Dynamic Incentive Mechanisms

  • David C. Parkes Harvard University
  • Ruggiero Cavallo University of Pennsylvania
  • Florin Constantin Georgia Institute of Technology
  • Satinder Singh University of Michigan


Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.
How to Cite
Parkes, D. C., Cavallo, R., Constantin, F., & Singh, S. (2010). Dynamic Incentive Mechanisms. AI Magazine, 31(4), 79-94. https://doi.org/10.1609/aimag.v31i4.2316