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Technology diffusion by learning from neighbours

Part of: Operations research and management science Game theory Mathematical sociology Special processes

Published online by Cambridge University Press:  01 July 2016

Kalyan Chatterjee  and
Susan H. Xu
Kalyan Chatterjee*
Affiliation:
Pennsylvania State University
Susan H. Xu*
Affiliation:
Pennsylvania State University
*
∗ Postal address: Department of Economics, Pennsylvania State University, 504 Kern, University Park, PA 16802, USA
∗∗ Postal address: Department of Management Science and Information Systems, Pennsylvania State University, University Park, PA 16802, USA. Email address: shx@psu.edu
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Abstract

In this paper, we consider a model of social learning in a population of myopic, memoryless agents. The agents are placed at integer points on an infinite line. Each time period, they perform experiments with one of two technologies, then each observes the outcomes and technology choices of the two adjacent agents as well as his own outcome. Two learning rules are considered; it is shown that under the first, where an agent changes his technology only if he has had a failure (a bad outcome), the society converges with probability 1 to the better technology. In the other, where agents switch on the basis of the neighbourhood averages, convergence occurs if the better technology is sufficiently better. The results provide a surprisingly optimistic conclusion about the diffusion of the better technology through imitation, even under the assumption of extremely boundedly rational agents.

Keywords

Social learning in networkstechnology diffusioncultural evolutionrandom walk

MSC classification

Primary: 91A22: Evolutionary games 91A26: Rationality, learning 91D30: Social networks 90B15: Network models, stochastic 60K35: Interacting random processes; statistical mechanics type models; percolation theory

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 36 , Issue 2 , June 2004 , pp. 355 - 376
Copyright
Copyright © Applied Probability Trust 2004 

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