文档介绍：Bayesian Learning in works1
Douglas Gale (Corresponding Author)
Department of Economics, New York University
269 Mercer St., 7th Floor, New York, NY, 10003-6687.
E-mail: douglas.******@
Url: /user/galed/
Phone: (212) 998-8944
Fax: (212) 995-3932
and
Shachar Kariv
Department of Economics, New York University
269 Mercer St., 7th Floor, New York, NY, 10003-6687.
E-mail: ******@
Url: /~sk510
Version: March 13, 2003.
We extend the standard model of social learning in two ways. First, we
introduce a work and assume that agents can only observe the actions
of agents to whom they are connected by work. Secondly, we allow agents
to choose a different action at each date. If work satisfies a connectedness
assumption, the initial diversity resulting from diverse private information is
eventually replaced by uniformity of actions, though not necessarily of beliefs,
in finite time with probability one. We look at works to illustrate
the impact work architecture on speed of convergence and the optimality
of absorbing states. Convergence is remarkably rapid, so that asymptotic results
are a good approximation even in the medium run.
Journal of Economic Literature Classification Numbers: D82, D83
Key Words: Networks, Social learning, Herd behavior, Informational
cascades.
Running Title: Bayesian Learning in works.
1 One of us discussed this problem with Bob Rosenthal several years ago, when we
were both at Boston University. At that time, we found the problem of learning in
networks fascinating but made no progress and were eventually diverted into working on
boundedly rational learning, which led to our paper on imitation and experimentation.
We thank seminar participants at NYU, DELTA, INSEAD, Cergy, Cornell and Iowa
for ments. The financial support of the National Science Foundation through
Grant No. SES-0095109 is gratefully acknowledged.
1
1. INTRODUCTION
The canonical model of social prises a