Transfer of information in collective decisions by artificial agents

Gabriele Valentini, Douglas G. Moore, Jake R. Hanson, Theodore P. Pavlic, Stephen C. Pratt, Sara Imari Walker

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

Collective decision-making systems rely on many agents to gather, process and exchange information to arrive at a group decision. Critical to group success is the transfer of information among agents and between agents and their environment. Without information transfer, no consensus can be achieved. Yet, the role of individual rules in determining information transfer at the group level is poorly understood. With the aim to shed a light on how the decision mechanism of individuals affects information transfer in collectives, we analyze the information landscape of two decision-making strategies: one based on the majority rule and one based on the voter model. For each strategy, we consider a binary site-selection scenario and use transfer entropy to measure the flow of information in a spatial, multi-agent system. We find that information transferred among agents is dependent on the decision mechanism, increases with the time necessary to make a collective decision, and is loosely modulated by the uncertainty of the final outcome. This is the first study that compares collective decision making mechanisms through the lens of information dynamics. Although this approach is limited to simulated agents, similar approaches could in principle be used to study collective decisions in biological systems.

Original languageEnglish (US)
Pages641-648
Number of pages8
StatePublished - 2020
Event2018 Conference on Artificial Life: Beyond AI, ALIFE 2018 - Tokyo, Japan
Duration: Jul 23 2018Jul 27 2018

Conference

Conference2018 Conference on Artificial Life: Beyond AI, ALIFE 2018
CountryJapan
CityTokyo
Period7/23/187/27/18

ASJC Scopus subject areas

  • Modeling and Simulation

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