Nothing better to do? Environment quality and the evolution of cooperation by partner choice

Abstract

The effects of partner choice have been documented in a large number of biological systems such as sexual markets, inter-specific mutualisms, or human cooperation. By contrast, this mechanism has never been demonstrated in a large number of intra-specific interactions in non-human animals such as collective hunts, although one would expect it to play a role as well.Here we use individual-based simulations to solve this apparent paradox. We show that the conditions for partner choice to operate are in fact restrictive. They entail that individuals can compare social opportunities and choose the best. The challenge is that social opportunities are often rare because they necessitate the co-occurrence of (i) at least one available partner, and (ii) a resource to exploit together with this partner. This has three consequences. Firstly, partner choice cannot lead to the evolution of cooperation when resources are scarce, which explains that this mechanism could never be observed in many cases of intraspecific cooperation in animals. On the other hand, partner choice can operate when partners constitute in themselves a resource, which is the case in sexual interactions and inter-specific mutualisms. Lastly, partner choice can lead to the evolution of cooperation when individuals are highly efficient at finding resources in their environment, which sheds light on the relationship between cognitive abilities and cooperation, in particular in the human species.

Social learning in swarm robotics

“…Implementing or learning a complex social structure (e.g. leader election or partner choice [38]) as well as introducing physically distinct types of robot hardware may benefit a robot swarm’s ability to handle challenging environments and problems, by enabling cooperation and division of labour (see [39] in this issue for a discussion on the benefits of a multi-level social structure in social learning). Also, individual robots are currently limited to displaying reactive behaviours (feed-forward neural networks or decision trees, see [19]) and basic social interactions based on direct information transfer (sharing control parameters in one form or another).…”

Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with

“…The situation that is modelled here corresponds to many collective tasks observed in nature [3,25,28], where each agent has to balance between looking for partners and cooperating with the current partner, the latter possibly taking significant time. As a matter of fact, it has been shown elsewhere [4,7,9,10,17] that optimal partner choice strategies can be reached only when the cost of cooperation is large (ie. the duration of cooperation is long with regards to looking for cooperative partners).…”

“…With 𝑎, 𝑏 ≥ 0 and 𝑎 + 𝑏 > 0. This payoff function combines both a prisoner's dilemma and a public good game, and was first introduced in Ecoffet et al [10]. Two different equilibria 1 can be reached for 𝑥 • :…”

“…Results from theoretical biology [4,7,10,17] have shown that cooperation with partner choice is optimal only under certain conditions. First, the number of cooperation opportunities must be large enough that an agent can refuse to cooperate with a potential partner and still have the opportunity to meet a more interesting partner.…”