Argenziano Rossella, Gilboa Itzhak
Department of Economics, University of Essex, Colchester CO4 3SQ, United Kingdom.
Department of Economics and Decision Sciences, HEC Paris, Jouy-en-Josas 78351, France.
Proc Natl Acad Sci U S A. 2025 Jul 22;122(29):e2509985122. doi: 10.1073/pnas.2509985122. Epub 2025 Jul 18.
We study the decision problem of a Proposer who has a set of applications to submit for approval to an Authority and can choose an order of submission. The Proposer's utility depends on the Authority's rulings. The Authority has to be consistent with its past decisions, which we model using the nearest-neighbor criterion. If the Proposer's utility increases with the set of approved applications, then any greedy strategy is optimal for her: She should submit any application that, given the current history, would be approved. However, if her utility increases with some approvals but decreases with others, the Proposer's problem becomes significantly more complex. In the single-dimensional case, an optimal strategy can be computed in polynomial time. In the general case, however, finding an optimal strategy is NP-hard. Thus, even in the absence of uncertainty or strategic behavior on the part of the Authority, evaluating the impact of current submissions on future outcomes can be computationally intractable.
我们研究了一个提议者的决策问题,该提议者有一组申请要提交给一个权威机构以获得批准,并且可以选择提交顺序。提议者的效用取决于权威机构的裁决。权威机构必须与它过去的决策保持一致,我们使用最近邻准则对此进行建模。如果提议者的效用随着获批申请的集合而增加,那么任何贪婪策略对她来说都是最优的:她应该提交任何在当前历史情况下会被批准的申请。然而,如果她的效用随着某些批准而增加,但随着其他批准而减少,提议者的问题就会变得明显更加复杂。在单维情况下,可以在多项式时间内计算出最优策略。然而,在一般情况下,找到最优策略是NP难的。因此,即使在权威机构不存在不确定性或策略行为的情况下,评估当前提交对未来结果的影响在计算上也可能是难以处理的。