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Learning by doing vs. learning from others in a principal-agent model

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  • Arifovic, Jasmina
  • Karaivanov, Alexander

Abstract

We 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.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:dyncon:v:34:y:2010:i:10:p:1967-1992
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    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Joseph E. Stiglitz, 1974. "Incentives and Risk Sharing in Sharecropping," Review of Economic Studies, Oxford University Press, vol. 41(2), pages 219-255.
    3. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    4. Patrick Bolton & Mathias Dewatripont, 2005. "Contract Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262025760, January.
    5. Masten, Scott E & Snyder, Edward A, 1993. "United States versus United Shoe Machinery Corporation: On the Merits," Journal of Law and Economics, University of Chicago Press, vol. 36(1), pages 33-70, April.
    6. Stokey, Nancy L, 1988. "Learning by Doing and the Introduction of New Goods," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 701-717, August.
    7. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    8. Rogerson, William P, 1985. "The First-Order Approach to Principal-Agent Problems," Econometrica, Econometric Society, vol. 53(6), pages 1357-1367, November.
    9. Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(02), pages 373-401, March.
    10. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-541, June.
    11. Marks, Robert, 1998. "Evolved perception and behaviour in oligopolies," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1209-1233, August.
    12. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February.
    13. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
    14. Sunil Dutta, 2002. "Revenue Recognition in a Multiperiod Agency Setting," Journal of Accounting Research, Wiley Blackwell, vol. 40(1), pages 67-83, March.
    15. Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, May.
    16. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    17. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    18. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    19. Holmstrom, Bengt & Milgrom, Paul, 1987. "Aggregation and Linearity in the Provision of Intertemporal Incentives," Econometrica, Econometric Society, vol. 55(2), pages 303-328, March.
    20. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    21. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    22. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 333-354, November.
    23. Chao, Kang, 1983. "Tenure Systems in Traditional China," Economic Development and Cultural Change, University of Chicago Press, vol. 31(2), pages 295-314, January.
    24. Alexander Karaivanov & Robert M. Townsend, 2014. "Dynamic Financial Constraints: Distinguishing Mechanism Design From Exogenously Incomplete Regimes," Econometrica, Econometric Society, vol. 82(3), pages 887-959, May.
    25. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, January.
    26. Rebecca Achee Thornton & Peter Thompson, 2001. "Learning from Experience and Learning from Others: An Exploration of Learning and Spillovers in Wartime Shipbuilding," American Economic Review, American Economic Association, vol. 91(5), pages 1350-1368, December.
    27. Xiaobo Zhang & Shenggen Fan & Ximing Cai, 2002. "The Path Of Technology Diffusion: Which Neighbors To Learn From?," Contemporary Economic Policy, Western Economic Association International, vol. 20(4), pages 470-478, October.
    28. Caves, Richard E & Crookell, Harold & Killing, J Peter, 1983. "The Imperfect Market for Technology Licenses," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 45(3), pages 249-267, August.
    29. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    30. Rose Cunningham, 2004. "Investment, Private Information and Social Learning: A Case Study of the Semiconductor Industry," Macroeconomics 0409021, EconWPA.
    31. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
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    Cited by:

    1. repec:eee:jeborg:v:141:y:2017:i:c:p:64-82 is not listed on IDEAS
    2. Luba Petersen & Jasmina Arifovic, 2015. "Escaping Expectations-Driven Liquidity Traps: Experimental Evidence," Discussion Papers dp15-03, Department of Economics, Simon Fraser University.

    More about this item

    Keywords

    Learning Principal-agent model Moral hazard;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • 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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