Weekly Course Guide
Thirteen weeks covering the foundations of mathematical and computational modeling in behavior science.
Week 1
Introduction to Modeling in Behavior Science
Types of models, description vs explanation, parameters vs variables, and a first pass through an 8-step modeling framework.
Linear ModelVerbal ModelsMathematical Models
Week 2
Historical Models: Matching and Discounting
The matching law, generalized matching equation, hyperbolic discounting, and model comparison across choice and delay paradigms.
Herrnstein's HyperbolaGeneralized Matching EquationMazur's Hyperbolic ModelExponential Discounting
Week 3
Historical Models: Demand
Hursh-Silberberg exponential demand equation, essential value, elasticity.
Exponential Demand EquationSimple Demand CurveUnit Price Analysis
Week 4
Associative Learning Models
The Rescorla-Wagner model, blocking, conditioned inhibition, overshadowing, overexpectation, and Mackintosh's attentional model.
Rescorla-WagnerMackintosh Attention Model
Week 5
Behavioral Momentum and Response Persistence
Behavioral momentum theory as a Pavlovian account of response persistence, with applications to treatment relapse, resurgence, and clinical intervention design.
Behavioral Momentum TheoryAugmented Model of Momentum
Week 6
Model Comparisons
AIC, BIC, cross-validation, parsimony, and the bias-variance tradeoff.
AICBICCross-Validation
Week 7
How to Construct a Model
Hands-on model building, sensitivity analysis, parameter estimation, dimensional analysis.
Life-Cycle DiagramsFlow DiagramsSensitivity Analysis
Week 8
Probability Theory and Probabilistic Models
Probability distributions, Bayes' theorem, maximum likelihood estimation, signal detection.
Poisson ProcessBayesian UpdatingMaximum Likelihood Estimation
Week 9
Multilevel Modeling and Time-Series Forecasting
Random effects, ICC, shrinkage, autoregressive models, ARIMA.
Multilevel ModelAR(1)ARIMA
Week 10
Dynamical Systems Models
Differential equations, phase portraits, stability, attractors, and bifurcations applied to behavioral phenomena.
Logistic ODECoupled ODEsDifference Equations
Week 11
Computational Models
Agent-based models, Monte Carlo simulation, Q-learning, reinforcement learning.
Q-LearningAgent-Based ModelsMonte Carlo Simulation