Uncertainty in Artificial Intelligence
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Dynamic Mechanism Design for Markets with Strategic Resources
Swaprava Nath, Onno Zoeter, Yadati Narahari, Christopher Dance
The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.
Pages: 539-546
PS Link:
PDF Link: /papers/11/p539-nath.pdf
AUTHOR = "Swaprava Nath and Onno Zoeter and Yadati Narahari and Christopher Dance",
TITLE = "Dynamic Mechanism Design for Markets with Strategic Resources",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "539--546"

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