Learning by Doing vs. Learning from Others in a Principal-Agent Model
AbstractWe introduce learning in a principal-agent model of stochastic output sharing under moral hazard. Without knowing the agents' preferences and technology the principal tries to learn the optimal agency contract. We implement two learning paradigms - social (learning from others) and individual (learning by doing). We use a social evolutionary learning algorithm (SEL) to represent social learning. Within the individual learning paradigm, we investigate the performance of reinforcement learning (RL), experience-weighted attraction learning (EWA), and individual evolutionary learning (IEL). Overall, our results show that learning in the principal-agent environment is very difficult. This is due to three main reasons: (1) the stochastic environment, (2) a discontinuity in the payoff space in a neighborhood of the optimal contract due to the participation constraint and (3) incorrect evaluation of foregone payoffs in the sequential game principal-agent setting. The first two factors apply to all learning algorithms we study while the third is the main contributor for the failure of the EWA and IEL models. Social learning (SEL), especially combined with selective replication, is much more successful in achieving convergence to the optimal contract than the canonical versions of individual learning from the literature. A modified version of the IEL algorithm using realized payoff evaluation performs better than the other individual learning models; however, it still falls short of the social learning's ability to converge to the optimal contract.
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Date of creation: Nov 2007
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Postal: Working Paper Coordinator, Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
Other versions of this item:
- Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-04-21 (All new papers)
- NEP-BEC-2008-04-21 (Business Economics)
- NEP-CMP-2008-04-21 (Computational Economics)
- NEP-CTA-2008-04-21 (Contract Theory & Applications)
- NEP-DGE-2008-04-21 (Dynamic General Equilibrium)
- NEP-EVO-2008-04-21 (Evolutionary Economics)
- NEP-EXP-2008-04-21 (Experimental Economics)
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