The Evolutionary Dynamics of Cooperation in Collective Search

Abstract

How does cooperation arise in an evolutionary context? We approach this problem using a collective search paradigm where interactions are dynamic and there is competition for rewards. Using evolutionary simulations, we find that the unconditional sharing of information can be an evolutionary advantageous strategy without the need for conditional strategies or explicit reciprocation. Shared information acts as a recruitment signal and facilitates the formation of a self-organized group. Thus, the improved search efficiency of the collective bestows byproduct benefits onto the original sharer. A key mechanism is a visibility radius, where individuals have unconditional access to information about neighbors within a limited distance. Our results show that for a variety of initial conditions-including populations initially devoid of prosocial individuals-and across both static and dynamic fitness landscapes, we find strong selection pressure to evolve unconditional sharing.

Social learning with a grain of salt

“…We used tournament selection in our evolutionary simulations (Tump, Wu, Bouhlel, & Goldstone, 2019): randomly sampled groups of four agents competed in one round of the socially correlated bandit task. The highest performing agent was selected to seed the next generation.…”

Social learning with a grain of salt

“…We used tournament selection in our evolutionary simulations (Tump, Wu, Bouhlel, & Goldstone, 2019): randomly sampled groups of four agents competed in one round of the socially correlated bandit task. The highest performing agent was selected to seed the next generation.…”

Asking better questions: How presentation formats influence information search.

“…Additionally, instead of only assuming information is unilaterally shared along a deterministic social network, we also examined the rationality of when to freely share information (Bouhlel, Wu, Hanaki, & Goldstone, 2018;Tump, Wu, Bouhlel, & Goldstone, 2019). Inspired by the open source movement, we used both agent-based simulations and evolutionary algorithms to study the conditions in which it is beneficial to openly share information without any expectations of reciprocity.…”

Social learning with a grain of salt

“…We used tournament selection in our evolutionary simulations (Tump, Wu, Bouhlel, & Goldstone, 2019): randomly sampled groups of four agents competed in one round of the socially correlated bandit task. The highest performing agent was selected to seed the next generation.…”

Asking better questions: How presentation formats influence information search.

“…Additionally, instead of only assuming information is unilaterally shared along a deterministic social network, we also examined the rationality of when to freely share information (Bouhlel, Wu, Hanaki, & Goldstone, 2018;Tump, Wu, Bouhlel, & Goldstone, 2019). Inspired by the open source movement, we used both agent-based simulations and evolutionary algorithms to study the conditions in which it is beneficial to openly share information without any expectations of reciprocity.…”

Social learning with a grain of salt

“…We used tournament selection in our evolutionary simulations (Tump, Wu, Bouhlel, & Goldstone, 2019): randomly sampled groups of four agents competed in one round of the socially correlated bandit task. The highest performing agent was selected to seed the next generation.…”

Asking better questions: How presentation formats influence information search.

“…Additionally, instead of only assuming information is unilaterally shared along a deterministic social network, we also examined the rationality of when to freely share information (Bouhlel, Wu, Hanaki, & Goldstone, 2018;Tump, Wu, Bouhlel, & Goldstone, 2019). Inspired by the open source movement, we used both agent-based simulations and evolutionary algorithms to study the conditions in which it is beneficial to openly share information without any expectations of reciprocity.…”