Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Structural leverage and fictitious play in sequential auctions
Eighteenth national conference on Artificial intelligence
Efficient agents for cliff-edge environments with a large set of decision options
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Efficient Bidding Strategies for Simultaneous Cliff-Edge Environments
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
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In this paper we propose a model for human leaming and decision making in environments of repeated Cliff-Edge (CE) interactions. In CE environments, which include common daily interactions, such as sealed-bid auctions and the Ultimatum Game (UG), the probability of success decreases monotonically as the expected reward increases. Thus, CE environments are characterized by an underlying conflict between the strive to maximize profits and the fear of causing the entire deal to fall through. We focus on the behavior of people who repeatedly compete in one-shot CE interactions, with a different opponent in each interaction. Our model, which is based upon the Deviated Virtual Reinforcement Learning (DVRL) algorithm, integrates the Learning Direction Theory with the Reinforcement Learning algorithm. We also examined several other models, using an innovative methodology in which the decision dynamics of the models were compared with the empirical decision patterns of individuals during their interactions. An analysis of human behavior in auctions and in the UG reveals that our model fits the decision patterns of far more subjects than any other model.