Water quality trading with asymmetric information, uncertainty and transaction costs: A stochastic agent-based simulation

We examine the efficiency of emissions trading in bilateral and clearinghouse markets with heterogeneous, boundedly rational agents making decisions under imperfect and asymmetric information, and transaction costs. Results are derived using a stochastic agent-based simulation model of agents’ decision-making and interactions. Trading rules, market structures, and agent information structures are selected to represent emerging water quality trading programs. The analysis is designed to provide a strong test of the efficiency of trading occurring through the two market structures. The Differential Evolution algorithm is used to search for market trade strategies that perform well under multiple states of the world. Our findings suggest that trading under both bilateral and clearinghouse markets yields cost savings relatively to no trading. The clearinghouse is found to be more efficient than bilateral negotiations in coordinating point–nonpoint trading under uncertainty and transaction costs. However, the market under both structures is unlikely to achieve or even approximate least-cost pollution control allocations. Expectations of gains from water quality trading should, therefore, be tempered.