Inferring a graph's topology from games played on it

Douglas G. Moore, Sara I. Walker

Research output: Contribution to conferencePaper

Abstract

We consider an iterated model of agents playing a two-player game on a graph. The agents change their strategies as the game progresses based on anticipated payoffs. Using only the time series of the agents' strategies, we determine the pairwise mutual information between all agents in the graph, and use these values as a predictors of the graph's topology. From this, we assess the influence of various model parameters on the effectiveness of mutual information at recovering the actual causal structure. It is found that the degree to which the functional connectivity reflects the actual causal structure of the graph strongly depends on which game is being played and how the agents are changing their strategies. Further, there is evidence that the edge density of the graph may also have some impact on the accuracy of the inferred network. This approach allows us to better connect the dynamics of the systems under study with the difference in their functional and actual connectivity, and has broad implications for the interpretation and application of information-based network inference. The methods and analyses described can be generalized and applied to other types of network models.

Original languageEnglish (US)
Pages271-277
Number of pages7
StatePublished - Jan 1 2020
Event2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019 - Newcastle upon Tyne, United Kingdom
Duration: Jul 29 2019Aug 2 2019

Conference

Conference2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019
CountryUnited Kingdom
CityNewcastle upon Tyne
Period7/29/198/2/19

ASJC Scopus subject areas

  • Modeling and Simulation

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  • Cite this

    Moore, D. G., & Walker, S. I. (2020). Inferring a graph's topology from games played on it. 271-277. Paper presented at 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019, Newcastle upon Tyne, United Kingdom.