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Generalized Bounded Rationality and Robust Multi-Commodity Network Design

Longsheng Sun(lsun4***at***buffalo.edu)
Mark Karwan(mkarwan***at***buffalo.edu)
Changhyun Kwon(chkwon***at***usf.edu)

Abstract: Often network users are not perfectly rational, especially when they are satisficing---rather than optimizing---decision makers and each individual's perception of the decision environment reflects personal preferences or perception errors due to lack of information. While the assumption of satisficing drivers has been used in modeling route choice behavior, this research uses a link-based perception error model to describe driver's uncertain behavior, without assuming stochasticity. In congestion-free networks, we show that the perception error model is more general than the existing bounded rationality models with satisficing drivers with special cases when the two approaches yield the same results; that is, satisficing under accurate perception is equivalent to optimizing under inaccurate perception. This motivates us to define generalized bounded rationality in route choice behavior modeling. The proposed modeling framework is general enough to capture link-specific cost-perception of drivers. We use a Monte Carlo method to estimate modeling parameter values to guarantee a certain coverage probability in comparison with the random utility model. We demonstrate how the notion of generalized bounded rationality can be used in robust multi-commodity network design problems and devise a cutting plane algorithm. We illustrate our approaches in the context of hazardous materials transportation.

Keywords: bounded rationality; satisficing; perception; network design; robust optimization; inverse optimization

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Robust Optimization

Category 3: Network Optimization

Citation: Working Paper, University of South Florida, December 2016

Download: [PDF]

Entry Submitted: 01/13/2017
Entry Accepted: 01/13/2017
Entry Last Modified: 01/13/2017

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