COLQ 201, Multiagent Modeling

Time and place: MWF 3:30 - 4:30, Newcomb Hall 404
Prof. Harry Howard
Office: Newcomb Hall 322-D
862-3417 (voice mail 24 hours a day)
Office hours: MW 4:30-5:30, T 4-5 and by appointment

Description: Multiagent Modeling teaches you that simple rules underlie complex phenomena. In particular, it demonstrates how a population of agents that interact with one another and with their environment according to simple instructions can simulate a variety of seemingly unrelated phenomena in the earth sciences, biology, urban studies, artificial intelligence, epidemiology, ecology, evolutionary biology, anthropology, economics, decision theory, organizational psychology, political science, communications, and linguistics. Rather than having you go out into the real world and perform costly and painstaking experiments on the atmosphere, forests, wolves and sheep, banks, farmers, superhighways, cities or villages, you will learn how to use a 2D modeling environment called NetLogo to populate an artificial world of your design with agents that do your bidding, or at least follow the rules that you impose on them. When you run your model world, its agents will interact over time, and you may see complex, and often unexpected, patterns develop among them. As a by-product, you should be able to talk to almost anyone about almost anything. This can be useful in a job interview or a cocktail party. Don't think that Tulane doesn't teach you practical stuff.

Did I mention that this is going to be fun? Go ahead and try out the NetLogo simulation Mimicry. Read the text and then click the link "Run Mimicry in your browser". Move the slider at the top right more towards the right so that the simulation will run faster, click the setup button and then the go button. What happens to the viceroys? And more importantly, do you understand how it happens?

Objectives:

• You will learn how to run and understand a multiagent simulation using the NetLogo modeling environment.
• You will learn how to program in the NetLogo language. NetLogo was designed to be "low threshold and no ceiling", which is to say that it enables easy entry by novices and yet meets the needs of high powered users. I hope you strive to be high powered.
• You will learn the basics of game theory and dynamical systems theory.
• You will learn a lot of disparate facts about the earth sciences, biology, urban studies, artificial intelligence, epidemiology, ecology, evolutionary biology, anthropology, economics, decision theory, organizational psychology, political science, communications, and linguistics, which can be unified by their reduction to a Multiagent dynamical system.
• You will sharpen your skills at critical analysis. For every simulation that we discuss in class, you will be able to answer the questions:
  • How does it work?
  • Does it do what it claims to do accurately?
  • Are the assumptions of the simulation – the structure of the environment and the rules that the agents obey – reasonable?
  • Does the real world really behave the way the simulation says it should?
• You will become an agile and discerning thinker, able to pierce the veil of distracting superficial details of a problem to find its computational essence.
• You will become intellectually better-rounded.

Outcomes: For you to demonstrate your attainment of these objectives, you will perform the following tasks:

• Take a quiz or turn in a project almost every week, on Monday. [11-1 * 7.5% = 75%]
  • No quiz/project can be accepted late, though I will drop the lowest grade.
  • Even though these look like a lot of small grades, missing just one lowers your final grade almost an entire letter, as an unfortunate few have found out the hard way.
  • If you know ahead of time that you will miss a quiz/project, send me an e-mail beforehand, and I will excuse you with no penalty. Beforehand does not mean the morning that the quiz/project is due, however.
• Present a final project to the class on the final exam day (???) and turn in a written report of your project within two days. [25%]
  • You final project will be a NetLogo simulation of some problem of interest to you. It can be a deeper investigation of one of the simulations that we will review in class, or one of your own confection. If you build on an existing simulation, you must acknowledge the work that you draw on.
  • This may be a group effort, but the entire group will receive the same grade.
• Note that there is no credit for class participation, though I will raise a Y+ to an X– if your class participation is worthy of recognition.
  • There is no class participation grade because:
    • I will pod-cast my lectures (record them as mp3 files and make them available on this page, in the 'mp3' column in the Schedule of Assignments below), and
    • I will also make my PowerPoint presentations available on this page, in the 'ppt' column in the Schedule of Assignments below.
  • So all of the information that is imparted in class is available to you outside of class. So I don't care whether you show up for class or not, but if you do not, you do not get to ask me questions or brainstorm with your fellow students. And that is how most people really learn something. And you will miss out on all the fun.

Code of Academic Integrity

“The integrity of Newcomb-Tulane College is based on the absolute honesty of the entire community in all academic endeavors. As part of the Tulane University community, students have certain responsibilities regarding work that forms the basis for the evaluation of their academic achievement. Students are expected to be familiar with these responsibilities at all times. No member of the university community should tolerate any form of academic dishonesty, because the scholarly community of the university depends on the willingness of both instructors and students to uphold the Code of Academic Conduct. When a violation of the Code of Academic Conduct is observed it is the duty of every member of the academic community who has evidence of the violation to take action. Students should take steps to uphold the code by reporting any suspected offense to the instructor or the associate dean of the college. Students should under no circumstances tolerate any form of academic dishonesty.” For further information, point your browser at http://college.tulane.edu/honorcode.htm.

Violations of the Code of Academic Integrity will not be tolerated in this class. I will rigorously investigate and pursue any such transgression.

Students with disabilities who need academic accommodation should:

• Contact and register with the Office of Disability Services (ODS). For more information, visit the ODS website at http://erc.tulane.edu/disability/.
• Bring official notice to me from the ODS indicating that you need academic accommodation. This should be done before the first quiz/project.

Schedule of assignments, Spring 2010
The links in the Assignment column are to the simulations that we will discuss in class that day.
Many simulations will have background readings assigned from other sources which I will make available on MyTulane in due course.

Date	Day	Topic	Assignment	ppt	mp3	Q/P
Jan 11 (M)	1	Introduction to the course
13 (W)	2	Introduction to NetLogo	NetLogo User Manual: What is NetLogo? Sample Model: Party
15 (F)	3	Models in NetLogo	NetLogo User Manual: Tutorial #1: Models - Wolf Sheep Predation
18 (M)		MLK Birthday
20 (W)	4	Earth sciences: diffusion	NetLogoGreenHouse, Climate change,
22 (F)	5	Biology: flocking, herding & schooling	Boids, Biology > Flocking
25 (M)	6	Biology: from foraging to graph theory	Biology > Ants, AntSystem (errors), Earth Sciences > Fire		--	Q1
27 (W)	7	Biology: migration	Social science > Scatter, Randomly walking (??), Programming
29 (F)	8	AI: navigation	Path finder, Programming	--
Feb 1 (M)	9			--	--	Q2
3 (W)	10	Social science: traffic	Social science > Traffic basic, Traffic grid (Traffic simulation)
5 (F)	11	Social science: individual vs. collective movement	Independence vs. mimetism
8 (M)	12	Biology: communication Epidemiology: spread of disease	Quorum sensing (skip) Malaria control, Multiple-drug resistant tuberculosis, Biology > AIDS	--
10 (W)	13	Epidemiology: the SIR model	Disease in groups, Epidemic typhoid fever on disaster area,			Q3
12 (F)	14	Epidemiology: night of the living dead	Zombie infection, Zombie infection 2, another Zombie infection 2	--	--
15 (M)		Lundi Gras
17 (W)	15	Ecology: predator-prey models	Biology > Wolf-sheep predation, Wolf-sheep predation refuge, Curricular models > Urban suite > Pollution
19 (F)	16	Ecology: predator-prey evolution	Bug hunt evolution (update errors), Community structure v4	--
22 (M)	17	Biology: population genetics	PopGen fishbowl 1	--		Q4
24 (W)	18	Evolutionary biology: game theory 1	Evolutionary game theory Mayberry ESS	--
26 (F)	19	Evolutionary biology: game theory 2	Evolutionary game theory big bird replicator dynamic		--
Mar 1 (M)	20	Evolutionary biology: social factors	Sample models > Social science > Altruism, Divide the cake	--		Q5
3 (W)	21	Anthropology: agrarian societies	Sample models > Social science > Cooperation [Southern African Agrarian Humans Suite NA]	--	--
5 (F)	22	Anthropology: aggression & ethnocentrism	Ethnocentrism [Homo bellicus NA]	--	--
8 (M)	23	Anthropology: cultural dissemination	Axelrodv2,	--
10 (W)	24	Urbanism: why live in cities?	Brainstorm P6 [Urbanization MC (on web), Urban transition (won't update)	--
12 (F)	25	Urbanism: clustering 1	Structure from randomness 1 and 2	--	--
15 (M)	26	Urbanism: clustering 2	Cluster,	--		Q6
17 (W)	27	Urbanism: misc	Urban Suite > Structure from randomness, Social science > Segregation, Urban Suite > Path dependence, [Shopsim]	--		Q7
19 (F)	28	Urbanism: real example	Urban Suite > Cells, Urban Suite > Tijuana Bordertowns	--
22 (M)	29	Economics:	Urban Suite > Positive feedback, Sprawl effect, Awareness	--
24 (W)	30	Economics: distribution of wealth	Social Science > Wealth distribution, Urban Suite > Economic disparity	--		Q8
26 (F)	31	Economics: rational agent model & banking		--	--
29 (M)		Spring Break
31 (W)		Spring Break
Apr 2 (F)		Spring Break
5 (M)	32	Economics: customers & consumers
7 (W)	33	Decision making: prisoner's dilemma	Social sciences > El Faro; SS > unverified > [Minority game], BankReserves, Cash flow			Q9
9 (F)	34	Decision making: negotiation	LogoMoth, Negotiations, consumerism project, [customerBehavior]
12 (M)	35	Organizational psychology	March, Miller,
14 (W)	36	Political science: civil violence, colonialism	Rebellion, Colonialism,			Q10
16 (F)	37	Political science: voting	Voting, Voting - network knowledge, Voting - network vote choice
19 (M)	38	Political science: international environmentalism	Cooperative countries,
21 (W)	39	Communications: spread of rumors & innovation	Rumor, Innovation
23 (F)	40	Linguistics	Language change,
26 (M)	41	Last quiz, party				Q11
30 (F)	42	FINAL EXAM DAY 1-5 pm	Present projects

Go back to Harry Howard's home page

Inception: 26-Sept-09. Last revision: April 9, 2010 . HH