Correlated equilibria in graphical games
Proceedings of the 4th ACM conference on Electronic commerce
Computing pure nash equilibria in graphical games via markov random fields
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Biased Voting and the Democratic Primary Problem
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Journal of Artificial Intelligence Research
History-dependent graphical multiagent models
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Graphical models for game theory
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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Factored models of multiagent systems address the complexity of joint behavior by exploiting locality in agent interactions. History-dependent graphical multiagent models (hGMMs) further capture dynamics by conditioning behavior on history. The challenges of modeling real human behavior motivated us to extend the hGMM representation by distinguishing two types of agent interactions. This distinction opens the opportunity for learning dependence networks that are different from given graphical structures representing observed agent interactions. We propose a greedy algorithm for learning hGMMs from time-series data, inducing both graphical structure and parameters. Our empirical study employs human-subject experiment data for a dynamic consensus scenario, where agents on a network attempt to reach a unanimous vote. We show that the learned hGMMs directly expressing joint behavior outperform alternatives in predicting dynamic human voting behavior, and end-game vote results. Analysis of learned graphical structures reveals patterns of action dependence not directly reflected in the original experiment networks.