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Economics of Complex Systems, Econ 154 (At UCLA): Hum CS M130/Mgmt M118A Winter 2008 Foundations of NEW Social Science: Syllabus version: 01-02-2008 Postings of revisions will be announced by email to enrolled students. Readings will be added to this syllabus before the course begins.
Instructors: Bill McKelvey Duran Bell Email: bill.mckelvey@anderson.ucla.edu Email: dbell@uci.edu Phone: 310-825-7796 Cell: 949-412-7211 Office: 4171 Social Science Plaza, Assisted by John Bragin, jbragin@ucla.edu Office hours: Tu Th 12-2:00
Course Meeting Times: Anteater Instructional and Research Building 3030 20 sessions. TuTh: 10:00am to 11:20am (with continuing class at UCLA until 11:50). Scope of the Course: This course uses complexity science to bridge between old and new conceptions of social science. Newtonian science, neo-classical economics, and existing social sciences, in general, all build on the assumptions: 1. That all the basic agents comprising phenomena (atomic particles, atoms, molecules, organisms, people, groups, firms, etc.) are homogeneous and that the behavior of one is independent of the behavior of the others; and 2. Go forward in time under equilibrium conditions (interspersed with occasional, short-term disequilibrium periods). None of these assumptions hold for most of human behavior in social settings. So, what to do to do good science? New Economics, New Management, New Social Science, Complexity Science, and Agent-based Models posit that order-creation is the dominant condition of social systems and that order-creation is the outcome of interactions among autonomous heterogeneous agents. In New Science, equilibrium conditions are not things to be assumed but rather to be marveled at and studied if, when, and where they occur. New Science (mostly complexity science) simply accepts agents as stochastically idiosyncratic and then asks how and why macro structures emerge. Complexity science focuses on order-creation rather than the order-translation process underlying the 1st Law of Thermodynamics (energy conservation), and replaces the 19th century mathematics of neo-classical economics, management, and social science with agent-based computational models (ABMs). Since order-creation is a more characteristic aspect of social phenomena than order-translation, it follows that New Science ABMs map onto social phenomena better than math models styled after Classical physics and now dominating neo-classical economics. After all, People are the Brownian motion! The key question becomes, How to research social systems as complex adaptive systems, in which agents and emergent structures coevolve in the context of pressures from ever changing environmental contexts? New Science is often called rule-based or bottom-up science. The idea is to explain the emergence of macro social phenomena such as networks, groups, organizations, and larger structures by taking extant theories and translating them into the rules that autonomous heterogeneous agents have to be following in order for such structures to emerge. Furthermore, agents (people) adaptively learn and coevolve with other learning agents and higher-level social structures both upward and downward causality involved. Some of the research questions are: 1. What are the active agent rules? 2. Why do agents follow some rules and not others? 3. How and when do agents rules change? 4. What kinds of emergent social phenomena arise from interacting and learning agents? 5. What role do contextual energy differentials (adaptive tension) play in motivating agent behaviors? 6. How to manage agents and get them to produce more economically viable teams, new product developments, entrepreneurial ventures, and generally, more effective socioeconomic and/or organizational (complex adaptive) systems? Complexity scientists use agent-based models often termed adaptive learning models to 1. Meet the model-centered epistemology of modern philosophy of science; 2. Model social phenomena without the warping homogeneity, independence, and equilibrium assumptions inherent in math models; 3. Run computational experiments over time to more fully understand the interactions of nonlinearly related variables (rather than simply linearizing them) related to self-organizing phenomena. Modern computers allow the use of increasingly sophisticated agent-based adaptive-learning models such as cellular automata, genetic algorithms, and neural networks. These offer methods of studying how macro structures emerge from the interactions of stochastically idiosyncratic, learning, agents. They are the methods of choice of many complexity scientists. Since people are the Brownian motion in social systems, it is surely ironic that the use of these models in the social sciences considerably lags their use in the physical and life sciences. There are far more cites per journal in natural science than in social science. This course introduces you to the logic of agent-based theorizing, the different kinds of model platforms, and gets you started in the process of developing the agent “simple rules” that allow one to translate from old to new ways of modeling social phenomena. Course Goal: By the end of this course a student should be able to discuss the general differences between order-translation and order-creation science; critically read and discuss intermediate-level writings on complexity, multi-agent models and scale-free theories in physical, biological, social and organizational science; and begin to build rule-based models for basic theories in his or her major field. Texts: You will not need to buy any texts nor use the library. All the study materials are on the course website. However, you will need to download and study these. After the first Class meeting we will not hand out hardcopies of the materials. You will find the links to these on the List of Links page that can be accessed from the Main Page of the course website. Readings: Readings in bold type are required. In general, readings progress from easiest to hardest. And, in general, the bold items need to be read before class, except in some cases that will be pointed out as we go along. There will be a short writing assignment on each paper marked with an arrow: Using the Course Website: In general for each Class session you’ll find the following materials on the course website under a separate heading for each Class session: 1) Copies of papers and book chapters to download. 2) A PowerPoint slide lecture used during the Class session. 3) A writing assignment for each Class session. Study Time: You should spend an average of four hours outside study time for each Class session. Attendance: On-time for all sessions, following the first class session. Absence or lateness can only be excused by documentation for such things as illness, car trouble, death in the family, legal summons, or because a student is the principal caregiver for someone who is ill. Documented absences will be made up by homework assignments that will take about two hours each to complete. Grading: Letter Grade. For a passing grade or better in the course you must attend (on time) at least 18 sessions. At any rate, you are fully responsible for the content of any missed sessions. Assignments for each session (20% total), two short quizzes (10% each), final exam (25%) and a final paper/project (35%). Approximately 90 to 100% is an A; 80 to 90% is a B; 70 to 80% is a C; and 60 to 70% is a D. In the case of borderline totals, good work on the individual session assignments can boost your grade by one grade point. Exams: There will be two short midterm quizzes, and a final exam (given during finals week). The quizzes are not cumulative, but the final is. Except in the most extraordinary circumstances, there will be no make-up exams. Plagiarism: Know what it is, know what the UCLA rules about it are, and then don’t do it.
Schedule: I: Introduction to Order-Creation Science: Studying Emergent Complexity
Jan 08 (Session 01): INTRODUCTION TO NEW SOCIAL SCIENCE Overview: i. Session Design and Performance Requirements ii. Non-Equilibrium, Complexity, and Order-Creation Science iii Scientific Realism and Model-Centered Science iv Bottom-Up Science and Agent-based Computational Modeling b. Nobel Laureate Murray Gell-Mann on What is Complexity i. Effective Complexity ii. Two Kinds of Regularities to be Studied (1) Law-Like Regularities (2) Scale-free Regularities iii. Scalability (1) Annie Oakley, Kaiser Wilhelm and Frozen Accidents (2) Middle-level Theory (3) Disasters and Disaster Prevention—and some Good Extremes as well (4) Tiny Initiating Events, Butterfly Events, Butterfly Effects, Butterfly Levers c. Readings i. (required) Weaver, W. (1947) Science and Complexity. In W. Weaver (ed.), The Scientists Speak. ii. McKelvey, B. (2006). Note on Gell-Manns 2002 Chapter: What is Complexity? iii. Gell-Mann, M. (2002). What is Complexity? In Curzio & Fortis (eds.), Complexity and Industrial Clusters. iv. Wikipedia.Complex System. http://en.wikipedia.org/wiki/Complex_system OR v. Wikipedia.Complexity. http://en.wikipedia.org/wiki/Complexity vi Casti, J. (2007). Complexity Encyclopedia Britannica [WWW]
Jan 10 (Session 02): tension in a teapot: THE EUROPEAN SCHOOLOrder Creation among the Dead a. Order-Creation Essentials: Imposed Energy and the 1st Critical Value i. The Teapot and Henri Banard’s Dissertation in 1901 ii. Fluids, Energy, and the 1st & 2nd Critical Values b. Thermodynamics and Energy-effects in Physical Systems i. Prigogine’s Dissipative Structures and Irreversibility of Time ii Prigogine’s Tension between 1st and 2nd Laws of Thermodynamics c. ORDER CREATION AT THE EDGE OF ORDER i. The Region of Emergent Complexity ii Order via Chaotic Enslavement at the Edge iii Toward the 0th Law of Thermodynamics d. Organizational Application e. Readings: i.(required) Haken, H. (1983). Goal: Why You Might Read This Book. Synergetics. Springer-Verlag, 18. ii. McKelvey, B. (2007). Emergent Order Creation between the Edges of Order & Chaos. (Session Note) iii. McKelvey, B. (2004). Toward a 0th Law of Thermodynamics: Order-Creation Complexity Dynamics Journal of Bioeconomics,6: 65 96.
Jan 15 (Session 03): Slime, ants & Emergence: THE AMERICAN SCHOOL Order Creation among the Living a From Force-based (Cue-stick) Science to Bottom-up (Living) Agent-based Science b. Order-Creation Essentials: Motive-to-Connect (fitness), Heterogeneity & Connections c. Key Aspects of Complex Adaptive Systems: Holland’s Six Criteria d. Levels of Biological Emergence: Emergent Scalability i. Primordial Pool, DNA ii Biomolecules, Organelles, Cells iii Organisms iv. Slime Mold, Ant & Bee Colonies, etc. v. Species and Ecologies vi. And then Bigger Brains and Human Dominance (except for anti-drug resistant bacteria!) e. Tension vs. the Interaction of Agents: i. Agent Activation by Tension vs. by Fitness ii. When does it become Important to Study Agent Rules rather than Imposed Tension? f. Readings: i. Johnson, (2001). Street Level [about ants]. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Scribner. ii. (required) Chu, D., R. Strand & R. Fjelland (2003). Theories of Complexity. Complexity, 8: 1930. iii. Camazine, et al. (2001): What is Self-Organization? (Chapter 1) and How Self-Organization Works (Chapter 2), in Self-Organization in Biological Systems, Princeton.
Jan 17: (Session 04): Emergent Social Phemonema SFI Continued a. From Chaos to Complexity i. The Edge of Order ii. The Edge of Chaos iii. Chaos & Complexity Theory The Map iv. Fractals and Scalability in Common b. Basic Bio-Social Order-creation Dynamics What is Carried Over from Biology i. From Animals to People and Social Systems: How Different is Emergence? ii. What is different between the rabbit/fox ecology and social ecologies in, US, China, India, Africa? c. Key Differences i. Cognition and Memory ii. More Kinds of Connectivity iii. More Kinds of Change iv. More Complicated Complexities d. Emergence in Societies/Economies: Does Complexity Science Offer Anything New? e. Why are Organizations Different from Physical, Biological, and Other Social Systems? f. Readings: i. Seel (2003). Emergence in Organizations. [WWW] ii. Wikipedia. Emergence. http://en.wikipedia.org/wiki/Emergence iii. (required) Rauch, J. (2002). Seeing Around Corners. Atlantic Monthly, April: 35 48. iv. Heylighen, F. (2008). Complexity and Self-organization. In Encyclopedia of Library and Information Sciences. M. J. Bates & M. N. Maack (eds.).
Jan 22 (Session 05): Living at the Edge Self-organized Criticality a. Bak’s Self-Organized Criticality i. Sandpiles and Avalanches ii. Defining it iii. Universal Law? b. Sandpiles and Avalanches c. At the Edge of Adaptive Success in Biology i. Criticality defined in terms of the power law negatively sloping straight line ii. Criticality required for species adaptation in rank/frequency ecosystems d. Self-organized Criticality in Social Systems i. Criticality in rank/frequency social phenomena ii. Criticality and organizational adaptation iii. Criticality and stock markets iv.Criticality and business ecosystems. e. Readings: i. Wikipedia. Self-organized Criticality. http://en.wikipedia.org/wiki/Self-organized_criticality ii (required) Pascale, R., et al. (2000). Self-organization and Emergence & Self-organization & the Corporation, In Surfing the Edge of Chaos: Laws of Nature & ¦Business: Chapters 7 & 8. iii Bak, P. (1996). Complexity & Criticality. In How Nature Works, Chapter 1.
Jan 24 (Session 06): Why Jack Welch Should be Preaching Complexity Theory a. Why Jack Welch? i. CEO for 20 years; Most CEOs are now temp workers! ii. He was Manager of the Century. iii. Created more shareholder value than anyone else! b. The AIDS Cocktail Analogy i. One Pill vs. the Cocktail ii. One or Two Rules vs. the Whole Set c. Summarizing Complexity Theory: Why These 12? d. The 12 Simple Rules e. Readings: i. Wikipedia. Organizational studies http://en.wikipedia.org/wiki/Organizational_theory McKelvey 2007 1st Principles of Adaptation ii. (required) Iansiti, M. & Levien, R. (2004). Strategy as Ecology. Harvard Business Rev. (March): 69 78. iii. Mackey. A., et al. (2006). Churning, AIDS, and Welch.
Unit II: Doing Science Better
Jan 29 (Session 07): Getting the Philosophy Right: SCIENTIFIC REALISM a. Logical Positivism & Logical Empiricism b. Scientific Realism i. The Semantic Conception ii. Evolutionary Epistemology iii. Campbellian Realism c. Theories as Maps d. Readings: i. Fine. A. (1998) Scientific Realism and Antirealism. Routledge Encyclopedia of Philosophy. ii.(required) Azevedo, J. (2002). Updating Organizational Epistemology. In J. A. C. Baum (ed.), Companion to Organizations. Oxford, UK: Blackwell, pp. 715 732. iii. McKelvey, B. (1999). Toward a Campbellian Realist Organization Science. In J. A. C. Baum & B. McKelvey (eds.), Variations in Organization Science. Thousand Oaks, CA: Sage, pp. 383 411.
Jan 31 (Session 08): MODEL-CENTERED SCIENCE: Science is Based on Models! a. The Legacy of Positivism i. Centrality of Models in Science ii. Centrality of Experiments in Science b. Models as Autonomous Agents and Mediators c. Model-Centered Science d. Readings: i. Wikipedia. Epistemology http://en.wikipedia.org/wiki/Epistemology ii.(required) Morgan, M. & M. Morrison & (1999). Models as Mediating Instruments (Ch. 2). In M. S. Morgan & M. Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge U. Press: 10 37. iii. McKelvey, B. (2002). Model-Centered Organization Science Epistemology, plus Glossary of Epistemology Terms. In J. A. C. Baum, ed. Companion to Organizations. Oxford, UK: Blackwell, 752 780, 889 898.
Unit III: Interlude: Chaos and Statistics
Feb 05 (Session 09): NON-LINEAR CHOATIC SYSTEMS (Bragin) a. Scientific Laws, Determinism, Predictability b. Linear systems, Classical non-linear systems, chaotic non-linear systems c. Sensitive dependence on initial conditions, attractors, basins of attraction d. The Logistic Growth Model of Population Dynamics. e. The Lotka-Volterra Model of Predator-Prey Dynamics f. The Lorenz Model of Atmospheric Evolution g. Readings i. (required but not available, yet) Taylor (1989): The Strange New Science of Chaos (BBC-TV), on-line viewing. ii. Trump (1998): What is Chaos? http://order.ph.utexas.edu/chaos/ iii. Percival (1991): Chaos: a science for the real world, in Hall, ed: Exploring Chaos.
Feb 07 (Session 10): Review of Descriptive Statistics (Bragin) a. Measures of Central Tendency (Mean, Median and Mode) b. Measures of Spread (Variance, Inter-Quartile Range, Range) c. Histograms and Probability Distributions d. Normal and Pareto Distributions e. Log-Log Plots. f. Readings (i) Statistics: http://en.wikipedia.org/wiki/Statistics (ii) Descriptive statistics From the Wiki (ii) Niederman, et al (2003): Live by Pareto’s Law, What the Numbers Say, pp 16-20.
g. Quiz on Sessions 01 through 09 Unit IV: Multi-Agent Models: Design and Applications
Feb 12 (Session 11): INTRODUCTION TO AGENT-BASED MODELING & BOTTOM-UP SCIENCE a. From Force-based to Rule-based Science b. Defining Bottom Up Science c. Defining Agents d. Introduction to Basic Modeling Platforms: i. CA Cellular Automata ii. GA Genetic Algorithms iii. Neural Networks e. Strengths and Weaknesses of Each Platform f. Readings: Wikipedia. “Epistemology” http://en.wikipedia.org/wiki/Epistemology Morgan, M. & M. Morrison & (2000). “Models as Mediating Instruments” (Ch. 2). In M. S. Morgan & M. Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge U. Press: 10–37. McKelvey, B. (2002). “Model-Centered Organization Science Epistemology,” plus “Glossary of Epistemology Terms.” In J. A. C. Baum, ed. Companion to Organizations. Oxford, UK: Blackwell, 752–780, 889–898
Feb 14 (Session 12): MIKE MACY’S USE OF THE GENETIC ALGORITHM a. Biological Basis of the Genetic Algorithm (GA) i. Chromosomes and Genes ii. Crossover, Mutation, Mating & Offspring b. Translation into the Computational GA c. GA Design i. The Macy/Skvoretz Example ii. Going Through Their Paper: Structure of the Paper; Design of the GA iii. Their Approach Compared to Other Options d. Advantages and Disadvantages of the GA for Organizational and Social Modeling e. Reading: i. Wikipedia - "Epistemology" article ii Morrison, et al (1999) - "Models as Mediating Instruments" iii McKelvey (2002) - "Model-Centered Organization Science Epistemology" Macy, et al (1998) - "Evolution of Trust & Cooperation between Strangers" Lecture 11 - Power Point Slides Feb 19 (Session 13): STU KAUFFMAN’S NK MODEL: A CLASSIC CELLULAR AUTOMATA APPLICATION a. CA Platform; Started circa 1969 b. Analysis of N and K effects; and the C effects c. Peaks, Valleys, Nash Equilibria d.Tuning the (Adaptive) Fitness Landscape e. Rugged Landscapes f.Complexity Catastrophe and Moderate Complexity Effects g. Readings: i. Flake, G. W. (1998). Chapter 15: Genetics and Evolution i. Axelrod (2001) - "Evolution of Strategies in Iterated Prisoner's Dilemma" ii. Thomas (2004) - "Games, Life and the Game of Life" iii. McKelvey (1999) - "Avoiding Complexity Catastrophe in Coevolutionary Pockets” iv. Yuan, et al (2004) - "Situational Learning and the NK Model" Writing Assignment 12 - "Genetic Algorithms"
Feb 21, Friday (Session 14): BLAKE LEBARON’S MODEL OF THE STOCK MARKET a. Modeling the Stock Market Dynamics b. Validation of the Model c. Modeling Economic Rational Agents d. LeBaron’s Multi-Platform Model i. Basic Market Behavior Uses a CA Model ii. Agent-Investor Behavior Uses a GA Model iii. Development of Investment Strategies Uses a Neural Net Model e. What Happens When Agents Lose Heterogeneity f. Readings: ii Best (1990+) - An Overview of Neural Networks iii Wikipedia - "Neural Netwoks" iv LeBaron (2001a) - "Volatility Magnification and Persistance in an Agent-Based Financial Market" v Le Baron (2002) - "Calibrating an Agent-Based Financial Market"
Feb 26 (Session 15): KATHLEEN CARLEY’S ORGAHEAD and CONSTRUCT-O MODELS a. Multi-Platform Models b. Tasks c. Hierarchy d. Emergent Teams e. Environment f. Reading: i.Carley, K. M. (2001). Smart Agents and Organizations of the Future. Working paper, CMU. ii. Carley, K. M. & D. Svoboda (1996). Modeling Organizational Adaptation as a Simulated Annealing Process. Sociological Methods and Research, 25: 138-168. iii. Carley, K. M. & L. Gasser (1999). Computational Organization Theory. In Multivalent Systems: A Modern Approach to Distributed Artificial Intelligence. G. Weiss (ed.), MIT Press.
Unit V: Issues and Choices in Model Design
Feb 28 (Session 16): BASICS ON MODEL DESIGN & VALIDATION a. Contractor et al.’s Model of Anthony Giddens’s Structuration Theory i. Anthony Gidden’s Theory Finding the Key Variables ii. The Basic Model: How it Works iii. Translating the Variables into Stylized Facts and then into Agent Simple Rules iv. Validating the Rules in Basic Social Science Research v. Designing the Human Experiment vi. Model Results and Implications vii Pluses and Minuses of Their Approach b Docking as a Means of Model Validation i What Docking Means ii. What Can Be Wrong in Models (1) Bad Theory-to-Model Connection (2) Model is Improperly Designed (3) Model is Cooked or Wrapped (4) Model is Too Simple or Too Complicated c. Readings: i.¨Wikipedia: Structuration http://en.wikipedia.org/wiki/Structuration_theory ii. Contractor, N., et al. (2000). Structuration Theory and Self-Organizing Networks. Working paper. iii. Rouchier, J. (2003). Re-Implementation of a Multi-agent Model Aimed at Sustaining Experimental Economic Research. JASSS: http://jasss.soc.surrey.ac.uk/6/4/7.html
Mar 04 (Session 17): CHOOSING BETWEEN COMPLEX REAL WORLD AND IDEALIZED MODEL PowerPoints for this lecture a. Models as Maps; Subway Maps b. Review of Role of Models in Good Science c. KISS vs. Veridicality: Too Simple vs. Too Complex d. What is Just Right? i. Docking with Other Models ii. Validating Against Real-World Criteria iii.Clear Connection between Manipulated Independent Variable and Outcome e. Building an Agent-based Computational Model i. Building Multi-agent Systems ii. From Simple Models to Complex Results f. LeBaron’s Guidelines for Model Building g. Readings: i.Carley, K. M. 2002. Simulating Society: The Tension between Transparency and Veridicality. Proceedings of Agent 2002, Chicago, IL. ii. Gilbert, N., & P. Terna (1999). How to Build and Use Agent-based Models in Social Science. Working paper. iii. LeBaron, B. (2001). A Builder’s Guide to Agent-Based Financial Markets, Quantitative Finance, 1, 254-261. d. Quiz on sessions 10 through 16
UNIT VI: POWER LAWS, SCALE-FREE CAUSES, & RANK-FREQUENCY RESEARCH
Mar 06 (Session 18): On the Power of Power Laws: Lessons from Econophysics a. Defining Fractals, Power Laws, and Scale-free Theory b. Math-based Fractal Geometry vs. Living Adaptive Fractals c. Power Laws as Indicators of Rank/Frequency Pareto Distributions vs. Causes d. 80 Kinds of Power Laws e. Social and Organizational Application f. Readings: i. ¨Blog by John Hagel (2006). The Power of Power Laws. [WWW] ii. Buchanan, M. (2004). Power Laws & the New Science of Complexity Management. [www.strategy-business.com]. iii. Andriani, P. & B. McKelvey (2005/06): Tables of 80 Kinds of Power Laws & 15 Scale-free iv. Andriani, P., & B. McKelvey (2007). Beyond Gaussian Averages: Redirecting Management Research to Extreme Events and Power Laws.Journal of International Business Studies, 38: (Nov.-Dec.).
Mar 11 (Session 19): Scale-free Causes of Power Laws and Scalability a. Romanesque Broccoli and Scalability i. Tiny Initiating Events, Butterfly Events and Butterfly Levers ii. Multiple Levels; Gell-Mann’s Middle Level Theory b. Scalability Examples i. Negative Extremes: Challenger & Pioneer Disasters, 9/11, ii. Positive Extremes: Organizations, Microsoft, Google iii Chris Anderson’s Long Tailing in Web-based Markets c. 15 Scale-free Theories i. Fixed Exponents ii. Combinations iii. Positive Feedback Spirals iv. Contextual Effects d. Managing the Tails of Rank/Frequency Distributions i. Enabling Positive Levers ii. Shutting Down Negative Levers e. Readings: i. Strogatz, S. H. (2005). Romanesque Networks. Nature, 433 (Jan.): 365-366. ii.Bragin (2008): Fractals: The Real Geometry of Nature (Slide presentation) iii. Andriani, P. & B. McKelvey (2007). Extremes & Scale-Free Dynamics in Organization Science iv. Gladwell, M. (2006). Million Dollar Murray. The New Yorker. v. Iansiti, M. & Levien, R. (2004). Strategy as Ecology. Harvard Business Review, (March): 69-78.
Mar 13 (Session 20): Doing Research on Rank/Frequency Distributions a. Rank/Frequency Pareto Distributions: Short Review i. Mosquitoes vs. Elephants; Ma & Pa stores vs. Wal-Mart; You vs. Bill Gates ii. Researching the Upper-left Tail: Gaussian Statistics and Micro-Niches iii. Research the Extremes in the Lower-right Tail: When N = 1, or Very Small. b. Researching in a Pareto World i. When is it Normal and When is it Pareto ii. Searching for Scale-free Causes and Scalability iii. Getting Better Samples of Outliers iv. Learning from How Geologists Study #9 Quakes c. Readings: i. (required) Bell, D. (2008) Identity and innovation ii. (required) McKelvey, B (2008). Pareto-based Science: Researching Rank/Frequency Phenomena
Study questions fo the Final Examination Final Exam Mar 19, Wednesday, 3:00pm-6:00pm,
Foundational Books (for your information, not required reading) 1 Order Out of Chaos (Ilya Prigogine & Isabelle Stengers, 1984). 2. Complexity: Life at the Edge of Chaos (Roger Lewin, 1992/1999). 3. Origins of Order (Stewart Kauffman, 1993). 4. Complexification (John Casti 1994). 5. Thinking in Complexity (Klaus Mainzer, 1994/2004). 6. At Home in the Universe (Stewart Kauffman, 1995). 7. How Nature Works: The Science of Self-Organized Criticality (Per Bak, 1996). 8. The Self-Organizing Economy (Paul Krugman, 1996). 9. The Lure of Modern Science: Fractal Thinking(Bruce West &B. Deering,1995). 10. Complexity and Postmodernism (Paul Cilliers, 1998). 11. The Complexity Advantage (Susanne Kelly & Mary Allison, 1998). 12. Butterfly Economics (Paul Ormerod, 1998). 13. Evolutionary Systems: Biological and Epistemological Perspectives on Selectionand Self-Organization (Gertrudis Van de Vijver, Stanley N. Salthe & Manuala Delpos, eds., 1998). 14. The Complexity Vision and the Teaching of Economics, (David Colander, ed. 2000). 15. Surfing the Edge of Chaos (Richard Pascale, et al., 2000). 16. Emergence: The Connected Lives of Ants, Brains, Cities, & Software (Steven Johnson, 2001). 17. An Introduction to Econophysics:…Complexity in Finance (Rosario Mantegna & Eugene Stanley, 2000) 18. Linked: The New Science of Networks 19. Hollywood Economics: How Extreme Uncertainty Shapes the Film Industry. (Arthur De Vany, 2004). 20 The Structure and Dynamics of Networks (Mark Newman, Duncan Watts, Albert-Laszlo Barbarasi, eds. 2006). 21. A Realist Theory of Science, (R. Bhaskar, 1975). [2nd ed., Verso, 1997]. 22. Mapping Reality (Jane Azevedo: 1997). 23 Economics & Reality (Tony Lawson, 1997). 24. Models as Mediators (Mary Morgan & Margaret Morrison, eds., 2000) 25. Hidden Order (John Holland, 1996). 26. Growing Artificial Societies (Joshua Epstein & Robert Axtell, 1996) 27. Emergence: From Chaos to Order (John Holland, 1998). 28. Would-Be Worlds: How Simulation is Changing the Frontiers of Science (John Casti, 1997). 29. Simulation for the Social Scientist (Nigel Gilbert & Klaus Troitzsch, 1999). 30. Simulating Organizations (Michael Prietula, Kathleen Carley, & Les Gasser, 1998). 31. Computational Modeling of Behavior in Organizations (Daniel Ilgen & Charles Hulin, eds., 2000). 32. The Handbook of Computational Economics, Vol. 2 (Leigh Tesfatsion & Kenneth Judd, 2006) 33. North, M. & C. Macal 2007. Managing Business Complexity: Discovering Strategic Solutions with Agent-based Modeling and Simulation. Oxford University Press. 34. Miller, J. & S. Page 2007. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press. 35. Epstein, Joshua M. 2007. Generative Social Science: Studies in Agent-based Computational Modeling. Princeton University Press. The Best Agent Modeling Programs NetLogo: http://ccl.northwestern.edu/netlogo/ (~Java-based multi-agent platform) Repast: http://repast.sourceforge.net/ (Java-based multi-agent platform) |
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