  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.
