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Measuring utility with diffusion models.

Renato Berlinghieri1, Ian Krajbich2,3,4, Fabio Maccheroni5

  • 1Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.

Science Advances
|August 23, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method to estimate parameters in the drift diffusion model (DDM) for decision-making. This approach directly derives decision thresholds and utilities from data, simplifying the analysis of economic choices.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Decision Science

Background:

  • The drift diffusion model (DDM) is a leading framework for understanding decision-making processes.
  • Many decisions involve comparing stimuli, such as economic values, requiring accurate parameter estimation.

Purpose of the Study:

  • To propose a consistent estimator for drift diffusion model parameters in comparative decision tasks.
  • To enable direct derivation of decision thresholds, drift rates, and subjective utilities from experimental data.

Main Methods:

  • Developed a novel consistent estimator for drift diffusion model parameters.
  • Applied the estimator to datasets involving comparisons of probabilities and reward values.
  • Validated the model's ability to predict unobserved drift rates.

Main Results:

  • The proposed estimator successfully derives decision thresholds, drift rates, and utilities directly from data.
  • Analysis of two datasets confirmed good fit with the drift diffusion model.
  • Subjective utilities were found to be linear with probability and slightly convex with reward value.

Conclusions:

  • The new method offers a robust way to analyze decision-making within the drift diffusion model framework.
  • It eliminates the need for separate measurements or assumptions about functional forms of decision parameters.
  • The findings provide insights into the utility functions governing economic choices.