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Parikshit N. Mahalle Aboli H. Patil
Turkish Journal of Computer and Mathematics Education (TURCOMAT), Volume 12, pp 7-15; https://doi.org/10.17762/turcomat.v12i4.455

Abstract:
Peer review is one of the most crucial and important tasks that are associated with academic conferences, journals and grant proposals; and assignment of an appropriate reviewer plays vital role for accurate and fair review process. This paper presents a learning based proactive system that assigns reviewer(s) whose expertise matches with the domain(s) of the paper satisfying constraints. The assignment of reviewer to paper needs to satisfy various constraints such as maximum number of papers per reviewer, minimum number of reviewers per paper and conflict of interest. he core challenge in reviewer paper assignment is to make the computer understand the subject domain of experts and papers. In proposed system, features are extracted from title, abstract and introduction section of publications of reviewer and submitted papers. These features help the model learn the domain features of experts and submitted papers more accurately. Once the training set is built utilizing the inherent correlation between abstract and title, the model is trained and the similarity between reviewers and papers is predicted. The experimental results on test data set of AAAI 2014 and NIPS 2019 demonstrate the effectiveness of the proposed system.
Andres Garcia-Saavedra, Xi Li, Alexandros Kostopoulos, George Iosifidis, Danny De Vleeschauwer, Chia-Yu Chang, Lidia Fuentes, Daniel-Jesus Munoz, Brendan McAuliffe, Paul Sutton, et al.
Published: 29 March 2021
by Zenodo
Abstract:
The main contributions of this document are: • Presentation of the communication, dissemination, and exploitation plan (CoDEP) • Presentation of the early achievements according to this plan. DAEMON´s CoDEP includes communication, dissemination, and exploitation activities. Communication includes all the activities related to the promotion of the project and its results beyond the project’s own community. This includes explaining its research in layman's terms to the media and the public. Dissemination includes activities related to raising awareness of its results in a technical community working on the same research field. In general, this will be done through publications, and participation and organization of technical events. Finally, exploitation (in accordance with the European IPR Helpdesk) covers activities aiming at using the results in further research activities other than those covered by the project, such as developing, creating and marketing products or processes, creating and providing a service, or standardization activities. Particular emphasis is given to standardization in this deliverable; the initial roadmap defined is also presented. The Raise Awareness phase, during the initial stages of the project, focuses on communication activities (e.g., setting up the web portal and social media accounts and high-level project presentations) to make the project known not just to the technical community but to a larger audience outside the project topics, including the public. Dissemination and standardization activities also start in this phase with preliminary technical ideas. In the Presentation of Results phase, dissemination and exploitation activities increase their intensity, since the architecture and NI design efforts will have produced meaningful results. Initial demonstrations of specific concepts of the architecture and NI solutions are also expected during this phase. However, it is in the Demos and PoCs phase where demonstration activities involving multiple blocks of the architecture working in an integrated way are expected. Finally, the Long-lasting Impact phase refers to other research or exploitation actions taken once the project is finished. This includes shaping the topics and framework of future research projects, exploiting DAEMON NI concepts in a market-oriented way by incorporating them into products and services, and leaving a footprint in standards through project contributions. The plan defines the various undertaken activities, and defines related target metrics. In accordance with the plan, some early results have already been produced. Some highlights follow: • Communication o Design of promotion material, website, and creation of news, social media accounts, along with initial actions to raise awareness about the project through these means. This already resulted in over 1000 visits to the website and an increasing impact through social media. o Communication talks, a brochure and other events to present the scope of the project. • Dissemination o Nine accepted conference publications, some of them jointly with other projects and in top venues (e.g, IEEE INFOCOM, AAAI, etc.). o Four accepted publications in top-tier (Q1) journals (e.g., IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, etc.). o Participation in various working groups to join efforts with other 5GPPP projects. • Exploitation, including standardization o Creation of the standardization management role. o Identification of relevant SDOs for networking (e.g., 3GPP, IETF, ETSI, O-RAN) and monitoring of groups of interest for potential contributions. o Identification of relevant open-source projects (e.g., srsRAN [3], Eclipse Zenoh [5], Eclipse fog05 [6]). o Definition of the initial standardization roadmap according to the timelines of the SDOs and the project.
Andres Garcia-Saavedra, Xi Li, Alexandros Kostopoulos, George Iosifidis, Danny De Vleeschauwer, Chia-Yu Chang, Lidia Fuentes, Daniel-Jesus Munoz, Brendan McAuliffe, Paul Sutton, et al.
Published: 29 March 2021
by Zenodo
Abstract:
The main contributions of this document are: • Presentation of the communication, dissemination, and exploitation plan (CoDEP) • Presentation of the early achievements according to this plan. DAEMON´s CoDEP includes communication, dissemination, and exploitation activities. Communication includes all the activities related to the promotion of the project and its results beyond the project’s own community. This includes explaining its research in layman's terms to the media and the public. Dissemination includes activities related to raising awareness of its results in a technical community working on the same research field. In general, this will be done through publications, and participation and organization of technical events. Finally, exploitation (in accordance with the European IPR Helpdesk) covers activities aiming at using the results in further research activities other than those covered by the project, such as developing, creating and marketing products or processes, creating and providing a service, or standardization activities. Particular emphasis is given to standardization in this deliverable; the initial roadmap defined is also presented. The Raise Awareness phase, during the initial stages of the project, focuses on communication activities (e.g., setting up the web portal and social media accounts and high-level project presentations) to make the project known not just to the technical community but to a larger audience outside the project topics, including the public. Dissemination and standardization activities also start in this phase with preliminary technical ideas. In the Presentation of Results phase, dissemination and exploitation activities increase their intensity, since the architecture and NI design efforts will have produced meaningful results. Initial demonstrations of specific concepts of the architecture and NI solutions are also expected during this phase. However, it is in the Demos and PoCs phase where demonstration activities involving multiple blocks of the architecture working in an integrated way are expected. Finally, the Long-lasting Impact phase refers to other research or exploitation actions taken once the project is finished. This includes shaping the topics and framework of future research projects, exploiting DAEMON NI concepts in a market-oriented way by incorporating them into products and services, and leaving a footprint in standards through project contributions. The plan defines the various undertaken activities, and defines related target metrics. In accordance with the plan, some early results have already been produced. Some highlights follow: • Communication o Design of promotion material, website, and creation of news, social media accounts, along with initial actions to raise awareness about the project through these means. This already resulted in over 1000 visits to the website and an increasing impact through social media. o Communication talks, a brochure and other events to present the scope of the project. • Dissemination o Nine accepted conference publications, some of them jointly with other projects and in top venues (e.g, IEEE INFOCOM, AAAI, etc.). o Four accepted publications in top-tier (Q1) journals (e.g., IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, etc.). o Participation in various working groups to join efforts with other 5GPPP projects. • Exploitation, including standardization o Creation of the standardization management role. o Identification of relevant SDOs for networking (e.g., 3GPP, IETF, ETSI, O-RAN) and monitoring of groups of interest for potential contributions. o Identification of relevant open-source projects (e.g., srsRAN [3], Eclipse Zenoh [5], Eclipse fog05 [6]). o Definition of the initial standardization roadmap according to the timelines of the SDOs and the project.
Chan Rory, Kuo Chris RuiWen,
Archives of Asthma, Allergy and Immunology, Volume 5, pp 008-013; https://doi.org/10.29328/journal.aaai.1001023

Abstract:
Irway hyperresponsiveness (AHR) is a hallmark of persistent asthma measured using direct or indirect airway bronchial challenge testing. The purpose of this study is to investigate the putative relationships between type 2 inflammatory biomarkers, airway geometry (FEV1 and FEF25-75) and specific IgE (RAST or skin prick) to AHR. We performed a retrospective analysis of our database (n = 131) of patients with asthma. Of these subjects, 75 had a histamine challenge and 56 had a mannitol challenge. Fractional exhaled nitric oxide (FeNO) and specific immunoglobulin E (IgE) but not blood eosinophils were significantly higher in patients with AHR to either histamine or mannitol. FEV1 % and FEF25 - 75 % were significantly lower in patients with AHR. Elevated Type 2 biomarkers including FeNO and specific IgE but not blood eosinophils were associated with AHR. Highlights: FeNO and specific IgE but not blood eosinophils are raised in patients with airway hyperresponsiveness.
, Shaoyuan Li
Published: 2 November 2020
Control Theory and Technology, Volume 18, pp 459-461; https://doi.org/10.1007/s11768-020-00008-w

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Nika Levikov, Daniela Quacinella, Edward Duca
International Journal of Higher Education Management, Volume 7; https://doi.org/10.24052/ijhem/v07n01/art-2

Abstract:
Collaboration, Fragmentation, Engagement, Responsible Research and Innovation Responsible Research and Innovation (RRI) has recently gained recognition as a guiding principle for research to be more inclusive of societal needs. In response, the University of Malta led an internal qualitative study to assess attitudes and perceptions towards RRI. This approach paved the way for cultural and institutional changes that may not have developed otherwise. Academics, non-academic staff and students were interviewed alongside an online questionnaire totaling 29 face-to-face interviews and 226 survey responses. Thematic coding analysis revealed the core theme of fragmentation. Sub-themes stemming from fragmentation include challenges around collaboration, communication, politics, knowledge systems thinking and varied ideas of responsibility in research. While most respondents are in favor of RRI practice, several barriers affect an individual’s capacity to practice this approach, including lack of time and resources, and lack of recognition of public engagement (PE) efforts in the university’s current policies and governance structure. This research allowed for the development of a targeted Action Plan and set of initiatives to successfully begin implementing a culture of RRI best practice, including the establishment of the Committee for Engaged Research and fostering an internal network of individuals who are exemplary in RRI best practice. The thorough and targeted process has produced more significant and tangible results than moving directly into implementation, while also reducing the risk of future problems emerging from rushed initiatives. The authors conclude that such an approach is imperative for successful RRI implementation within institutions, especially when considering cultural/local context. 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Published: 23 March 2020
Abstract:
<p><strong>Key Words</strong>: Uncertainty Quantification, Deep Learning, Space-Time POD, Flood Modeling</p><p><br>While impressive results have been achieved in the well-known fields where Deep Learning allowed for breakthroughs such as computer vision, language modeling, or content generation [1], its impact on different, older fields is still vastly unexplored. In computational fluid dynamics and especially in Flood Modeling, many phenomena are very high-dimensional, and predictions require the use of finite element or volume methods, which can be, while very robust and tested, computational-heavy and may not prove useful in the context of real-time predictions. This led to various attempts at developing Reduced-Order Modeling techniques, both intrusive and non-intrusive. One late relevant addition was a combination of Proper Orthogonal Decomposition with Deep Neural Networks (POD-NN) [2]. Yet, to our knowledge, in this example and more generally in the field, little work has been conducted on quantifying uncertainties through the surrogate model.<br>In this work, we aim at comparing different novel methods addressing uncertainty quantification in reduced-order models, pushing forward the POD-NN concept with ensembles, latent-variable models, as well as encoder-decoder models. These are tested on benchmark problems, and then applied to a real-life application: flooding predictions in the Mille-Iles river in Laval, QC, Canada.<br>For the flood modeling application, our setup involves a set of input parameters resulting from onsite measures. High-fidelity solutions are then generated using our own finite-volume code CuteFlow, which is solving the highly nonlinear Shallow Water Equations. The goal is then to build a non-intrusive surrogate model, that&#8217;s able to <em>know what it know</em>s, and more importantly, <em>know when it doesn&#8217;t</em>, which is still an open research area as far as neural networks are concerned [3].</p><p><br><strong>REFERENCES</strong><br>[1] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, &#8220;Inception-v4, inception-resnet and the impact of residual connections on learning&#8221;, in Thirty-First AAAI Conference on Artificial Intelligence, 2017.<br>[2] Q. Wang, J. S. Hesthaven, and D. Ray, &#8220;Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem&#8221;, Journal of Computational Physics, vol. 384, pp. 289&#8211;307, May 2019.<br>[3] B. Lakshminarayanan, A. Pritzel, and C. Blundell, &#8220;Simple and scalable predictive uncertainty estimation using deep ensembles&#8221;, in Advances in Neural Information Processing Systems, 2017, pp. 6402&#8211;6413.</p>
Published: 1 September 2019
Abstract:
Hálózati struktúra és centralitás a diplomáciai hálózatokban Network Structure and Centrality and Diplomatic Networks Kacziba Péter PhD, egyetemi adjunktus, Pécsi Tudományegyetem Bölcsészettudományi Kar Politikatudományi és Nemzetközi Tanulmányok Tanszék [email protected] Összefoglalás Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p8#matud_f28654_p8 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p8#matud_f28654_p8 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Jelen tanulmány célja, hogy multidiszciplináris megközelítéssel vizsgálja az 1817–2015 közötti diplomáciai hálózatok strukturális jellegzetességeit, egyúttal elemezze, hogy a kapcsolatszerkezetben mely államok töltöttek be központi pozíciót. A vizsgálat során negyven hálózati modell elemzéséből következő részeredmények kerülnek bemutatásra: a tanulmány felvázolja a csúcspontokkal, élekkel és azok történeti változásaival kapcsolatos jellemzőket; áttekinti a sűrűséggel és reciprocitással kapcsolatos adatokat; illetve a fokszámcentralitásból kinyert információk alapján megkísérli detektálni a hálózatokban központi szerepet játszó aktorokat. Abstract Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p13#matud_f28654_p13 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p13#matud_f28654_p13 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. This study seeks to analyze the structural characteristics of diplomatic networks between 1817 and 2015 with a multidisciplinary approach and attempt to examine which states have occupied a central position in the network structures. The paper presents partial results of the analysis of forty diplomatic network models: it outlines the topological characteristics of nodes, edges, and their historical transformations; reviews data on density and reciprocity; and on the basis of information obtained from degree centricity, it attempts to detect central actors of the network structures. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p15#matud_f28654_p15 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p15#matud_f28654_p15 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Kulcsszavak: nemzetközi kapcsolatok, diplomácia, hálózatkutatás, digitális bölcsészet Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p17#matud_f28654_p17 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p17#matud_f28654_p17 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Keywords: international relations, diplomacy, network research, digital humanities DOI: 10.1556/2065.180.2019.9.10 Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p21#matud_f28654_p21 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p21#matud_f28654_p21 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Cikk letöltése Bevezetés Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p26#matud_f28654_p26 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p26#matud_f28654_p26 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Az előző évtizedekben a globális tér és az abban lezajló interakciók fokozódó komplexitása kihívás elé állította a nemzetközi kapcsolatokkal foglalkozó kutatásokat. Az állami és nem állami szereplők számának drasztikus növekedése, valamint a technikai innovációk és a globalizáció következményei új módszertani alternatívák kidolgozását és átvételét követelték meg. Ezek közül az egyik legígéretesebb lehetőséget a hálózattudomány alkalmazása biztosította, amely fogékony – ennek ellenére periferikus – módszertani alternatívának bizonyult a nemzetközi kapcsolatok fejlődésének lekövetésében. Ugyan a hálózati megközelítés diszciplináris alkalmazása korántsem tekinthető új jelenségnek, a kapcsolatszerkezeti modellezés és elemzés az informatikai, illetve digitális technikák széles körű elterjedéséig ezen a tudományterületen periferikus módszertani alternatíva maradt. A nemzetközi tanulmányokban alkalmazott hálózati módszertan az 1960-as évek óta kiemelt figyelmet szentel a rendszerszintű, globális összefüggések jellemzőivel foglalkozó kutatásoknak. Ezek a strukturális elemzések részletesen vizsgálták az államközpontú nemzetközi rendszer sajátosságait; a kereskedelmi folyamatok hálózati összefüggéseit; a nemzetközi interakciók, tranzakciók és kohéziók tulajdonságait; a szervezeti tagságok következményeit; a regionális klaszterezettséget vagy a szövetségi rendszerek alapvetéseit (Victor et al., 2017, 11.). Utóbbiak mellett a rendszerszintű megközelítések egyik leghangsúlyosabb vonulata az államok közötti diplomáciai kapcsolatokra fókuszált, a diplomáciai linkek meglétéből, hiányából és jellegéből próbált a nemzetközi rendszerre, a centrális vagy periferikus, illetve a regionális vagy individuális szereplőkre vonatkozó tulajdonságokat kinyerni (például: Russet–Lamb, 1969; Neumayer, 2008, Westerwinter, 2017). Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p27#matud_f28654_p27 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p27#matud_f28654_p27 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Jelen tanulmány ezt a kutatási trendet követve a globális diplomáciai hálózatok általános szerkezeti tulajdonságainak bemutatására, illetve a kapcsolatszerkezetek centrális szereplőinek detektálására vállalkozik. Bár a kutatási eredmények sokrétűek, ehelyütt terjedelmi korlátok miatt az általános szerkezeti tulajdonságok esetében a csúcspontok és kapcsolati linkek összetételére és változásaira, a sűrűségi és reciprocitási adatokra, illetve a fokszámeloszlására fókuszálok; a centralitásra vonatkozó attribútumokat pedig csak a fokszámközpontiság mérőszámai alapján elemzem. Az írásban röviden összefoglalt kutatási eredmények újdonságát elsősorban a mintavételi eljárás széles idősávja biztosítja: míg a korábbi elemzések rövid periódusokra vonatkozóan vizsgálták a diplomáciai hálózatok jellegzetességeit, addig jelen kutatás és tanulmány relatíve széles időskálára, az 1817–2015 közötti időszakra helyezi a hangsúlyt. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p28#matud_f28654_p28 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p28#matud_f28654_p28 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A tanulmányban található ábrák saját szerkesztés eredményei, amelyek a modellezett hálózatokból kinyert adatok alapján kerültek összeállításra. Helytakarékossági okokból ezen adatok és hálózatok forrásait külön-külön nem tüntettem fel, ehelyütt azonban szükséges jelezni, hogy az összeállított hálózatok és az abból kinyert adatok forrásai: Bayer, 2006; Moyer et al., 2016; Europa Publications, 2016. Módszertan Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p33#matud_f28654_p33 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p33#matud_f28654_p33 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A tanulmány kiindulópontjául szolgáló kutatás kifejezetten a diplomáciai kapcsolódások felépítésének és változásainak hosszú idősoros leírására vállalkozott, emiatt azonban számos módszertani kihívással találta szemben magát. Ezek közül az első nagyobb problémát az adatfelvétel jelentette, egységes adatbázis hiányában ugyanis az elemzett periódus diplomáciai kapcsolatait csak három, eltérő módszertant alkalmazó forrásból lehetett kinyerni. Ennek megfelelően az 1817 és 1960 közötti hálózatok forrása a Correlates of War projekt legfrissebb, Reşat Bayer által jegyzett adatbázisa (v2006.1.); az 1960–2010 közötti adatok Jonathan D. Moyer és munkatársai Diplometrics elnevezésű adatgyűjtéséből származnak; míg az utóbbiak által még nem tárgyalt 2015-ös év a The Europa World Year Book diplomáciai információinak eredménye (Bayer, 2006; Moyer et al., 2016; Europa Publications, 2016). Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p34#matud_f28654_p34 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p34#matud_f28654_p34 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Az adatbázisok jellegéből adódóan a modellezett és vizsgált hálózatok az államok közötti diplomáciai kapcsolatokat ábrázolták, a nemzetközi szervezetekhez küldött állandó képviseletek tehát nem képezték a kutatás tárgyát. Szintén az adatforrások eltérő módszertani sajátosságaiból következően a hálózatokban 1960 előtt diplomáciai akkreditáció eredményezett hálózati kapcsolatot, azt követően viszont már rezidens képviselet jelenléte. Mivel a használt adatbázisok képviseleti osztályozása is eltér egymástól, ezért a kutatás külképviseleti missziók eltérő formáit és szintjeit nem osztályozta vagy kategorizálta: a hálózatok az adott mintavételi évben jelen lévő kapcsolatokat nagyköveti, főképviselői 1 , ügyvivői, miniszteri, esetleg egyéb képviselői 2 szinten ábrázolták, ezek között azonban nem tettek különbséget. Az 1960-at megelőző, akkreditációs adatgyűjtés folyományaként a rezidens képviseletek esetén is minden esetben csak maximum egy diplomáciai képviselet került ábrázolásra, tehát az adott országba delegált vezető képviseleten kívül létesített alsóbbrendű missziók külön nem kerültek feltüntetésre. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p35#matud_f28654_p35 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p35#matud_f28654_p35 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A kutatás jobbára ötéves mintavételi időszakokban vizsgálta a külképviseleti kapcsolatokat, bizonyos években (például a második világháború alatt és után) a felhasznált adatbázisok ugyanakkor nem szolgáltattak adatokat. 3 Az elemzésben az adatbázisok által kiválasztott állami entitások szerepelnek, azonban a Correlates of War adatgyűjtési eljárásának megfelelően a Diplometrics gyűjtéséből is ki lettek szűrve az önálló szuverenitással nem vagy csak korlátozottan rendelkező államok. Ez például a 2015-ös évet alapul véve azt jelenti, hogy a 193 ENSZ tagsággal rendelkező országon kívül csak olyan államok kerültek be a hálózatba, amelyek ténylegesen és széles körben tudták gyakorolni szuverenitásukat. 4 Szintén fontos leszögezni, hogy a hálózatok izolált részeket nem tartalmaznak, azaz csak azok az országok kerültek bele a hálózatokba, amelyek az adott mintavételi évben legalább egy másik országgal a kritériumoknak megfelelő diplomáciai kapcsolatban álltak. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p36#matud_f28654_p36 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p36#matud_f28654_p36 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A kapcsolatok vizuális modellezését a Gephi 0.9.2 verziószámú szoftver végezte el (Bastian et al., 2009). A modellezett hálózatokban az államok (csúcspontok) közötti diplomáciai interakciókat a bejövő és kimenő képviseletek irányított linkjei jelzik, minden egyes diadikus pár egymáshoz maximum két éllel kapcsolódhat, egy bejövő és egy kimenő misszióval (lásd: melléklet). Utóbbiból következően az összeállított kapcsolati szerkezetek irányított hálózatok. Végezetül fontos kiemelni, hogy az adatbázisok saját bevallásuk szerint is tartalmazhatnak anomáliákat, ekkora adatmennyiség esetében a hibalehetőség tehát nem elhanyagolható tényező (Moyer et al., 2016, 7.). Hálózati struktúra Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p41#matud_f28654_p41 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p41#matud_f28654_p41 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Az elemzés tárgyát képező negyven hálózat összevetésekor az egyik legszembetűnőbb jellegzetesség a szerkezeti komplexitás folyamatos és fokozatos növekedése, amely bizonyos években kisebb-nagyobb mértékben fluktuált ugyan, a mintavételi időszak egészére nézve azonban jellemző. Ez a komplexitás-növekedés következik egyrészt a hálózatban részt vevő államok számának emelkedéséből, másrészt a közöttük lévő interakciók arányának fokozódásából. A kutatás által modellezett diplomáciai hálózatokban részt vevő államok/csúcspontok (N) száma 1817–1914 között 91,3%-kal emelkedett, amit jóval meghaladott az 1914 és 2015 közötti 345,5%-os növekedés (1. ábra). Ebben a vonatkozásban a német egyesítés alatti és utáni, valamint a második világháború alatti időszak annexiói jelentettek számottevő kivételt, mindkét esetben átmeneti visszaesés mutatkozott a hálózatokban részt vevő államok számában. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p43#matud_f28654_p43 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p43#matud_f28654_p43 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 1. ábra. Államok/csúcspontok száma (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p46#matud_f28654_p46 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p46#matud_f28654_p46 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A hálózati komplexitás növekedésének másik mozgatórugója az interakciók számának emelkedése volt. Az ezeket reprezentáló hálózati élek (L) száma 1817–1914 között 365,7%-kal, 1914–2015 között pedig 895,2%-kal növekedett (2. ábra). Bár ezek a számok kétségkívül jelentős növekedésről tanúskodnak, fontos azonban megjegyezni, hogy a hálózatokban részt vevő államok maximális kapcsolódási lehetőségeiknek (Lmax) csak átlagosan 33,3%-át használták fel (3. ábra). A hálózati sűrűség (D) értéke az elemzett évek során egyetlenegyszer sem haladta meg a D = 0,5-öt 5 , az arány pedig látványosan csökkent a hálózatok méretének növekedésével. Míg a hálózatokban részt vevő államok 1817–1955 között átlag 37,6%-át teljesítették maximális kapcsolódási lehetőségeiknek, addig 1960 és 2015 között – L max 286,06% növekedése mellett – csak 23,4%-át. A sűrűség látványos csökkenésének egyik oka az államok számának folyamatos emelkedése, amely a maximális kapcsolódási lehetőségek arányát megnövelte. Ez a növekedés az államok közötti interakciók számát fokozta, komplexebb hálózatokat hozott létre, ugyanakkor a diplomáciai költségeket is megemelte. Ezt a költségnövekedést csak a gazdaságilag legfejlettebb államok tudták követni, a többség tehát a rendelkezésre álló források korlátozottsága miatt továbbra is csak a legfontosabb partnerekre koncentrált. A sűrűség csökkenése emellett a felhasznált adatbázisok különböző adatgyűjtési technikáiból is ered. Míg az 1960-as adatok feltételrendszere tartalmazza a többszörös akkreditációkat, addig az 1960 utáni hálózatok csak a rezidens képviseleteket modellezik. Az 1960 utáni csökkenés tehát a multiakkreditációk hiányából is következik. Mintavételi eljárástól függetlenül megállapítható ugyanakkor, hogy az államok számának gyors növekedését a diplomáciai képviseletek számai nem tudták lekövetni, tehát a hálózatok tényleges mérete messze elmarad a lehetséges maximális mérettől. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p48#matud_f28654_p48 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p48#matud_f28654_p48 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 2. ábra. Hálózati kapcsolatok/élek száma (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p51#matud_f28654_p51 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p51#matud_f28654_p51 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 3. ábra. Hálózati sűrűség (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p54#matud_f28654_p54 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p54#matud_f28654_p54 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A sűrűség mért értékei természetesen összefüggésben vannak a hálózat részei­nek összekapcsoltságával, azaz azzal ténnyel, hogy nem minden állam küld minden más államba diplomáciai képviseletet, illetve nem minden ország viszonozza a hozzá érkező missziókat, egyes kapcsolatok tehát aszimmetrikusak maradnak. Az elemzett hálózatok érdekessége azonban az, hogy a kapcsolatok jóval szimmetrikusabbak az átlagos 6 irányított hálózatoknál, a vizsgált időszakban a reciprocitás (r) átlagos értéke r ≈ 0,76 volt. 7 A 4. ábrát elemezve kitűnik, hogy a diplomáciai kölcsönösség a 19. század második felében átlépte ezt az r = 0,7 pontos küszöböt, ettől kezdődően mintavételi eljárástól függetlenül végig átlag felett maradt. A kisebb-nagyobb visszaeséseket áttekintve a szimmetrikusság kiterjedt háborúk, gazdasági válságok következtében (1920, 1935, 1950), illetve tömbök közötti ellenségeskedés (1955), továbbá a dekolonizációs folyamatok (1965) eredményeként átmenetileg csökkent. A diplomáciai képviseletek relatíve magas szimmetrikussága ugyanakkor az alacsony sűrűségi adatokkal együtt értelmezendő. Ahogyan korábban láttuk, a hálózatokban részt vevő államok lehetséges kapcsolataik számának csak relatíve kevés hányadát használták ki, ezekre a megvalósuló kapcsolatokra vonatkoznak az imént ismertetett magas reciprocitási adatok. Az alacsony sűrűségi adatokból azonban az is kiütközik, hogy a hálózatokban létrehozható kapcsolatok többsége nem alakul ki, a hálózati szimmetrikusság magas aránya tehát végső soron nemcsak a létrejövő kapcsolatokra, de a nem megvalósuló kapcsolatokra is igaz. Másként megfogalmazva, az elemzett hálózatokban a létező és a nem létező kapcsolatok többségében egyaránt szimmetrikusságot mutatnak: ha létrejött egy kapcsolat, akkor azt többségében viszonozták 8 , ha viszont nem, akkor az egyoldalú kezdeményezés jobbára elmaradt. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p56#matud_f28654_p56 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p56#matud_f28654_p56 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 4. ábra. Reciprocitás (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p59#matud_f28654_p59 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p59#matud_f28654_p59 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A hálózat alkotóelemeinek, sűrűségének és szimmetrikusságának arányváltozásai természetesen hatást gyakoroltak a hálózatokban létrejövő kapcsolatok mennyiségére is. Utóbbiak közül az új alkotóelemek megjelenése rövid távon csökkentette a hálózatok sűrűségét, ugyanakkor hosszú távon növelte a létrehozható kapcsolatok számát. Az újonnan belépők kapcsolatrendszerének folyamatos növekedését a kapcsolatok szimmetrikusságra való törekvése is felerősítette, amely – mintegy hálózati befolyásként – a létrejövő egyirányú kapcsolatok többségét idővel szimmetrikussá tette. Bár a hálózatba belépők nagy száma miatt a hálózatok sűrűsége soha nem lépte át a D = 0,5-ös értéket, azonban a fentebb részletezett folyamatok révén az alkotóelemek és a hálózatok fokszáma (k) folyamatosan nőtt. Ehelyütt egyelőre csak az átlagos fokszámot vizsgálva (‹k›) kijelenthető, hogy ez az érték 1817–1914 között 143,4%-kal, 1914 és 2015 között pedig 123,4%-kal növekedett (5. ábra). A fokszám mennyisége természetesen a hálózat különböző alkotóelemeit vizsgálva eltérő módon változott, kijelenthető ugyanakkor, hogy a középértéket a centrális szereplők rendkívül magas értéke jelentősen befolyásolta. Ennek mértéke az idő előrehaladtával folyamatosan nőtt: a legmagasabb fokszámmal rendelkező államok 1817-ben 22,4; 1914-ben 61,1; 1955-ben 121,6; 2015-ben pedig már 307,2 kapcsolattal haladták meg az átlagos fokszám értékét. A centrális és periferikus szereplők kapcsolatainak sűrűsége között tehát jelentős eltérések mutatkoznak (Duque, 2017, 13.). Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p61#matud_f28654_p61 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p61#matud_f28654_p61 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 5. ábra. Átlagos fokszámeloszlás (1817–2015) Centrális szereplők Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p67#matud_f28654_p67 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p67#matud_f28654_p67 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Utóbbiból is jól kivehető, hogy a centrális szereplők hálózati befolyása a vizsgált periódus alatt fokozatosan növekedett. Ennek a folyamatnak a követésében a közeliség, a köztiség és a fokszámcentralitás értékeinek elemzése nyújthat segítséget, jelen tanulmány terjedelmi korlátok miatt ezek közül kizárólag utóbbira koncentrál. A fokszámcentralitás a központiság mérésének legkézenfekvőbb mérőszáma, amely az egyes pontok kapcsolati számát viszonyítja egyrészt más csúcspontok fokszámához, másrészt az összes kapcsolat mennyiségéhez (Barabási, 2016, 63.; Newman, 2010, 168–169.). Mivel az elemzett példák irányított hálózatok, ezért a bejövő (kbe ) és kimenő (kki ) fokszámokat különválasztottuk, előbbi a fogadott külképviseleteket, utóbbi a küldött missziók számát jelöli. A bejövő fokszám tekintetében jól kirajzolódik, hogy az elemzett mintegy kétszáz év alatt pusztán négy ország került olyan előnyös helyzetbe, hogy a legmagasabb bejövő fokszámú pozíciót begyűjtse. A negyven mintavételi évben 47,5%-ban Franciaország, 35%-ban az USA, 2,5%-ban az Egyesült Királyság, 15%-ban pedig több ország (Franciaország, Egyesült Királyság, Németország, USA) együttesen osztozott az adott évre vonatkozó legmagasabb fogadott külképviseleti mennyiségen (6. ábra). Az adatokból szintén kitűnik, hogy míg a francia vezető szerep a második és különösen az első világháború előtti időszakra volt jellemző, addig 1950 után az USA vált a diplomáciai külképviseletek legfontosabb célországává. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p69#matud_f28654_p69 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p69#matud_f28654_p69 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 6. ábra. Legmagasabb bejövő fokszámmal rendelkező csúcspontok/államok (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p72#matud_f28654_p72 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p72#matud_f28654_p72 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A legmagasabb bejövő fokszámmal rendelkező országok listája kiválóan tükrözi a hálózatelmélet presztízsről kialakított elképzeléseit, és igazolja, hogy a magas bejövő kapcsolatszámmal rendelkező csúcspontokhoz érkező kapcsolatok interakciós befektetésnek tekinthetők. A diplomácia világában ez a tulajdonság az úgynevezett szelekciós kényszerből következik, azaz abból a jellegzetességből, hogy az államok korlátozott anyagi erőforrásaikat kizárólag a számukra legelőnyösebb külképviseletek fenntartásába próbálják fektetni. Brandon J. Kinne álláspontja szerint ez a szelekciós preferencia nem pusztán a diadikus/bilaterális párok viszonyrendszeréből következik: az újonnan létesített képviseletek a hálózati szerkezet révén is determináltak, mivel az államok diplomáciai kötelékeiket más országok kapcsolathálója alapján alakítják ki (Kinne, 2014, 247.). Ez a hálózati hatás egy diplomáciai képviselet létesítésekor érvényesül a költségminimalizálás és feladatmaximalizálás kapcsán, valamint a külügyi döntések közvetett külpolitikai jelzései révén. Másként megfogalmazva, a missziót létrehozó állam a bilaterális viszonyok mellett figyelembe veszi a kirendeltséget fogadó ország diplomáciai hálózatának összetételét is, s a képviselet hatásfokának maximalizálására, illetve a költségek minimalizálására törekedve ezután dönt saját külképviseleti szerkezetéről. Ebből következően a legmagasabb bejövő fokszámmal rendelkező országok a képviselet-létesítés szelekciós kényszere miatt a nemzetközi diplomáciai hierarchia csúcsán helyezkednek el, ők rendelkeznek a legtöbb diplomáciai hozzáféréssel, emiatt befolyásuk számottevő (Maliniak–Plouffe, 2011, 4–6.). Széles körű mozgásterüket jól mutatja, hogy a legmagasabb bejövő fokszámok az 1817–1864 és az 1965–1995 közötti időszakoktól eltekintve végig megközelítették az adott évre vonatkozó lehetséges maximumot, ugyanakkor azt ténylegesen soha nem érték el. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p73#matud_f28654_p73 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p73#matud_f28654_p73 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Bár a legmagasabb bejövő fokszámmal rendelkező országok presztízse és hálózati befolyása központi szerepet játszik a centralitás vizsgálatakor, azonban nem elhanyagolhatók azok az államok sem, amelyek a mért legmagasabb értékeket megközelítették. A kutatás ezen centrális szereplők kiválasztása érdekében összegyűjtötte azon államokat is, amelyek a mintavételi években bekerültek a tíz legmagasabb bejövő fokszámmal rendelkező ország közé (7. ábra). Annak ellenére, hogy a csoport szereplőinek bejövő fokszámai között jelentős eltérések mutatkoztak, a top 10-ek tagjai mind a diplomáciai tér olyan aktív és nagy befolyással bíró szereplőinek tekinthetők, akikre az államok többsége hasznot eredményező diplomáciai befektetésként tekintett. A csoport elemzésekor az egyik legszembetűnőbb jellegzetesség az abban részt vevők alacsony száma. A mintavételi időszakban összesen harminckét állam 9 tudott bekerülni a mért évben legtöbb bejövő fokszámmal rendelkező tíz ország közé. Ezek közül a központi szereplők közül húsz állam tízszer vagy annál kevesebbszer került be a csoportba, Belgium, Németország, Olaszország, az USA, valamint Franciaország és az Egyesült Királyság viszont harmincnál is többször, utóbbi kettő pedig mind a negyven mintavételi alkalommal. Az imént említett hat ország központi szerepét jól jellemzi, hogy a teljes periódusban létrejövő 98 347 bejövő kapcsolat 14,12%-a ezekbe az országokba irányult. A mért időszakban legmagasabb diplomáciai presztízzsel rendelkezők tehát egy meglehetősen zárt csoportot alkottak, az abba való bekerülés és bent maradás során pedig nem pusztán a diplomáciai vonzerő számított, de az is, hogy az adott állam mióta volt tagja a nemzetközi rendszernek, abban milyen szerepet töltött be, illetve hogy külső és belső viszonyainak stabilitását meddig tudta megőrizni. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p75#matud_f28654_p75 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p75#matud_f28654_p75 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 7. ábra. A 10 legmagasabb bejövő és kimenő fokszámmal rendelkező országok csoportja (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p78#matud_f28654_p78 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p78#matud_f28654_p78 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Míg a bejövő fokszám az államok egymással szembeni megítélésére utal, addig a kimenő kapcsolatok az egyes országok diplomáciai befektetéseinek fokmérői. Mivel ez a mérőszám nem a hálózaton belüli alkotóelemek döntéseitől, hanem az individuális szereplők egyéni elhatározásától függ, ezért a legfontosabb államok összetétele a bejövő fokszámhoz képest valamelyest diverzifikáltabb képet mutat. Ez jól látszódik a legmagasabb kimenő fokszámmal rendelkezők összetételében, amelyben a mért időszak 32,5%-ában több ország együttesen osztozott a legmagasabb kimenő fokszámon. Ebből következően az önállóan betöltött pozíciók aránya csökkent: az USA a negyven mintavételi év 25%-ában, Franciaország 22,5%-ban, az Egyesült Királyság 12,5%-ban, Oroszország, Ausztria–Magyarország és Olaszország pedig egyenként 2,5–2,5%-ban tudta magáénak a legtöbb kimenő külképviseleti mennyiséget (8. ábra). A legmagasabb kimenő értékkel rendelkező pozíciók diverzifikáltsága az egyes történeti periódusokat vizsgálva is szembetűnő. Míg a legmagasabb bejövő fokszámú adatok esetében egyértelmű volt Franciaország 19. századi és az USA 20. századi vezető pozíciója, addig a kimenő fokszámcentralitás esetében a hosszabb távú egyoldalú elsőbbség csak 1990-től jellemző. Az USA vezető szerepének fokozatos előtérbe kerülését magyarázza az államok számának második világháború utáni drasztikus növekedése, amelynek hatására a diplomáciai költségek megnőttek, és csak a gazdaságilag legfejlettebb államok tudtak extenzív kapcsolathálózatot fenntartani. A kimenő kapcsolatok centrális szereplőinek maximális fokszámhoz viszonyított kimenő fokszáma a folyamat hatására csökkent, a maximális lehetséges kapcsolatok, valamint a megvalósuló linkek száma között egyre nagyobb eltérések mutatkoztak. A 8. ábrán megfigyelhető folyamatot utóbbiak mellett természetesen az adatgyűjtésből kivett multiakkreditációk hiánya is indukálja. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p80#matud_f28654_p80 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p80#matud_f28654_p80 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 8. ábra. Legmagasabb kimenő fokszámmal rendelkező csúcspontok/államok (1817–2015) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p83#matud_f28654_p83 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p83#matud_f28654_p83 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Szintén a nagyszámú külképviselet fenntartásával kapcsolatos költségekkel állhat összefüggésben, hogy a magas kimenő fokszámmal rendelkező centrális szereplők száma a bejövő fokszámnál tapasztalható arányoknál is alacsonyabb. A mintavételi időszakban huszonhárom ország került be a tíz legtöbb kimenő fokszámot összesítő diagramba (7. ábra), ezek összetétele pedig egyértelműen jelzi, hogy kiterjedt külképviseleti hálózatot csak gazdaságilag fejlett országok tarthattak fenn. Ezek közül a központi szereplők közül kilenc állam tízszer vagy annál kevesebbszer került be a csoportba, a középmezőnybe nyolc ország sorolható, egyenként tíz és harminc közötti előfordulással. A top 10-ek csoportjában legtovább jelen lévők száma ismételten hat: Németország, Olaszország, Oroszország, az USA, valamint Franciaország és az Egyesült Királyság harmincnál is többször került be a csoportba, utóbbi kettő ismételten mind a negyven mintavételi alkalommal. Belgium középmezőnybe történő visszaesése jelzi az ország bejövő és kimenő kapcsolatai között lévő számottevő különbséget, amit az országba delegált nemzetközi szervezetek generálnak, a bejövő kapcsolatok javára. Oroszország belépése a top 6-ok csoportjába szintén érthető fejlemény, ami jól reprezentálja az orosz külpolitika rezsimváltások során is fennmaradó alapvetéseit. A legmagasabb kimenő fokszámmal rendelkező centrális szereplők tehát a legfontosabb célországokhoz hasonlóan egy rendkívül zárt csoportot alkotnak. Erről a zártságról sokat elmond, hogy utoljára Kína volt képes új csatlakozóként belépni a top 10-ek csoportjába, ettől eltekintve a legfontosabb célországok csoportjának tagjai között 1975 óta, míg a legtöbb képviseletet küldő centrális szereplők között pedig 1985 óta nincs változás. Következtetések Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p88#matud_f28654_p88 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p88#matud_f28654_p88 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A tanulmány által ismertetett adatok a nemzetközi tanulmányok és a hálózatelméleti kutatások számára is kézzelfogható eredményeket produkáltak. A nemzetközi tanulmányok esetében a kutatás egyebek mellett bizonyította, hogy megfelelő adatgyűjtés esetén rendszerszintű összefüggések is modellezhetők, ezek a kapcsolatszerkezetek elemezhetők, a nemzetközi rendszer rétegzettségei, illetve a hatalom absztrakt jellegzetességei pedig kvantitatív módszerekkel kimutathatók. A kapott eredmények cáfolják a feltételezést mely szerint a digitális kapcsolattartás elterjedése miatt a külképviseleti érdekképviselet elvesztheti jelentőségét: a nagykövetségi posztok aránya 2000 és 2015 között – az államok számának 1,6%os növekedése mellett – kb. 19%-kal növekedett, az utolsó mintavételi évben pedig már több mint kilencezer diplomáciai főképviselet működött a világon. Szintén számottevő trend, hogy az eredmények tanulsága szerint a diplomáciai kapcsolódások kialakulását nem pusztán bilaterális (vagy diadikus) viszonyok határozzák meg, a hálózati szerkezet önmagában is döntően befolyásolja a diplomáciai kapcsolatok formálódását, egyúttal az államok abban kifejtett effektivitását. Az elemzés szintén ráirányította a figyelmet arra, hogy a diplomáciai befektetés legfontosabb forrása a külképviselet-létesítés, amely a magas viszonzási hajlandóság miatt növeli a kezdeményező államok nemzetközi beágyazottságát: mivel a kimenő kapcsolatok nagy arányban eredményeznek bejövő kapcsolatokat, ezért az adott állam centralitása, presztízse és diplomáciai hatékonysága a befektetések révén növekszik. Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p89#matud_f28654_p89 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p89#matud_f28654_p89 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A nemzetközi tanulmányok tudományspecifikus következtetéseinek további listázása helyett érdemes rámutatni néhány olyan eredményre is, amelyek általános hálózatelméleti jelenségekre reflektálnak. Ezek közül az egyik legszembetűnőbb jelenség, hogy a diplomáciai kapcsolódásokat más valódi hálózatokhoz hasonlóan a növekedés jellemzi, ennek a növekedésnek a dinamikája azonban az utolsó mintavételi években fokozatosan lassult, amely természetesen hatást gyakorolt a topológiára és annak tulajdonságaira is. Ez a hálózatok transzformációját nézve azt jelentette, hogy a növekedés dinamikus időszakában egyes országok hosszabb távú hálózati jelenlétük és népszerűségük okán centrális szereplőkké váltak, a megnövekedett szerep azonban sosem eredményezte óriás komponens vagy központosított (csillag formájú) topológia kialakulását. A hálózatokat inkább egy olyan decentralizált struktúra jellemzi, amelyben egyes csúcspontok központi pozíciója a növekedési időszak dinamikus periódusában növekedett, míg annak lassulása során a linkek számának fokozatos kiegyenlítődése révén csökkent. A topológia változása ugyanakkor sosem alakított ki elosztott hálózati szerkezetet, a népszerűségi és erőforrásbeli különbségek a mintavételi periódusban végig fenntartották a diplomáciai hálózatok hierarchikusságát. Melléklet Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p94#matud_f28654_p94 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p94#matud_f28654_p94 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Diplomáciai képviselek globális hálózatai Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p95#matud_f28654_p95 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p95#matud_f28654_p95 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Referenciahálózatok Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p97#matud_f28654_p97 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p97#matud_f28654_p97 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 1817 Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p100#matud_f28654_p100 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p100#matud_f28654_p100 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 1914 Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p103#matud_f28654_p103 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p103#matud_f28654_p103 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. 2015 Saját szerkesztés (források: Bayer, 2006; Moyer et al., 2016; Europa Publications, 2016; szoftver: Gephi 0.9.2 201709242018) Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p106#matud_f28654_p106 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p106#matud_f28654_p106 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. A tanulmányhoz kapcsolódó kutatás a Nemzeti Tehetség Program támogatásával valósult meg. Pályázati azonosító: NTP-NFTÖ-18-B-0352 Hivatkozás Kérjük, válassza ki az önnek megfelelő formátumot: (2017). Magyar Tudomány [Digitális kiadás.] Budapest: Akadémiai Kiadó Letöltve: https://mersz.hu/?xmlazonosito=matud_f28654_p108#matud_f28654_p108 (2019.09.06.) Magyar Tudomány (Budapest: Akadémiai Kiadó, 2017.), letöltve: 2019.09.06. https://mersz.hu/?xmlazonosito=matud_f28654_p108#matud_f28654_p108 BibTeX EndNote Mendeley Zotero Jegyzet elhelyezéséhez, kérjük, lépjen be. Projekt címe: A nemzetközi kapcsolatok hálózattudományi megközelítései Irodalom Barabási A.-L. (2016): A hálózatok tudománya. Budapest: Libri Könyvkiadó Bastian, M. – Heymann, S. – Jacomy, M. (2009): Gephi: An Open Source Software for Exploring and Manipulating Networks. International AAAI Conference on Weblogs and Social Media. Bayer, R. (2006): Diplomatic Exchange Dataset, v2006.1. http://correlatesofwar.org (letöltve: 2018. 09. 20.) Duque, M. (2017): Core-Periphery Structure in the International Status Hierarchy. Belfer Center for Science and International Affairs, Harvard Kennedy School, 1–38. https://politicalscience.nd.edu/assets/236649/ (letöltve: 2019. 04. 29.) Europa Publications (2016): The Europa World Year Book. 57th edition. London: Routledge Kinne, B. J. (2014): Dependent Diplomacy: Signaling, Strategy, and Prestige in the Diplomatic Network. International Studies Quarterly, 58, 247–259. DOI: 10.1111/isqu.12047 Maliniak, D. – Plouffe, M. (2011): A Network Approach to the Formation of Diplomatic Ties. WORKING DRAFT, San Diego: University of Chicago, 1–24. Moyer, J. D. – Bohl, D. K. – Turner, S. (2016): Diplometrics: Diplomatic Representation. S. Fre­derick S. Pardee Center for International Futures. link (letöltve: 2018. 09. 20.) Neumayer, E. (2008): Distance, Power and Ideology: Diplomatic Representation in a World of Nation-states. Area, 40, 2, 228–236. DOI: https://doi.org/10.1111/j.1475-4762.2008.00804.x Newman, M. E. J. (2010): Networks: An Introduction. New York: Oxford University Press DOI: 10.1093/acprof:oso/9780199206650.001.0001 Russett, B. M. – Lamb, C. W. (1969): Global Patterns of Diplomatic Exchange 1963–1964. Journal of Peace Research, 6, 1, 37–55. https://doi.org/10.1177/002234336900600104 Victor, J. N. – Montgomery, A. H. – Lubell, M. (eds.) (2017): The Oxford Handbook of Political Networks. Oxford: Oxford University Press. DOI: 10.1093/oxfordhb/9780190228217.001.0001 Westerwinter, O. (2017): Uncertainty, Network Change and Costly Signaling: How the Network of Diplomatic Visits Affects the Initiation of International Conflict. Department of Political Science, University of St. Gallen, 1–46. https://www.alexandria.unisg.ch/252634/ (letöltve: 2019. 04. 29.) 1 A Brit Nemzetközösség egymáshoz delegált, nagyköveti rangú képviselői, angolul: High Commisioner. 2 Például apostoli nuncius. 3 A konkrét mintavételi évek a következők: 1817, 1824, 1827, 1832, 1836, 1840, 1844, 1849, 1854, 1859, 1864, 1869, 1874, 1879, 1884, 1889, 1894, 1899, 1904, 1909, 1914, 1920, 1925, 1930, 1935, 1940, 1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015. 4 Névlegesen: Tajvan, a Szentszék és Koszovó. A kutatási következetesség miatt nem került az elemzett országok közé Palesztina és Észak-Ciprus sem, mivel ezekben az esetekben a szuverenitás tényleges gyakorlása erősen korlátozott. 5 Sűrűségi értékhatár: 0 ≤ D ≤ 1. 6 Példának okáért Mark Newman (2010) a World Wide Web irányított hálózatában mért r ≈ 0,54 értéket már „szokatlanul magasnak” minősítette. 7 Reciprocitási értékhatár: 0 ≤ r ≤ 1. 8 Az előzőekben felvázolt számadatok alapján a mintavételi időszakban a létrejövő kapcsolatok átlagosan 76%-os eséllyel váltak szimmetrikussá. 9 A kutatás az adatok összevetésének effektivitása érdekében egyes államok elnevezéseit a vizsgált időszak alatt nem változtatta, azaz egyes releváns esetekben a megszűnő államot és jogutódját kontinuitásként kezelte. Ilyen eset volt például Németország vagy Oroszország. Előbbi esetében a Német Birodalmat, a Weimari Köztársaságot vagy az NSZK-t egyaránt a Németország elnevezés jelöli, utóbbi esetében pedig az Oroszország megjelölés a cári időszakot és a Szovjetuniót egyaránt takarja. Az adatok megfeleltetése természetesen csak releváns esetekben volt lehetséges, példának okáért Ausztria–Magyarország a felbomlás után érthető okokból már külön államként szerepel.
Anita Pacholik-Żuromska
Published: 17 July 2019
Abstract:
The claim that a cognizer needs to act with the environment to gain knowledge about the world is trivial. No more does the claim sound trivial than when it is said that the cognizer1 also needs to interact with the environment to know himself, i.e., to gain self-knowledge (SK), defined generally as the subject's knowledge of his mental states, such as feeling, beliefs, or desires (cf. Peacocke, 1999). But why should the cognizer interact with the external world to know the content of his states if they are given to him directly by introspection? In this paper, I support the thesis that to meet the requirements put on SK as knowledge (i.e., as justified, true belief), it must be both embodied and social. Otherwise, the subject has no tool to correct his false beliefs about himself since he is simply unaware that they are false. The vision of such a self-blind subject seems not quite optimistic; hence, in this article, I would like to investigate certain solutions which could help in the argumentation against such vision. The traditional account of SK separated the cognizer from the influence of other subjects by giving him the first-person-authority grounded on his privileged access to his internal psychological states. On such account, a society consisted of individual minds, interacting however with one another, but with no access to others' minds. On the early stage of computationalism, the already classic paradigm in cognitive science, an intuitive approach to SK was the one according to which a cognizer knew his own mental states by virtue of their appearance in mind (Haugeland, 1987; Guttenplan, 1994; Dretske, 1995). SK was then characterized by the propositional form of “I believe that I believe that p.” An explanation could easily be formulated with the nomenclature of computationalism by saying that to know himself, a subject needs to present two abilities (or in terms of functionalism: dispositions): to have a concept of I/Me to ascribe the attitude to oneself as to the subject of the experienced state, and to have a concept of an attitude such as BELIEF or DESIRE in order to identify the mental state in which he is (cf. Peacocke, 1992). If the concepts were understood as representations falling under computational operations (Fodor, 1998, 2000), then the SK also had a representational form composed of two basic representations: the one of I and the other of an experienced phenomenal state such as pain or belief (Newen and Vosgerau, 2007). The computability of SK, also called information processing, was determined by algorithmic processes on representations (Dretske, 1981; Fodor, 1987, 1991; Leake, 1995; David et al., 2004; Miłkowki, 2017). On such computational account of SK, a subject was closed in the internal loop of self-representational mind, which needed no non-neural body to gain the knowledge about itself. One of the newest examples of such an internalistic model of SK is the Epistemic Agent Model (EAM,) formed on the level of conscious processing and representing its owner as an individual capable of keeping autonomous epistemic self-control, i.e., monitoring and voluntary modification of his own mental states (Metzinger, 2017, p. 8). The components of EAM are two smaller models: a model of an entity exerting control (the self) as well as a model of the satisfaction conditions of the specific mental action and the asymmetric dynamic relation connecting these two models, the one which can be interpreted simply as an intentional attitude toward a content of the mental state such as belief (cf. Metzinger, 2017). All the components are internal and based only on the neural information processing. The subject (self) aiming at some action in the world first needs to be aware of the belief according to which he acts. To know this belief, he needs to be equipped with the model of himself as the subject having that belief. Therefore I interpret the EAM as a model of SK. Although this model does not include external elements (which are needed in the conception of the social and embodied SK) it points to a very important constituent of SK, namely the minimal phenomenal selfhood—the subjective experience of being a self. The processes responsible for physical self-specification are neuronal and hence internal. Basically, these are both homeostatic regulation as well as proprioception, which is understood as sensorimotor integration (Christoff et al., 2011, p. 104). They underlie higher level processes giving rise to self-experience. This self-experience is a fundament of EAM and, hence, SK. The constitution of SK as relying on the constitution of the self is crucial here. On the one hand, the development of self-experience constitutes a necessary element of SK, but on the other hand, it is the source of errors in self-cognition. The errors in self-cognition are reported in many empirical studies: Rubber Hand Illusion (RHI), Full Body Illusion (FBI), or Body Swap Illusion (BSI) have shown that the perception of self-location and first-person perspective can be experimentally influenced and changed (Lenggenhager et al., 2007; Blanke and Metzinger, 2009; Ionta et al., 2011; Aspell et al., 2012), and that certain dimensions of minimal phenomenal selfhood can be manipulated (cf. Limanowski, 2014, p.1). The cases of experiencing a phantom (de facto missing) limb as still belonging to the body (Ramachandran and Blakeslee, 1998; Ramachandran and Altschuler, 2009; Case et al., 2010; Ramachandran et al., 2011) well exemplify lack of resistance to an error in self-cognition. The examples involving self-illusions explicitly show that we can artificially induce the experience of self-location and ownership from the outside to evoke a false self-identification, and hence, to create a false content of SK. The newest empirical findings show the connection between impairment of the self in Schizophrenia (SZ) and Autism Spectrum Disorders (ASD) accompanied by disturbances during interaction of those affected by the abovementioned mental condition with the social environment. In these cases, a subject possesses either a sharper self-others boundary which extends beyond the norm (ASD) or has weaker distinction (SZ) (Noel et al., 2017). The experiments with FBI involving ASD patients showed that the patients do not experience FBI as intensively as the healthy subjects do (Mul et al., 2019). The conclusion therefore drawn was that the multisensory integration, which constitutes the base for the minimal phenomenal selfhood formation, may be related to deficits in social functioning. The abovementioned cases indicate the connection between the internal subjective sphere with the external sphere of the social, without giving up the role of the body in the constitution of the self. The question of SK is the question of how the body (something private and individual) interacts with the world (public and social). According to this issue called body-social problem (Kyselo, 2015), the social interaction relies on the tension between what is objective and what is subjective in cognition expressed in terms of distinction and participation (Kyselo, 2015). Self-cognition can be formed from the bottom up, as the basic representation of the subject as an individual distinct from other entities, but also it is shaped top-down through a subject's participation in joint actions. Both, distinction and participation lead to the development of the cognizer's beliefs as belonging to him as an individual entity with privileged access to his own states and first-person authority. They both are complementary components of the process of the cognizer's constitution as an autonomous individual in the process of continuous balancing between what is his own and what is social (cf. Kyselo, 2015). The uniqueness of human cognition is characterized by the ability to participate with others in collaborative activities with shared goals and intentions. This ability is the so-called shared intentionality defined as an ability to share the mental states (e.g., beliefs) of others owing to the ability to represent these states. The shared intentionality can be interpreted as a reasonable conscious participation (in opposition to unreflective imitation) in social practices (Tomasello and Rakoczy, 2003; Tomasello et al., 2005). It helps to develop one's own self exemplified in the set of beliefs constituting SK. Two-year-old children are ready to understand others as intentional agents, but by age four, show the ability to read others' minds skillfully enough to be able to look from others' perspectives and understand that others can have beliefs different from their own (Baron-Cohen et al., 1985; Tomasello and Rakoczy, 2003; Tomasello et al., 2005). The ability to take the perspective of others—to think like others—to understand that others can have different beliefs is the symptom that a child has developed the theory of mind, i.e., accepts that others have their own individual minds distinctive from that of a child. This is one of the milestones in the development of SK. Due to the conception of embodied and social SK self-cognition is the result of the body interactions with the world (Figure 1). Mind is not only “in the head” but also “in the body.” This general idea was presented by Seth (2015) and is based on empirical research on how self-experience emerges, or how the phenomenal selfhood is constructed. For the body-world interaction to be effective, the organism must present adequate abilities to control the body. These include, among others, the sense of ownership and self-identification (Seth, 2015, p. 11). The integration of bodily information in the form of bodily awareness is required because in this manner the brain creates the body model as a whole (Seth, 2015, p. 11). The self is thus an effect of interoceptive, exteroceptive, and proprioceptive sensory stimuli (Seth, 2015, p. 12). The interaction between interoceptive and exteroceptive signals is significant here (Seth, 2015, p.13), which means that, as an essential component, the self-model must also contain an external element, whose presence allows the constitution of this model. In such an externalist model of the self, an action will be a tester of SK and it will be a verifier of the beliefs concerning the subject's own states, showing that the subject in specific cases such as RHI can be wrong about the perceived object as belonging to his body, although the first information is about its integration within the body. Worth emphasizing is the fact that the presented internal and external models are based on the mechanism of predictive coding, showing that the same mechanism can underlie different models. Predictions running in the brain allow to “properly read” the current states of the world on the basis of a sensory input for the purpose of performing an appropriate action (Friston et al., 2009). I think, however, that it works properly only in the external model of SK, owing to the probability which increases after the interaction of the subject with the environment. The interaction with the world (action performing) serves as a tester of sensory input (Seth, 2015). Figure 1. Factors influencing SK. As it has already been said, the empirical evidence shows that the cognizer may be wrong about his experienced states. If an error arises on the basic level of information processing, for instance, an error in proprioception where the minimal phenomenal self is constituted, it is inherited by consequent levels (i.e., from sub-personal neuronal level via phenomenal up to the level of propositional mental content) until the false information appears in self-consciousness, giving the subject a wrong representation about his state. The social element constituting SK is the answer to this problem. The social constitution of SK allows us to step out from the first-person perspective and take the third-person perspective by judging the reliability of the beliefs about the subject's own mental states. This ability opens the mind to the possibility that the cognizer can be wrong about the content of the experienced state. Although bottom up processes determine the inheritance of errors in self-experience, SK prepares us for being mistaken about our own mental states. 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(2003) What makes human cognition unique? From individual to shared to collective intentionality. Mind Lang. 18, 121–147. doi: 10.1111/1468-0017.00217 CrossRef Full Text | Google Scholar Keywords: self-knowledge, embodiement, externalism, self-illusions, social cognition Citation: Pacholik-Żuromska A (2019) Self-knowledge as a Result of the Embodied and Social Cognition. Front. Psychol. 10:1679. doi: 10.3389/fpsyg.2019.01679 Received: 15 February 2019; Accepted: 03 July 2019; Published: 17 July 2019. Edited by: Reviewed by: Copyright © 2019 Pacholik-Żuromska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Anita Pacholik-Żuromska, [email protected]
Industrial Robot: the international journal of robotics research and application, Volume 46, pp 332-336; https://doi.org/10.1108/ir-04-2019-0069

Abstract:
Purpose: The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding her pioneering efforts and the challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach: The interviewee is Dr Maja Matarić, Chan Soon-Shiong Distinguished Professor in the Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. In this interview, Matarić shares her personal and business perspectives on socially assistive robotics. Findings: Matarić received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990 and BS in Computer Science from the University of Kansas in 1987. Inspired by the vast potential for affordable human-centered technologies, she went on to found and direct the Interaction Lab, initially at Brandeis University and then at the University of Southern California. Her lab works on developing human–robot non-physical interaction algorithms for supporting desirable behavior change; she has worked with a variety of beneficiary user populations, including children with autism, elderly with Alzheimer’s, stroke survivors and teens at risk for Type 2 diabetes, among others. Originality/value: Matarić is a pioneer of the field of socially assistive robotics (SAR) with the goal of improving user health and wellness, communication, learning and autonomy. SAR uses interdisciplinary methods from computer science and engineering as well as cognitive science, social science and human studies evaluation, to endow robots with the ability to assist in mitigating critical societal problems that require sustained personalized support to supplement the efforts of parents, caregivers, clinicians and educators. Matarić is a Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the IEEE and AAAI, recipient of the Presidential Awards for Excellence in Science, Mathematics & Engineering Mentoring (PAESMEM), the Anita Borg Institute Women of Vision Award for Innovation, Okawa Foundation Award, NSF Career Award, the MIT TR35 Innovation Award, the IEEE Robotics and Automation Society Early Career Award and has received many other awards and honors. She was featured in the science documentary movie “Me & Isaac Newton”, in The New Yorker (“Robots that Care” by Jerome Groopman, 2009), Popular Science (“The New Face of Autism Therapy”, 2010), the IEEE Spectrum (“Caregiver Robots”, 2010), and is one of the LA Times Magazine 2010 Visionaries. Matarić is the author of a popular introductory robotics textbook, “The Robotics Primer” (MIT Press 2007), an associate editor of three major journals and has published extensively.
Malte Schilling, , Katharina Muelling, Britta Wrede, Helge Ritter
Frontiers in Neurorobotics, Volume 13; https://doi.org/10.3389/fnbot.2019.00016

Abstract:
Editorial on the Research TopicShared Autonomy—Learning of Joint Action and Human-Robot Collaboration Advancing the autonomy of artificial agents, such as robots, comes with an important challenge: how to shape the autonomy of an individual agent in such a manner that bringing several such agents together leads to a suitable fusion or sharing of (parts of) their individual autonomy spaces. This challenge has opened up important new perspectives on how to make robots more skillful, versatile, and easier to deploy for tasks and scenarios where robots can no longer act solitarily. The special issue aims at, first, providing a theoretical perspective to describe autonomy in collaborative settings, and, secondly, presenting novel results that highlight common traits and underlying dimensions and can guide further developments on adaptive interaction in teams of humans and robots. Accordingly, Shared Autonomy has to address the question on how to mediate autonomy between participating actors. This question can be approached through a distinction of different levels of autonomy and by defining characteristic dimensions describing collaborative scenarios (see Figure 1 and Schilling et al., 2016). In their review, Alonso and de la Puente identify transparency as one such characteristic which is understood as observability and predictability. They show that this translates well to interactive multi-agent scenarios and provides useful dimensions to account for interaction patterns. As predictability decreased in more complex tasks, systems were found to act more autonomously. They reasoned that this is due to limited observability which can be counteracted by subsuming low-level details in higher-level representations as are goals. Here, Gildert et al. continue with their review on joint action which highlights a shared context as a requirement for prediction. Furthermore, they point out the role of attention and how implicit and explicit communication can guide attention between agents in order to mediate interaction patterns. Both articles connect the issue of shared information in a further step toward the notion of trust in interactive settings. Figure 1. Distinguishing multiple levels of autonomy (shown on the left). Conceptually, these different levels characterize the freedom of decision making arising at different levels of an abstraction hierarchy. The notion of Shared Autonomy allows to analyze and design types of interaction patterns between human users and other agents (shown on the right). For details see Schilling et al. (2016). The special issue also provides examples that highlight current advances in human-robot collaboration and show that different levels of a representational hierarchy may call for different mechanisms and strategies: lower levels deal with the optimal selection of means to achieve immediate goals; autonomy on an intermediate level determines strategies that fulfill higher level purposes which are given on the highest level (as intentions). Importantly, studies on coordination with robots on a higher-level further stress that this can't be addressed solely on an abstract level, but always requires grounding in lower action levels. This further emphasizes the importance of a differentiated perspective on Shared Autonomy. For coordination on the lower level of immediate action, Trinh et al. provide a solution to navigation for multiple agents as a prototypical example for Shared Autonomy scenarios. Each agent preplans its own route to a target location which is represented as a static flow field. To avoid collisions, this representation is overlaid with a constantly updated dipole flow field of other agents' locations. The agents' behavior is mostly driven by the preplanned flow field, but the dipole flow field shapes the routes of the agents autonomously. Ewerton et al. directly address this change of control and autonomy. Their framework deals with visual and haptic feedback to users while learning motor skills. In their case users are intended to learn drawing Japanese characters. Probabilistic models of skills are learned following a reinforcement learning approach and the skill likelihood is used to regulate the amount of feedback. The system autonomously steps in when needed and starts to provide guidance through haptic feedback. The focus of Akkaladevi et al. is on how cooperation can be organized between multiple agents in a collaborative assembly process. On the lowest level, this entails learning basic actions. On a higher level, the system aims at putting these into a sequence. In addition, the system attempts to propose suitable actions that are chosen depending on background knowledge about users' capabilities, current context and current goals. Knowledge was learned using incremental reinforcement learning. The Interactive Shared Solution Shaping paradigm by Reardon et al. focuses further on an intermediate level of representation for negotiating a shared plan: while the autonomous agent has its own planning process, a human user provides feedback based on his expert domain knowledge. This was realized for planning the route of a surveillance robot. While the user doesn't need to know all the details about, e.g., collision avoidance, mediation of the planning process requires transparency between the robot system and the user. A form of interaction is needed that details current routes to the user and allows to influence the (re)planning process. Establishing such a form of fast and reliable communication is crucial for systems that should work autonomously, but at the same time contribute toward a user's intention and provide valuable information. In Shukla et al. such multimodal coordination is further analyzed and applied. They introduce the probabilistic Proactive Incremental Learning framework that learns to associate hand gestures with manipulation actions in a collaborative assembly task. There, communication is required to establish common ground and align mutual beliefs between robot and user which exploits information about gaze. The system anticipated users' behaviors and goals in order to proactively assist the user. In a study with non-roboticist users they found that their proactive system reduced the interaction effort and was mostly preferred, but further cautioned that this requires trust. In a further user study, Schulz et al. turn toward the question of how preferred interaction with robots is task dependent. They categorize forms of collaboration along two dimensions: first, distinguishing independent actions and joint interaction; secondly, distinguishing sequences where order is crucial versus not. This was assessed in a series of table-top scenarios in which a human collaborated with a robot to build a given design in a blocks world. They found that mostly autonomous actions of the robot are more efficient and preferred. The special issue brings together current work and trends in Shared Autonomy. Collaboration in teams of humans and robots shifts more and more toward higher level tasks that not only include low level coordination of immediate action. This requires coordination of complex plans between multiple agents that can be adapted at runtime. Therefore, each agent is tasked with autonomous control on different levels, but also has to respect the autonomy of other agents and therefore has to adjust its degree of autonomy. Shared Autonomy provides a useful perspective on the underlying interaction patterns, how these are adapted and what requirements this poses to systems, like establishing common ground, transparency and shared beliefs as well as goals, trust, and efficient forms of communication. MS prepared the figure and wrote the paper. HR, WB, KM, and BW discussed and wrote the paper. This work was supported by the Cluster of Excellence Cognitive Interaction Technology CITEC (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Schilling, M., Kopp, S., Wachsmuth, S., Wrede, B., Ritter, H., Brox, T., et al. (2016). “Towards a multidimensional perspective on shared autonomy,” in Proceedings of the AAAI Fall Symposium Series 2016 (Stanford, CA). Google Scholar Keywords: shared autonomy, human-robot interaction, learning, collaboration, interactive robots Citation: Schilling M, Burgard W, Muelling K, Wrede B and Ritter H (2019) Editorial: Shared Autonomy— Learning of Joint Action and Human-Robot Collaboration. Front. Neurorobot. 13:16. doi: 10.3389/fnbot.2019.00016 Received: 01 February 2019; Accepted: 09 April 2019; Published: 14 May 2019. Edited by: Reviewed by: Copyright © 2019 Schilling, Burgard, Muelling, Wrede and Ritter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Malte Schilling, [email protected]
, Pavel Hamet, Johanne Tremblay, Christian A. Koch,
Published: 28 March 2019
Frontiers in Endocrinology, Volume 10; https://doi.org/10.3389/fendo.2019.00185

Abstract:
The rapid growth of technology in the past couple of decades has paved the way for development of novel techniques that can solve scientific questions at a rate that is far beyond the capability of humans. One such example is the field of Artificial Intelligence (AI) and Machine Learning (ML). AI is a discipline that deals with the study and design of intelligent agents, that is, devices that intricately perceive their environment and take actions that maximize the chances of achieving their goals (1). AI, in a way, mimics the structure and operating methodologies of a human brain (2). AI has two forms of application: physical and virtual (3). The physical component is mainly represented by robots. Derived from a Czech word robota, meaning “forced labor,” the physical robotic forms were conceptualized by inventors such as Leonardo Da Vinci (3). This component has been widely used in the field of endocrinology, such as robot-assisted surgery of adrenal or prostate cancer. Examples of virtual applications of AI are electronic medical records (EMR), where specific algorithms are used to identify subjects, and harness health related data (3). ML is a field of AI that deals with the development of models and intricate networks that enable computer systems to improve their performance on a specific task progressively (4). ML algorithms can be: (i) unsupervised (spontaneous pattern detection), (ii) supervised (building algorithms based on prior examples), or (iii) reinforcement learning (utilization of reward/punishment techniques to obtain the desired result) (3). A common use of ML in daily life includes flagging spam in an e-mail, autonomous driving and selecting the best route for daily commute. In the field of medicine, AI/ML technology can have substantial impact at three levels: physicians, by improving the diagnostic accuracy and assisting with therapeutic and surgical interventions; health systems, by enabling improved workflow and reduction in errors; patients, through tailoring of diagnostic, and treatment modalities based on the unique phenotypic and genetic features of individual patients (5). In this review, we focus on the virtual components of AI and ML and provide some examples for the utility of AI/ML in endocrinology and metabolism. From early ML tools like logistic regression which found their utility in medicine several decades ago, AI/ML methods have become far more multifaceted and have revolutionized the field of medicine through their ability to compute and analyze vast and complex array of datasets which would not be feasible solely with trained human skillsets (2). Several AI/ML methods have proven their utility in the diagnosis and management of various endocrinopathies. Gradient forest analysis, a ML technique, was applied in a study to identify factors contributing to variation in all-cause mortality among subjects in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (6). This technique detected four risk groups based on hemoglobin glycosylation index (HGI), BMI, and age. The lowest risk group (with HGI < 0.44, BMI < 30 kg/m2, and age 0.44) experienced a 3.7% increase in absolute mortality risk attributable to intensive glycemic therapy. These mortality variations in the intensive treatment group were previously not detected by older, univariate subgroup analyses (6). Another study developed a prototype support vector regression model that outperformed diabetologists in predicting blood glucose levels at 30 and 60 min from a given time in patients with type 1 diabetes, and predicted about one quarter of hypoglycemic events 30 min ahead of the actual event (7). AI/ML-based algorithms have been extensively utilized and validated for diagnosis and classification of diabetic retinopathy (8–11). Deep learning systems and even purely database-driven AI algorithms have demonstrated the ability to diagnose diabetic retinopathy and related retinal diseases in large, multiethnic cohorts with high degree of sensitivity and specificity (8, 10). ML algorithms have also demonstrated the ability to incorporate associated risk factors such as duration of diabetes and insulin use into risk-stratification of diabetic retinopathy, which could potentially facilitate the development of better clinical decision support systems (11). A proprietary system IDx (Iowa City, IA) that uses ML technology to analyze retinal images in diabetic retinopathy had a sensitivity of 87% and specificity of 91% for autonomous detection of disease and received FDA approval in 2018 (12). This example represents the first prospective assessment of AI/ML in the clinic (5). AI/ML technologies has also been used in the analysis of large datasets generated from genomic technology. Deep-coverage whole-genome sequencing performed in 8,392 individuals of European and African descent to identify single-nucleotide variants and copy-number variations in Lipoprotein (a) revealed that LPA risk genotypes conferred greater relative risk for incident cardiovascular disease than direct measurements of Lipoprotein (a) levels. These risk genotypes were also associated with increased sub-clinical atherosclerotic disease in individuals of African ancestry (13). ML techniques were utilized in developing a novel mRNA based molecular test to detect BRAF V600E mutations in thyroid fine needle aspirate samples, which demonstrated sensitivity equal to that of established DNA-based assay and had lower non-diagnostic rates (14). By utilizing functional enrichment analysis followed by module analysis performed on protein-protein interaction network, the differential gene expression in anaplastic thyroid carcinoma was assessed (15). There were 247 up-regulated genes which were predominantly involved in cell cycle and 275 down-regulated genes that were mostly involved in thyroid hormone synthesis, insulin resistance, and cancer pathways, thus expanding on the current knowledge of genetics of thyroid carcinomas (15). AI/ML methods can accurately interpret medical images and provide a computer-aided diagnosis. ML algorithms like convolutional neural network and support vector machine (SVM) demonstrated higher sensitivity, specificity, and positive and negative predictive values with early detection of facial changes in acromegaly when compared with doctors' evaluation, thus allowing for earlier clinical diagnosis and evaluation (16). A simple diagrammatic representation of utilization of facial landmark recognition for diagnosis of acromegaly is provided in Figure 1. ML has enabled individuals to make optimal decisions in real time by enabling organizational performance (3). For example, utilization of ML techniques such as principal component analysis and SVM, and matrix-assisted laser desorption/ionization mass spectrometry (MS) imaging demonstrated the potential to differentiate hormone-secreting from non-secreting pituitary adenomas, and demarcate tumor from normal gland at a molecular level under 30 minutes, therefore potentially enabling real-time, intra-operative tumor delineation, and improve patient outcomes (17). Figure 1. A simplified schematic representation of one of the several machine learning (ML) technologies that can be utilized for diagnosis and management of endocrine disorders. The example demonstrated in the above diagram deals with early recognition of acromegaly based on facial features. Photographs of several individuals with normal facial features are utilized to obtain data on facial patterns through processes such as facial feature extraction (represented by blue dotted lines), facial detection, normalization, and frontalization (also displayed in the figure). This data is then fed into ML algorithms for recognizing normal facial features. These ML tools then perform complex analyses and generate output data that is used to determine whether the face presented in the test photograph is consistent with features of acromegaly (Image courtesy: Sriram Gubbi, NIDDK, NIH). Variants of SVM, neural networks, and other ML techniques, with immunohistochemical methods were utilized in categorizing Cushing syndrome with adrenocortical lesions (18). Based on the gene expression profiling, the highest expressions of Ki-67 and PCNA were found in adrenocortical carcinoma while highest FHIT expression was found in adrenocortical hyperplasia, and adrenal adenomas had intermediate expression of all three antigens. These techniques diagnosed the adrenocortical disease type with 92.6% accuracy. In another study, urinary steroid profiling using gas chromatography/MS and subsequent ML analysis generated a pattern of immature, early stage steroid metabolites in adrenocortical cancer (19). This enabled differentiation of cancer from an adenoma with a sensitivity and specificity of 90%, a diagnostic value that was higher than CT, MRI, or PET scans. Another emerging field based on AI technology that could potentially have a wider scope in the future is “pre-emptive medicine” (20). Pre-emptive medicine is a novel concept proposed in Japan, which aims at delaying the onset, or even preventing the occurrence of chronic diseases, such as diabetes, hypertension, cancer, or dementia by using a combination of AI techniques, genomic analysis and environmental interaction data (20). The above examples reinforce the promising role of AI/ML in diagnosis and management of endocrine disorders which, in several instances, can outperform skilled physicians, minimize resource use and allocation, and yield tangible benefits by supporting physicians and accelerating clinical decision-making (7, 16). Despite the substantial evidence for the ability of AI/ML to deliver cost-effective healthcare and improve patient outcomes, medicine has trailed behind other scientific fields in implementing these techniques into practice (21). Potential hurdles include the longitudinal nature of variations in human disease, inadequacies in the quality and reliability, heterogeneity of healthcare data, personal data confidentiality, need for informed consent from patients, requirement of supportive policies and efficient business models, unpredictable reimbursement, and increasing necessity for data sharing (21, 22). The so-called “digital biomarkers” that are obtained through big data analyses performed using AI/ML techniques are not readily interpretable clinically, in the sense, even if a certain newer AI/ML algorithm has been shown to be superior to older techniques in certain population cohorts; its implementation in clinical practice across more diverse populations might not necessarily result in better diagnosis or outcome; and could potentially even lead to over-diagnosis and over-treatment in certain patient cohorts (23). Ethical issues, including misuse of AI/ML to manipulate quality metrics to make unscrupulous profit, potential in-built discriminatory biases toward under-represented populations, and physician over-dependence on AI/ML pose some of the foreseeable challenges in this field (24). Although AI/ML can theoretically “replace” physicians with regards to performing certain diagnostic, therapeutic, or surgical tasks, these technologies, almost certainly, will never be able to provide the emotional, social, and ethical support that a physician can offer the patient, and will never replace the unique bond of a doctor-patient relationship (25, 26). Then the question arises: What aspects of patient care can physicians focus on in the era of AI/ML technology? With the ever-growing complexity and quantity of knowledge in the medical field, it is almost impossible for physicians to mentally organize and retain all of this data (27). Therefore, the medical fraternity should focus on strengthening the following aspects in order to re-define the role of physicians in the era of AI/ML: (1) Medical school curricula must shift their focus from information acquisition to knowledge management and communication skills, (2) Physicians must be trained to manage and collaborate with AI/ML applications, (3) Emphasis must be placed on training physicians to interpret AI/ML output data and to effectively utilize these results in clinical decision making, and (4) Reinforcing cultivation of empathy and compassion among physicians (27). Eventually, physicians and AI technology need to develop a mutually supportive relationship than a competitive one. While physicians can provide appropriate feedback for AI/ML techniques and tools to improve, these tools in turn can facilitate physicians in solving uncertain clinical scenarios (28). This can result in mutual identification of key clinical or algorithmic biases, which can be then tackled by the combined efforts of physicians and AI/ML through better data collection and through model improvements (28). In conclusion, utilization of AI/ML will enable diagnosing endocrine disorders with higher accuracy, potentially avoid unnecessary investigations, and reduce healthcare expenditures and facilitate better digital storage of vast patient data, be it individual profiles or aggregated data for epidemiological research and planning, and these benefits may one day transform clinical endocrine practice. Today, AI technologies like artificial pancreas for management of diabetes have already become a reality (29). Major efforts are required from academia and the information technology industry to push for further development of AI/ML technology in endocrinology. Endocrinologists are well suited to play a vital role in the advancement of AI/ML. However, this has not been the focus of training programs for endocrine subspecialties, which do not provide the necessary education for trainees to feel confident in the use of these technologies for diagnosis or research. There is certainly a need to spread awareness, acquire funding, introduce these concepts into training programs, and encourage further research in this new, exciting branch of endocrinology and metabolism—a deep future awaits! SG prepared the manuscript and the figure. FH-S, PH, JT, and CK wrote parts of the manuscript and critically reviewed the manuscript. This work was supported in part by the Intramural Research Program of Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), protocol 00-CH-0160. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 1. 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(2018) 12:303–10. doi: 10.1177/1932296817710475 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: artificial intelligence, machine learning, diabetes, thyroid, endocrinology, metabolism Citation: Gubbi S, Hamet P, Tremblay J, Koch CA and Hannah-Shmouni F (2019) Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era. Front. Endocrinol. 10:185. doi: 10.3389/fendo.2019.00185 Received: 05 December 2018; Accepted: 06 March 2019; Published: 28 March 2019. Edited by: Reviewed by: Copyright © 2019 Gubbi, Hamet, Tremblay, Koch and Hannah-Shmouni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Fady Hannah-Shmouni, [email protected]
, Lina M Sulieman, Bradley A Malin
Journal of the American Medical Informatics Association, Volume 26, pp 561-576; https://doi.org/10.1093/jamia/ocz009

Abstract:
Objective User-generated content (UGC) in online environments provides opportunities to learn an individual’s health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations. Materials and Methods We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review. Results We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support. Conclusions The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.
Proceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition; https://doi.org/10.3390/mol2net-04-06123

Abstract:
Abstract: Multi-Objective Evolutionary Algorithms (MOEAs) are methods which are strongly recommended to approximate the Pareto frontier whenever the characteristics of objective functions and constraints make it difficult for mathematical programming (cf. Coello, 1999). These approaches construct a solution set formed by non-dominated solutions. Alternatively, an MOEA can approximate toward the set of best compromise solutions, i.e. the Region of Interest or RoI, through the incorporation of information about the preference of a decision maker (DM). According to (Branke, Salvatore, Greco, Slowiński & Zielniewicz, 2016), the DM preference information within an MOEA search process can be motivated by the necessity of sampling of the Pareto frontier, or the reduction of the DM’s cognitive effort to handle only the RoI, or because the DM’s preference information reinforces the necessary selective pressure. Let us observe that the incorporation of preferences is a method that offers support to the limited capacity of the human mind to handle several conflicting objectives at the same time (Miller, 1956); so, this method has become a very powerful tool that aids in the solution of many-objective problems. However, most of the time the behavior of a complex system often depends on parameters whose values are unknown in advance (Ling, 2010). Such is the case of MOEAs based on outranking approaches. The outranking approaches are methods that construct outranking relations among potential actions or decision alternatives and exploit such relations to find solutions to decision problems. These approaches have found application in approximating the RoI in Multi-objective Optimization Problems (MOPs) because they allow computational models of preferences of DM’s that can be used to guide the search in MOEAs toward solutions that are closely related to his/her interests. Ideally, the parameter values of an outranking approach should be defined by the DM; however, given the cognitive effort required from the DM, this task can be extremely difficult and time-consuming, and hence prohibited to be handled directly. This situation is aggravated in cases when a DM is not capable of providing a clear explanation of his/her decisions. These situations prevent from the use of a direct assessment of the parameter values required by a complex system. Instead, the most convenient strategy to overcome such problems lies in the use of preference disaggregation methods. A preference disaggregation method (PDM) is an indirect elicitation approach that can indirectly infer the values of a predefined set of parameters from a set of examples provided by the DM. In these approaches, the provision of preference information is far simpler for a DM because it can be done based on decisions taken in the past or formulated recently by means of manageable examples. So far, PDMs have been implemented using evolutionary metaheuristics, and they have shown their effectiveness on the parameter elicitation for outranking approaches used in the solution of the Portfolio Selection Problem (PSP). However, the success in the parameter elicitation does not depend only on the quality of the provided set examples but also on the performance of the used algorithms; in this aspect, it has been observed in other problems that the achievement of good solutions in a wider range of instances might require the use of several metaheuristics combined. Recently, hyperheuristics gain more attention because of their capacity to integrate characterization models of problem instances with a set of metaheuristics in order to improve the construction of solution in an optimization problem. Based on the fact that parameter elicitation has been modeled previously by optimization problems, this work proposes the study of the impact on the parameter elicitation of outranking approaches for PSP due to the implementation of a hyperheuristic that selects adequately the best metaheuristics given the instance of the problem and the state of the search process. References Bechikh, S. (2013). Incorporating Decision Maker’s Preference Information in Evolutionary Multi-objective Optimization, Diss. PhD thesis, High Institute of Management of Tunis, University of Tunis, Tunisia. Branke, J., Salvatore, C., Greco, S., Slowiński, Zielniewicz, P. (2016). Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research, 250:884–901. Coello, C.A. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, an International Journal, 1(3):269–308. Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Ozcan, E., & Woodward, J. R. (2009). Exploring hyper-heuristic methodologies with genetic programming. In Computational intelligence (pp. 177-201). Springer Berlin Heidelberg. Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2010). A classification of hyper-heuristic approaches. In Handbook of metaheuristics (pp. 449-468). Springer US. Coello, C.A. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, an International Journal, 1(3):269–308. DOI:10.1007/ BF03325101. Coello, C. C. (2000). Handling preferences in evolutionary multiobjective optimization: A survey. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on (Vol. 1, pp. 30-37). IEEE. Coello, C. (2017). Introducción a la computación evolutiva. Notas de Curso CINVESTAV-IPN. Cruz-Reyes, L, Fernandez, E., Rangel-Valdez, N. (2017). A metaheuristic optimization-based indirect elicitation of preference parameters for solving many-objective problems. International Journal of Computational Intelligence Systems, 10(2017): 56 – 77. Dias, L., Mousseau, V. (2006). Inferring electre’s veto-related parameters from outranking examples. European Journal of Operational Research, 170:172–191. Fernandez, E., Lopez, E., Lopez, F., and Coello Coello, C.A. (2011). Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method. Information Science (IS). 181: 44 – 56. Ling-Ko, L., Hsu, D., Lee, W.S., Ong, S.C.W. (2010). Structured Parameter Elicitation. APPEARED IN: Proc. AAAI Conference on Artificial Intelligence, 2010. Available at https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1798. Date accessed: 1/Nov/2018. Miller, G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2):81–97, 1956. Rangel-Valdez, N. Fernandez, E., Cruz Reyes, L., Gomez-Santillan, C., Hernández-López, R.I. (2015). Multiobjective Optimization Approach for Preference-Disaggregation Analysis Under Effects of Intensity. MICAI (2) 2015: 451-462. Roy, B. (1991). The outranking approach and the foundations of electre methods. Theory and Decisions, 31(1):49–73. DOI: Soubeiga, E. (2003). Development and application of hyperheuristics to personnel scheduling (Doctoral dissertation, University of Nottingham). Zhu, Y. & Luo, Y. (2016) Multi-objective optimisation and decision-making of space station logistics strategies. International Journal of Systems Science, 47(13):3132 – 3148.
Published: 19 November 2018
Frontiers in Big Data, Volume 1; https://doi.org/10.3389/fdata.2018.00006

Abstract:
Machine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them. Big Data is no fad. The world is growing at an exponential rate and so is the size of the data collected across the globe. Data is becoming more meaningful and contextually relevant, breaking new grounds for machine learning (ML), in particular for deep learning (DL) and artificial intelligence (AI), moving them out of research labs into production (Jordan and Mitchell, 2015). The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems. High-throughput genomic and proteomic experiments can be used to enable personalized medicine. Large data sets of search queries can be used to improve information retrieval. Historical climate data can be used to understand global warming and to better predict weather. Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger. Game data can turn pixels into actions within video games, while observational data can help enable robots to understand complex and unstructured environments and to learn manipulation skills. However, is AI, ML, and DL really synonymous, as recently suggested in the news, blogs, and media? For example, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used by the media to describe how AlphaGo won. In addition to this, even Gartner's list (Panetta, 2017) of top 10 Strategic Trends for 2018 places (narrow) AI at the very top, specifying it as “consisting of highly scoped machine-learning solutions that target a specific task.” Artificial intelligence and ML are very much related. According to McCarthy (2007), one of the founders of the field, AI is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” This is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots. Moreover, the behavior of a machine is not just the outcome of the program, it is also affected by its “body” and the enviroment it is physically embedded in. To keep it simple, however, if you can write a very clever program that has, say, human-like behavior, it can be AI. But unless it automatically learns from data, it is not ML: ML is the science that is “concerned with the question of how to construct computer programs that automatically improve with experience,” (Mitchell, 1997). So, AI and ML are both about constructing intelligent computer programs, and DL, being an instance of ML, is no exception. Deep learning (LeCun et al., 2015; Goodfellow et al., 2016), which has achieved remarkable gains in many domains spanning from object recognition, speech recognition, and control, can be viewed as constructing computer programs, namely programming layers of abstraction in a differentiable way using reusable structures such as convolution, pooling, auto encoders, variational inference networks, and so on. In other words, we replace the complexity of writing algorithms, that cover every eventuality, with the complexity of finding the right general outline of the algorithms—in the form of, for example, a deep neural network—and processing data. By virtue of the generality of neural networks—they are general function approximators—training them is data hungry and typically requires large labeled training sets. While benchmark training sets for object recognition, store hundreds or thousands of examples per class label, for many AI applications, creating labeled training data is the most time-consuming and expensive part of DL. Learning to play video games may require hundreds of hours of training experience and/or very expensive computing power. In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves. When a machine has to interact with a human, this seems to be especially valuable. This illustrates that ML and AI are indeed similar, but not quite the same. Artificial intelligence is about problem solving, reasoning, and learning in general. Machine learning is specifically about learning—learning from examples, from definitions, from being told, and from behavior. The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside (with DL fitting inside both), since ML also requires writing algorithms that cover every eventuality, namely, of the learning process. The crucial point is that they share the idea of using computation as the language for intelligent behavior. What kind of computation is used and how should it be programed? This is not the right question. Computation neither rules out search, logical, probabilistic, and constraint programming techniques nor (deep) (un)supervised and reinforcement learning methods, among others, but does, as a computational model, contain all of these techniques. Reconsidering AlphaGo: AlphaGo and its successor AlphaGo Zero (Silver et al., 2017) both combine DL and tree search—ML and AI. Alternatively, the “Allen AI Science Challenge” (Schoenick et al., 2017) should be considered. The task was to comprehend a paragraph that states a science problem, at the middle school level and then to answer a multiple-choice question. All winning models employed ML yet failed to pass the test at the level of a competent middle schooler. All winners argued that it was clear that applying a deeper, semantic level of reasoning with scientific knowledge to the question and answers, is the key to achieving true intelligence. In other words, AI has to cover knowledge, reasoning, and learning, using programmed and learning-based programmed models in a combined fashion. Using computation as the common language, we have come a long way, but the journey ahead is still long. None of today's intelligent machines come close to the breadth and depth of human intelligence. In many real-world applications, as illustrated by AlphaGo and the Allen AI Science Challenge, it is unclear whether problem formulation falls neatly into fully learning. The problem may well have a large component, which can be best modeled using an AI algorithm without the learning component, but there may be additional constraints or missing knowledge that take the problem outside its regime, and learning may help to fill the gap. Similarly, programmed knowledge and reasoning may help learners to fill their gaps. There is a symmetric difference between AI and ML, and intelligent behavior in machines is a joint quest, with many vast and fascinating open research problems: • How can computers reason about and learn with complex data such as multimodal data, graphs, and uncertain databases? • How can preexisting knowledge be exploited? • How can we ensure that learning machines fulfill given constraints and provide certain guarantees? • How can computers autonomously decide the best representation for the data at hand? • How do we orchestrate different algorithms, involving learned or not learned ones? • How do we democratize ML and AI? • Can learned results be physically plausible or easily understood by us? • How do we make computers learn with us in the loop? • How do we make computers learn with less help and data provided by us? • Can they autonomously decide the best constraints and algorithms for a task at hand? • How do we make computers learn as much about the world, in a rapid, flexible, and explainable manner, as humans? Answering these and other similar questions will put the dream of intelligent and responsible machines into reach. Fully programmed computations, together with learning-based programmed computations, will help to better generalize, beyond the specific data that we have seen, whether a new pronunciation of a word or an image will significantly differ from those we have seen before. They allow us to go significantly beyond supervised learning, towards incidential and unsupervised learning, which does not depend so much on labeled training data. They provide a common ground for continuous, deep, and symbolic manipulations. They allow us to derive insights from cognitive science and other disciplines for ML and AI. They allow us to focus more on acquiring common sense knowledge and scientific reasoning, while also providing a clear path for democratizing ML-AI technology, as suggested by De Raedt et al. (2016) and Kordjamshidi et al. (2018). Building intelligent systems requires expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low-level of abstraction. Building intelligent systems also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. There is actually a lack of theoretical understanding that could be used to remove these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address the challenges faced by AI and ML practitioners, such as dealing with messy, real-world data at the right level of abstraction and with constantly changing problem definitions. Finally, data-driven science is an exploratory task. Starting from a substantial foundation of domain expert knowledge, relevant concepts as well as heuristic models can change, and even the problem definition is likely to be reshaped concurrently in light of new evidence. Interactive ML and AI can form the basis for new methods that model dynamically evolving targets and incorporate expert knowledge on the fly. To allow the domain expert to steer data-driven research, the prediction process additionally needs to be sufficiently transparent. Machine learning and AI complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them. Our algorithms should support (re)trainable, (re)composable models of computation and facilitate reasoning and interaction with respect to these models at the right level of abstraction. Multiple disciplines and research areas need to collaborate to drive these breakthroughs. Using computation as the common language has the potential for progressing learning concepts and inferring information that is both easy and difficult for humans to acquire. To this end, the “Machine Learning and Artificial intelligence” section in Frontiers in Big Data welcomes foundational and applied papers as well as replication studies from a wide range of topics underpinning ML, AI, and their interplay. It will foster the scholarly discussion of the causes and effects of achievements providing a proper perspective on the obtained results. Using the common language of computation, we can fully understand how to achieve intelligent behavior in machines. The author confirms being the sole contributor of this work and has approved it for publication. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. De Raedt, L., Kersting, K., Natarajan, S., and Poole, D. 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Nature 550, 354–359. doi: 10.1038/nature24270 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: machine learning, artificial intelligence, deep learning, computation, learning methods Citation: Kersting K (2018) Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines. Front. Big Data 1:6. doi: 10.3389/fdata.2018.00006 Received: 14 June 2018; Accepted: 24 October 2018; Published: 19 November 2018. Edited and reviewed by: Dan Roth, University of Pennsylvania, United States Copyright © 2018 Kersting. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Kristian Kersting, [email protected]
, Paola Corsano
Published: 18 April 2018
Frontiers in Psychology, Volume 9; https://doi.org/10.3389/fpsyg.2018.00558

Abstract:
The Internet was born in the United States in the second half of the twentieth century; it was initially used for military purposes but has since become a powerful instrument for nonmilitary use, including the exchange of information all over the world, thanks to the introduction of tools such as the web browser. From the start, the World Wide Web assumed several functions (e.g., recreation, education, and business) but preserved a private dimension. To connect, people needed access to an Internet-connected computer, which represented a separation from real life, or a virtual reality. A video-terminal device helped these people to immerse themselves in salient but virtual images and sounds; this immersion could induce symptoms such as dissociation (Schimmenti and Caretti, 2010). In the 1990s, scientists developed a conceptualization of the misuse of the Internet and of Internet-addiction disorder (IAD) that was coherent with their conception of the Internet as virtual reality. The strongest criterion for distinguishing healthy Internet use from misuse was connection time; this criterion was supported by several empirical studies regarding its relationship with psychopathological symptoms (Young, 1998; Quayle and Taylor, 2003; Musetti et al., 2016, 2017). However, over the last two decades, Internet use has given rise to global sociocultural changes and has had important implications for the functioning of people's minds (Clowes, 2015). Today, digital and connectable tools such as smartphones are powerful, very small, portable, and (thanks to WiFi and cloud technology) able to store a great deal of salient information about people's lives. These tools thus assume the function of an e-memory (electronic memory) by expanding cognitive memory (Clowes, 2015). Virtual reality is no longer synonymous with the Internet, so there is a need to reformulate the conceptualization of the Internet by taking into account its evolution. The extent of digital information in every sphere of people's lives has caused the integration of the Internet into the cognitive tasks people perform in their daily routines, leading to the consideration of the Internet as part of an extended concept of cognition (Smart et al., 2017). The concept of the Internet as a tool to connect to a virtual reality that is separate from the real world is no longer current, so a new concept of the Internet that takes its environmental features into account is needed. This concept is in line with Floridi's (2014) idea of an infosphere that shapes people's reality. The conceptualization of the Internet as an environment rather than as a tool leads to the reformulation of IAD theory. If the Internet is not just a tool to be utilized, the theoretical model of IAD cannot be based on behavior connected to its overuse, misuse, or abuse. Based on this opinion, we present arguments in favor of reconsidering the Internet as an environment rather than as a tool. In the following section, we explore the Internet's role in cognitive ecology, as well as the inadequacy of treating the Internet as a tool and thus of the current Internet-addiction model. One conceptualization that could help explain the idea that the Internet is a superstructure within which people operate is that of cognitive ecology (Smart, 2017), which has been defined as “the multidimensional contexts in which we remember, feel, think, sense, communicate, imagine, and act, often collaboratively, on the fly, and in rich ongoing interaction with our environments” (Tribble and Sutton, 2011, p. 94). Today's society is digital (Lupton, 2015), and the Internet represents the main part of its cognitive ecology. In the theory of situated cognition (Robbins and Aydede, 2009), cognition is embodied (Gallagher, 2005), embedded (Rupert, 2004), extended, and distributed or collective (Smart et al., 2017). These theories reconceptualize cognition; instead of the classical, individualistic and intra-brain conception of cognition, these theories take into account the relationships among the brain, the body, and the environment to determine the functional products of the mind (Smart et al., 2017). Thanks to the Internet's development (in terms of devices, apps, and social platforms), it can be seen as the principal structure of embodied, embedded, extended, and distributed cognition. Proponents of the embodied-cognition thesis claim that extra-neural bodily factors shape the course of cognitive processing (Anderson, 2003; Shapiro, 2007, 2011). Mobile or wearable devices such as smartphones are today part of people's daily engagements, and they allow continuous online access, which shapes the course of their daily activities and interactions (Smart et al., 2017). By contrast, proponents of the embedded-cognition thesis claim that the extra-organismic environment plays a role (although not a constitutive one) in cognitive states and processes (Rupert, 2004), thus reallocating cognition to within biological boundaries (Smart et al., 2017). The Internet can be inserted within this vision of cognition. For example, augmented reality devices (Smart et al., 2017) such as Google Glass can enrich the sensory experience and have repercussions on cognitive processes. Advocates for the extended-cognition thesis claim that cognitive processes supervene on the relation between a cognitive agent and the social environment in which that agent is situated (Smart et al., 2017). Internal (biological) structures and external devices work in a pair relationship in which biological structures can perform the same operations as external factors (see Clark and Chalmers, 1998) or in a complementary relationship in which external devices can perform operations that biological structures cannot, and vice-versa (see Sutton, 2010; Heersmink, 2015, 2016). The debate regarding the parity or complementarity of the Internet and the brain has not yet been resolved (Smart et al., 2017), and it is not our aim to discuss that issue here. What is important in this context is that Internet devices are so widespread in the social environment that they are the principal external factor through which people's brains relate to and structure external representations; these devices have thus become integrated in people's cognitive architectures (Halpin et al., 2010). Consider the examples of how the use of GPS has modified people's spatial navigation, including its important impact on the neural mechanisms of spatial cognition (Maguire et al., 2000), or considering how Facebook use shapes the representation of the self, including an important impact on the self-concept. This effect is not merely about the interaction between a cognitive agent and environmental devices or about the scaffolding function that external factors have within the mind. The Internet is more than just a scaffold that guides and integrates the mind as it performs functions that the mind cannot accomplish alone (Sterelny, 2010). Rather, people created the Internet to meet people's needs, and the Internet's functions, such as that of e-memory, have changed the ways in which people remember and behave in the world (i.e., a person can recover remote information without having to store every piece of information from day to day). The Internet has changed people's brain structures, which have in turn evolved in such a way as to change how the Internet meets new needs (Clowes, 2013). This view requires consideration of the Internet as an extended function of the mind, including its actual effects on the development of the brain's circuits. In a similar vein, the advent of cooked food changed not only people's tastes but also their digestive functions and the structures of their jaws and teeth; it thus had repercussions on environmental adaptation and species conservation (Wrangham, 2009; Sterelny, 2010). The last thesis regarding the Internet's crucial role is that of distributed cognition. This thesis relates to the cognitive processes (e.g., focusing, reasoning, remembering, and problem-solving) that a collection of individuals share. Again, the Internet has allowed people to take advantage of a huge network of geographically distributed individuals who process cognitive operations at the same time and on the same issue. This opportunity boosts collaboration, information exchange, and the coordination of collective efforts and collective decision-making (Chi et al., 2008; Chi, 2009; Smart et al., 2017). These theories of cognition are today a matter of debate. Some authors have preferred one vision over others; others have considered the theories to not be mutually exclusive and to instead by various integrated aspects of cognition. In the article, we want to underline that, irrespective of the vision that one embraces, the Internet represents a fundamental part of cognitive processing. It not only boosts cerebral operations but also shapes, modulates, and changes neurobiological structures, functioning, and development; the Internet is also, in turn, shaped and developed in a process that resembles a spiral of mutual influence toward ever-higher steps of development. In this sense, a view of the Internet as a mere tool to be utilized functionally or dysfunctionally, as in the model of Internet addiction, is reductive in this era. Thus, considering the Internet as a digital environment that encloses and characterizes cognitive processes is more useful for understanding the phenomenon that we are studying. Consider the people of the nineteenth century, who began to deal with great technological changes (due to the Second Industrial Revolution). The invention of the train, for example, represented a substantial change in the connection between long distances and/or in the amount of people or material carried. People also had to learn to use trains by acquiring new behaviors such as buying tickets and waiting for the departure time; these behaviors could be functional or dysfunctional (examples of the latter include buying an expensive ticket or getting on the wrong train). Although the train was intended as an instrument for traveling to a destination, its growth into a global network and its various functions (industrial, civil, and military) fostered the sociocultural revolution of the 1800s. The train changed the way people thought about industry; thus, in the nineteenth century, the bourgeoisie affirmed its power, and science and literature became more liberal. In other words, what began as a mere instrument evolved into an environmental change that people had to adapt to. The example of the train concretely describes the difference between a tool and a sociocultural environment. The dynamics of the person–tool interaction have been thoroughly studied and represent the basis for the strong Vygotskian psychological tradition (Luria and Vygotsky, 1992). According to this tradition, children organize their behavior by learning to use tools or through external stimuli (Vygotsky, 1997). For example, a child might pay attention to a tool and then name the tool; the name of the tool thus becomes a word in the child's internal speech, thus inducing a new step in the child's reasoning and language functions (Bodrova et al., 2011). This explains how the development of higher brain functions is mediated by the utilization of tools, a view that fits well with the thesis of embodied cognition, according to which external tools shape the course of cognitive processing. It also fits with the thesis of scaffolding cognition, according to which external tools drive cognitive functioning. Within the latter conceptualization, the Internet can be seen as a tool through which people interact and whose use shapes the course of their cognitive processing. However, this view is reductive because it does not take into account the extra-brain operations that the Internet can provide but that the brain cannot. For instance, in the scaffolding view, people can interact with a social platform that reminds them of a salient episode that occurred in their past, thus shaping their emotional reactions and/or thoughts. However, in this view, social-platform interaction does not allow for the improvement of memory systems to provide a better ability to remember salient episodes from the past. Rather, the social platform is seen as a context inside which a limited memory system can take advantage of externally stored information, thus optimizing its work and allowing cognitive resources to be delivered to other processes. In other words, although the Internet—at least in its embryonic form, when recreation was the main online activity—was once considered a tool that shaped and mediated cognition and behavior, today, it is considered an environment that characterizes the people of today. To return to the example of the train, at the beginning, it was considered to be a tool for enhancing travel, but after a few decades, it began to shape the environment that characterized people in the industrial era. Interestingly, Floridi (2014) explained how tools, in addition to being utilized to boost behaviors, have also changed the sociocultural fabrics of various eras, thereby marking the evolution of humanity. The use of bronze (starting in 3000 BC) changed the prehistoric world into the Bronze Age. Similarly, today, people are part of an information society (also known as the infosphere) and can access whatever information they lack (e.g., facts about laws, politics, or science), meaning that there are no boundaries between their online and offline lives—a state known as “onlife” (Floridi, 2014). As the reader may have noted, the arguments in favor of considering the Internet as an environment have multiplied and advanced. It is important to underline this vision here because the classical model and the resulting research into IAD are based on an obsolete conceptualization of the Internet as a tool. Over the last three decades, the literature on this phenomenon has been abundant, but scholars have not reached an agreement on which criteria must be focused on when determining the dividing line between pathological or nonpathological Internet use (Musetti et al., 2016). The main models of Internet-related pathologies retrace those of other addictions (Young, 1998). If the theorists of IAD do not consider the Internet to constitute the current information society, they risk pathologizing a normal behavior, similarly to what happened for new addictions (as with new terms such as “shopaholic” or “workaholic”; see, e.g., Billieux et al., 2015). Without the environmental framework of the Internet, the theorization of pathological Internet use is limited to a reductive list of potentially problematic behaviors (Schimmenti, 2017), such as using the Internet for pornography or gambling. It is noteworthy that the DSM-5 does not resolve this impasse, as it does not mention IAD; the only related disorder, online gaming disorder, is inserted in a section regarding diagnoses that require further study (American Psychiatric Association, 2013). The seven symptoms of IAD in the classical model are withdrawal; tolerance; concern over Internet use; heavier or more frequent Internet use than intended; centralized activities to obtain more from the Internet; loss of interest in other social, occupational, and recreational activities; and disregard for the physical or psychological consequences of Internet use (Young, 1998). These criteria must be present for at least 1 year. Clearly, these criteria are not applicable to the vision of the Internet as an environment. If the Internet constitutes the social fabric, it becomes impossible to withdraw from it, making it impossible to be concerned over Internet use; it likewise becomes impossible to focus on obtaining the Internet. In particular, the criterion of “heavier or more frequent use of the Internet than intended” lacks a comparative parameter in the environmental view of the Internet. How much Internet use is normal if the Internet is ingrained in every part of people's lives and also extends their cognition? In the environmental view, considering the amount of time spent online to be a pathological criterion would mean seeing the entire information society as pathological. Moreover, and paradoxically, a rehabilitation treatment based on this criterion would be centered on reduced Internet access, thus limiting the use of extended and collective cognition (Smart et al., 2017), which could have important repercussions with regard to social adaptation that, in turn, would favor an increase in other pathological criteria, such as withdrawal from social occupation or recreation. Our position is that the classical IAD model should be reformulated to match the vision of the Internet as a social environment. First, researchers must determine whether it is actually possible to be addicted to the Internet. In other words, can people become addicted to their social fabrics? Perhaps it is possible for a person to manifest difficulties or abnormalities when adapting to a social environment. In a similar vein, new models should ignore utilization-related criteria and instead focus on the symptoms that indicate social maladaptation, which may resemble manifestations of known symptoms such as dissociation, depression, anxiety, and personality disorder (Musetti et al., 2018). If this new focus were applied, a question would need be raised about what preexisting pathological conditions would predispose a person to have difficulty adapting to an environment (Caplan, 2002). Considering the Internet as the current socio-cognitive environment, a person's preexisting intra-brain features could favor the success or failure of the adaptation process. In an interesting model, scholars have suggested that maladaptive cognitions precede the symptomatology of IAD (Davis, 2001; Taymur et al., 2016), thus underlining the comorbidity of IAD with heterogeneous psychopathological diagnoses (Orsal et al., 2013). A child presenting with an attention disorder will have some difficulty adapting to a school environment and to a social network of peers, and this difficulty will often impair the development of the child's intellectual and other cognitive functions. Similarly, a person who is cognitively poorly equipped could fail to take advantage of the Internet's contextual affordances (Ryding and Kaye, 2017). This could result in the unsuccessful extension and/or distribution of cognition processes, with repercussions for the person's cognitive development and risks of pathological adaptation to the digitized environment. A similar view could be used in studies on the appropriate treatments for cognitively predisposing features and to help explain the adaptation processes. We are in favor of treating the Internet as a social environment in which a cognitive agent exists. Our proposal is that Internet use should not be seen as a mere instrumental action to achieve a goal (and which could be functional or dysfunctional); rather, we propose treating Internet use as an action situated in the digital context, as part of a system with a proper structure and rules. Considering the concept of the Internet as a social environment, the classical IAD model should be reformulated, as its implications are obsolete and misleading when applied to studies on the pathological population or on potential treatments. AM: devised and structured the paper; PC: contribute to development and deep revision of the work, with literature analysis and agreement for final approval of the paper. 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Edited by: Reviewed by: Copyright © 2018 Musetti and Corsano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Alessandro Musetti, [email protected]
Alan M. Thompson
AI Magazine, Volume 1; https://doi.org/10.1609/aimag.v1i1.83

Abstract:
As a major scientific society, the AAAI has a responsibility for promoting its field as well as informing its members of the latest technical developments. Since the latter function is adequately performed by the several journals and conference proceedings already mentioned, the editorial committee chose to assign to AI Magazine the task of providing AAAI members and the public as well with a broader perspective on the research activities within AI. The approach we intend to take includes publishing informative expository and survey articles designed not so much for those working within a particular problem domain, but for those outside it who would like to gain a better understanding of the issues and methods currently being studied without having to cull all the technical literature.
Comment
Rose Martin, Ivaylo Kusev, Alex J. Cooke, Victoria Baranova, Paul Van Schaik,
Published: 24 May 2017
Frontiers in Psychology, Volume 8; https://doi.org/10.3389/fpsyg.2017.00808

Abstract:
A commentary onThe Social Dilemma of Autonomous Vehiclesby Bonnefon, J.-F., Shariff, A., and Rahwan, I. (2016). Science 352, 1573–1576. doi: 10.1126/science.aaf2654 An autonomous vehicle (AV) car with 1 passenger (e.g., the car owner) inside is traveling within the speed limit down the road. However, unexpectedly, 10 pedestrians have appeared in its path and it is too late for the car to brake. The car must either save the passenger by driving into the 10 pedestrians and killing them, or save the 10 pedestrians by swerving into a barrier and killing the passenger. Should the AV algorithm be programmed to save the passenger, or to save the greater number of people? This question is of great importance to the AV industry, policy makers, the potential buyers, and the general public. Recent research (Bonnefon et al., 2016) has investigated how humans judge the morality of the two AV algorithms—a utilitarian (saving the greater number of lives; Bentham, 1970) and a non-utilitarian passenger-protective (saving the passenger). Using moral dilemma scenarios in which an AV is programed to be utilitarian or passenger-protective, participants were required to rate (on a 0–100 slider) “what action they thought was the most moral,” Bonnefon et al. (2016) found that participants rated the utilitarian algorithm (e.g., sacrificing 1 passenger to save 10 pedestrians) as the moral course of action. However, when the respondents were asked to rate on a scale the likelihood of buying a car with each of the algorithms (to what extent they are inclined), they indicated higher likelihood of purchasing the passenger-protective algorithm than the utilitarian one. This surprising result demonstrates what appears to be a social dilemma—an agent temptation to act in accordance with self-interest (Bonnefon et al., 2016), which often results in the worst outcome for all individuals involved, including the decision-maker (Dawes, 1980; Kollock, 1998). The authors have not explained theoretically the results from this social dilemma; yet they discounted the possibility of uncertainty (e.g., the possibility that people may not be aware or have access to the utilitarian actions and their consequences; Kusev et al., 2016; Zhao et al., 2016). Therefore, the aim of this paper is to provide an insight (theoretical and methodological) and explanation for the surprising reversals of the moral utilitarian preferences reported in Bonnefon et al. (2016). Here, we argue that methodological issues in Bonnefon's et al. (2016) research may have induced uncertainty amongst participants. Accordingly, it is plausible that the difference in response to the two questions (what action they thought was the most moral, and to what extent are they are inclined to purchase an AV with each algorithm) is caused by the restricted accessibility of moral utilitarian information. For example, respondents may not realize that a car purchaser is also inevitably a pedestrian too. We suggest that full utilitarian descriptions provide accessibility to utilitarian tasks and their consequences and can eliminate the conflicting responses to these two questions. Psychological uncertainty has been found to account for respondents' differences in utilitarian choice for morally sensitive scenarios (Kusev et al., 2016); comprehensive moral tasks and questions reduced decision uncertainty and boosted utility maximization. Kusev et al. (2016) argued that in moral decision-making tasks (the trolley and footbridge dilemma; Thomson, 1985; Greene et al., 2001) participants are given (i) a partial moral task description which outlines what will happen should they throw the switch/push the stranger, and (ii) asked a partial appropriateness of action question for only one of the two possible moral actions (yes/no answers). Hence, the respondents are left to infer what will happen should they refrain from this action and asked to judge the appropriateness of only one of the actions. Thus, the moral dilemma and question contain only “partial utilitarian descriptions,” inducing uncertainty. Accordingly, we argue that all of the scenarios presented by Bonnefon et al. (2016) contained partial utilitarian information, inducing decision uncertainty amongst respondents. For instance, in some of the scenarios each respondent is required to imagine themselves as a passenger inside the car and are therefore presented with only one side of the situation. We propose that if the respondents imagine themselves as pedestrians as well, uncertainty may be reduced as respondents would be able to access both possibilities—being a passenger and a pedestrian. For instance, being in the car and benefiting from the passenger-protective algorithm does not expose the respondents to the greater danger/risk of other cars employing the same algorithm when they are not in the car (e.g., as pedestrians). In addition to the partial information in the scenarios presented by Bonnefon et al. (2016), we further argue that the questions the authors claim to produce conflicting results in studies 3, 4, and 6 do not fully account for the willingness to buy an AV car. In the experiments participants were presented with a moral scenario where an AV can either be programmed to be utilitarian, passenger-protective, or select either option at random. This scenario was followed by questions, one of which required the participants to rate their relative willingness to buy an AV for themselves—“How would you rate your relative willingness of having an AV with each of these algorithms?.” The results revealed that participants preferred to purchase the passenger-protective AV, which once again conflicted with their general preference for utilitarian AV. Due to this conflict, our ongoing research aims to comprehensively understand utilitarian behavior by providing respondents with two moral questions regarding their willingness to purchase an AV, and their willingness for other people to purchase an AV: “Please rate how willing you would be to purchase an AV that is programmed with each of these algorithms” and “Please rate how willing you would be for other people to purchase an AV that is programmed with each of these algorithms.” It is plausible that full accessibility to moral tasks and questions reduces decision uncertainty and maximizes utility in moral decision-making with AVs. In our proposal, we argue that utility maximization can be increased by enabling participants to imagine themselves as not only as a passenger of an AV, but also as a pedestrian, and measure their judgments appropriately. RM drafted and revised the manuscript. PK (corresponding author) initiated and revised the general commentary. 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Keywords: autonomous vehicles, utility theory, moral dilemmas, uncertainty, accessibility Citation: Martin R, Kusev I, Cooke AJ, Baranova V, Van Schaik P and Kusev P (2017) Commentary: The Social Dilemma of Autonomous Vehicles. Front. Psychol. 8:808. doi: 10.3389/fpsyg.2017.00808 Received: 28 January 2017; Accepted: 02 May 2017; Published: 24 May 2017. Edited by: Reviewed by: Copyright © 2017 Martin, Kusev, Cooke, Baranova, Van Schaik and Kusev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Petko Kusev, [email protected]
Published: 8 July 2015
Abstract:
The use of domestic service robots is becoming widespread. While in industrial settings robots are often used for specified tasks, the challenge in the case of robots put to domestic use is to afford human-robot collaboration in a variety of non-predefined and different daily tasks. Herein, we aim at identifying and understanding the conditions that will facilitate flexible collaboration between humans and robots. Past research of social and personality psychology was mainly focused on individual's self-regulation, defined as the ability to govern, or direct attention, resources, or action toward the realization of a particular goal (Higgins, 1989; Kruglanski et al., 2002). There is evidence that pursuing goals with the presence of others influences self-control (Fishbach and Trope, 2005), however only little is known on dyadic processes of self-regulation. Additionally, whereas research of goal pursuit in social psychology has mainly been associated with general processes of the structure and function of goals (Gollwitzer and Bargh, 1996; Carver and Scheier, 1998; Kruglanski et al., 2002; Fishbach and Ferguson, 2007; Custers and Aarts, 2010), human-robot interaction involves pragmatic interpersonal dilemmas such as how to coordinate human-robot activity and what knowledge should be shared between humans and robots over the course of action. To fill this gap, in what follows, we will define the unique characteristics of what we term as human-robot coupled self-regulation, which has the unique features of a dyadic asymmetric team aimed to increase the affordances of an individual in different activities. We will describe the unique characteristics of human-robot interaction and its special challenges toward goal pursuit. Our first assumption is that self-regulation of a human-robot couple could be conceptualized as a unique team configuration. A team is “a distinguishable set of two or more people who interact, dynamically, interdependently, and adaptively toward a common and valued goal/objective/mission, who have each been assigned specific roles or functions to perform, and who have a limited life-span of membership” (Salas et al., 1992, p. 4; Salas et al., 2010). Team members have differentiated responsibilities and roles (Cannon-Bowers et al., 1993). Therefore, essential for a team's successful performance is the understanding of the abilities and behaviors of its members that fit their experience and unique expertise for the task at hand. Because humans and robots differ in their level of agency (the capacity to act and do) and their level of experience (the capacity to feel and sense), (Gray and Wegner, 2012), we argue that their contribution to the team is not symmetric. Based on the reasoning that genuine authorship of an action or situation may not always be clear (Dijksterhuis et al., 2008), we suggest that defined requirements of person, robot, and situation are essential to reduce the expectation gap. Our perspective is that human-robot collaboration should be viewed in terms of functionality, to extend possibilities for the kinds of goals that humans want to pursue. These instrumental relations between a person and her tool, used to increase the fit between person and environment, are termed affordances (Gibson, 1979). Following this view, we argue that robots can be perceived as self-regulatory tools to increase affordances across different situations (Koole and Veenstra, 2015). Our instrumental relational approach enables flexibility in tuning the robot's level of responsiveness and dominance in human-robot social contexts. For example, whereas the human member of the team holds a fixed ownership position, the robot's level of dominance could vary by user demands, or depending on the situation. To understand the usefulness of this principle, let us take for example 80 year old Mrs. Brown. She is physically fragile, but it is important for her to maintain an independent life style. This is why she has “Rupert,” a multi-functional platform robot that serves as her aid. When she leaves the house she may want “Rupert” to lead and find the safest walking path to the store, thus she may set it to high dominance and responsiveness, in case she startles. At home, she may not desire high level of proactive care-taking and leave “Rupert” to be on call. Our second assumption is that human-robot coupled self-regulation is based on concrete rather than abstract level of agreement. Carrying out human-robot joint actions demands continuous coordination on at least five elements: (1) who takes part; (2) what is the role of each member; (3) what is the joint goal; (4) how does each team member contribute to the timing and synchronization; and (5) where the actions take place (Clark, 2005). To address this, the robot should identify where the focus of attention of the human is, to what degree the attention of the human is focused on team actions, and how to convey feedback. Similarly, the human needs to calibrate expectations from the robot, i.e., be invested in the robot's immediate action or approval of action, and how to respond to the robot's requests (Alami et al., 2005). Coupled self-regulation of goals requires agreement on goal setting and goal striving as two basic phases in goal pursuit (Gollwitzer and Oettingen, 2011). Whereas, robots may act automatically from initiation to completion of the task, humans' possible reflection on their performance may involve conscious awareness and create new representations of behavior, thus leading to communication gaps (Baumeister and Bargh, 2014). According to the action identification theory, a specific action can be verbally identified and interpreted from different levels of abstraction, ranging from low-level identities that specify how the action is performed, to high-level identities that signify why the action is performed. For instance, a person who “drinks water” can identify it as “holding a glass” (low level), or as “relieving thirst” (high level) (Vallacher and Wegner, 1987, 1989). This helps explain why different action identifications by human and robot may lead to dissimilar systems of goals and means of attainment (Kruglanski et al., 2002; Shah et al., 2002). To address these challenges, we suggest the use of multiple human-robot forms of communication to pursue the joint goal. Lohan et al. (2014) proposed a distinction between two kinds of actions: path-oriented and manner-oriented, that can be communicated via two different linguistic utterance styles. Whereas, in path-oriented utterances the goal is stressed, in manner-oriented utterances, the means of motion are emphasized (e.g., Talmy, 1991). In our example, Mrs. Brown and “Rupert” carry a recliner to the porch (Path-“let's move the chair to the porch” or Manner-“I want to read my book on the porch”). Suddenly the phone rings and Mrs. Brown wants to go and answer ((Path-“let me go get the phone” or Manner-“I need to answer this call”). “Rupert” must understand that the goal has changed and pause. Research indicates that professional and social interactions between team members can develop the team's social cognition (Klimoski and Mohammed, 1994). There is evidence that a team's fluent on-going communication regarding goal pursuit reduces the need for preexisting knowledge (Kozlowski and Bell, 2003). In social HRI, it is critical to generate many levels of interaction with the automation. Hence, the robot should always be present and aim to facilitate the goal, even if only to provide recommendations. In civil aviation, for example, communication is key especially if things turn out unexpectedly. In the Northwest 2009 incident in Minneapolis the automation had the capability, but was not designed to point out that the task was not performed as planned and that the pilots missed their destination. To borrow from our previous example, let us suppose Mrs. Brown wants to grab a pillow from the upper cabinet. The robot may not be able to reach so high, but it should continue to collaborate by providing feedback and advice; I cannot reach the uppermost cupboard (failure to complete task) but it is too dangerous for you to try to reach it on your own, if not urgent, perhaps we should call your son, or is there another pillow on a lower shelf? Much of human communication over goal pursuit is based on social cues (e.g., gestures, and mimicry) that automatically generate social judgment and behavior (Chartrand and Bargh, 1999; van Baaren et al., 2003; Leander et al., 2010). Similarly, translation of social cues to social signals leads to inference of human intentions by robotic agents (Fiore et al., 2013). The relevance of automatic embodied cues for joint goal pursuit was demonstrated in human-human and human-robot synchronicity, suggesting that physical synchronicity is associated with experience of responsiveness and empathy (Sebanz and Knoblich, 2009; Cohen et al., 2010; Paladino et al., 2010; Boucher et al., 2012; Hoffman et al., 2014). Embodied communication is not only “used” by robots, but integrated in them to support both the recognition of the human's behavior and the generation of their behavior. Research of social signal processing and modeling multimodal communication, suggests that social and behavioral cues may be detectable from a machine, hence perceivable. Likewise, models of behavior are integrated in a way that a robot exhibits a more natural behavior, aiming at a more successful interaction with the human (Pentland, 2007; Vinciarelli et al., 2012). However, despite emerging findings from the field of embodied cognition on the potential of physical and social cues as an alternative route for communication, it was also claimed that embodied cognition cues can lead to different patterns of activation across different contexts (Loersch and Payne, 2011), thus prediction of behavior may be difficult (Shalev, 2015). A possible way to address this limitation is to use robots in fixed context, where interpretation to human's embodied signals is less ambiguous. For example Loth et al. (2013), have demonstrated that bar staff responded to a set of two non-verbal signals. Foster (2014), indicated that robotic sensors can similarly detect and respond to these signals. Individuals frequently use embodied cues for functional self-regulatory purposes (Balcetis and Cole, 2009; Schnall et al., 2010; Bargh and Shalev, 2012; Shalev, 2014). However, using embodied cues as diagnostic inputs (Williams et al., 2009; Ackerman et al., 2010; Meier et al., 2012; Robinson and Fetterman, 2015; Winkielman et al., 2015) may lead to human-robot miscommunications. For example, human speakers expect co-located listeners to link visually perceivable objects and the verbally described references to them. Thus, humans may expect a co-located robot to have the same visual-verbal linking abilities (e.g., look at the green object on the right), thus developers must integrate the robot's visual system with natural language components to enable this flow of communication (Kopp, 2010; Cantrell et al., 2012; Vollmer et al., 2013). Furthermore, there is also anecdotal evidence of human-human communication misunderstandings in complex scenes. For example orientation can be relative to egocentric, or exocentric (absolute or relative) locations. Soldiers for example, are taught to communicate via the exocentric coordinates of the compass rose. However, most humans tend to naturally orient relative to their egocentric perspective, which may be difficult for robots to depict. Interestingly, Cassenti et al. (2012) found that instructors used exocentric references to direct the robot and that it improved their performance relative to egocentric-only commands. To address this communication gap, we argue that shared database, sensors and multiple types of displays and interaction means (e.g., physiological measures, eye tracking, voice, touch, text, button presses etc.) can enrich the robot's capacity of perception and expression. Similarly, to reduce expectation issues, technology can shape the way the user acts on the robot, how individuals understand what to expect from it, and how they can interact with a robot to refine their mutual understanding of the task at hand. 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The scaffolded mind: higher mental processes are grounded in early experience of the physical world. Eur. J. Soc. Psychol. 39, 1257–1267. doi: 10.1002/ejsp.665 PubMed Abstract | CrossRef Full Text | Google Scholar Winkielman, P., Niedenthal, P., Wielgosz, J., Eelen, J., and Kavanagh, L. C. (2015). “Embodiment of cognition and emotion,” in APA handbooks in psychology. APA handbook of personality and social psychology, Vol. 1. Attitudes and social cognition eds M. Mikulincer, P. R. Shaver, E. Borgida and J. A. Bargh (Washington, DC: American Psychological Association), 151–175. doi: 10.1037/14341-004 CrossRef Full Text Keywords: human-robot interaction (HRI), embodied cognition, self-regulation, goal pursuit, teamwork Citation: Shalev I and Oron-Gilad T (2015) What do we think we are doing: principles of coupled self-regulation in human-robot interaction (HRI). Front. Psychol. 6:929. doi: 10.3389/fpsyg.2015.00929 Received: 15 March 2015; Accepted: 22 June 2015; Published: 08 July 2015. Edited by: Reviewed by: Copyright © 2015 Shalev and Oron-Gilad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Idit Shalev, [email protected]
Claus Atzenbeck
ACM SIGWEB Newsletter pp 1-4; https://doi.org/10.1145/2749279.2749280

Abstract:
Munmun De Choudhury is an assistant professor at the School of Interactive Computing, Georgia Tech and a faculty associate with the Berkman Center for Internet and Society at Harvard. Munmun's research interests are in computational social science, with a specific focus on reasoning about health behaviors from social digital footprints. She has been a recipient of the Grace Hopper Scholarship, recognized with an IBM Emergent Leaders in Multimedia award, and recipient of ACM SIGCHI 2014 best paper award and ACM SIGCHI honorable mention awards in 2012 and 2013. Munmun's work has been extensively covered in popular press as well, including the New York Times, the TIME magazine, the Wall Street Journal, and the NPR. She served as the Program Chair of the International AAAI Conference on Weblogs and Social Media (ICWSM) in 2014, and currently is a member of the Steering Committee of this interdisciplinary conference. Earlier, Munmun was a postdoctoral researcher at Microsoft Research, a research fellow at Rutgers, and obtained a PhD in Computer Science from Arizona State University in 2011.
Mohammad T. Irfan, Luis E. Ortiz
Published: 1 October 2014
Artificial Intelligence, Volume 215, pp 79-119; https://doi.org/10.1016/j.artint.2014.06.004

The publisher has not yet granted permission to display this abstract.
Yun R. Fu
Manifold Learning for 3D Shape Description and Classification; https://doi.org/10.21236/ada606874

Abstract:
Periodically, the US Army conducts detailed measurement surveys of its soldiers as a way to understand the impact that changes in soldier body size have for the design, fit and sizing of virtually every piece of clothing and equipment that Soldiers wear and use in combat. Recently finished US Army Anthropometric Survey (ANSUR II) has collected 3D body scan data of soldiers at the Natick Solider Center (NSC), as shown in Figure 1. By applying new techniques for shape analysis and classification to these 3D body scan data will help designers of clothing and personal protection equipment to understand and fit Army population. The overall research goal of this proposal is to create a new manifold learning framework for large-scale graph decomposition and approximation problems by low-rank approximation and guarantee computable, stable and fast optimizations for 3D shape description and classification. The PI's group has published (or accepted for publication) 1 book through Springer and 13 scientific papers partially supported by this grant. In particular, these papers are in top journals and conference proceedings such as TPAMI, IJCV, TCSVT, ICCV, AAAI, SDM, ACM MM, etc. One paper, 1 out of 384, receives the Best Paper Award in SDM 2014. The PI, Dr. Y. Raymond Fu has received the 2014 INNS Young Investigator Award, from International Neural Networks Society (INNS), 2014. Leveraged by this grant, the PI has been granted an ARO Young Investigator Program (YIP) Award and a Defense University Research Instrumentation Program (DURIP) award.
Published: 11 April 2014
Frontiers in Psychology, Volume 5; https://doi.org/10.3389/fpsyg.2014.00322

Abstract:
The research traditions of memory, reasoning, and categorization have largely developed separately. This is especially true for reasoning and categorization, where the former has focused on logic and probability rules and the latter on similarity processes. For example, classical rules of logic are often considered the basis for human reasoning (Evans et al., 1991) in tasks such as the Wason selection task (Wason, 1966), which requires participants to use deductive reasoning to solve a logic puzzle involving four cards. Reasoning models are typically developed in terms of hypotheses for how relevant rules should be combined and applied to reach conclusions from the relevant premises (Braine et al., 1995). By contrast, in categorization, the predominant theoretical traditions (i.e., prototype theory and exemplar theory) have involved a similarity process (Wills and Pothos, 2012, provide an overview). Such sharp distinctions between cognitive processes have started to break down. For example, Oaksford and Chater (1994) proposed a model of the Wason selection task based on information theory, rather than logical rules. Their model used information maximization in relation to uncertain hypotheses (cf. Anderson, 1991), which is an idea that could plausibly translate across other cognitive processes. Pothos (2010) showed how information maximization could apply to a learning task. Heit et al. (2012) argued that researchers should explore the connections between memory and reasoning. Tversky and Kahneman (1983), Shafir et al. (1990) proposed that when assessing the relative probability of statements about a hypothetical person, Linda, participants employ a process of similarity. Thus, the idea that similar or identical cognitive processes may underlie superficially disparate processes, like categorization and reasoning, is not new. What has been perhaps lacking is the development of specific models, which can be applied across different areas. Our purpose is to outline our ideas regarding such a model for probabilistic reasoning and similarity. We do so in the context of recent work with cognitive models based on quantum probability (QP) theory. Many people are familiar with quantum mechanics. What is perhaps less known is that the ingenious physicists who developed quantum mechanics also invented a new theory of probability, since classical probability (CP) theory was inconsistent with their bold new theory of the physical world. QP theory refers to the rules for assigning probabilities to events from quantum mechanics, without the physics. QP theory is potentially applicable to any area where there is a need to compute probabilities. The motivation to adopt QP theory is typically informed by whether the empirical situation of interest reflects some key properties of QP theory, such as incompatibility, interference, superposition, and entanglement. For example, when two possibilities are incompatible, this means that it is impossible to concurrently assign a truth-value to both. So, if we are certain about one possibility, then we are necessarily uncertain about the other. In CP theory it is always possible to create a complete joint probability distribution for all available alternatives. The quantum cognition research program is relatively new, but there have already been several notable applications, spanning conceptual combination (Aerts, 2009), perception (Atmanspacher et al., 2004), memory (Bruza et al., 2009), reasoning and decision-making (Busemeyer et al., 2011), and similarity (Pothos et al., 2013; for overviews see Busemeyer and Bruza, 2011 and Pothos and Busemeyer, 2013). We note that all these applications of QP theory in cognition have the form of standard cognitive models; they make claims regarding cognitive representations and processes, without any assumptions regarding the underlying neural substrate. In particular, quantum cognitive models do not require a quantum brain (the quantum brain hypothesis has been extremely controversial; Litt et al., 2006; Hammeroff, 2007). QP theory is a geometric approach to probability where different possibilities (or events or questions) are represented as subspaces, of varying dimensionality, in a multidimensional Hilbert space. Hilbert spaces are like vector spaces, but with some additional properties. The system of interest (e.g., the cognitive state of a participant in an experimental task) is a vector in the Hilbert space, called the state vector. Probabilities are determined by projecting the state vector onto different subspaces and computing the squared length of this projection. For example, consider Tversky and Kahneman's (1983) famous Linda problem, where participants read a story about a hypothetical person, Linda, and were asked to judge the likelihood of different features of Linda. We assume that the mental state vector corresponds to the representation of the information about Linda, after reading the story. As illustrated in Figure 1, the relevant Hilbert space includes subspaces for all relevant features of Linda, such as whether she is a feminist. Then, to extract the probability that a participant in the experiment will consider Linda to be a feminist, we project the state vector onto the feminist subspace, and compute the squared length of this projection. Generally, probability assignment in QP theory essentially involves a process of overlap between the cognitive state (modeled by the state vector) and different possibilities (modeled by subspaces). Thus, probability assignment in QP theory is a plausible candidate for a similarity process as well. Indeed, Sloman (1993) independently presented a model of induction, based on ideas closely resembling the formal properties of probabilistic computation in QP theory. Figure 1. The QP model for the conjunction fallacy. To calculate the probability that Linda is a bank teller, we project the state vector (shown in black) onto the bank teller subspace (shown in blue) and compute the squared length of this projection (shown in orange). To calculate the conjunction that Linda is both a feminist and a bank teller, we first project the state vector onto the feminist subspace (shown in red) and then project it onto the bank teller subspace and compute the squared length of this projection (shown in green). The process of projection in QP theory cognitive models is assumed to correspond to a process of thinking/evaluating the corresponding premise. However, for incompatible possibilities, it is impossible to identify a common projection to both relevant subspaces. This is equivalent to saying that it is impossible to concurrently assign a truth-value to incompatible observables. In order to compute a conjunction for incompatible observables (e.g., as needed for modeling the Linda experimental task), Busemeyer et al. (2011) postulated a process of sequential projection: the state vector is first projected onto the more likely predicate (feminism in the Linda example; cf. Gigerenzer and Goldstein, 1996) and then it is projected onto the less likely one (bank teller). The squared length of the resulting vector is the probability for the conjunction, according to QP theory. Note this number decomposes to the product of the probability of the marginal times the conditional, as one would expect from CP theory. Similar ideas of sequential projection have been used in other QP models to account for order effects in inference and causal reasoning (Trueblood and Busemeyer, 2011, 2012). The basic computation Pothos et al. (2013) proposed for the quantum similarity model is nearly identical. Suppose we are modeling the similarity of object A to object B. Similarity assessment is assumed to involve thinking about the first object and then the second, a process which can be modeled by projecting first onto the A subspace and then onto the B one, and computing the squared length of the resulting vector. Thus, the computation for assessing the similarity between A and B is exactly the same as for computing the conjunction between A and B, with two exceptions. The first relates to the order of projection. In decision-making tasks, we assume that the task does not typically impose any constraints on which premise is considered first. A corresponding choice has to be made and choosing the more likely premise first is a reasonable assumption. In similarity tasks, the similarity question is often phrased in a way that imposes a particular directionality (Tversky, 1977) and it is this directionality which constraints the order of projection. The second exception relates to how the initial mental state vector is determined. In decision-making applications, the state vector is plausibly determined by the information initially presented. In similarity, the state vector is set so that it does not bias the similarity judgment toward either of the compared objects. One may question these assumptions. However, QP theory is a mathematical theory of probability and we cannot expect psychological predictions to emerge without introducing some psychological assumptions too. The assumptions regarding projection order are exactly of this kind. It is worth noting that the quantum model for the conjunction fallacy predicts that if a person judges an unlikely event U before judging the conjunction of the unlikely event U and a likely event L, the effect is smaller than when the questions are in the opposite order, which has been confirmed experimentally by Stolarz-Fantino et al. (2003) and Gavanski and Roskos-Ewoldsen (1991). Overall, the decision-making model for the conjunction fallacy (Busemeyer et al., 2011) and the similarity model for Tversky's (1977) findings have similar forms. We think that this is a novel accomplishment, in that we provide a formal and testable expression of the idea that similarity and reasoning processes may involve the same or very similar cognitive mechanisms. Of course, the value of the modeling lies in the extent to which it can inform empirical prediction. Though speculative, we currently think there are a number of promising directions. For example, order effects in similarity judgments can arise because of differences in the degree of knowledge we have for one of the compared elements vs. the other (such differences can be related to the relative dimensionality of the corresponding subspaces). It is likewise possible that, in decision-making tasks, conjunctions in certain directions will be more natural than others. Tversky (1977) also reported a diagnosticity effect; where the similarity between two options resulted in a third, separate option being preferred in a matching task. Participants in this task were asked to decide which country was most similar to Austria. In one condition, the candidate choices were Sweden, Hungary, and Poland, and participants favored Sweden. However, when the candidate choices where changed to Sweden, Norway, and Hungary, participants preferred Hungary. Tversky explained the effect through a change in grouping–Eastern vs. Western Europe in the first condition and Nordic vs. non-Nordic in the second. This diagnosticity effect is analogous to a similarity effect in decision-making: when introducing an option similar to one of the existing options, this decreases the desirability of the similar options (cf. Trueblood et al., 2013). The quantum similarity model can capture the diagnosticity effect and the same mechanism has the potential to accommodate the corresponding result in decision-making. Likewise, the order of projections in the quantum similarity model allows for violations of the triangle inequality in similarity judgments and perhaps similar violations in decision-making can arise in the same way. The triangle inequality is a fundamental property of distance that must be obeyed by similarity measures based on simple functions of distance. In similarity judgments, the triangle inequality can be expressed as Similarity(A, B) > Similarity(A, C) + Similarity(C, B). Tversky (1977) reported a violation of the triangle inequality by having participants judge the similarity between three countries: Russia, Jamaica, and Cuba. He found that the similarity of Russia and Jamaica was smaller than the similarity of Russia and Cuba plus the similarity of Cuba and Jamaica. Is it meaningful to consider whether a general model of cognition could be based on QP principles? This is certainly an exciting proposition for researchers in the quantum cognition area. But a few qualifications need be noted. First, CP theory cognitive models can be and have been extremely successful too (e.g., Griffiths et al., 2010). A preference for the unique features of QP theory, against CP theory, can be motivated in situations where there is evidence of context effects, interference effects, and sequence effects. It is also possible that there will be empirical findings beyond both CP theory and QP theory. In fact, QP theory is a highly constrained framework and we already have simple, non-parametric tests, which can test for consistency with QP principles (Wang and Busemeyer, 2013). All of these possibilities provide exciting directions for future research. Although we did not discuss memory processes explicitly, there has been initial work in developing a QP model of memory recognition (Busemeyer and Trueblood, 2010). In sum, we think that the current quantum cognitive models provide an encouraging starting point in exploring the commonalities between memory, reasoning, and categorization, because of the simplicity of the basic ideas and their applicability across areas. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Jennifer S. Trueblood was supported by NSF grant SES-1326275, Emmanuel M. Pothos by Leverhulme Trust grant RPG-2013-004, and Jerome R. Busemeyer by NSF grant ECCS – 1002188. Emmanuel M. Pothos and Jerome R. Busemeyer were supported by Air Force Office of Scientific Research (AFOSR), Air Force Material Command, USAF, grants FA 8655-13-1-3044 and FA 9550-12-1-0397 respectively. 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L. (2009). Is there something quantum-like about the human mental lexicon? J. Math. Psychol. 53, 362–377. doi: 10.1016/j.jmp.2009.04.004 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Busemeyer, J. R., and Bruza, P. (2011). Quantum Models of Cognition and Decision Making. Cambridge, UK: Cambridge University Press. Busemeyer, J. R., Pothos, E., Franco, R., and Trueblood, J. S. (2011) A quantum theoretical explanation for probability judgment “errors”. Psychol. Rev. 118, 193–218. doi: 10.1037/a0022542 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Busemeyer, J. R., and Trueblood, J. S. (2010). “Quantum model for conjoint recognition,” in AAAI Fall Symposium Series (Arlington, VA: Association for the Advancement of Artificial Intelligence). Evans, St B. T. J., Newstead, S. E., and Byrne, R. J. M. (1991). Human Reasoning: The Psychology of Deduction. Hove: Lawrence Erlbaum Associates. Gavanski, I., and Roskos-Ewoldsen, D. R. (1991). Representativeness and conjoint probability. J. Pers. Soc. Psychol. 61, 181–194. doi: 10.1037/0022-3514.61.2.181 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Gigerenzer, G., and Goldstein, D. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–669. doi: 10.1037/0033-295X.103.4.650 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., and Tenenbaum, J. B. (2010). Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci. (Regul. Ed.) 14, 357–364. doi: 10.1016/j.tics.2010.05.004 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Hammeroff, S. R. (2007). The brain is both a neurocomputer and quantum computer. Cogn. Sci. 31, 1035–1045. doi: 10.1080/03640210701704004 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Heit, E., Rotello, C., and Hayes, B. (2012). “Relations between memory and reasoning,” in Psychology of Learning and Motivation, Vol. 57, ed B. H. Ross (San Diego, CA: Academic Press), 57–101. doi: 10.1016/B978-0-12-394293-7.00002-9 CrossRef Full Text Litt, A., Eliasmith, C., Kroon, F. W., Weinstein, S., and Thagard, P. (2006). Is the brain a quantum computer? Cogn. Sci. 30, 593–603. doi: 10.1207/s15516709cog0000_59 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Oaksford, M., and Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychol. Rev. 101, 608–631. doi: 10.1037/0033-295X.101.4.608 CrossRef Full Text Pothos, E. M. (2010). An entropy model for artificial grammar learning. Front. Cogn. Sci. 1:16. doi: 10.3389/fpsyg.2010.00016 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Pothos, E. M., and Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behav. Brain Sci. 36, 255–327. doi: 10.1017/S0140525X12001525 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Pothos, E. M., Busemeyer, J. R., and Trueblood, J. S. (2013). A quantum geometric model of similarity. Psychol. Rev. 120, 679–696. doi: 10.1037/a0033142 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Shafir, E. B., Smith, E. E., and Osherson, D. N. (1990). Typicality and reasoning fallacies. Mem. Cogn. 18, 229–239. doi: 10.3758/BF03213877 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Sloman, S. A. (1993). Feature-based induction. Cogn. Psychol. 25, 231–280. doi: 10.1006/cogp.1993.1006 CrossRef Full Text Stolarz-Fantino, S., Fantino, E., Zizzo, D. J., and Wen, J. (2003). The conjunction effect: new evidence for robustness. Am. J. Psychol. 116, 15–34. doi: 10.2307/1423333 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Trueblood, J. S., Brown, S. D., Heathcote, A., and Busemeyer, J. R. (2013). Not just for consumers: context effects are fundamental to decision-making. Psychol. Sci. 24, 901–908. doi: 10.1177/0956797612464241 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Trueblood, J. S., and Busemeyer, J. R. (2011). A quantum probability account of order effects in inference. Cogn. Sci. 35, 1518–1552. doi: 10.1111/j.1551-6709.2011.01197.x Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Trueblood, J. S., and Busemeyer, J. R. (2012). A quantum probability model of causal reasoning. Front. Cogn. Sci. 3:138. doi: 10.3389/fpsyg.2012.00138 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Tversky, A. (1977). Features of similarity. Psychol. Rev. 84, 327–352. doi: 10.1037/0033-295X.84.4.327 CrossRef Full Text Tversky, A., and Kahneman, D. (1983). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev. 90, 293–315. doi: 10.1037/0033-295X.90.4.293 CrossRef Full Text Wang, Z., and Busemeyer, J. R. (2013). A quantum question order model supported by empirical tests of an a priori and precise prediction. Top. Cogn. Sci. 5, 689–710. doi: 10.1111/tops.12040 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Wason, P. C. (1966). “Reasoning,” in New Horizons in Psychology, ed B. M. Foss (Harmondsworth: Penguin), 135–151. Wills, A. J., and Pothos, E. M. (2012). On the adequacy of current empirical evaluations of formal models of categorization. Psychol. Bull. 138, 102–125. doi: 10.1037/a0025715 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text Keywords: quantum probability theory, conjunction fallacy, similarity judgment, decision-making, classical probability theory Citation: Trueblood JS, Pothos EM and Busemeyer JR (2014) Quantum probability theory as a common framework for reasoning and similarity. Front. Psychol. 5:322. doi: 10.3389/fpsyg.2014.00322 Received: 03 March 2014; Accepted: 27 March 2014; Published online: 11 April 2014. Edited by: Reviewed by: Copyright © 2014 Trueblood, Pothos and Busemeyer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: [email protected]
, Umut Oztok
Published: 9 November 2012
EMBnet.journal, Volume 18; https://doi.org/10.14806/ej.18.b.551

Abstract:
Motivation and Objectives Storing biomedical data in various structured forms, like biomedical databases or ontologies, and at different locations have brought about many challenges for answering complex queries about the knowledge represented in these resources. For instance, here are two queries about some genes, drugs and diseases: “What are the drugs that treat the disease Depression and that do not target the gene ACYP1?”, “What are the 3 most similar drugs that target the gene DLG4?” One of the challenges of answering such complex queries is to represent the queries in a natural language and present the answers in an understandable form. Another challenge is to efficiently find answers to complex queries that require appropriate integration of relevant knowledge stored in different places and in various forms, and/or that require auxiliary definitions, such as, chains of drug-drug interactions, cliques of genes based on gene-gene relations, similarity/diversity of genes/drugs. Furthermore, once an answer is found for a complex query, the experts may need further explanations about the answer. We have developed novel computational methods and built a software system, called BioQuery-ASP, to handle all these challenges Methods We have addressed the challenges described above using a declarative programming paradigm, called Answer Set Programming (ASP) (Lifschitz, 2008; Brewka et al., 2011). ASP provides an expressive high-level knowledge representation formalism that allows recursive definitions, aggregates, default negation, etc. and efficient automated reasoners, such as Clasp (Gebser et al., 2007), which has recently won first places at ASP and SAT (Boolean Satisfiability) competitions in automated reasoning. Due to these attractive features, ASP has been used in various applications, such as phylogeny reconstruction (Brooks et al., 2006), systems biology (Gebser et al., 2011), service robotics (Aker et al., 2012), decision support systems (Nogueira et al., 2001), automatic music construction (Boenn et al., 2009), workforce management (Ricca et al., 2012). To address the first challenge (i.e., representing queries in natural language), we have developed a controlled natural language (called BioQuery-CNL) for biomedical queries about drug discovery (Erdem and Yeniterzi, 2009; Oztok 2012). For instance, the queries above are in BioQuery-CNL. Then we have built an intelligent user interface that allows users to enter biomedical queries in BioQuery-CNL and that presents the answers with links to related webpages (Erdem et al., 2011b). Queries in BioQuery-CNL are translated into a set of ASP rules by a novel algorithm. For instance, the first query above is translated into the following ASP rules: what _ drug(DRG) <- drug _ name(DRG), drug _ treats _ disease(DRG,"Depression"), not drug _ targets _ gene(DRG,"ACYP1") which describe the drugs DRG that treat the disease Depression and that do not target the gene ACYP1. To address the second challenge (i.e., efficiently answering complex queries), first we have developed a rule layer over biomedical ontologies and databases that not only integrates the concepts in these knowledge resources but also provides definitions of auxiliary concepts (Bodenreider et al., 2008). For instance, the predicate drug_treats_disease is defined in the rule layer as follows: drug _ treats _ disease(DRG,DIS) <- drug _treats _ disease _ pkb(DRG,DIS) drug _ treats _ disease(DRG,DIS) <- drug _treats _ disease _ ctd(DRG,DIS) integrating the knowledge extracted from the knowledge bases PharmGKB (McDonagh et al., 2011) and CTD (Davis et al., 2011), about "which drug treats which disease." The auxiliary concept of "chains of gene-gene relations" is defined recursively in the rule layer as well: gene _ reachable _ from(X,1) <- gene _gene(X,Y), start _ gene(Y) gene _ reachable _ from(X,N+1) <- gene _gene(X,Z), gene _ reachable _ from(Z,N), N < L, max _chain _ length(L) to be able to answer queries like "What are the genes related to the gene ADRB1 via a gene-gene relation chain of length at most 3?" Then, for an efficient query answering, we have introduced an algorithm to identify the relevant parts of the rule layer and the knowledge resources with respect to the given query, and used automated reasoners of ASP to answer queries considering these relevant parts (Erdem et al., 2011a). Essentially, our algorithm identifies the relevant predicates that the query-predicates depend on (using a "dependency graph"), and considers the rules that contain these relevant predicates. For some queries, the relevant knowledge consists of about 500 thousand rules whereas the total size of all the knowledge resources (with the rule layer) is over 21 million rules; considering the relevant rules only decreases the computation time of answering a query by almost a factor of 100. To address the third challenge (i.e., generating explanations), we have developed an intelligent algorithm to generate an explanation (i.e., a tree of "applicable" ASP rules) for a given answer, with respect to the query and the relevant parts of the rule layer and the knowledge resources. We have also developed algorithms to generate shortest/different explanations for a biomedical query taking into account the provenance information as well (Oztok 2012). For instance, an answer to the query "What are the genes that are targeted by the drug Epinephrine and that interact with the gene DLG4?" is ADRB1; and a shortest explanation that justifies this answer is as follows: "The drug Epinephrine targets the gene ADRB1 according to CTD and the gene DLG4 interacts with the gene ADRB1 according to BioGrid." Based on these methods, we have developed a software system, BioQuery-ASP, that guides the user to represent a complex query in a natural language, finds answers to the query (if an answer exists), returns links to related web pages for further information, and generates explanations (if the user asks for one). A demo of BioQuery-ASP is available at BioQuery-ASP Website: http://krr.sabanciuniv.edu/projects/BioQuery-ASP/ (Last accessed on September 25, 2012)). Results and Discussion We have shown the applicability of BioQuery-ASP to answer complex queries that are specified by experts, over large biomedical knowledge resources about genes, drugs and diseases, such as PharmGKB, DrugBank (Knox et al., 2011), BioGrid (Stark et al., 2006), CTD, Sider (Kuhn et al., 2010), etc., using efficient solvers of ASP. BioQuery-ASP could find answers to most of the complex queries in 3-10 CPU seconds, over 10 million facts extracted from these knowledge resources and over 10 million rules integrating them (using a computer with two 1.60GHz Intel Xeon E5310 Quad-core Processors and 16GB RAM). No existing biomedical query answering systems (e.g., web services built over the available knowledge resources, which answer queries by means of keyword search) can directly answer such queries, or can generate explanations for answers. In that sense, BioQuery-ASP is a novel biomedical query answering system that can be useful for experts in automating deep reasoning about knowledge about genes, drugs and diseases available via various biomedical databases and ontologies. Acknowledgements This work has been partly supported by TUBITAK Grant 108E229. References Aker E, Patoglu V, et al. (2012) Answer Set Programming for Reasoning with Semantic Knowledge in Collaborative Housekeeping Robotics. In Proc. of the 10th IFAC Symposium on Robot Control. Bodenreider O, Coban Z, et al. (2008) A preliminary report on answering complex queries related to drug discovery using answer set programming. In Proc. of the 3rd International Workshop on Applications of Logic Programming to the Semantic Web and Web Services. Boenn G, Brain M, et al. (2009) Anton: Composing logic and logic composing. In Proc. of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning, pages 542-547. Brewka G, Eiter T, et al. (2011) Answer set programming at a glance. Communications of ACM 54(12):92-103. Brooks DR, Erdem E, et al. (2006) Inferring Phylogenetic Trees Using Answer Set Programming. Journal of Automated Reasoning 39(4): 471-511. Davis AP, King BL, et al. (2011) The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Research 39(Database issue):D1067-72. Erdem E, Erdem Y, et al. (2011a) Finding answers and generating explanations for complex biomedical queries. In Proc. of the 25th Conf. on Artificial Intelligence (AAAI), pages 785-790. Erdem E, Erdogan H, et al. (2011b) BioQuery-ASP: Querying biomedical ontologies using answer set programming. In Proc. of [email protected] Challenge. Erdem E and Yeniterzi R (2009) Transforming controlled natural language biomedical queries into answer set programs. In Proc. of BioNLP Workshop, pages 117-124. Gebser M, Kaufmann B, et al. (2007) clasp: A Conflict-Driven Answer Set Solver. In Proc. of the 9th Int'l Conference on Logic Programming and Nonmonotonic Reasoning, pages 260-265. Gebser M, Schaub T, et al. (2011) Detecting inconsistencies in large biological networks with answer set programming. Theory and Practice of Logic Programming, 11(2):1-38. Knox C, Law V, et al. (2011) DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Research 39(Database issue):D1035-41. Kuhn M, Campillos M, et al. (2010) A side effect resource to capture phenotypic effects of drugs. Molecular Systems Biology 6:343. Lifschitz V (2008) What Is Answer Set Programming? In Proc. of the 23rd Conference on Artificial Intelligence (AAAI), pages 1594-1597. McDonagh EM, Whirl-Carrillo M, et al. (2011) From pharmacogenomic knowledge acquisition to clinical applications: the PharmGKB as a clinical pharmacogenomic biomarker resource. Biomarkers in Medicine 5(6):795-806. Nogueira M, Balduccini M, et al. (2001) An A-Prolog decision support system for the space shuttle. In Proc. of the 3rd Int'l Symposium on Practical Aspects of Declarative Languages, pages 169-183. Oztok U (2012) Generating Explanations for Complex Biomedical Queries. M.S. Thesis, Sabanci University. Stark C, Breitkreutz BJ, et al. (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34(Database issue):D535-9. Ricca F, Grasso G,et al. (2012) Team-building with answer set programming in the Gioia-Tauro seaport. Theory and Practice of Logic Programming 12(3):361-381.
C. Basu, H. Hirsh, W. W. Cohen, C. Nevill-Manning
Journal of Artificial Intelligence Research, Volume 14, pp 231-252; https://doi.org/10.1613/jair.739

Abstract:
The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their fields of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More specifically, we focus on a variant of this problem -- recommending conference paper submissions to reviewing committee members -- which offers us a testbed to try different approaches. Using WHIRL -- an information integration system -- we are able to implement different recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions.
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