Journal ArticleDOI
Learning and Cooperation in Sequential Games
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TLDR
This work model agents with a reinforce ment learning algorithm and analyze cooperative behavior in a sequential prisoner's dilemma game and attributes the reciprocal-like behavior to the structural flow of information, which reduces the risks of exploitation faced by the second-mover.Abstract:
The predictions of classical game theory for one-shot and finitely repeated play of many 2x2 simulta neous games do not correspond to human behavior observed in laboratory experiments. The promis ing results of learning models in tracking human behavior coupled with the growing electronic market and the number of e-commerce applications has resulted in an increased interest in studying the behavior of adaptive artificial agents in different economic games. We model agents with a reinforce ment learning algorithm and analyze cooperative behavior in a sequential prisoner's dilemma game. Our results demonstrate the ability of artificial agents to learn cooperative behavior even in sequential games where defection is the subgame perfect Nash equilibrium. We attribute the reciprocal-like behavior to the structural flow of information, which reduces the risks of exploitation faced by the second-mover. Additionally, we analyze the impact of the second-mover's temptation payoff and pay off risks on the rate of cooperative behavior.read more
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Posted Content
Perfect versus imperfect observability---An experimental test of Bagwell's result
Steffen Huck,Wieland Mueller +1 more
TL;DR: An experimental test of Bagwell's claim that the first mover advantage vanishes completely if this action is only imperfectly observed by second movers finds some support for the noisy Stackelberg equilibrium emphasised by van Damme and Hurkens (1997).
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A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems
Kathryn E. Merrick,Kamran Shafi +1 more
TL;DR: This paper uses agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game, and discusses the implications both for modeling human behavior and for designing artificial agents or robots.
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Is There a Violation of Savage's Sure-Thing Principle in the Prisoner's Dilemma Game?
TL;DR: It was found that the sure-thing principle was violated in the domain of gains as expected by Shafir and Tversky but obeyed in thedomain of losses.
Journal ArticleDOI
Social contracts and human-computer interaction with simulated adapting agents
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References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
The Evolution of Cooperation
TL;DR: In this paper, a model based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game was developed for cooperation in organisms, and the results of a computer tournament showed how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established.
Journal ArticleDOI
Moral Hazard and Observability
TL;DR: In this article, the role of imperfect information in a principal-agent relationship subject to moral hazard is considered, and a necessary and sufficient condition for imperfect information to improve on contracts based on the payoff alone is derived.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.