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Adm. Sci. 2013, 3(3), 53-75; doi:10.3390/admsci3030053
Article

Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers

1,*  and Jr. 2
Received: 17 May 2013; in revised form: 28 June 2013 / Accepted: 2 July 2013 / Published: 12 July 2013
(This article belongs to the Special Issue Computational Organization Theory)
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Abstract: The dynamic choice between individual and social learning is explored for a population of autonomous agents whose objective is to find solutions to a stream of related problems. The probability that an agent is in the individual learning mode, as opposed to the social learning mode, evolves over time through reinforcement learning. Furthermore, the communication network of an agent is also endogenous. Our main finding is that when agents are sufficiently effective at social learning, structure emerges in the form of specialization. Some agents focus on coming up with new ideas while the remainder of the population focuses on imitating worthwhile ideas.
Keywords: individual learning; social learning; organizational learning individual learning; social learning; organizational learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Chang, M.-H.; Harrington, J.E., Jr. Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers. Adm. Sci. 2013, 3, 53-75.

AMA Style

Chang M-H, Harrington JE, Jr. Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers. Administrative Sciences. 2013; 3(3):53-75.

Chicago/Turabian Style

Chang, Myong-Hun; Harrington, Joseph E., Jr. 2013. "Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers." Adm. Sci. 3, no. 3: 53-75.

Adm. Sci. EISSN 2076-3387 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert