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149
More order with less law: On contract enforcement, trust, and crowding
, 2000
"... Most contracts, whether between voters and politicians or between house owners and contractors, are incomplete. “More law,” it typically is assumed, increases the likelihood of contract performance by increasing the probability of enforcement and/or the cost of breach. This paper studies a contract ..."
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Cited by 88 (17 self)
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Most contracts, whether between voters and politicians or between house owners and contractors, are incomplete. “More law,” it typically is assumed, increases the likelihood of contract performance by increasing the probability of enforcement and/or the cost of breach. This paper studies a contractual relationship where the first mover has to decide whether she wants to enter a contract without knowing whether the second mover will perform. We analyze how contract enforceability affects individual performance for exogenous preferences. Then we apply a dynamic model of preference adaptation and find that economic incentives have a non–monotonic impact on behavior. Individuals perform a contract when enforcement is strong or weak but not with medium enforcement probabilities: Trustworthiness is “crowded in” with weak and “crowded out” with medium enforcement. In a laboratory experiment we test our model’s implications and find support for the crowding prediction. Our finding is in line with the recent work on the role of contract enforcement and trust in formerly Communist countries.
Analyzing complex strategic interactions in multiagent systems
 In AAAI03 Workshop on Game Theoretic and Decision Theoretic Agents
, 2002
"... We develop a model for analyzing complex games with repeated interactions, for which a full gametheoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristicpayoff table specifying the expe ..."
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Cited by 61 (3 self)
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We develop a model for analyzing complex games with repeated interactions, for which a full gametheoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristicpayoff table specifying the expected payoffs of the joint heuristic strategy space. We analyze a particular game based on the continuous double auction, and compute Nash equilibria of previously published heuristic strategies. To determine the most plausible equilibria, we study the replicator dynamics of a large population playing the strategies. To account for errors in estimation of payoffs or improvements in strategies, we analyze the dynamics and equilibria based on perturbed payoffs.
Deterministic approximation of stochastic evolution in games
, 2002
"... This paper provides deterministic approximation results for stochastic processes that arise when finite populations recurrently play finite games. The processes are Markov chains, and the approximation is defined in continuous time as a system of ordinary differential equations of the type studied ..."
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Cited by 47 (3 self)
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This paper provides deterministic approximation results for stochastic processes that arise when finite populations recurrently play finite games. The processes are Markov chains, and the approximation is defined in continuous time as a system of ordinary differential equations of the type studied in evolutionary game theory. We establish precise connections between the longrun behavior of the discrete stochastic process, for large populations, and its deterministic flow approximation. In particular, we provide probabilistic bounds on exit times from and visitation rates to neighborhoods of attractors to the deterministic flow. We sharpen these results in the special case of ergodic processes.
Evolving Aspirations and Cooperation
 Journal of Economic Theory
, 1998
"... This paper therefore builds on [3], in which a model of consistent aspirationsbased learning was introduced ..."
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Cited by 46 (2 self)
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This paper therefore builds on [3], in which a model of consistent aspirationsbased learning was introduced
An Economist's Perspective on Probability Matching
, 1998
"... . The experimental phenomenon known as "probability matching" is often offered as evidence in support of adaptive learning models and against the idea that people maximise their expected utility. Recent interest in dynamicbased equilibrium theories means the term reappears in Economics. ..."
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Cited by 36 (0 self)
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. The experimental phenomenon known as "probability matching" is often offered as evidence in support of adaptive learning models and against the idea that people maximise their expected utility. Recent interest in dynamicbased equilibrium theories means the term reappears in Economics. However, there seems to be conflicting views on what is actually meant by the term and about the validity of the data. The purpose of this paper is therefore threefold: First, to introduce today's readers to what is meant by probability matching, and in particular to clarify which aspects of this phenomenon challenge the utilitymaximisation hypothesis. Second, to familiarise the reader with the different theoretical approaches to behaviour in such circumstances, and to focus on the differences in predictions between these theories in light of recent advances. Third, to provide a comprehensive survey of repeated, binary choice experiments. Keywords. Probability Matching; Stochastic Learning; Optimis...
Why imitate, and if so, how? A bounded rational approach to multiarmed bandit problems
, 1996
"... ..."
Rules of thumb versus dynamic programming
 AMERICAN ECONOMIC REVIEW
, 1999
"... This paper studies decisionmaking with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are ..."
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Cited by 33 (2 self)
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This paper studies decisionmaking with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are only applicable in good states. Correcting this good state bias requires solving the dynamic program. We provide a general framework and characterize the asymptotic properties. We apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.
Online learning control by association and reinforcement
 IEEE Transactions on Neural Networks
"... Abstract—This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects. ..."
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Cited by 28 (0 self)
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Abstract—This paper focuses on a systematic treatment for developing a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming. This online learning system improves its performance over time in two aspects. First, it learns from its own mistakes through the reinforcement signal from the external environment and tries to reinforce its action to improve future performance. Second, system states associated with the positive reinforcement is memorized through a network learning process where in the future, similar states will be more positively associated with a control action leading to a positive reinforcement. A successful candidate of online learning control design will be introduced. Realtime learning algorithms will be derived for individual components in the learning system. Some analytical insight will be provided to give guidelines on the learning process took place in each module of the online learning control system. The performance of the online learning controller is measured by its learning speed, success rate of learning, and the degree to meet the learning control objective. The overall learning control system performance will be tested on a single cartpole balancing problem, a pendulum swing up and balancing task, and a more complex problem of balancing a triplelink inverted pendulum. Index Terms—Neural dynamic programming (NDP), online learning, reinforcement learning. I.
A selectionmutation model for qlearning in multiagent systems
 In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS
, 2003
"... Although well understood in the singleagent framework, the use of traditional reinforcement learning (RL) algorithms in multiagent systems (MAS) is not always justified. The feedback an agent experiences in a MAS, is usually influenced by the other agents present in the system. Multi agent envir ..."
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Cited by 21 (12 self)
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Although well understood in the singleagent framework, the use of traditional reinforcement learning (RL) algorithms in multiagent systems (MAS) is not always justified. The feedback an agent experiences in a MAS, is usually influenced by the other agents present in the system. Multi agent environments are therefore nonstationary and convergence and optimality guarantees of RL algorithms are lost. To better understand the dynamics of traditional RL algorithms we analyze the learning process in terms of evolutionary dynamics. More specifically we show how the Replicator Dynamics (RD) can be used as a model for Qlearning in games. The dynamical equations of Qlearning are derived and illustrated by some well chosen experiments. Both reveal an interesting connection between the exploitationexploration scheme from RL and the selectionmutation mechanisms from evolutionary game theory.