Boston University Libraries OpenBU
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item

    Data-Driven Estimation in Equilibrium Using Inverse Optimization

    Thumbnail
    License
    Attribution 4.0 International
    Date Issued
    2015-11
    Publisher Version
    10.1007/s10107-014-0819-4
    Author(s)
    Bertsimas, Dimitris
    Gupta, Vishal
    Paschalidis, Ioannis Ch.
    Share to FacebookShare to TwitterShare by Email
    Export Citation
    Download to BibTex
    Download to EndNote/RefMan (RIS)
    Metadata
    Show full item record
    Permanent Link
    https://hdl.handle.net/2144/18017
    Citation (published version)
    Bertsimas, D., Gupta, V. & Paschalidis, I.C. "Data-Driven Estimation in Equilibrium Using Inverse Optimization". Mathematical Programming. (2015) 153: 595.
    Abstract
    Equilibrium 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.
    Description
    36 pages, 5 figures Additional theorems for generalization guarantees and statistical analysis added
    Rights
    Attribution 4.0 International
    Collections
    • BU Open Access Articles [3847]


    Boston University
    Contact Us | Send Feedback | Help
     

     

    Browse

    All of OpenBUCommunities & CollectionsIssue DateAuthorsTitlesSubjectsThis CollectionIssue DateAuthorsTitlesSubjects

    Deposit Materials

    LoginNon-BU Registration

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Boston University
    Contact Us | Send Feedback | Help