Computational Modeling of Complex Socio-Technical Systems



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UNIVERSITY POLICY ON CHEATING AND PLAGIARISM

It is extremely important that the home-works, assignments, papers and tests that you turn in during the course reflect your own understanding. To copy answers from another person not only denies you the necessary feedback on whether or not you really understand the material, but it also compromises your integrity. In addition, those who do not succumb to cheating feel that they are “getting the short end of the stick” when they see others getting away with it. For these reasons we expect everyone to behave with integrity. And, to support those who do, we will institute measures to apprehend students who are cheating. For example, to control the alteration of graded exams, we will sporadically make copies of exams before returning them. Any discrepancy between the copy and an exam turned in for re-grading will be taken as clear evidence of cheating. In addition, because the crowded lecture hall makes it possible to copy answers from another students’ paper during an exam, we may distribute several different versions of written exams, rotating between versions.


Cheating is an extremely serious action. University policy requires that any student caught cheating will receive an R and that the facts of the case be reported to the Dean of Student Affairs. Multiple cases of cheating can be grounds for expulsion from CMU. Students are encouraged to discuss homework and laboratory projects but the submitted solutions must involve only an individual’s effort. To make that more clear, you are permitted, and even encouraged to discuss problem set solutions with your fellow students at the level of “what equations should I be using to solve that kind of problem” or “how do I interpret that problem”. However, students should never copy directly from another student’s problem set. Any student who copies from someone else’s homework, quiz, test, or exam solutions, or any student who willingly allows another student to copy his or her work, or any student who submits someone else’s work as their own will be deemed guilty of cheating.
In this class, without explicit permission of the instructor, the following do not count as original work and would constitute cheating:


  • Turning in the same or largely similar paper to two classes.

  • Joint work on a problem set.

  • Copying material from the web without citing it correctly.

  • Plagiarism, including – copying images, graphs, and tables from published work.

REQUIRED TEXTS:
1. Law, A., Simulation Modeling and Analysis, 2007, McGraw Hill, ISBN: 978-0-07-298843-7, edition: 4. (SMA)
2. Sterman, J., Business Dynamics: Systems thinking and modeling for a complex world, 2000, Irwin/McGraw-Hill, ISBN: 9780072389159. (BD)
3. Gilbert N. and Troitzsch, K., Simulation for the Social Scientist, 2005, Open University Press, ISBN: 9780335216000, edition: 2. (SSS)

REQUIRED AND BACKGROUND READINGS:

There are also a series of non-textbook readings; all papers are available via Blackboard.


A tentative ordering of material for each lecture is provided in the course outline. Please read the required items for the week BEFORE the Monday class. In addition, as needed, additional material will be added, or the readings changed based on the background of the participants.

PROGRAMMING:


Students can do programming in any language or using any operating system; however, existing tools are in C and C++.
Agent based models may be built in a system such as RePast, NetLogo, Swarm or Mason.
Machine learning models do NOT constitute a simulation and will not be counted as acceptable for the final project. However, machine learning can be used to test, analyze or validate a simulation model by assessing it’s output and/or the relation to real empirical data.

Computational Modeling of Complex Socio-Technical Systems: Course Outline

08-810 Spring 2012

(Please read the required items BEFORE class)

Legend


SMA = Law, A., Simulation Modeling and Analysis

BD = Sterman, J., Business Dynamics: Systems thinking and modeling for a complex world



SSS = Gilbert, N. and Troitzsch, K., Simulation for the Social Scientist


Date

Title

Notes












Week 1: Introduction & Overview


M 1/16

Homework #1 Out- Implementation and extension




W 1/18

SMA – ch 1 – (skim) (Basic Simulation Modeling)

Required




SSS – ch 1 (Simulation and Social Science)

Required




SSS – ch 2 (Simulation as a method)

Required




Jeffrey R. Young (1998) "Using computer Models to Study the Complexities of Human society"

Background




Casti, John L. (1997) Would-Be Worlds: How Simulation is Changing the Frontiers of Science.

Background




J. G. March and R. M. Cyert (1992) A Behavioral Theory of the Firm.

Background




Relevant Web Sites







Gilbert & Troitzsch: Book website: http://cress.soc.surrey.ac.uk/s4ss/links.html

Background













CLASSIC MODELS







- The Garbage Can Model







A Garbage Can Model of Organizational Choice. Administrative Sciences Quarterly, 17(1), 1-25. Cohen, M.D., March, J.G. and J.P. Olsen. (March 1972).

Required




Beyond Garbage Cans: An AI Model of Organizational Choice. Administrative Science Quarterly, 34, 38-67. Masuch M. and LaPotin. (1989).

Background




Kathleen Carley, 1986, "Efficiency in a Garbage Can: Implications for Crisis Management." Pp. 195-231 in James March & Roger Weissinger-Baylon (Eds.), Ambiguity and Command: Organizational Perspectives on Military Decision Making .Boston, MA: Pitman.

Background




Padgett, J. (1980). Managing Garbage Can Hierarchies. Administrative Science Quarterly, 25(4): 583-604.

Background













The NK Model







Kauffman, S.A., 1993, The Origins of Order, Oxford University Press, Oxford pp. 36-45.

Required




Levinthal,D.A. 1997, Adaptation on Rugged Landscapes, Management Science, 43: 934-950.

Background




Kauffman, S.A. and S. Johnsen, 1991, Co-Evolution to the Edge of Chaos: Coupled Fitness Landscapes, Poised States, and Co-Evolutionary Avalanches, Artificial Life II, Santa Fe Institute.

Background




Weinberger, E.D. and S.A. Kauffman 1989. The NK Model of rugged fitness landscapes and its application to maturation of the immune response. Journal of Theoretical Biology, 141: 211-245.

Background




Yuan, Y. & McKelvey, B. (2004). Situated Learning Theory: Adding rate and complexity effects via Kauffman’s NK model. Nonlinear Dynamics, Psychology, and Life Sciences, 8, 65-102.

Background













The Segregation Model







Schelling, T (1969) Models of segregation. American economic review 59. Pp. 488-493.

Required




Schelling, T (1971) Dynamic models of segregation. Journal of mathematical sociology 1. Pp. 143-186.

Required




Schelling, T (1978) Micromotives and Macrobehavior.

Background




Sakoda, J M (1971) The checkerboard model of social interaction. Journal of mathematical sociology 1. Pp. 119-132.

Background




A Description of the Schelling Model of Racial Segregation by Bruce Edmonds. http://bruce.edmonds.name/taissl/taissl-appendix.htm

Background




The Schelling Segregation Model

Demonstration Software by Chris Cook. http://www.econ.iastate.edu/tesfatsi/demos/schelling/schellhp.htm



Background










R 1/19

Lab











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