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Rational Choice Models: The Temporal Tree Representation

SSRN Electronic Journal
2023
  • 0
    Citations
  • 1,073
    Usage
  • 0
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Usage
    1,073
    • Abstract Views
      782
    • Downloads
      291
  • Ratings
    • Download Rank
      216,237

Article Description

Choice models, specifying the consumer’s choice probability of an option over a given choice set, are widely studied and applied in many fields. We propose a temporal tree representation of choice that covers all rational choice models. Compared with the existing structural choice models, the tree representation exhibits two major advantages that overcome the major challenges of model identification. First, all rational choice models have a tree representation, and a subclass of tree representation (with set-dependent branching) has a one-to-one correspondence to the rational choice models. This bridges the gap in the existing characterizations of structural models, which are unidentifiable, impose uninterpretable conditions, or do not cover the entire space of rational choice models. Second, the tree representation allows for the flexibility of systematically specifying the choice model structure based on available knowledge and data. In particular, the number of parameters needed to specify a tree representation can be primarily determined by the sufficient knowledge level, which corresponds to a specific layer of the tree branching. The sufficient knowledge level can be empirically determined based on the amount of available data, which in turn determines the number of parameters needed for model estimation. Therefore, the tree representation allows for a natural way of data integration, avoiding misspecification due to restrictive assumptions and overfitting for general models.

Bibliographic Details

Qi Feng; J. George Shanthikumar; Mengying Xue

Elsevier BV

Rational choice model; Structural choice model; Temporal tree representation; Identifiability

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