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

  • Arifovic, Jasmina
  • Karaivanov, Alexander

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.

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Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 34 (2010)
Issue (Month): 10 (October)
Pages: 1967-1992

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Handle: RePEc:eee:dyncon:v:34:y:2010:i:10:p:1967-1992
Contact details of provider: Web page: http://www.elsevier.com/locate/jedc

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  1. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
  2. Rogerson, William P, 1985. "The First-Order Approach to Principal-Agent Problems," Econometrica, Econometric Society, vol. 53(6), pages 1357-67, November.
  3. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  4. 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 701-17, August.
  5. Cars Hommes & Thomas Lux, 2008. "Individual Expectations and Aggregate Behavior in Learning to Forecast Experiments," Kiel Working Papers 1466, Kiel Institute for the World Economy.
  6. 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.
  7. 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 249-67, August.
  8. repec:zbw:cauewp:1122 is not listed on IDEAS
  9. 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.
  10. 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, 05.
  11. 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.
  12. Stiglitz, Joseph E, 1974. "Incentives and Risk Sharing in Sharecropping," Review of Economic Studies, Wiley Blackwell, vol. 41(2), pages 219-55, April.
  13. 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 848-81, September.
  14. Holmstrom, Bengt & Milgrom, Paul, 1987. "Aggregation and Linearity in the Provision of Intertemporal Incentives," Econometrica, Econometric Society, vol. 55(2), pages 303-28, March.
  15. Robert E. Marks, . "Evolved Perception and Behaviour in Oligopolies," Computing in Economics and Finance 1996 _038, Society for Computational Economics.
  16. Lux, Thomas & Schornstein, Sascha, 2002. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Discussion Paper Series 1: Economic Studies 2002,29, Deutsche Bundesbank, Research Centre.
  17. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
  18. Timothy G. Conley & Christopher R. Udry, 2000. "Learning About a New Technology: Pineapple in Ghana," Working Papers 817, Economic Growth Center, Yale University, revised May 2004.
  19. Drew Fudenberg & David K. Levine, 1996. "The Theory of Learning in Games," Levine's Working Paper Archive 624, David K. Levine.
  20. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Society for Computational Economics, vol. 28(4), pages 333-354, November.
  21. 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 510-41, June.
  22. Chao, Kang, 1983. "Tenure Systems in Traditional China," Economic Development and Cultural Change, University of Chicago Press, vol. 31(2), pages 295-314, January.
  23. 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.
  24. Patrick Bolton & Mathias Dewatripont, 2005. "Contract Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262025760, June.
  25. Rose Cunningham, 2004. "Investment, Private Information and Social Learning: A Case Study of the Semiconductor Industry," Macroeconomics 0409021, EconWPA.
  26. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-37, October.
  27. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  28. 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.
  29. 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.
  30. 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.
  31. Sunil Dutta, 2002. "Revenue Recognition in a Multiperiod Agency Setting," Journal of Accounting Research, Wiley Blackwell, vol. 40(1), pages 67-83, 03.
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