Many systems can be modeled as being composed of agents interacting with one another and their environment. As a method, agent based modeling (ABM) can explain phenomena in the biological and social sciences, ranging from evolution to epidemic spread to flocking to cooperation to racial segregation in neighborhoods. Very simple rules governing agent behavior can lead to complex and emergent phenomena. In this course students will use NetLogo to examine and modify well-studied agent based models of complex systems, as well as formulate models of their own.

Learning objectives. At the conclusion of this course, students should:
1) be familiar with classic agent based models in complex systems
2) be able to design and implement their own agent based models
3) be proficient in NetLogo

Assignments:
There will be 8 homework assignments. In each assignment, students will be tasked with constructing or modifying an agent based model based on material introduced in lecture or reading. The subjects of the models may include evolution, animal and plant behavior, epidemic spread, social networks, and human interaction. Students will comment on the effects of varying parameters on outcomes of the models.
The final project will consist of a research paper and presentation based on an agent based model of the students’ design.

Primary texts
NGA: Nigel Gilbert: Agent-Based Models (Quantitative Applications in the Social Sciences)
MPC: Miller & Page: Complex Adaptive Systems

Accommodations for students with disabilities

Academic integrity policy

You should bring a laptop to every class for hands-on in-class exercises and for using LectureTools. If you don't have one, please contact the instructor to arrange for a loaner laptop during classtime.

Course Syllabus

date subject reading assignment due (at 12 pm) 1 Thu 1/6 introduction NGA Ch1: the idea of agent based modeling Nino Boccara: Modeling Complex Systems p.1-5   2 Tue 1/11 NetLogo tutorial I introduction to NetLogo NetLogo Tutorial #1: Models   3 Thu 1/13 NetLogo tutorial II MPC Ch 2: Complexity in Social Worlds NetLogo Tutorial #2: Commands NetLogo Tutorial #3: Procedures   4 Tue 1/18 model properties MPC Ch 6: Why agent-based objects NGA Ch 2: Agents, environments, and timescales   5 Thu 1/20 biological systems: fireflies, flocking, slime mold, bees, ants flocking behavior slime mold HW1 6 Tue 1/25 "open" lab: modeling locust swarms 7 Thu 1/27 biological systems: predator/prey, "debugging" NGA Ch 3.2-3.4 Verification and validation HW 2 8 Tue 2/1 social systems: segregation Schelling, Micromotives and Macrobehavior, A self forming neighborhood model   9 Thu 2/3 cellular automata Janssen, Cellular Automata HW 3 10 Tue 2/8 critical phenomena, sandpiles MPC Ch 7 A basic framework   11 Thu 2/10 games Easley and Kleinberg, Networks, Crowds and Markets, Ch. 6: Games   12 Tue 2/15 social systems: coordination, El Farol MPC Ch. 3, Ch. 5, Ch. 9 social dynamics   13 Thu 2/17 network formation: random Newman, The Structure and Function of Complex Networks,Sec. 3.1-3.3, 4.1, 6, 7.1-2,5 HW 4 14 Tue 2/22 network formation: strategic Jackson, Social and Economic Networks: Strategic network formation   15 Thu 2/24 modeling disease spread Easley and Kleinberg, Networks, Crowds and Markets, Ch 21 Epidemics, Sec. 1-6 HW 5   Tue 3/1 -- winter break --       Thu 3/3 -- winter break --     16 Tue 3/8 opinion formation on networks Jackson, Social and Economic Networks, Ch 8   17 Thu 3/10 model review presentations   model review 18 Tue 3/15 learning & evolution genetic algorithms Zimmer, Testing Darwin, Discover Magazine Feb 2005   19 Thu 3/17 games on networks Jackson Chapter 9 project proposal 20 Tue 3/22 social systems: culture "Disseminating Culture", Chapter 7 of R. Axelrod's book The Complexity of Cooperation.   21 Thu 3/24 social systems: culture II     22 Tue 3/29 trading agent competition Epstein, J. & Axtell R. (1996). Growing Artificial Societies: Social Science from the Bottom Up., Chapter 2 Wilensky and Rand, Making Models Match, JASSS HW 6: trading agent 23 Thu 3/31 financial markets Farmer & Foley, The economy needs agent-based modeling, Nature, 2009:460(6) Blake LeBaron, Agent based computational finance   24 Tue 4/5 game competition   HW 7: game playing agent 25 Thu 4/7 guest lecture by Rick Riolo Active Nonlinear Tests (ANTs) of Complex Simulation Models John H. Miller Management Science, Vol. 44, No. 6. (Jun., 1998), pp. 820-830. Di Paolo, E. A., Noble, J. and Bullock, S. (2000) Simulation models as opaque thought experiments. In: Seventh International Conference on Artificial Life, pp. 497-506, MIT Press, Cambridge, MA. Jorge Luis Borges: Map Makers Joshua Epstein, Why Model, JASSS 2008.   26 Tue 4/12 guest talk by Elizabeth Bruch TBD   27 Thu 4/14 project demos   28 Tue 4/19 project demos       Tue 4/26     project write-up due   Grading: assignment type % course grade in class discussion 10 8 problem sets 40 model review 20 project 30