ABSTRACT
This paper proposes an efficient agent for competing in Cliff Edge (CE) environments, such as sealed-bid auctions, dynamic pricing and the ultimatum game. The agent competes in one-shot CE interactions repeatedly, each time against a different human opponent, and its performance is evaluated based on all the interactions in which it participates. The agent, which learns the general pattern of the population's behavior, does not apply any examples of previous interactions in the environment, neither of other competitors nor its own. We propose a generic approach which competes in different CE environments under the same configuration, with no knowledge about the specific rules of each environment. The underlying mechanism of the proposed agent is a new meta-algorithm, Deviated Virtual Learning (DVL), which extends existing methods to efficiently cope with environments comprising a large number of optional decisions at each decision point. Experiments comparing the performance of the proposed algorithm with algorithms taken from the literature, as well as another intuitive meta-algorithm, reveal a significant superiority of the former in average payoff and stability. In addition, the agent performed better than human competitors executing the same task.
- P. Bourgine and B. Leloup. May learning explain the ultimatum game paradox? Technical Report GRID Working Paper No. 00--03, Ecole Polytechnique, 2000.Google Scholar
- T. Brenner and N. Vriend. On the behavior of proposers in ultimatum games. J. Econ. Behav. Organ. Forthcoming.Google Scholar
- A. Byde, C. Preist, and N. Jennings. Decision procedures for multiple auctions. In AAMAS, pages 613--620, 2002. Google ScholarDigital Library
- J. DiMicco, A. Greenwald, and P. Maes. Dynamic pricing strategies under a finite time horizon. In EC'01, 2001. Google ScholarDigital Library
- S. Fatima, M. Wooldridge, and N. Jennings. Sequential auctions for objects with common and private values. In AAMAS, pages 635--642, 2005. Google ScholarDigital Library
- Y. Gal, A. Pfeffer, F. Marzo, and B. Grosz. Learning social preferences in games. In AAAI, pages 226--231, 2004. Google ScholarDigital Library
- J. Gittins. Multiarmed Bandits Allocation Indices. Wiley, New York, 1989.Google Scholar
- B. Grosskopf. Reinforcement and directional learning in the ultimatum game with responder competition. Experimental Economics, 6(2):141--158, 2003.Google ScholarCross Ref
- W. Guth and S. Huck. From ultimatum bargaining to dictatorship - an experimental study of four games varying in veto power. Metroeconomica, 48(3):262--279, 1997.Google ScholarCross Ref
- V. Krishna and J. Morgan. An analysis of the war of attrition and the all-pay auction. J. Econ. Theory, 72:343--362, 1997.Google ScholarCross Ref
- B. Leloup and L. Deveaux. Dynamic pricing on the internet: Theory and simulations. JECR, 1(3):265--276, 2001. Google ScholarDigital Library
- L. Niklasson, H. Engstrom, and U. Johansson. An adaptive rock, scissors and paper player based on a tapped delay neural network. In ADCOG21, pages 130--136, 2001.Google Scholar
- A. Roth and I. Erev. Learning in extensive form games: Experimental data and simple dynamic models in the intermediate term. Games Econ. Behav., 8:164--212, 1995.Google ScholarCross Ref
- R. Sutton and A. Barto. An Introduction to Reinforcement Learning. MIT Press, 1998. Google ScholarDigital Library
- P. Todd and B. Borges. Designing socially intelligent agents for the ultimatum game. In K. Dautenhahn, editor, Socially intelligent agents-Papers from the 1997 Fall Symposium, pages 134--136. AAAI Press, Menlo Park, CA, 1997.Google Scholar
- N. Vreind. Will reasoning improve learning? Econ. Lett., 55(1):9--18. 1997.Google ScholarCross Ref
- F. Zhong, D. Wu, and S. Kimbrough. Cooperative agent systems: Artificial agents play the ultimatum game. Journal of Group Decision and Negotiation, 11(6):433--447, 2002.Google ScholarCross Ref
Recommendations
Efficient bidding strategies for Cliff-Edge problems
In this paper, we propose an efficient agent for competing in Cliff-Edge (CE) and simultaneous Cliff-Edge (SCE) situations. In CE interactions, which include common interactions such as sealed-bid auctions, dynamic pricing and the ultimatum game (UG), ...
On cheating in sealed-bid auctions
Special issue: The fourth ACM conference on electronic commerceMotivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions. The first type of cheating we consider occurs when the seller examines the bids of a second-price auction ...
On cheating in sealed-bid auctions
EC '03: Proceedings of the 4th ACM conference on Electronic commerceMotivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions. The first type of cheating we consider occurs when the seller spies on the bids of a second-price auction and ...
Comments