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Title: | Improving the Effort Concept: A Revision of the Traditional Approach in the Context of Controlled Dynamic Stochastic Environments |
Keywords: | Optimal trajectory Closed-loop strategy Controlled stochastic environment Rational decision-maker Endogenous dynamic active learning Adaptive effort management Optimal effort threshold Effort aversion Excessive behavior Learning of preferences |
Description: | Submitted for publication to the Journal of Economic Theory. The objective of this paper is to re-evaluate the attitude to effort of a risk-averse decision-maker in an evolving environment. In the classic analysis, the space of efforts is generally discretized. More realistic, this new approach emploies a continuum of effort levels. The presence of multiple possible efforts and performance levels provides a better basis for explaining real economic phenomena. The traditional approach (see, Laffont, J. J. & Tirole, J., 1993, Salanie, B., 1997, Laffont, J.J. and Martimort, D, 2002, among others) does not take into account the potential effect of the system dynamics on the agent's behavior to effort over time. In the context of a Principal-agent relationship, not only the incentives of the Principal can determine the private agent to allocate a good effort, but also the evolution of the dynamic system. The incentives can be ineffective when the environment does not incite the agent to invest a good effort. This explain why, some efficient strategic incentive-compatible constraints that cover the entire period of contract do not generally exist. The present approach improves our understanding as regards the agent's psychology to effort as decision-maker. It provides new perspectives of research for theorists and empirical analysts. This article is based on research undertaken with support from European Community's PHARE ACE Programme 1998 under grant P98-2103-S. Peer reviewed |
URI: | http://dspace.mediu.edu.my:8181/xmlui/handle/10261/4345 |
Other Identifiers: | http://hdl.handle.net/10261/4345 |
Appears in Collections: | Digital Csic |
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