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The role of inertia in modeling decisions from experience with instance-based learning
Varun Dutt1, Cleotilde Gonzalez
1School of Computing and Electrical Engineering and School of Humanities and Social Sciences, Indian Institute of Technology Mandi, India.
Frontiers in Psychology
|June 12, 2012
Summary
Inertia, the tendency to repeat past choices, improves computational models of decision-making from experience (DFE) by better predicting average risk-taking and alternations. However, it does not enhance predictions of trends over time.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Behavioral Economics
Background:
- Decision-making from experience (DFE) involves learning from past outcomes.
- Instance-Based Learning (IBL) theory models DFE, incorporating inertia (repeating past decisions).
- The precise role of inertia in IBL models remains debated, as models without it also explain risk-taking.
Purpose of the Study:
- To demonstrate the predictive benefits of incorporating a specific inertia mechanism into an IBL model.
- To evaluate the impact of inertia on accounting for average risk-taking and alternations in choices.
- To assess inertia's effect on predicting trends in decision-making over repeated trials.
Main Methods:
- Utilized two large datasets (estimation and competition) from the Technion Prediction Tournament.
- Employed a repeated binary-choice task paradigm.
- Compared an IBL model with an inertia mechanism against an IBL model without inertia.
- Generalized model parameters from an estimation set to a competition set.
Main Results:
- Incorporating inertia improved the IBL model's ability to account for average risk-taking and alternations.
- Inertia did not improve the model's ability to predict trends in risk-taking and alternations over trials.
- Generalization of models (with and without inertia) to a new dataset revealed both benefits and drawbacks of including inertia.
Conclusions:
- A specific inertia mechanism offers predictive advantages for average choice behavior in IBL models.
- Inertia's contribution is limited to explaining average patterns, not dynamic trends in decision-making.
- The inclusion of inertia in IBL models presents a trade-off between explaining average behavior and overall predictive generalization.
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