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dc.contributor.authorBertsimas, Dimitrisen_US
dc.contributor.authorGupta, Vishalen_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date.accessioned2016-08-28T01:01:47Z
dc.date.accessioned2016-09-29T17:38:55Z
dc.date.available2016-09-29T17:38:55Z
dc.date.issued2015-11
dc.identifierhttp://arxiv.org/abs/1308.3397v2
dc.identifier.citationBertsimas, D., Gupta, V. & Paschalidis, I.C. "Data-Driven Estimation in Equilibrium Using Inverse Optimization". Mathematical Programming. (2015) 153: 595.
dc.identifier.urihttps://hdl.handle.net/2144/18017
dc.description36 pages, 5 figures Additional theorems for generalization guarantees and statistical analysis addeden_US
dc.description.abstractEquilibrium modeling is common in a variety of fields such as game theory and transportation science. The inputs for these models, however, are often difficult to estimate, while their outputs, i.e., the equilibria they are meant to describe, are often directly observable. By combining ideas from inverse optimization with the theory of variational inequalities, we develop an efficient, data-driven technique for estimating the parameters of these models from observed equilibria. We use this technique to estimate the utility functions of players in a game from their observed actions and to estimate the congestion function on a road network from traffic count data. A distinguishing feature of our approach is that it supports both parametric and \emph{nonparametric} estimation by leveraging ideas from statistical learning (kernel methods and regularization operators). In computational experiments involving Nash and Wardrop equilibria in a nonparametric setting, we find that a) we effectively estimate the unknown demand or congestion function, respectively, and b) our proposed regularization technique substantially improves the out-of-sample performance of our estimators.en_US
dc.relation.ispartofseriesMathematical Programming: v.;
dc.relation.replaceshttp://hdl.handle.net/2144/17760
dc.relation.replaces2144/17760
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEquilibriumen_US
dc.subjectNonparametric estimationen_US
dc.subjectUtility estimationen_US
dc.subjectTraffic assignmenten_US
dc.titleData-Driven Estimation in Equilibrium Using Inverse Optimizationen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10107-014-0819-4
pubs.notes36 pages, 5 figures Additional theorems for generalization guarantees and statistical analysis addeden_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-group/Boston Universityen_US
pubs.organisational-group/Boston University/College of Engineeringen_US
pubs.organisational-group/Boston University/College of Engineering/Department of Electrical & Computer Engineeringen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International