Information sharing in competitive environments may seem counterintuitive, yet it is widely observed in humans and other animals. For instance, the open-source software movement has led to new and valuable technologies being released publicly to facilitate broader collaboration and further innovation. What drives this behavior and under which conditions can it be ben- eficial for an individual? Using simulations in both static and dynamic environments, we show that sharing information can lead to individual benefits through the mechanisms of pseudo- reciprocity, whereby shared information leads to by-product benefits for an individual without the need for explicit recipro- cation. Crucially, imitation with a certain level of innovation is required to avoid a tragedy of the commons, while the mecha- nism of a local visibility radius allows for the coordination of self-organizing collectives of agents. When these two mecha- nisms are present, we find robust evidence for the benefits of sharing—even when others do not reciprocate.

How does cooperation arise in an evolutionary context? We ap-proach this problem using a collective search paradigm whereinteractions are dynamic and there is competition for rewards.Using evolutionary simulations, we find that the unconditionalsharing of information can be an evolutionary advantageousstrategy without the need for conditional strategies or explicitreciprocation. Shared information acts as a recruitment sig-nal and facilitates the formation of a self-organized group.Thus, the improved search efficiency of the collective bestowsbyproduct benefits onto the original sharer. A key mecha-nism is a visibility radius, where individuals have uncondi-tional access to information about neighbors within a lim-ited distance. Our results show that for a variety of initialconditions—including populations initially devoid of prosocialindividuals—and across both static and dynamic fitness land-scapes, we find strong selection pressure to evolve uncondi-tional sharing.