Is transparency always beneficial in complex systems such as traffic networks and stock markets? How is transparency defined in multi-agent systems, and what is its optimal degree at which social welfare is highest? We take an agent-based view to define transparency (or its lacking) as delay in agent observability of environment states, and utilize simulations to analyze the impact of delay on social welfare. To model the adaptation of agent strategies with varying delays, we model agents as learners maximizing the same objectives under different delays in a simulated environment. Focusing on two agent types - constrained and unconstrained, we use multi-agent reinforcement learning to evaluate the impact of delay on agent outcomes and social welfare. Empirical demonstration of our framework in simulated financial markets shows opposing trends in outcomes of the constrained and unconstrained agents with delay, with an optimal partial transparency regime at which social welfare is maximal.

翻译：透明度在复杂系统（如交通网络和股票市场）中是否总是有益的？在多智能体系统中，透明度如何定义，其使社会福利最大化的最优程度是什么？我们采用基于智能体的视角，将透明度（或其缺失）定义为智能体对环境状态观测的延迟，并利用仿真分析延迟对社会福利的影响。为建模智能体在不同延迟下的策略适应性，我们将智能体视为在仿真环境中具有不同延迟的、最大化相同目标的学习者。聚焦于两类智能体——受约束与不受约束型，我们使用多智能体强化学习评估延迟对智能体结果和社会福利的影响。在模拟金融市场中对该框架的实证演示表明，随着延迟变化，受约束与不受约束智能体的结果呈现相反趋势，且存在一个使社会福利最大化的最优部分透明度制度。