When is social computation better than the sum of its parts?
Vadas Gintautas, Aric Hagberg, Luis M. A. Bettencourt

TL;DR
This paper uses information theory to identify conditions under which coordinated multi-agent search strategies outperform independent searches, focusing on sources with correlated signals to achieve synergy.
Contribution
It introduces a theoretical framework linking information theory to multi-agent search coordination, classifies sources by their potential for synergy, and guides optimal algorithm design.
Findings
Uncorrelated sources offer no synergy for coordinated search.
Correlated sources enable strong synergy among searchers.
Mathematical conditions for optimal multi-agent search are derived.
Abstract
Social computation, whether in the form of searches performed by swarms of agents or collective predictions of markets, often supplies remarkably good solutions to complex problems. In many examples, individuals trying to solve a problem locally can aggregate their information and work together to arrive at a superior global solution. This suggests that there may be general principles of information aggregation and coordination that can transcend particular applications. Here we show that the general structure of this problem can be cast in terms of information theory and derive mathematical conditions that lead to optimal multi-agent searches. Specifically, we illustrate the problem in terms of local search algorithms for autonomous agents looking for the spatial location of a stochastic source. We explore the types of search problems, defined in terms of the statistical properties of…
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Taxonomy
TopicsDiffusion and Search Dynamics · Game Theory and Applications · Evolutionary Game Theory and Cooperation
