Darren Schreiber is currently a Senior Lecturer in Politics at the University of Exeter, his previous appointments were at Central European University in Budapest, Hungary; University of California, San Diego; and the University of Pennsylvania. His research centers on emergence and complexity in political systems. In his first career as an attorney, Darren specialized in federal litigation and had his first federal jury trial at age 23. While earning his Ph.D. in Political Science at UCLA, Darren developed an agent-based computer simulation of the formation and dynamics of political parties. He has pioneered the subfield of neuropolitics with the first use of functional brain imaging (fMRI) to study the neural foundations of politics.

Though models sit at the centre of lines of social inquiry as diverse as game theory, statistical analysis, qualitative analysis, and political philosophy, all involve an attempt to describe core elements of the world in a way that helps us to understand, value, and predict that world. With Agent Based Models, computer simulations of the behaviours of many agents work deductively from simplified assumptions to creates dynamic interactions that can be examined over a range of conditions to make inductive arguments about the nature of the world. In this generative reasoning approach, agents with very simple micromotives can lead to complex adaptive systems in which qualitatively different macrobehaviours emerge. How do very simple assumptions about drivers, city dwellers, and voters lead to complex emergent phenomena like traffic jams, housing segregation, and party realignment? In this course, we will answer these questions by building models of these problems and beginning to develop our own agent based models.

Day 1 — Foundations What is an Agent Based Model? Why Agent Based Models? Introduction to Emergence and Complexity Introduction to NetLogo Simple Models John Conway’s Game of Life Simple Economy Introduction to designing your own social science models Day 2 — Building a Simple Model Modelling principles The Cocktail Party Model The Schelling Segregation Model Day 3 — Extending Models Schelling Segregation Model Extended Multiple Ethnicities Diverse preferences Individuals who prefer diversity Day 4 — Creating Your Own Model Agents’ Properties and Behaviors Environments Interactions Scheduling Day 5 — Understanding Your Model Analyzing Agent-Based Models Trace analysis Statistical analysis Verification, Validation, and Replication