Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-04T22:59:12.628Z Has data issue: false hasContentIssue false
  • English
  • Français

Technology diffusion by learning from neighbours

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

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aldous, D. and Fill, J. A. (2002). Random Walks on Graphs and Networks. Unpublished monograph. Available at http://www.stat.berkeley.edu/users/aldous/.Google Scholar
Anderlini, L. and Ianni, A. (1996). Path dependence and learning from neighbors. Games Econom. Behavior 13, 141–177.Google Scholar
Anderlini, L. and Ianni, A. (1997). Learning on a torus. In The Dynamics of Norms, eds Bicchieri, C. et al., Cambridge University Press, pp. 87–107.Google Scholar
Bala, V. and Goyal, S. (1998). Learning from neighbours. Rev. Econom. Stud. 65, 595–622.CrossRefGoogle Scholar
Banerjee, A. V. and Fudenberg, D. (2004). Word of mouth learning. Games Econom. Behavior 46, 1–22.Google Scholar
Bhargava, S. C., Kumar, A. and Mukherjee, A. (1993). A stochastic cellular automata model of innovation diffusion. Tech. Forecasting Social Change 44, 87–97.CrossRefGoogle Scholar
Binmore, K. G. and Samuelson, L. (1997). Muddling through: noisy equilibrium selection. J. Econom. Theory 74, 235–265.Google Scholar
Bisin, A. and Verdier, T. (2001). The economics of cultural transmission and the dynamics of preferences. J. Econom. Theory 97, 298–319.Google Scholar
Conley, T. G. and Udry, C. R. (2000). Learning about a new technology: pineapple growers in Ghana. Preprint, Northwestern University.Google Scholar
Durrett, R. T. (1988). Lecture Notes on Particle Systems and Percolation. Wadsworth and Brooks/Cole, Pacific Grove, CA.Google Scholar
Ellison, G. (1993). Learning, local interaction, and coordination. Econometrica 61, 1047–1071.Google Scholar
Ellison, G. and Fudenberg, D. (1993). Rules of thumb for social learning. J. Political Economy 101, 612–643.Google Scholar
Ellison, G. and Fudenberg, D. (1995). Word-of-mouth communication and social learning. Quart. J. Econom. 110, 93–125.Google Scholar
Eshel, I., Samuelson, L. and Shaked, A. (1998). Altruists, egoists and hooligans in a local interaction model. Amer. Econom. Rev. 88, 157–179.Google Scholar
Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. 1. Wiley Eastern, New Delhi.Google Scholar
Gittins, J. C. (1989). Multiarmed Bandit Allocation Indices. John Wiley, New York.Google Scholar
Glazebrook, K. D. (1983). Optimal strategies for families of alternative bandit processes. IEEE Trans. Automatic Control 28, 858–861.Google Scholar
Mehta, A. and Luck, J. M. (1999). Models of competitive learning: complex dynamics, intermittent conversions and oscillatory coarsening. Phys. Rev. E 60, 5218–5230.Google Scholar
Morris, S. (2000). Contagion. Rev. Econom. Stud. 67, 57–78.Google Scholar
Ross, S. M. (1996). Stochastic Processes, 2nd edn. John Wiley, New York.Google Scholar
Rothschild, M. (1974). A two-armed bandit theory of market pricing. J. Econom. Theory 9, 185–202.CrossRefGoogle Scholar
Thorisson, H. (2001). Coupling, Stationarity and Regeneration. Springer, New York.Google Scholar