Understanding how to design agents that sustain cooperation in multi-agent systems has been a long-lasting goal in distributed artificial intelligence. Proposed solutions rely on identifying free-riders and avoiding cooperating or interacting with them. These mechanisms of social control are traditionally studied in games with linear and deterministic payoffs, such as the prisoner’s dilemma or the public goods game. In reality, however, agents often face dilemmas in which payoffs are uncertain and non-linear, as collective success requires a minimum number of cooperators. The collective risk dilemma (CRD) is one of these games, and it is unclear whether the known mechanisms of cooperation remain effective in this case. Here we study the emergence of cooperation in CRD through partner-based selection. First, we discuss an experiment in which groups of humans and robots play a CRD. This experiment suggests that people only prefer cooperative partners when they lose a previous game (i.e., when collective success was not previously achieved). Secondly, we develop an evolutionary game theoretical model pointing out the evolutionary advantages of preferring cooperative partners only when a previous game was lost. We show that this strategy constitutes a favorable balance between strictness (only interact with cooperators) and softness (cooperate and interact with everyone), thus suggesting a new way of designing agents that promote cooperation in CRD. We confirm these theoretical results through computer simulations considering a more complex strategy space. Third, resorting to online human–agent experiments, we observe that participants are more likely to accept playing in a group with one defector when they won in a previous CRD, when compared to participants that lost the game. These empirical results provide additional support to the human predisposition to use outcome-based partner selection strategies in human–agent interactions.

中文翻译：

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挑剔的失败者和无忧无虑的获胜者在选择合作伙伴时会面临集体风险困境

理解如何设计在多智能体系统中维持协作的智能体已成为分布式人工智能的长期目标。提出的解决方案依赖于确定搭便车者，并避免与其合作或互动。传统上，在具有线性和确定性收益的游戏中研究社会控制的这些机制，例如囚徒困境或公益游戏。然而，实际上，由于集体成功需要最少数量的合作者，代理人通常会面临两难的局面，即收益不确定和非线性。集体风险困境（CRD）就是其中之一，目前尚不清楚已知的合作机制在这种情况下是否仍然有效。在这里，我们研究基于合作伙伴的选择在CRD中出现合作的情况。第一，我们讨论了一组人类和机器人扮演CRD的实验。该实验表明，人们只有在输掉以前的比赛时（即以前没有取得集体成功的时候）才喜欢合作伙伴。其次，我们建立了一个演化博弈理论模型，指出只有在前一场博弈失败后才倾向于选择合作伙伴的演化优势。我们表明，该策略在严格性（仅与合作者交互）和柔和性（与每个人合作和交互）之间取得了良好的平衡，从而提出了一种设计促进CRD合作的代理的新方法。我们通过考虑更复杂的策略空间的计算机仿真来确认这些理论结果。第三，借助在线人工代理实验，我们观察到，与输掉比赛的参与者相比，当参与者在先前的CRD中获胜时，他们更有可能接受一个背叛者的小组比赛。这些实证结果为人类在人与人互动中使用基于结果的伙伴选择策略的倾向提供了额外的支持。