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

Jasmina Arifovic; Alexander Karaivanov

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

机译：边做边学与在代理人模型中向他人学习

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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.

机译：我们将学习引入道德风险下产出共享的委托-代理模型中。我们使用社会进化学习来表示社会学习和强化，经验加权吸引力（EWA）和个体进化学习（IEL）来表示个体学习。由于以下原因，在委托代理模型中学习很困难：最优合同的收益不连续性；对IEL和EWA放弃的收益的错误评估。社会学习在适应最佳合同方面比标准的个人学习算法成功得多。使用已实现的收益评估的改良IEL表现更好，但仍缺乏社会学习能力。

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《Journal of Economic Dynamics and Control》 |2010年第10期|p.1967-1992|共26页
作者
Jasmina Arifovic; Alexander Karaivanov;
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作者单位

Department of Economics, Simon Fraser University, 8888 University Drive, Bumaby, BC, Canada V5A 1S6;

Department of Economics, Simon Fraser University, 8888 University Drive, Bumaby, BC, Canada V5A 1S6;

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正文语种 eng
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关键词
learning; principal-agent model; moral hazard;

机译：学习;委托代理模型道德风险;

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7. Learning by Doing vs. Learning from Others in a Principal-Agent Model∗ [O] . Jasmina Arifovic Alex, Er Karaivanov 2009

机译：在委托 - 代理模型中边做边学，与他人学习*

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

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