Optimal Stopping With Multiple Priors
发展了奈特不确定性下的最优停止理论，将经典动态规划推广到多先验情形，并通过极小化极大定理与经典框架建立联系，讨论了微观经济学、运筹学和金融学中的例子。
We develop a theory of optimal stopping under Knightian uncertainty. A suitable martingale theory for multiple priors is derived that extends the classical dynamic programming or Snell envelope approach to multiple priors. We relate the multiple prior theory to the classical setup via a minimax theorem. In a multiple prior version of the classical model of independent and identically distributed random variables, we discuss several examples from microeconomics, operation research, and finance. For monotone payoffs, the worst-case prior can be identified quite easily with the help of stochastic dominance arguments. For more complex payoff structures like barrier options, model ambiguity leads to stochastic changes in the worst-case beliefs. Copyright 2009 The Econometric Society.