No abstract

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.

We introduce a multi-agent simulator for economic systems comprised of heterogeneous Households, heterogeneous Firms, Central Bank and Government agents, that could be subjected to exogenous, stochastic shocks. The interaction between agents defines the production and consumption of goods in the economy alongside the flow of money. Each agent can be designed to act according to fixed, rule-based strategies or learn their strategies using interactions with others in the simulator. We ground our simulator by choosing agent heterogeneity parameters based on economic literature, while designing their action spaces in accordance with real data in the United States. Our simulator facilitates the use of reinforcement learning strategies for the agents via an OpenAI Gym style environment definition for the economic system. We demonstrate the utility of our simulator by simulating and analyzing two hypothetical (yet interesting) economic scenarios. The first scenario investigates the impact of heterogeneous household skills on their learned preferences to work at different firms. The second scenario examines the impact of a positive production shock to one of two firms on its pricing strategy in comparison to the second firm. We aspire that our platform sets a stage for subsequent research at the intersection of artificial intelligence and economics.