The Impact of Complex and Informed Adversarial Behavior in Graphical Coordination Games

Paarporn, Keith; Canty, Brian; Brown, Philip N.; Alizadeh, Mahnoosh; Marden, Jason R.

doi:10.1109/tcns.2020.3038842

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Rational imitation dynamics can guarantee finite time convergence to an imitation equilibrium profile for spatial public goods games [28]. Log-linear learning [29], [30], [31], a wellknown distributed learning algorithm, guarantees the emergent behavior optimizes the system-level objective for multi-player nonzero-sum games. In [32], how to select a player to play a best response is investigated to avoid undesirable equilibria for anticoordination network games.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Learning in Two-Player Symmetric Games via Best and Better Responses

Yan¹,

Zhang²,

Deng³

et al. 2022

Preprint

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Artificial intelligence and robotic competitions are accompanied by a class of game paradigms in which each player privately commits a strategy to a game system which simulates the game using the collected joint strategy and then returns payoffs to players. This paper considers the strategy commitment for two-player symmetric games in which the players' strategy spaces are identical and their payoffs are symmetric. First, we introduce two digraph-based metrics at a meta-level for strategy evaluation in two-agent reinforcement learning, grounded on sink equilibrium. The metrics rank the strategies of a single player and determine the set of strategies which are preferred for the private commitment. Then, in order to find the preferred strategies under the metrics, we propose two variants of the classical learning algorithm self-play, called strictly best-response and weakly betterresponse self-plays. By modeling learning processes as walks over joint-strategy response digraphs, we prove that the learnt strategies by two variants are preferred under two metrics, respectively. The preferred strategies under both two metrics are identified and adjacency matrices induced by one metric and one variant are connected. Finally, simulations are provided to illustrate the results.

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Section: Introductionmentioning

confidence: 99%

Evaluation and Learning in Two-Player Symmetric Games via Best and Better Responses

Yan¹,

Zhang²,

Deng³

et al. 2022

Preprint

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“…Optimal seeding and other intervention problems for network coordination games have been studied in [44] and, in the more general setting of supermodular games, in [45]. Recently, vulnerability of network coordination games against adversarial attacks has been investigated in [46], [47], while [48] uses network coordination games as a micro-foundation for community structure in networks.…”

Section: Introductionmentioning

confidence: 99%

Robust Coordination of Linear Threshold Dynamics on Directed Weighted Networks

Arditti¹,

Como²,

Fagnani³

et al. 2021

Preprint

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We study the robustness of binary-action heterogeneous network coordination games equipped with an external field modeling the different players' biases towards one action with respect to the other one. We prove necessary and sufficient conditions for global stability of consensus equilibria under best response type dynamics, robustly with respect to (constant or time-varying) values of the external field. We then apply these results to the analysis of mixed network coordination and anticoordination games and find sufficient conditions for existence and global stability of pure strategy Nash equilibria. Our results apply to general weighted directed interaction networks and build on supermodularity properties of the coordination games in order to characterize conditions for the existence of a novel notion of robust improvement and best response paths.

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“…Our goal is different in this paper, as we are mainly interested in understanding the resilience of the system against external attacks. Recently, vulnerability of network coordination games against adversarial attacks has been investigated in [40], [41], while [1], [42], [43] study games with a mix of coordinating and anti-coordinating players, and [44] proposes network coordination games as a micro-foundation for community structure in networks.…”

mentioning

confidence: 99%

Robust Coordination of Linear Threshold Dynamics on Directed Weighted Networks

Arditti,

Como,

Fagnani

et al. 2024

IEEE Trans. Automat. Contr.

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We study dynamics in a network of interacting agents updating their binary states according to a timevarying threshold rule. Specifically, agents revise their state asynchronously by comparing the weighted average of the current states of their neighbors in the interaction network with possibly heterogeneous time-varying threshold values. Such thresholds are determined by an exogenous signal representing an external influence field modeling the different agents' biases towards one state with respect to the other one. We prove necessary and sufficient conditions for global stability of consensus equilibria, robustly with respect to the (constant or time-varying) external field. Our results apply to general weighted directed interaction networks and build on super-modularity properties of certain network coordination games whose best response dynamics coincide with the linear threshold dynamics. In particular, we introduce a novel notion of robust improvement paths for such games and characterize necessary and sufficient conditions for their existence.

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The Impact of Complex and Informed Adversarial Behavior in Graphical Coordination Games

Cited by 8 publications

References 32 publications

Evaluation and Learning in Two-Player Symmetric Games via Best and Better Responses

Evaluation and Learning in Two-Player Symmetric Games via Best and Better Responses

Robust Coordination of Linear Threshold Dynamics on Directed Weighted Networks

Robust Coordination of Linear Threshold Dynamics on Directed Weighted Networks

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