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When adaptation fails: an agent-based explanation of inefficient bargaining under private information

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In this study, we develop a rationale based on adaptive learning tha(t can explain inefficient bargaining under private information. We build a dynamic bargaining model in which independent, but interacting artificial agents co-evolve their offers with the help of genetic algorithms. In the context of this model, we conduct a computational experiment that serves to compare bargaining outcomes under complete and incomplete information. Results from the experiment show that inefficient bargaining (i.e., delays and failures to agree) may be due to problems in the agents' adaptive processes that arise because of incomplete information. In addition, we find that adaptive failures may occur even under complete information, and that "fair" bargaining outcomes may be a consequence of adaptive learning.

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en

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application/pdf

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http://flora.insead.edu/fichiersti_wp/inseadwp1999/99-44.pdf

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Copyright INSEAD. All rights reserved