Handbook for Social Utility Modeling

In this handbook, we will walk, step-by-step, through the process of computationally modeling behavior in value-based social decision-making tasks - complete with conceptual examples and four fully fledged walk-throughs that you can complete yourself. You can expect to learn about what utility models are, why one would use them to capture behavior in social decision-making paradigms, and how to computationally implement them. There is no required foreknowledge to conceptually understand the course, but a basic knowledge of statistical programming (i.e. MatLab, R, or Python) is of course required to implement this in your own research.

Introduction

• Utility Theory
• Computational versus Linear Modeling
• Experimental Paradigms
• Overview
• Choosing a Tutorial

Before Data Collection

• Research Question
• Experimental Design
• Outline the Data Generation Process
• Model the Data Generation Process
• Simulating Data
• Recovering Free Parameters
• Grouping

Model Fitting

• Estimating Free Parameters
• Determine Model Fit Index
• Identify the Best Model
• Validate the Best Model

Hypothesis Testing

• Compare Models
• Test Modulatory Hypotheses
• Test for Individual Differences
• Explain Individual Differences

A special thank you to Alan Sanfey, Molly Crockett, Jeroen van Baar, and Christian Ruff for agreeing to allow me to create a tutorial based on their data. Your commitment to fair, free, and accessible education is appreciated immensely.

If you notice typos, grammatical errors, incorrect information, or have suggestions to improve this handbook, email me at epgalvan20@g.ucla.edu.