Generalized Bounded Rationality and Robust Multicommodity Network Design
Document Type
Article
Publication Date
2018
Keywords
bounded rationality, satisficing, perception, network design, robust optimization, inverse optimization
Digital Object Identifier (DOI)
https://doi.org/10.1287/opre.2017.1621
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 multicommodity network design problems and devise a cutting plane algorithm. We illustrate our approaches in the context of hazardous materials transportation.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Operations Research, v. 66, issue 1, p. 42-57
Scholar Commons Citation
Sun, Longsheng; Karwan, Mark H.; and Kwon, Changhyun, "Generalized Bounded Rationality and Robust Multicommodity Network Design" (2018). Industrial and Management Systems Engineering Faculty Publications. 58.
https://digitalcommons.usf.edu/egs_facpub/58
