Data-Driven Modeling of Human Decision Making Processes
Jacob Avery

Citation
Jacob Avery. "Data-Driven Modeling of Human Decision Making Processes". Talk or presentation, 28, May, 2015.

Abstract
Many systems, particularly in transportation, require significant interactions of automation with human decision makers. An understanding of the objective functions that drive these decision processes is required to model these systems. These objective functions can then be used for either for optimizing system performance, or in the development of decision support tools. In this presentation, we present a case study in identifying discrete-choice models of decision-making using operational data. Discrete choice models reflect the relative importance of various attributes that influence the decision by identifying both the structure of the decision process, and the underlying utility function. The proposed approach is used to learn models of the airport runway configuration selection process. Using forecasts of weather and traffic demand, and the current runway configuration, the identified model predicts the runway configuration up to 3 hours in advance. The proposed approach is evaluated using data from New York’s LaGuardia airport.

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  • HTML
    Jacob Avery. <a
    href="http://www.cps-forces.org/pubs/61.html"
    ><i>Data-Driven Modeling of Human Decision Making
    Processes</i></a>, Talk or presentation,  28,
    May, 2015.
  • Plain text
    Jacob Avery. "Data-Driven Modeling of Human Decision
    Making Processes". Talk or presentation,  28, May, 2015.
  • BibTeX
    @presentation{Avery15_DataDrivenModelingOfHumanDecisionMakingProcesses,
        author = {Jacob Avery},
        title = {Data-Driven Modeling of Human Decision Making
                  Processes},
        day = {28},
        month = {May},
        year = {2015},
        abstract = {Many systems, particularly in transportation,
                  require significant interactions of automation
                  with human decision makers. An understanding of
                  the objective functions that drive these decision
                  processes is required to model these systems.
                  These objective functions can then be used for
                  either for optimizing system performance, or in
                  the development of decision support tools. In this
                  presentation, we present a case study in
                  identifying discrete-choice models of
                  decision-making using operational data. Discrete
                  choice models reflect the relative importance of
                  various attributes that influence the decision by
                  identifying both the structure of the decision
                  process, and the underlying utility function. The
                  proposed approach is used to learn models of the
                  airport runway configuration selection process.
                  Using forecasts of weather and traffic demand, and
                  the current runway configuration, the identified
                  model predicts the runway configuration up to 3
                  hours in advance. The proposed approach is
                  evaluated using data from New Yorkâs LaGuardia
                  airport.},
        URL = {http://cps-forces.org/pubs/61.html}
    }
    

Posted by Carolyn Winter on 10 Jun 2015.
Groups: forces
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