Information socialtaxis and efficient collective behavior emerging in groups of information-seeking agents

Proceedings of the National Academy of Sciences of the United States of America
Ehud D KarpasElad Schneidman

Abstract

Individual behavior, in biology, economics, and computer science, is often described in terms of balancing exploration and exploitation. Foraging has been a canonical setting for studying reward seeking and information gathering, from bacteria to humans, mostly focusing on individual behavior. Inspired by the gradient-climbing nature of chemotaxis, the infotaxis algorithm showed that locally maximizing the expected information gain leads to efficient and ethological individual foraging. In nature, as well as in theoretical settings, conspecifics can be a valuable source of information about the environment. Whereas the nature and role of interactions between animals have been studied extensively, the design principles of information processing in such groups are mostly unknown. We present an algorithm for group foraging, which we term "socialtaxis," that unifies infotaxis and social interactions, where each individual in the group simultaneously maximizes its own sensory information and a social information term. Surprisingly, we show that when individuals aim to increase their information diversity, efficient collective behavior emerges in groups of opportunistic agents, which is comparable to the optimal group behavior. Impor...Continue Reading

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Citations

Sep 7, 2017·Proceedings of the National Academy of Sciences of the United States of America·Roy HarpazElad Schneidman
Aug 13, 2020·Proceedings. Biological Sciences·Oyita Udiani, Nina H Fefferman
Jul 31, 2019·Nature Communications·Shay O'FarrellAndrew Strelcheck
Sep 26, 2020·Current Biology : CB·Máté NagyTamás Vicsek
May 12, 2018·Trends in Ecology & Evolution·Michael A GilAndrew Sih

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