Introduction to Mathematical Modeling in Behavior Science

A doctoral course companion for building, evaluating, and applying formal models of behavior-environment relations.

About This Course

Behavior science has a rich tradition of quantitative thinking. From Herrnstein's matching law to Mazur's hyperbolic discounting model, the field has developed mathematical accounts of behavior that rival the precision found in more traditionally quantitative disciplines. Yet many doctoral students receive limited formal training in the logic of model construction, evaluation, and comparison.

This course seeks to help close that gap. Over thirteen weeks, students learn to read, evaluate, build, and communicate quantitative models of behavior. The backbone of the course is an eight-step modeling framework adapted from engineering and the physical sciences, translated into behavior-science language, and applied to every model encountered.

The goal is fluency in approach, not mastery of pure mathematics. Here, fluency means you can read a model and understand what it claims, evaluate whether its assumptions are reasonable, build a simple model from scratch, fit it to data, compare it to alternatives, and communicate the results clearly.

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Course Sequence

WeekTopic
1Introduction to Modeling
2Historical Models: Matching and Discounting
3Historical Models: Demand
4Associative Learning Models
5Behavioral Momentum and Response Persistence
6Model Comparisons
7How to Construct a Model
8Probability Theory & Probabilistic Models
9Multilevel Modeling & Time-Series
10Dynamical Systems Models
11Computational Models
12Machine Learning & AI
13Final Project Presentations