Department of Industrial Engineering and Management Sciences
Friday, April 17, 2015
Brickyard Engineering (BYENG) 210, Tempe campus [map]
Iravani and team study the effect of congestion on observational learning in environments where customers choose among several alternatives (“locations”). While the locations’ quality is known to informed customers, uninformed customers infer quality from the location’s congestion level. They show that uninformed customers should never choose empty locations when congestion costs are small, and they characterize the conditions under which uninformed customers should join long queues (“crowds”). Importantly, they show when uninformed customers should join short non-empty queues (“minorities”) such as the single customer that seemingly chose against the wisdom of the crowd. The presence of congestion cost reduces, but does not entirely eliminate, such observational learning. When congestion is costly, customers may choose a short queue even when they expect higher quality from a location with a long queue, obfuscating what subsequent customers can learn from the queue length at different locations. When testing their rational model in the laboratory, they find strong evidence for observational learning from congestion levels (i.e., queue length at each location). However, relative to theoretical predictions, uninformed subjects in low congestion cost environments exhibit an under-appreciation of minority locations, and tend to join more often the majority locations instead. This observation is consistent with the notion of “random choice”, i.e, the possibility that minority queues may have been formed only by mistake, significantly diminishes their signaling value in favor of longer queues.
Seyed Iravani is a professor in the department of Industrial Engineering and Management Sciences at Northwestern University. He got his Ph.D. from University of Toronto, and worked as postdoctoral fellow in the Industrial and Operations Engineering department at the University of Michigan. He has been working with GM, Ford, Cisco, Motorola, Feeding America, and Mobile CARE among others on several projects related to the design and control issues of manufacturing and service operation systems and non-profit supply chains. His research interests are in the applications of stochastic processes, game theory and queueing theory in production, service operations and supply chains. He has served as Associate Editor for Operations Research, Management Science, IIE Transactions, and Navel Research Logistics and as Department Editor for Service Operations Engineering for IIE Transactions. users.iems.northwestern.edu/~iravani/research.html