Willis Richard, Du Yali, Leibo Joel Z, Luck Michael
King's College London, London, UK.
Google DeepMind, London, UK.
Auton Agent Multi Agent Syst. 2024;38(2):49. doi: 10.1007/s10458-024-09675-4. Epub 2024 Oct 12.
Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the , to quantify the disparity between individual and group rationality in such scenarios. This metric represents the maximum proportion of their individual rewards that agents can retain while ensuring that a social welfare optimum becomes a dominant strategy. Our approach diverges from traditional concepts of altruism, instead focusing on strategic reward redistribution. By transferring rewards among agents in a manner that aligns individual and group incentives, rational agents will maximise collective welfare while pursuing their own interests. We provide an algorithm to compute efficient transfer structures for an arbitrary number of agents, and introduce novel multi-player social dilemma games to illustrate the effectiveness of our method. This work provides both a descriptive tool for analysing social dilemmas and a prescriptive solution for resolving them via efficient reward transfer contracts. Applications include mechanism design, where we can assess the impact on collaborative behaviour of modifications to models of environments.
社会困境在多智能体合作中构成了重大挑战,因为个体受激励采取的行为方式会破坏社会最优结果。因此,自利的智能体常常回避集体行为。作为回应,我们将社会困境形式化,并引入一种新颖的度量标准—— ,以量化此类场景中个体理性与群体理性之间的差异。该度量标准表示智能体在确保社会福利最优成为主导策略的同时,能够保留的个体奖励的最大比例。我们的方法有别于传统的利他主义概念,而是专注于策略性奖励再分配。通过以一种使个体激励与群体激励相一致的方式在智能体之间转移奖励,理性智能体在追求自身利益的同时将使集体福利最大化。我们提供了一种算法来为任意数量的智能体计算有效的转移结构,并引入新颖的多人社会困境博弈来阐释我们方法的有效性。这项工作既提供了一种用于分析社会困境的描述性工具,也提供了一种通过高效奖励转移合同来解决这些困境的规范性解决方案。其应用包括机制设计,在此我们可以评估对环境模型进行修改对协作行为的影响。