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1. Spheres of legislation: polarization and most influential nodes in behavioral context

   https://doi.org/10.1186/s40649-021-00091-2  

   Phillips, Andrew C.; Irfan, Mohammad T.; Ostertag-Hill, Luca (December 2021, Computational Social Networks)

   null (Ed.)

   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. 

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2. Spheres of Legislation: Polarization and Most Influential Nodes in Behavioral Context

   https://doi.org/10.1007/978-3-030-36683-4_6  

   Phillips, Andrew C; Irfan, Mohammad T; Ostertag-Hill, Luca. (January 2020, Studies in computational intelligence)

   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 different spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks in order 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 typical centrality measures fail to capture. We also show that for the same set of senators, some spheres of legislation are more polarizing than others. 

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3. Learning to Play Pursuit-Evasion with Visibility Constraints

   https://doi.org/10.1109/IROS51168.2021.9635959  

   Engin, Selim; Jiang, Qingyuan; Isler, Volkan (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We study the problem of pursuit-evasion for a single pursuer and an evader in polygonal environments where the players have visibility constraints. The pursuer is tasked with catching the evader as quickly as possible while the evader tries to avoid being captured. We formalize this problem as a zero-sum game where the players have private observations and conflicting objectives.One of the challenging aspects of this game is due to limited visibility. When a player, for example, the pursuer does not see the evader, it needs to reason about all possible locations of the evader. This causes an exponential increase in the size of the state space as compared to the arena size. To overcome the challenges associated with large state spaces, we introduce a new learning-based method that compresses the game state and uses it to plan actions for the players. The results indicate that our method outperforms the existing reinforcement learning methods, and performs competitively against the current state-of-the-art randomized strategy in complex environments. 

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4. Neural encoding of novel social networks: evidence that perceivers prioritize others’ centrality

   https://doi.org/10.1093/scan/nsac059  

   Schwyck, Miriam E.; Du, Meng; Natarajan, Pratishta; Chwe, John Andrew; Parkinson, Carolyn (October 2022, Social Cognitive and Affective Neuroscience)

   Abstract Knowledge of someone’s friendships can powerfully impact how one interacts with them. Previous research suggests that information about others’ real-world social network positions—e.g. how well-connected they are (centrality), ‘degrees of separation’ (relative social distance)—is spontaneously encoded when encountering familiar individuals. However, many types of information covary with where someone sits in a social network. For instance, strangers’ face-based trait impressions are associated with their social network centrality, and social distance and centrality are inherently intertwined with familiarity, interpersonal similarity and memories. To disentangle the encoding of the social network position from other social information, participants learned a novel social network in which the social network position was decoupled from other factors and then saw each person’s image during functional magnetic resonance imaging scanning. Using representational similarity analysis, we found that social network centrality was robustly encoded in regions associated with visual attention and mentalizing. Thus, even when considering a social network in which one is not included and where centrality is unlinked from perceptual and experience-based features to which it is inextricably tied in naturalistic contexts, the brain encodes information about others’ importance in that network, likely shaping future perceptions of and interactions with those individuals. 

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5. Interdependent Security Games in the Stackelberg Style: How First-Mover Advantage Impacts Free-Riding and Security (Under-)Investment

   Huang, Ziyuan; Naghizadeh, Parinaz; Liu, Mingyan (June 2023, The 22nd Workshop on the Economics of Information Security (WEIS'23))

   Network games are commonly used to capture the strategic interactions among interconnected agents in simultaneous moves. The agents’ actions in a Nash equilibrium must take into account the mutual dependencies connecting them, which is typically obtained by solving a set of fixed point equations. Stackelberg games, on the other hand, model the sequential moves between agents that are categorized as leaders and followers. The corresponding solution concept, the subgame perfect equilibrium, is typically obtained using backward induction. Both game forms enjoy very wide use in the (cyber)security literature, the network game often as a template to study security investment and externality – also referred to as the Interdependent Security (IDS) games – and the Stackelberg game as a formalism to model a variety of attacker-defender scenarios. In this study we examine a model that combines both types of strategic reasoning: the interdependency as well as sequential moves. Specifically, we consider a scenario with a network of interconnected first movers (firms or defenders, whose security efforts and practices collectively determine the security posture of the eco-system) and one or more second movers, the attacker(s), who determine how much effort to exert on attacking the many potential targets. This gives rise to an equilibrium concept that embodies both types of equilibria mentioned above. We will examine how its existence and uniqueness conditions differ from that for a standard network game. Of particular interest are comparisons between the two game forms in terms of effort exerted by the defender(s) and the attacker(s), respectively, and the free-riding behavior among the defenders. 

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