Mittagsseminar (in cooperation with A. Steger, D. Steurer and B. Sudakov)

Date and Time: Tuesday, May 15, 2007, 12:15 pm

Duration: This information is not available in the database

Location: CAB G51

Speaker: Julian Lorenz

Observational Learning in Random Networks

In the standard model of observational learning, n agents sequentially decide between two alternatives a or b, one of which is objectively superior. Their choice is based on a stochastic private signal and the decisions of others. Assuming a rational behavior, it is known that informational cascades arise, which cause an overwhelming fraction of the population to make the same choice, either correct or false. Assuming that each agent is able to observe the actions of all predecessors, it was shown by Bikhchandani, Hirshleifer, and Welch that, independently of the population size, false informational cascades are quite likely. In a more realistic setting, agents observe just a subset of their predecessors, modeled by a random network of acquaintanceships. We show that the probability of false informational cascades depends on the edge probability p of the underlying network. As in the standard model, the emergence of false cascades is quite likely if p does not depend on n. In contrast to that, false cascades are very unlikely if p = p(n) is a sequence that decreases with n. Provided the decay of p is not too fast, correct cascades emerge almost surely, benefiting the entire population.

Upcoming talks     |     All previous talks     |     Talks by speaker     |     Upcoming talks in iCal format (beta version!)

Previous talks by year:   2023  2022  2021  2020  2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  

Information for students and suggested topics for student talks

Automatic MiSe System Software Version 1.4803M   |   admin login