SySc 525/625 Agent-based Simulation Syllabus 2016

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1 Instructor: Dr. Wayne Wakeland Office: Harder House 101 Office Hrs.: Tuesdays 4:00-6:00 SySc 525/625 Agent-based Simulation Syllabus 2016 Course Description This course focuses on both the conceptual and the technical aspects of agent-based simulation. Students will learn how to use NetLogo to create agent-based models and use agent-based simulations in research and education. Reading assignments focus on the history and theories behind agent-based simulation and the decentralized paradigm in general. What is an agent? An agent is an entity, such as an organism, person or a social organization whose activities (including movements, as well as interactions with the physical and social environment) are programmed as a set of behavioral rules. Agent-based models differ from most computer models in that the computation is decentralized, not centralized. Each individual agent can have variables associated with it, instead of having variables representing the aggregate properties of the system. These variables can change as the agents move and interact with their environment. Agents can be identical or they can be of different 'breeds.' One can specify behaviors and decision-making rules for a each breed of agent and control each breed separately. The aggregate behavior "emerges" from the interaction of the agents and the environment. What is NetLogo? NetLogo is a user friendly agent-based programming language that is wellsuited for 2-D simulations. In certain respects it is more limited than packages like Mason, Repast, and SWARM that require a higher level of programming sophistication and are less well-documented. NetLogo has a well-designed user interface which lets the user view the behavior of the agents and create graphs of the changing values of variables. NetLogo also users to easily write the rules/procedures for the agents without requiring prior programming experience. This makes NetLogo easy to learn and easy to debug. Learning NetLogo Students will learn how to make agents move, interact, and engage in other behaviors by modify existing programs and creating original simulations in NetLogo. The image above is from a model of slime mold aggregation. To view that simulation and other examples, go to the NetLogo website. One can also download a free copy of the NetLogo software and obtain a straightforward tutorial from this website. NetLogo has a user interface with a built in grid for viewing the behavior of the agents. Other elements can be added to this user interface in order to make the program easier to use. Students will learn how to create a user-interface for a simulation so that it can be run by somebody with no programming experience. NetLogo has tools that make creating your own user interface (with sliders for setting variables, monitors for keeping track of changing variables and buttons for running different commands) very easy. Students will also learn how to create graphs for viewing the changing values of variables and export data for visualization and statistical analysis.

2 Applications of Agent-based Simulations A large variety of systems can be modeled using NetLogo. There are many examples of physical and biological simulations in NetLogo on the Netlogo website. NetLogo is also especially useful for modeling social systems. Psychologists, sociologists and political scientists can use NetLogo to make models of individuals who change their behavior based on their interactions with other agents. For example, one could construct a model in which agents communication with other agents about who to support in an election, where to look for 'food' or who to cooperate with. One can make models involving agents working together in an organization, making decisions in varying environments, or buying and selling in a market. It is also possible to create complex game theoretic models in NetLogo. Course Format In addition to attending weekly labs, students will access course information online through D2L, including syllabus information and multiple discussion boards. Students will be expected to post answers to weekly discussion questions on the discussion boards and these discussion boards will also provide a forum for students to post their questions and concerns. Students will also be able to submit assignments and take the final exam through D2L. All readings and assignments should be completed before class on the day listed at the beginning of each row. A description of the books required for the class can be found below the table. Be sure to review the grading and assignment information at the end of this document. Week Readings Assignments Lab Component Key Concepts Intro to agent-based modeling 1 Introduction to NetLogo session with NetLogo begin tutorial What is a model & the modeling cycle Agents, environment (patches) Decentralization Emergence Complex systems An alternative representation Randomness, heterogeneity Benefits; When useful 2 3 TTT Ch. 1 (pdf provided) UW Preface, Ch. 0, 1 R&G Preface, Ch.1, 2 UW Ch. 2 OW Ch. Four (Wk2) (Wk3) Select lit. review topic and begin Slime Mold program Netlogo Intro: Primatives and User Interface enter program create user Primitives: commands & reporters interface Animation, graphs, other outputs (scripted) Examples explore commands Examples Artificial Ants program enter ant program Life, Heroes & cowards, Simple economy

3 GAS Ch. I R&G Ch. 3 modify program (scripted) Complexity Economics Sugarscape Intro ODD Overview, Design, Details Example: butterfly mate-finding Example models UW Ch. 3 Forest Fire program Agents, economics, and science 4 OW Ch. Six R&G Ch. 4,5 (SSS Ch.1,2 recomm.) (Wk4) enter forest fire program create graphs and output files Add'l Modeling concepts Prediction vs. understanding Components of simulation Stages of simulation 5 UW Ch. 4 OW Ch. Seven R&G Ch. 6 SSS Ch.7 sel., Ch.8 sel. (Wk5) Project Proposal (10%) SIR Models create a SIR model SugarScape model Creating Models: Design, Build, Examine Networks Testing Models (introduction) CA's and agents: Cellular Automata, features of agents, agent attributes, archtecture, complexity 6 UW Ch. 5 (to pg. 247) (Wk6) GAS Ch. II, III explore Sugarscape model add novel components to model ABS details SugarScape and Simple Rules 7 8 UW Ch. 5 (rest), 6 OW Ch. Eight (Wk7) R&G Ch UW Ch. 7 (to pg.335) (Wk8) BehaviorSp ace Lab Netlogo Network Model Analysis Critical analysis of simulation Tools for studying simulation results o NL BehaviorSpace Emergence, Observation, Sensing Verification and Validation

4 9 10 Finals week GAS Ch. IV (V- VI recommended) OW Ch. Nine (R&G Ch. 11, 20, 22 recomm.) (R&G Ch opt.) Lit. review due, 3-5 pages (20%), share by posting on "Lit Review" discussion topic Take Exam via D2L (30%) Prepare for presentation of projects Project report due (20%) Modeling Lab informally share lit. review takeaways No formal lab session, could take the exam then Brief review of exam results/qs Project presentations (10%) SugarScape wrapup Evolution Texts UW (Req'd) An Intro. to Agent-based Modeling, Uri Wilensky and William Rand (2015) MIT Press. R&G (~50% Req'd; more recomm.) Agent-Based and Individual-Based Modeling, by Steven Railsback and Volker Grimm (2011). GAS (~80% Req'd)-Growing Artificial Societies : Social Science from the Bottom Up, Joshua M. Epstein, 2050 Project, Robert L. Axtell (Contributor) (1996), Brookings Institution Press. OW (Sel. Ch. Req'd)- Origin of Wealth, Eric Beinhocker (2006), Harvard Business Press. (pdfs provided) SSS (some pgs Req'd; Add'l recomm.) Simulation for the Social Scientist, Gilbert & Troitsch 2nd. Ed. (2005) (pdfs provided) TTT (Ch. 1 Req'd) Turtles, Termites, and Traffic Jams, Mitchell Resnick (1997) MIT Press (pdf provided) Grading (+,, -) 20% Simulation Project Proposal 10% Simulation Presentation 10% Simulation Project Report 20% Targeted Lit. Review (3-5 pages) 15% Exam 25%

5 Typical Lab/Discussion Session (classroom aspect of hybrid fmt.) 6:45 7:45 Mini-lecture and Q&A regarding readings and web discussions of the current week s questions <break> 8:00-8:30 Students begin lab activity (completed later if not during the lab) 8:30-10:00 [Optional] Students work on lab or project (poss. with team); instructor available, as needed Between Lab Sessions (web-based aspect of hybrid fmt.) 1. Review slidecast(s) 2. Complete the lab activity 3. Complete reading assignments 4. Two days before lab session, write and post answers to the discussion questions for the week 5. Complete project-related and/or lit. review assignments as required 6. Prior to the lab session: respond to at least one posting by another student Selected Reference Materials (there is much more; this is just a sampling & a bit old) Agent-Based Approach» Axelrod, Robert "Advancing the Art of Simulation in the Social Sciencess," Japanese J. for Mgmt. of Information Systems, Special Issue on Agent-based Modeling.Vol. 12, No. 3, pp 1-19.» Davidsson, P "Agent based Simulation: A Computer Science View," J. of Artificial Societies and Social Simulation, Vol. 6, No. 1.» Gilbert, Nigel; Troitzsch, Klaus Simulation for the Social Scientist. 2nd Ed. (Book)» Resnik, Mitchel, Turtles, Termites, and Traffic Jams: Explorations in Massibely Parallel Microworlds. (Book)»Davidson P. (2002) "Agent based simulation, a computer science view" JASSS 5(1).

6 Artificial Intelligence Weiss, Gerhard Multiagent Systems : A Modern Approach to Distributed Artificial Intelligence. (Book) Cellular Automata» Wolfram, Stephen Cellular Automata and Complexity. (Book, compilation of articles)» Wolfram, Stephen A New Kind of Science. (Book) Complexity» Bar-Yam, Yaneer Dynamics of Complexity, Westview Press. (book) 0.5 Concepts: Emergence & Complexity 1.5 Cellular Automata 6 Evolution 7.5 Principles of Self-Organization as Organization by Design 9.1 Complex Systems and Social Policy 9.4 Toward a Networked Global Economy Computational Biology» Grimm, V. and S. Railsback Individual-based Modelling and Ecology. Princeton U. Press. (book) Economics» Beinhocker, E. Origin of Wealth: Evolution, Complexity and the Radical Remaking of Economics, Harvard Business School Press (Book) Part I: Pardigm shift Part II: Complexity Economics Part III: How Evolution Creates Wealth Part IV: What it means for Business and Society]» Mantegna,Rosario N. and Stanley, H. Eugene An Introduction to Econophysics: Correlations and Complexity in Finance. (Book)» Ormerod, Paul Butterfly Economics. (Book) Emergence» Holland, John Hidden Order: How Adaptation Builds Complexity. (Book)» Holland, John Emergence: From Chaos to Order. (Book)

7 » Johnson, Steven Emergence: The Connected Lives of Ants, Brains, Cities, and Software. (Book) Game Theory» Axelrod, Robert The Evolution of Cooperation. (Book)» Axelrod, Robert The Complexity of Cooperation. (Book) Human Decision-Making» Barkow, Jerome; Cosmides, Leda; Tooby, John (editors) The Adapted Mind: Evolutionary Psychology and the Generation of Culture. (Book, collection of articles by different authors)» Gigerenzer, Gerd; Selten, R Bounded Rationality: The Adaptive Toolbox. (Book)» Gigerenzer, Gerd; Todd,Peter; ABC Research Group Simple Heuristics that Make Us Smart. (Book)»Simon, Herbert "Rational Choice and the Structure of the Environment," Psychological Review, 63, pp Institutions/Business Management» Olson, Edwin E.; Eoyang, Glenda H.; Beckhard, Richard; Vaill, pe Facilitating Organization Change: Lessons From Complexity Science. (Book) Networks» Barabasi, Albert-Laszlo Linked: The New Science of Networks. Perseus Publishing (Book)» Barabasi, Albert-Laszlo Linked: How Everything is Connected to Everything and What it Means for Business, Science and Everyday Life. Penguin Books.» Buchanan, Mark Nexus: Small Worlds and the Groundbreaking Science of Networks. (Book)» Galdwell, Malcom The Tipping Point: How Little Things can make a Big Difference. (Book)» Watts, Duncan Small Worlds: The Dynamics of Networks between Order and Randomness. (Book) Social Agents» multiple Proceedings of the Workshop on Social Agents: Architecture and Institutions. Decision and Information Sciences Div., Argonne Nat'l Laboratory, U. of Chicago.» multiple Proceedings of the Agent 2002 Conference on Social Agents: Ecology, Exchange, and Evolution. Decision and Information Sciences Div., Argonne Nat'l Laboratory, U. of Chicago.

8 Social Systems» Epstein, Joshua; Axtell, Robert Growing Artificial Societies: Social Science from the Bottom Up. (Book)» Epstein, Joshua "Agent-based Computational Models and Generative Social Science," Complexity, 4(5), pp » Gimblett, Randy (editor) Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes. (Book)» Kohler, Timothy; Gumerman, George (editors) Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes. (Book)» Rauch, Jonathan "Seeing Around Corners" Atlantic Monthly, April, pp