Observational Learning in Large-Scale Congested Service Systems

34 Pages Posted: 18 Oct 2017

See all articles by Chen Jin

Chen Jin

University of Pennsylvania - The Wharton School

Laurens Debo

Dartmouth College - Tuck School of Business

Seyed Iravani

Northwestern University - Department of Industrial Engineering and Management Sciences

Date Written: October 17, 2017

Abstract

We study the impact of observational learning in large scale congested service systems with servers having heterogenous quality levels and customers that are heterogonously informed about the server quality. Providing congestion information to all customers allows them to avoid congested servers, but, also implies that less informed customers learn about the quality from observing the choices of other customers. Due to an exponentially growing state space in the number of servers, identifying Bayesian equilibria is intractable with a large, discrete number of servers. In this paper, we develop a tractable model with a continuum of servers. We find that the impact of observational learning on the customers' choice behavior may lead to severe "imbalance" of server load in the system, such that a decentralized system significantly under-performs in terms of the social welfare, compared with a centralized system. The decentralized system performs well only when (a) either the congestion costs are high and there are sufficient informed customers, or (b) when the congestion costs are medium or low and the aggregate capacity of high-quality servers matches the aggregate demand of informed customers. We also find situations in which making more customers informed about service quality leads to a decrease in social welfare. Our paper highlights the tension between observational learning and social welfare maximization and thus observational learning in large-scale service systems might require intervention of the platform manager.

Keywords: Observation Learning, Bayesian Inference, Load-balancing

JEL Classification: D83, D7

Suggested Citation

Jin, Chen and Debo, Laurens and Iravani, Seyed, Observational Learning in Large-Scale Congested Service Systems (October 17, 2017). Tuck School of Business Working Paper No. 3054728. Available at SSRN: https://ssrn.com/abstract=3054728 or http://dx.doi.org/10.2139/ssrn.3054728

Chen Jin

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Laurens Debo (Contact Author)

Dartmouth College - Tuck School of Business ( email )

Hanover, NH 03755
United States

Seyed Iravani

Northwestern University - Department of Industrial Engineering and Management Sciences ( email )

Evanston, IL 60208-3119
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
82
Abstract Views
392
rank
321,677
PlumX Metrics