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The discounting model selector: Statistical software for delay discounting applications.

Shawn P Gilroy1, Christopher T Franck2, Donald A Hantula3

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Summary
This summary is machine-generated.

A new open-source software tool was developed for behavior analysts to analyze temporal discounting data. This validated software calculates effective delay 50 (ED50) using Bayesian model selection, offering a user-friendly graphical interface.

Keywords:
Bayesian methodsdecision makingdiscountingsoftware

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Area of Science:

  • Behavioral science
  • Computational psychology
  • Psychometrics

Background:

  • Delay discounting is a key concept in behavioral economics and psychology, measuring the devaluation of rewards over time.
  • Traditional methods for analyzing delay discounting data can be complex and may not fully capture underlying behavioral processes.
  • The need for accessible, statistically robust tools for analyzing temporal discounting data is evident in the field.

Purpose of the Study:

  • To develop and validate an open-source computer software for analyzing temporal discounting data.
  • To implement approximate Bayesian model selection methods for determining the best-fitting model to user data.
  • To compute the effective delay 50 (ED50) from the selected model, providing a key metric of discounting behavior.

Main Methods:

  • Development of original, open-source computer software with a graphical user interface (GUI).
  • Application of approximate Bayesian model selection to user-supplied temporal discounting data.
  • Independent validation of the software's methods against established literature and Monte Carlo simulations (n=50,000).
  • Application of the software to existing delay discounting datasets.

Main Results:

  • The software produced results identical to the original source paper's methods upon independent validation.
  • Monte Carlo simulations confirmed the accurate selection of the true model across various settings.
  • Comparisons of model-selected ED50 values were consistent with traditional indices of discounting.
  • The software successfully applied Bayesian model selection to multiple existing datasets.

Conclusions:

  • The developed open-source software provides behavior analysts with a convenient and statistically rigorous tool for temporal discounting analysis.
  • The software's validation and successful application demonstrate its utility and reliability.
  • Open-source software offers significant opportunities for advancing research and practice in behavioral science.
  • Potential future expansions of the software could further enhance its capabilities.