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This study presents a static self-control model where an agent's choices are driven by preferences, temptation ranking, and self-control costs. It simplifies analysis by showing temptation ranking can oppose preference, aiding parameter identification through revealed preference violations.

Keywords:
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Area of Science:

  • Behavioral Economics
  • Decision Theory
  • Microeconomic Theory

Background:

  • Agents often face choices involving immediate gratification versus long-term goals, a phenomenon studied in self-control models.
  • Understanding the interplay between preferences, temptations, and the cost of self-control is crucial for explaining observed economic behavior.
  • Existing models may require complex parameterizations to capture these dynamics.

Purpose of the Study:

  • To develop and analyze a static self-control model.
  • To simplify the representation of an agent's temptation ranking relative to their preferences.
  • To establish a method for identifying the model's parameters using choice data.

Main Methods:

  • Formulation of a static self-control model incorporating preferences, temptation ranking, and self-control costs.
  • Mathematical analysis to demonstrate that assuming temptation ranking is the opposite of preference does not limit the model's scope.
  • Characterization of the model by relaxing the Weak Axiom of Revealed Preference (WARP).

Main Results:

  • It is without loss of generality to assume an agent's temptation ranking is the opposite of her preference.
  • Violations of the Weak Axiom of Revealed Preference (WARP) can be systematically identified within this model.
  • These WARP violations provide a mechanism for empirically identifying the model's underlying parameters.

Conclusions:

  • The proposed static self-control model offers a parsimonious framework for understanding choice behavior.
  • The simplification of temptation ranking reduces analytical complexity.
  • The model's parameters can be estimated by observing deviations from rational choice axioms, specifically WARP.