Learning by doing vs. learning from others in a principal-agent model
AbstractWe introduce learning in a principal-agent model of output sharing under moral hazard. We use social evolutionary learning to represent social learning and reinforcement, experience-weighted attraction (EWA) and individual evolutionary learning (IEL) to represent individual learning. Learning in the principal-agent model is difficult due to: the stochastic environment; the discontinuity in payoffs at the optimal contract; and the incorrect evaluation of foregone payoffs for IEL and EWA. Social learning is much more successful in adapting to the optimal contract than standard individual learning algorithms. A modified IEL using realized payoffs evaluation performs better but still falls short of social learning.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Economic Dynamics and Control.
Volume (Year): 34 (2010)
Issue (Month): 10 (October)
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Learning Principal-agent model Moral hazard;
Other versions of this item:
- Jasmina Arifovic & Alexander Karaivanov, 2007. "Learning by Doing vs. Learning from Others in a Principal-Agent Model," Discussion Papers dp07-24, Department of Economics, Simon Fraser University.
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
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