The art of compensation: how hybrid teams solve collective risk dilemmas
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It is widely known how the human ability to cooperate has influenced the thriving of our species. However, as we move towards a hybrid human-machine future, it is still unclear how the introduction of AI agents in our social interactions will affect this cooperative capacity. Within the context of the one-shot collective risk dilemma, where enough members of a group must cooperate in order to avoid a collective disaster, we study the evolutionary dynamics of cooperation in a hybrid population made of both adaptive and fixed-behavior agents. Specifically, we show how the first learn to adapt their behavior to compensate for the behavior of the latter. The less the (artificially) fixed agents cooperate, the more the adaptive population is motivated to cooperate, and vice-versa, especially when the risk is higher. By pinpointing how adaptive agents avoid their share of costly cooperation if the fixed-behavior agents implement a cooperative policy, our work hints towards an unbalanced hybrid world. On one hand, this means that introducing cooperative AI agents within our society might unburden human efforts. Nevertheless, it is important to note that costless artificial cooperation might not be realistic, and more than deploying AI systems that carry the cooperative effort, we must focus on mechanisms that nudge shared cooperation among all members in the hybrid system.
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