Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests. In domains where agents can choose to take their own action or delegate their action to a central mediator, an open question is how mediators should take actions on behalf of delegating agents. The main existing approach uses delegating agents to punish non-delegating agents in an attempt to get all agents to delegate, which tends to be costly for all. We introduce a Pareto Mediator which aims to improve outcomes for delegating agents without making any of them worse off. Our experiments in random normal form games, a restaurant recommendation game, and a reinforcement learning sequential social dilemma show that the Pareto Mediator greatly increases social welfare. Also, even when the Pareto Mediator is based on an incorrect model of agent utility, performance gracefully degrades to the pre-intervention level, due to the individual autonomy preserved by the voluntary mediator.

中文翻译：

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通过帕累托调解器在保持自治的同时改善社会福利

机器学习算法通常代表利益不同且有时相互冲突的代理做出决策。在代理可以选择采取自己的行动或将其行动委托给中央调解人的领域中，一个悬而未决的问题是调解人应如何代表委托代理人采取行动。现有的主要方法使用委托代理来惩罚非委托代理，以试图让所有代理进行委托，这对所有人来说往往代价高昂。我们引入了帕累托调解器，旨在改善委托代理的结果，而不会使他们中的任何一个变得更糟。我们在随机范式游戏、餐厅推荐游戏和强化学习序列社会困境中的实验表明，帕累托中介极大地增加了社会福利。还，