Keywords:Other Foundations of Game Theory & Economic Parad, Planning and Decision Support for Human-Machine Teams, Behavioral Game Theory
AbstractThis paper explores the behavior of present-biased agents, that is, agents who erroneously anticipate the costs of future actions compared to their real costs. Specifically, the paper extends the origi- nal framework proposed by Akerlof (1991) for studying various aspects of human behavior related to time-inconsistent planning, including pro- crastination, and abandonment, as well as the elegant graph-theoretic model encapsulating this framework recently proposed by Kleinberg and Oren (2014). The benefit of this extension is twofold. First, it enables to perform fine grained analysis of the behavior of present-biased agents depending on the optimisation task they have to perform. In particular, we study covering tasks vs. hitting tasks, and show that the ratio be- tween the cost of the solutions computed by present-biased agents and the cost of the optimal solutions may differ significantly depending on the problem constraints. Second, our extension enables to study not only un- derestimation of future costs, coupled with minimization problems, but also all combinations of minimization/maximization, and underestima- tion/overestimation. We study the four scenarios, and we establish upper bounds on the cost ratio for three of them (the cost ratio for the origi- nal scenario was known to be unbounded), providing a complete global picture of the behavior of present-biased agents, as far as optimisation tasks are concerned.
How to Cite
Fomin, F. V., Fraigniaud, P., & Golovach, P. A. (2021). Present-Biased Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5415-5422. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16682
AAAI Technical Track on Game Theory and Economic Paradigms