让智能体学会公平决策，避免弱者被压迫。
Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents
- 用罗尔斯公平原则设计决策机制，平衡集体与个人利益。
- 实验显示其社会整体福利和最低收益均显著提升。
- 适合研究公平性、伦理智能体的学者与开发者。
社会规范是社会中普遍的行为标准。然而，当智能体在决策时不考虑对他人影响时，可能形成压制某些个体的规范。本文提出RAWL-E方法，使智能体在学习规范时贯彻罗尔斯主义的‘最大最小’原则，平衡社会福祉与个体目标，促进伦理规范。我们在模拟采集场景中评估了RAWL-E智能体。结果表明，采用该方法的社会所形成的规范显著提升了社会福利、公平性和鲁棒性，且最低体验值高于未应用罗尔斯伦理的智能体社会。
原文摘要 · Abstract (English)
Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics.
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