Prof. Peng Sun
Duke University
Talk:
Analyzing Scrip Systems
Abstract:
Scrip systems provide a non-monetary trade economy for exchange of resources. We model a scrip system as a stochastic game and study system design issues on selection rules to match potential trade partners over time. We show the optimality of one particular rule in terms of maximizing social welfare for a given scrip system that guarantees players’ incentives to participate. We also investigate the optimal number of scrips to issue under this rule. In particular, if the time discount factor is close enough to 1, or trade benefits one partner much more than it costs the other, the maximum social welfare is always achieved no matter how many scrips are in the system. When the benefit of trade and time discount are not sufficiently large, on the other hand, injecting more scrips in the system hurts most participants; as a result, there is an upper bound on the number of scrips allowed in the system, above which some players may default. We show that this upper bound increases with the discount factor as well as the ratio between the benefit and cost of service. Finally, we demonstrate similar properties for a different service provider selection rule that has been analyzed in previous literature.
Biography:
Peng Sun is a J.B. Fuqua Professor in the Decision Sciences area at the Fuqua School of Business, Duke University. He researches mathematical theories and models for resource allocation decisions under uncertainty, and incentive issues in dynamic environments. His work spans a range of applications areas, from operations management, economics, finance, marketing, to health care and sustainability. He serves a Department Editor at Management Science and an Associate Editor at Operations Research, two leading academic journals of the profession of Operations Research and Management Science. At the Fuqua School, Professor Sun has taught MBA core course Decision Models and elective course Strategic Modeling and Business Dynamics, and PhD course Dynamic Programming and Optimal Control.
