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(searched for: doi:10.1016/j.artint.2014.06.004)
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Cognitive Computation and Systems, Volume 3, pp 253-262; https://doi.org/10.1049/ccs2.12029

Abstract:
The Hipster Effect is a group of evolutionary ‘‘Diffusive Learning’’ processes of networks of individuals and groups (and their communication devices) that form Cyber-Physical Systems; and the Hipster Effect theory has potential applications in many fields of research. This study addresses decision-making parameters in machine-learning algorithms, and more specifically, critiques the explanations for the Hipster Effect, and discusses the implications for portfolio management and corporate bankruptcy prediction (two areas where AI has been used extensively). The methodological approach in this study is entirely theoretical analysis. The main findings are as follows: (i) the Hipster Effect theory and associated mathematical models are flawed; (ii) some decision-making and learning models in machine-learning algorithms are flawed; (iii) but regardless of whether or not the Hipster Effect theory is correct, it can be used to develop portfolio management models, some of which are summarised herein; (iv) the [1] corporate bankruptcy prediction model can also be used for portfolio-selection (stocks and bonds).
Andrew C. Phillips, , Luca Ostertag-Hill
Published: 21 March 2021
Computational Social Networks, Volume 8, pp 1-51; https://doi.org/10.1186/s40649-021-00091-2

Abstract:
Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and apply unsupervised machine learning techniques, such as fuzzy clustering algorithms and generative models, to identify spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks to gain an insight into the role played by the spheres of legislation in various interesting constructs like polarization and most influential nodes? To this end, we apply both game-theoretic and social network analysis techniques. In particular, we show that game-theoretic notion of most influential nodes brings out the strategic aspects of interactions like bipartisan grouping, which structural centrality measures fail to capture.
, Pablo Gonzalez-Cantergiani, Xavier Molinero, Maria Serna
Physica A: Statistical Mechanics and its Applications, Volume 528; https://doi.org/10.1016/j.physa.2019.121430

Xuequn Li, Shuming Zhou, Jiafei Liu, Gaolin Chen, Zhendong Gu, Yihong Wang
International Journal of Modern Physics B, Volume 33; https://doi.org/10.1142/s0217979219501868

The publisher has not yet granted permission to display this abstract.
ACM Transactions on Economics and Computation, Volume 6, pp 1-58; https://doi.org/10.1145/3232861

Abstract:
Performing interventions is a major challenge in economic policy-making. We present causal strategic inference as a framework for conducting interventions and apply it to large, networked microfinance economies. The basic solution platform consists of modeling a microfinance market as a networked economy, learning the model using single-sample real-world microfinance data, and designing algorithms for various causal questions. For a special case of our model, we show that an equilibrium point always exists and that the equilibrium interest rates are unique. For the general case, we give a constructive proof of the existence of an equilibrium point. Our empirical study is based on microfinance data from Bangladesh and Bolivia, which we use to first learn our models. We show that causal strategic inference can assist policy-makers by evaluating the outcomes of various types of interventions, such as removing a loss-making bank from the market, imposing an interest-rate cap, and subsidizing banks.
Jean Honorio
2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton) pp 830-836; https://doi.org/10.1109/allerton.2017.8262825

Abstract:
We analyze the sample complexity of learning graphical games from purely behavioral data. We assume that we can only observe the players' joint actions and not their payoffs. We analyze the sufficient and necessary number of samples for the correct recovery of the set of pure-strategy Nash equilibria (PSNE) of the true game. Our analysis focuses on directed graphs with n nodes and at most k parents per node. Sparse graphs correspond to k ϵ O(1) with respect to n, while dense graphs correspond to k ϵ O(n). By using VC dimension arguments, we show that if the number of samples is greater than O(kn log 2 n) for sparse graphs or O(n 2 log n) for dense graphs, then maximum likelihood estimation correctly recovers the PSNE with high probability. By using information-theoretic arguments, we show that if the number of samples is less than Q(kn log 2 n) for sparse graphs or Q(n 2 log n) for dense graphs, then any conceivable method fails to recover the PSNE with arbitrary probability.
, Bin Wang, , Yixiang Hu, Yihan Wang, Junming Shao
IEEE Access, Volume 5, pp 3777-3789; https://doi.org/10.1109/access.2017.2679038

Abstract:
The identification of influential nodes is essential to research regarding network attacks, information dissemination, and epidemic spreading. Thus, techniques for identifying influential nodes in complex networks have been the subject of increasing attention. During recent decades, many methods have been proposed from various viewpoints, each with its own advantages and disadvantages. In this paper, an efficient algorithm is proposed for identifying influential nodes, using weighted formal concept analysis (WFCA), which is a typical computational intelligence technique. We call this a WFCA-based influential nodes identification algorithm. The basic idea is to quantify the importance of nodes via WFCA. Specifically, this model converts the binary relationships between nodes in a given network into a knowledge hierarchy, and employs WFCA to aggregate the nodes in terms of their attributes. The more nodes aggregated, the more important each attribute becomes. WFCA not only works on undirected or directed networks, but is also applicable to attributed networks. To evaluate the performance of WFCA, we employ the SIR model to examine the spreading efficiency of each node, and compare the WFCA algorithm with PageRank, HITS, K-shell, H-index, eigenvector centrality, closeness centrality, and betweenness centrality on several real-world networks. Extensive experiments demonstrate that the WFCA algorithm ranks nodes effectively, and outperforms several state-of-the-art algorithms.
Published: 16 February 2017
by MDPI
Abstract:
We propose interdependent defense (IDD) games, a computational game-theoretic framework to study aspects of the interdependence of risk and security in multi-agent systems under deliberate external attacks. Our model builds upon interdependent security (IDS) games, a model by Heal and Kunreuther that considers the source of the risk to be the result of a fixed randomized-strategy. We adapt IDS games to model the attacker’s deliberate behavior. We define the attacker’s pure-strategy space and utility function and derive appropriate cost functions for the defenders. We provide a complete characterization of mixed-strategy Nash equilibria (MSNE), and design a simple polynomial-time algorithm for computing all of them for an important subclass of IDD games. We also show that an efficient algorithm to determine whether some attacker’s strategy can be a part of an MSNE in an instance of IDD games is unlikely to exist. Yet, we provide a dynamic programming (DP) algorithm to compute an approximate MSNE when the graph/network structure of the game is a directed tree with a single source. We also show that the DP algorithm is a fully polynomial-time approximation scheme. In addition, we propose a generator of random instances of IDD games based on the real-world Internet-derived graph at the level of autonomous systems (≈27 K nodes and ≈100 K edges as measured in March 2010 by the DIMES project). We call such games Internet games. We introduce and empirically evaluate two heuristics from the literature on learning-in-games, best-response gradient dynamics (BRGD) and smooth best-response dynamics (SBRD), to compute an approximate MSNE in IDD games with arbitrary graph structures, such as randomly-generated instances of Internet games. In general, preliminary experiments applying our proposed heuristics are promising. Our experiments show that, while BRGD is a useful technique for the case of Internet games up to certain approximation level, SBRD is more efficient and provides better approximations than BRGD. Finally, we discuss several extensions, future work, and open problems.
Asish Ghoshal, Jean Honorio
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) pp 1220-1227; https://doi.org/10.1109/allerton.2016.7852374

Abstract:
In this paper we study the problem of exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions of the players alone. We consider sparse linear influence games — a parametric class of graphical games with linear payoffs, and represented by directed graphs of n nodes (players) and in-degree of at most k. We present an ℓ1-regularized logistic regression based algorithm for recovering the PSNE set exactly, that is both computationally efficient — i.e. runs in polynomial time — and statistically efficient — i.e. has logarithmic sample complexity. Specifically, we show that the sufficient number of samples required for exact PSNE recovery scales as O(poly(k) log n). We also validate our theoretical results using synthetic experiments.
, Lakshmanan Kuppusamy
Published: 20 September 2016
Social Network Analysis and Mining, Volume 6; https://doi.org/10.1007/s13278-016-0386-1

The publisher has not yet granted permission to display this abstract.
V.N. Gudivada, M.T. Irfan, E. Fathi, D.L. Rao
Published: 1 January 2016
Handbook of Statistics pp 169-205; https://doi.org/10.1016/bs.host.2016.07.010

Yanru Zhang, , , , , Zhu Han
IEEE Transactions on Wireless Communications, Volume 14, pp 177-190; https://doi.org/10.1109/twc.2014.2334661

Abstract:
Device-to-device (D2D) communication is seen as a major technology to overcome the imminent wireless capacity crunch and to enable new application services. In this paper, a novel social-aware approach for optimizing D2D communication by exploiting two layers, namely the social network layer and the physical wireless network layer, is proposed. In particular, the physical layer D2D network is captured via the users' encounter histories. Subsequently, an approach, based on the so-called Indian Buffet Process, is proposed to model the distribution of contents in the users' online social networks. Given the social relations collected by the base station, a new algorithm for optimizing the traffic offloading process in D2D communications is developed. In addition, the Chernoff bound and approximated cumulative distribution function (cdf) of the offloaded traffic are derived and the validity of the bound and cdf is proven. Simulation results based on real traces demonstrate the effectiveness of our model and show that the proposed approach can offload the network's traffic successfully.
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