Instructor: Bill McKelvey
Saturdays:
SPRING 2002
1:30pm to 4:30pm
Anderson Complex
Room C303
“NEW” MANAGEMENT and “NEW” ECONOMICS:
Applications of Complexity Science and Agent-based Models
mckelvey@anderson.ucla.edu
This course uses complexity science to bridge between old and new conceptions of economics and management. Newtonian
science, neoclassical economics, and old style approaches to management science and practice all build on the assumptions that all
the basic “agents” comprising phenomena (atomic particles, atoms, molecules, organisms, people, groups, firms, etc.): 1) are
“homogeneous;” and 2) that social systems go forward in time under equilibrium conditions. “New” Economics, “New”
Management, 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 macro structures emerge from
the “stochastic agent soup.”
Complexity science focuses on “order-creation” rather than the “order-translation” process underlying the 1st law of
thermodynamics (energy conservation), and replaces the 19 th century mathematics of neoclassical economics and management
science with agent-based computational models. Since order-creation is a more characteristic aspect of social phenomena than
order-translation, it follows that New Science maps onto social phenomena better than math models styled after classical physics
and now dominating neoclassical economics. After all, People ARE the Brownian motion! The key question becomes, How to
research and manage firms—as complex adaptive systems—in which agents and emergent structures coevolve in the context of
pressures from a changing competitive context?
New Science is often called “rule-based science.” The idea is to explain the emergence of macro socioeconomic phenomena—
such as networks, groups, firms and larger structures—by taking extant theories and translating them into the “rules” that
autonomous heterogeneous agents would 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.
2.
3.
4.
5.
6.
What are the active agent rules?
Why do agents follow some rules and not others?
How and when do agents’ rules change?
What kinds of emergent social phenomena arise from interacting and learning agents?
What role do contextual energy differentials (adaptive tension) play in motivating agent behaviors?
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”—(1) to meet the model-centered
epistemology of modern philosophy of science, (2) model social phenomena without the warping homogeneity and equilibrium
assumptions inherent in math models, and (3) to 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. A couple of years ago there were over 200 more cites per journal in natural science than in
sociology. 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.
2


Easy Reads to do Before 1st Class!
The Self-Organizing Economy (Paul Krugman, 1996)
(A quick read: 100 pages on the self-organization of economies and power law applications)
Growing Artificial Societies (Epstein and Robert Axtell 1996)
(Fun Read: Multicolored introduction to agent-based modeling and bottom-up (rule-based) science—a classic!!)

As additional textbooks, choose ONE out of each of the following sets:
1. Complexification (John Casti 1994)
(Introduction to natural science complexity phenomena)
2. Complexity: Life at the Edge of Chaos (Roger Lewin, 1992, 1999)
(Sort of a biography of the emergence of the Santa Fe “vision” of complexity)
3. Emergence: The Connected Lives of Ants, Brains, Cities, & Software (Steven Johnson, 2001)
(An easy read about self-organization from slime mold to brains to cities)
4. Thinking in Complexity (Klaus Mainzer, 1997)—optional/advanced—NOT required
(Introduction to complexity science as “order-creation” science—starts from quantum physics and his
analysis moves up to biosphere, brain, artificial intelligence, and finally to socioeconomic systems)
1. Multi-Agent Systems (Jacques Ferber, 1999).
(Basic textbook on agent-based modeling. Aimed at people who work in organizations.)
2.  Simulation for the Social Scientist (Nigel Gilbert and Klaus Troitzsch, 1999).
(Basic text on agent-based modeling in the social sciences)
1.  Butterfly Economics, (Paul Ormerod, 1998).
(Economics based on the biological and complex adaptive systems coevolutionary models rather than
on the classical, Newtonian physics, machine-model. Builds from his, The Death of Economics, 1994.)
2. The Complexity Vision and the Teaching of Economics, (David Colander, ed. 2000).
(The Santa Fe complexity vision applied to a wide range of issues economists worry about)
1. Computational Modeling of Behavior in Organizations (Daniel Ilgen and Charles Hulin, 2000).
(Agent-Models of individual and group behavior IN organizations rather than models OF organizations)
2. Simulating Organizations (Michael Prietula, Kathleen Carley, & Les Gasser, 1998).
(Multi-level agent-based models of learning agents in multi-level adaptive organizations)
3. Dynamics of Organizations (Alessandro Lomi and Erik Larsen, 2001).
(Various kinds of models of mostly industry-level—ecological—dynamics of firms)
 Means the book is recommended for undergraduates and/or those without natural science majors.
The Honors Collegium advisors tell me that one book per week is the normal work load.
 Grades based on:
(1) Weekly Assignments: You must email to me—one day before class—a 1-page “paper” (1) telling me what you read; (2) telling
me why you liked what you read; and (3) telling me all the problems you had with the readings—such as logical flaws,
disagreements, modeling difficulties, misinterpretation of complexity science, economics, organization science; not relevant to
management, not tied in with real-world phenomena, etc., etc., etc.,
(2) Term Paper: A 10 page paper on a topic “contracted” with the instructor. Self-organizing groups may suggest alternatives.
(3) “Thought-Experiment:” A 2-page paper that translates an “old” science social science theory into an agent-based, adaptivelearning, self-organizing, “simple rule” model.
 Students will be treated as adaptive-learning agents. Class may or may not self-organize. There are three obvious paths of
specialization: New Economics; New Management; Agent-based Modeling. There are a vast number of other possible
specializations.

Relevant to any social science discipline and other fields such as linguistics and social psychology.

Details may be adjusted, depending on emergent needs and desires of the class.
Additional reading lists follow the weekly agenda, defining some of the related literature.
3
1.
BASIC COMPLEXITY SCIENCE
Topics:
Readings:
2.
Kinds of Complexity
Agents and Simple Rules
The Region of Emergence
Dissipative Structures
Emergence: The Connected Lives of Ants, Brains, Cities, & Software (Steven Johnson, 2001).
How the Leopard Changed Its Spots: The Evolution of Complexity (Brian Goodwin, 2001).
Chaos, Complexity, and Sociology: Myths, Models, and Theories
(Raymond Eve, Sara Horsfall and Mary Lee, eds., Sage, 1997).
How Nature Works: The Science of Self-Organized Criticality (Per Bak, 1996).
Chaos and Society (Alain Albert, ed., 1995).
Complexification (John Casti 1994).
Out of Control (Kevin Kelly, 1994).
Complexity: Metaphors, Models, and Reality (G. A. Cowan et al., eds., 1994).
Chaos and Order: The Complex Structure of Living Things (Fritz Cramer, 1993).
Chaos and Complexity (Brian Kaye, 1993).
Complexity (Mitchell Waldrop, 1993).
Interdisciplinary Approaches to Nonlinear Complex Systems (Hermann Haken
and A. Mikhailov, eds., 1993).
Complexity: Life at the Edge of Chaos (Roger Lewin, 1992/1999).
Exploring Complexity: An Introduction (Grégoire Nicolis and Ilya Prigogine, 1989).
Order Out of Chaos (Ilya Prigogine and Isabelle Stengers, 1984).
Horgan, J. (1995). “From Complexity to Perplexity,” Scientific American, 272, 104–111.
ORDER-CREATION SCIENCE APPLIED TO FIRMS and ECONOMIES
Topics:
Readings:
Origins of Order
Order-Creation at Different Levels of Analysis
Matter—Life—Brain—Artificial Intelligence—Social Systems
Cosmic Evolution: The Rise of Complexity in Nature (Eric Chaisson, 2001)
Self-Organization in Biological Systems (Scott Camazine et al., 2001).
Investigations (Stewart Kauffman, 2000).
Emergence: From Chaos to Order (John Holland, 1998).
Thinking in Complexity (Klaus Mainzer, 1997).
Self-Organization of Complex Structures: From Individual to Collective
Dynamics (Frank Schweitzer, ed., 1997).
At Home in the Universe (Stewart Kauffman, 1995).
Dynamic Patterns: Self-Organization of Brain and Behavior (Scott Kelso, 1995).
Development and Evolution: Complexity and Change in Biology (Stanley Salthe, 1993).
Self-Organizing Systems: The Emergence of Order (Eugene Yates, 1987).
Autopoiesis and Cognition (R. Maturana and F. H. Verela, 1980).
Synergetics (Hermann Haken, 1977, 1983).
*McKelvey, B. (2001b). “Social Order-Creation Instability Dynamics: Heterogeneous
Agents and Fast-Motion Science—on the 100th Anniversary of Bénard’s Paper,”
paper presented at the Workshop on Thermodynamics and Complexity Applied to
Organizations, EIASM, Brussels, Belgium, Sept. 28-29.
Arthur, W. B. (1993). “Why Do Things Become More Complex?” Scientific American (May), 144.
Simon, H. A. (1969). “The Architecture of Complexity,” The Sciences of the Artificial, 84–118.
Ashby, W. R. (1962). “Principles of the Self-Organizing System,” in Principles of SelfOrganization, H. von Foerster & G. W. Zopf, eds., New York: Pergamon, 255–278.
4
3.
BOTTOM-UP SCIENCE—THINKING LIKE AN AGENT
Topics:
Readings:
4.
Order-Creation continued
Agent-Based, Bottom-Up Science
Growing an Artificial Economy
Simulation for the Social Scientist (Nigel Gilbert and Klaus Troitzsch, 1999).
Multi-Agent Systems (Jacques Ferber, 1999).
Computational Techniques for Modelling Learning in Economics (Thomas Brenner, ed., 1999).
Would-Be Worlds: How Simulation is Changing the Frontiers of Science (John Casti, 1997).
Hidden Order (John Holland 1996).
Page, S. (1999). “Computational Models from A to Z,” Complexity, 5, 35–41.
Darley, V. M. and S. A. Kauffman (1997). “Natural Rationality,: in ADL, 45–80.
Weiss, G. (1995). “Adaptation and Learning in Multi-Agent Systems…” in G. Weiss,
ed., Adaptation and Learning in Multi-Agent Systems, Springer.
Vriend, N. (1995). “Self-Organization of Markets: An Example of a Computational
Approach,” Computational Economics, 8, 205–231.
Carley, K. M. and A. Newell (1994). “The Nature of the Social Agent,” Journal of
Mathematical Sociology, 19, 221–262.
Arthur, W. B. (1993). “On Designing Economic Agents that Behave Like Human
Agents,” J. Evolutionary Economics, 3, 1–22.
Holland, J. and J. Miller (1991). “Artificial Adaptive Agents in Economic Theory,”
American Economic Review Papers and Proceedings, 81, 365–370.
DYNAMICS OF COEVOLVING ECONOMIC ADAPTIVE SYSTEMS
Topics:
Readings:
Orthodox and Evolutionary Economics: A Critique
Economies as Complex Adaptive Systems
Agent-Based Computer Simulation of Dichotomous Economic Growth (Roger McCain, 2000).
Commerce, Complexity, and Evolution (William Barnett et al., eds. 2000).
Computable Economics (Kumaraswamy Velupillai, 2000).
Butterfly Economics (Paul Ormerod, 1998).
Evolution and Self-Organization in Economics (F. Schweitzer & G. Silverberg, eds. 1998)????
The Economy as an Evolving Complex System II (ADL)(B. Arthur, S. Durlauf, D. Lane, eds., 1997).
Computational Economic Systems: Models, Methods & Econometrics (Manfred Gilli, ed., 1996).
The Death of Economics (Paul Ormerod, 1994).
The Economy as an Evolving Complex System I (P. Anderson, K. Arrow and D. Pines, 1988).
*Zak, P. (2000). “Population Genetics and Economic Growth,” working paper, UC Riverside.
Tesfatsion, L. (1999). “Hysteresis in an Evolutionary Labor Market…” in
Evolutionary Computation in Economics and Finance, S-H. Chen, ed., Springer.
Arifovic, J., J. Bullard and J. Duffy (1997). “The Transition from Stagnation to Growth:
An adaptive Learning Approach,” J. of Economic Growth, 2, 185–209.
*De Vany, A. (1997). “Complexity at the Movies,” Complexity,
*De Vany, A. (2001). “Motion Picture Profit, the Stable Paretian Hypothesis, and the Curse of the
Superstar,” working paper, UC Irvine.
Durlauf, S. N. (1997). “Limits to Science or Limits to Epistemology?” Complexity, 2, 31–37.
Kollman, K., J. H. Miller and S. E. Page (1997). “Computational Political Economy,” ADL 461+.
Krugman, P. (1997). “How the Economy Organizes Itself in Space…,” in ADL, 239–262.
Lane, D. (1997). “Is What is Good for Each Best For All: Learning from Others…,” ADL, 105–127.
Leijonhufvud, A. (1997). “Macroeconomics and Complexity: Inflation Theory,” in ADL, 321–334.
Lindgren, K. (1997). “Evolutionary Dynamics in Game-Theoretic Models,” in ADL, 337–367.
Shubik, M. (1997). “Time and Money,” in ADL, 263–283.
Tesfatsion, L. (1997). “How Economists Can Get Alife,” in ADL, pp. 533–564.
Arifovic, J. (1996), “The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental
Economies,” Journal of Political Economy, 104, 510–541.
Arifovic, J. (1994). “Genetic Algorithm Learning and the Cobweb Model,” Journal of
Economic Dynamics and Control, 18, 3–28.
5
5.
STOCK MARKET DYNAMICS
Topics:
Readings:
6.
ECOLOGY and STRATEGY DYNAMICS and KAUFFMAN’S NK Model
Topics:
Readings:
7.
Modeling Economic “Rational” Agents
Modeling Market Dynamics
Computational Finance (Yaser S. Abu-Mostafa, Blake LeBaron, et al. 2000).
LeBaron, B. (2001). “Evolution and Time Horizons in an agent-based Stock Market,”
Brandeis University. (details the model)
*LeBaron, B. (2001). “Volatility Magnification and Persistence in an Agent-Based
Financial Market, Brandeis University.
*LeBaron, B. (2001). “Financial Market Efficiency in a Coevolutionary Environment.”
*LeBaron, B. (2001). “A Builder’s Guide to Agent Based Financial Markets.”
*LeBaron, B. (2000). “Empirical Regularities from Interacting Long and Short
Memory Investors in an Agent-Based Stock Market,” IEEE Transactions on
Evolutionary Computation.
Arthur, W. B. et al. (1997). “Asset Pricing Under Endogenous Expectations in an
Artificial Stock Market,” in ADL (1998), pp. 15–45.
Kauffman’s “Complexity Catastrophe”
Details of the NK Model and NK Applications
Policy Analysis
Surfing the Edge of Chaos (Richard Pascale, Mark Millemann and Linda Gioja, 2000).
Competing on the Edge (Shona Brown and Kathleen Eisenhardt, 1998).
Origins of Order (Stewart Kauffman, 1993).
*Rivkin, J. W. (2001). “Reproducing Knowledge: Replication Without Imitation at Moderate
Complexity,” working paper, Harvard Business School.
*Fleming, L. and O. Sorenson (forthcoming). “Technology As a Complex Adaptive System:
Evidence from Patent Data,” Research Policy.
*Yuan, Y. and B. McKelvey (2001). “Situativity of Learning Within Groups: An NK
Modeling Study,” working paper, Annenberg School, USC, Los Angeles.
Rivkin, J. W. (2000). “Imitation of Complex Strategies,” Management Science, 46, 824–844.
Carley, K. M. (2000). “Organizational Adaptation in Volatile Environments,” in IH, 241–268.
Levinthal, D. and M. Warglien (1999). “Landscape Design: Designing for Local
Action in Complex Worlds,” Organization Science, 10, 342–357.
McKelvey, B. (1999). Avoiding Complexity Catastrophe in Coevolutionary Pockets:
Strategies for Rugged Landscapes,” Organization Science, 10, 294–321.
Sorenson, O. (1997). “The Complexity Catastrophe: Interdependence and Adaptability in
Organizational Evolution,” working paper, University of Chicago.
Levinthal, D. (1997). “Adaptation on Rugged Landscapes,” Management Science, 43, 934–950.
INTERNAL ORGANIZATION STRUCTURE/CULTURE DYNAMICS
Topics:
Readings:
Horizontal and Vertical Structures
Coevolving Structure and Culture
The Boundaryless Organization (Ron Ashkenas, et al., 1995).
Simulating Organizations (Michael Prietula, Kathleen Carley, & Les Gasser, 1998).
Computational Modeling of Behavior in Organizations (IH) (Daniel Ilgen and Charles Hulin, eds.,
American Psychological Association, 2000).
Computational Organization Theory (CP) (Kathleen M. Carley and M. Prietula, eds., 1994).
Artificial Intelligence in Organization and Management Theory (MW)
(Michael Masuch and Massimo Warglien, eds. North Holland, 1992).
Schwab, D. P. and C. A. Olson (2000). “Simulating Effects of Pay-for-Performance
Systems on Pay-Performance Relationships,” in IH, 115–127.
Stasser, G. (2000). “Information Distribution, Participation, and Group Decision,” in IH, 135–156.
Latené, B. (2000). “Pressures to Uniformity and the Evolution of Cultural Norms,” in IH, 189–215.
McPherson, J. M. (2000). “Modeling Change in Fields of Organizations,” in IH, 221–234.
Padgett. J. F. (1997). “The Emergence of Simple Ecologies of Skill,” in ADL, 199–221.
Carley, K. M. and D. M. Svoboda (1996). “Modeling Organizational Adaptation as a
Simulated Annealing Process,” Sociological Methods and Research, 25, 138–168.
Crowston, K. (1994). “Evolving Novel Organizational Forms,” in CP,
6
8.
ALLIANCE and ORGANIZATIONAL NETWORK DYNAMICS
Topics:
Readings:
9.
ORGANIZATIONAL LEARNING DYNAMICS
Topics:
Readings:
10.
Alliance Formation and Management Challenges
Design vs. Coevolution
Managing by Thinking Like Agents
Strategic Alliances (Michael Yoshino and Srinivasa Rangan, 1995).
Networks In and Around Organizations (Steven Andrews and David Knoke, eds., 1999).
Dynamics of Organizations (Alessandro Lomi and Erik Larsen, 2001).
Carley, K. (1999). “On the Evolution of Social and Organizational Networks,” in S. B.
Andrews” and D. Knoke, eds., Research in the Sociology of Organizations, JAI Press, pp. 3–30.
Cohen, M. D., R. L. Riolo and R. Axelrod (1999), “The Emergence of Social
Organization in the Prisoners’ Dilemma: How Context-Preservation and other Factors
Promote Cooperation,” SFI working paper #99-01-002.
Epstein, J. (1997). “Zones of Cooperation,” SFI working paper #97-12-094.
Agent Rules
Kinds of Models
Individual, Collective, and Artificial Learning
Social Science Examples
Organizational Learning: Creating, Retaining and Transferring Knowledge (Linda Argote 1999).
Learning and Innovation in Organizations and Economies (Bart Nooteboom, 2000).
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
(Gerhard Weiss, ed. 1999).
Connectionist Models of Social Reasoning and Social Behavior
(Stephen Read and Lynn Miller, eds. 1998).
Distributed Artificial Intelligence Meets Machine Learning: Learning in
Multi-Agent Environments (Gerhard Weiss, ed. 1996).
*Carley, K. and J-S Lee (1998). “Dynamic Organization: Organizational Adaptation
in a Changing Environment,” Advances in Strategic Management, Vol. 15, 269–297.
Glance, N. S. and B. A. Huberman (1994). “Social Dilemmas and Fluid Organizations,”
in CM, Ch. 10.
Lant, T. (1994). “Computer Simulations of Organization as Experiential learning Systems…”
in CM, Ch. 9.
Selten, R. (1991). “Evolution, Learning, and Economic Behavior,” Games and Econ. Beh., 3, 3–24.
LEADERSHIP DYNAMICS
Topics:
Leadership, Social Capital, and Knowledge Management
Speeding up the Pace of Coevolution
Strategic Adaptation
GE, Jack Welch, and Complexity Theory: Why GE Wins!!
Aerospace Matrix Organizations: Why they Work
Readings: The Complexity Advantage (Susanne Kelly and Mary Allison, 1998).
The Unshackled Organization (Jeff Goldstein, 1994).
*McKelvey, B. (2002). “MicroStrategy from MacroLeadership: Distributed Intelligence via New
Science,” A. Y. Lewin and H. Volberda (eds.), Mobilizing the Self-Renewing Organization, Sage.
*McKelvey, B. (2002). “Emergent Order in Firms: Complexity Science vs. the Entanglement Trap,”
in E. Mitleton-Kelly, (ed.), Organizations Are Complex Social Systems, Elsevier.
Johnson, N. L. (1999). “Diversity and Robustness: Collective Problem Solving…,”
working paper, Los Alamos.
Hong. L. and S. Page (1999). “Problem Solving by Heterogeneous Agents,”
Journal Economic Theory,”
Guastello, S. J. (1998). “Self-Organization and Leadership Emergence, Nonlinear Dynamics,
Psychology, and Life Sciences, 2, 301–315.
Devany, A. (1996). “The Emergence and Evolution of Self-Organized Coalitions,” in
M. Gilli, ed., Computational Economic Systems: Models, Methods, and Econometrics, Kluwer.
Huberman, B. and N. S. Glance (1994). “Diversity and Collective Action,” in HM,
7
11.
APPENDIX: PHILOSOPHICAL FOUNDATIONS
Evolutionary Scientific Realism
Reading:
Mapping Reality (Jane Azevedo: 1997).
Economics & Reality (Tony Lawson, 1997).
*McKelvey, B. (1999). “Toward a Campbellian Realist Organization Science,” in J. A.
C. Baum & B. McKelvey (eds.), Variations in Organization Science…”, 383–411.
Model-Centered Social Science
Readings:
Models as Mediators (MM) (Mary Morgan and Margaret Morrison, eds., 2000).
*McKelvey, B. (2001). “Model-Centered Organization Science Epistemology,” in
J. A. C. Baum (ed.) Companion to Organizations.
*Henrickson, L, & B. McKelvey (2002). “Foundations of “New” Social Science: Institutional
Legitimacy from Philosophy, Complexity Science, Postmodernism, and Agent-based Modeling,”
Proceedings of the National Academy of Science.
*Henrickson, L. (2001). “Trends in Chaos and Complexity Theories and Computer Simulation in
the Social Sciences,” working paper, UCLA.
Morrison, M. & M. Morgan (2000). “Models as Mediating Instruments” in MM, 10–37.
Morrison, M. (2000). “Models as Autonomous Agents,” in M. S. Morgan and MM, 38–65.
*Contractor, N. S., Whitbred, R., Fonti, F., Hyatt, A. O’Keefe, B., and Jones, P. (2000).
“Structuration theory and self-organizing networks,” presented at Organization Science Winter
Conference, (Keystone, CO, 2000).
*Read, D. W. (1990). “The Utility of Mathematical Constructs in Building Archaeological Theory,”
in A. Voorrips (ed.), Mathematics and Information Science in Archaeology: A Flexible Framework.
Bonn: Holos, 29–60.
Epistemology and Complexity
Readings:
Dynamics in Action: Intentional Behavior as a Complex System (Alicia Juarrero, 1999).
Complexity and Postmodernism (Paul Cilliers 1998).
Evolutionary Systems: Biological and Epistemological Perspectives on Selection and
Self-Organization (Gertrudis Van de Vijver, Stanley N. Salthe, Manuala Delpos, eds., 1998).
Evolution, Order and Complexity (Elias Khalil and Kenneth Boulding, eds., 1996).
The Philosophy of Artificial Life (M. Boden, 1996).
*McKelvey, B. (2001). “What Is Complexity Science? It’s Really Order-Creation Science,”
Emergence, 3, 137–157.
*McKelvey, B. (2001b). “Social Order-Creation Instability Dynamics: Heterogeneous
Agents and Fast-Motion Science—on the 100th Anniversary of Bénard’s Paper,”
paper presented at the Workshop on Thermodynamics and Complexity Applied to
Organizations, EIASM, Brussels, Belgium, Sept. 28-29.
*Cilliers, P. (2000), “Boundaries, Hierarchies and Networks in Complex Systems”
International Journal of Innovation Management, June 2001, 5, 181–212.
Dooley, K. J. and A. H. Van de Ven (1999). “Explaining Complex Organizational Dynamics,”
Organization Science, 10, 358–372.
Juarrero, A. (1998). “Causality as Constraint,” in G. Van de Vijver, S. N. Salthe & M. Delpos, eds.,
Evolutionary Systems, Dordrecht, The Netherlands: Kluwer, 233–242.
Durlauf, S. N. (1997). “Limits to Science or Limits to Epistemology?” Complexity, 2, 31–37.
Newman, D. (1996). “Emergence and Strange Attractors,” Philosophy of Science, 63, 245–261.
Klee, R. (1984), “Micro-determinism and Concepts of Emergence,” Philosophy of Science, 51,
44–63.
8
Relevant Web Sites
Leigh Tesfatsion, Iowa State
Kathleen Carley, CMU
http://www.econ.iastate.edu//tesfatsi/
http://www.econ.iastate.edu/classes/econ308x/tesfatsion/
http://www.hss.cmu.edu/departments/sds/faculty/carley/39-350-2F750_2000_b_comporg.pdf
http://www.casos.ece.cmu.edu/home_frame.html
Art De Vany, UC Irvine:
http://aris.ss.uci.edu/econ/personnel/devany/devany.html
Robert Axlerod, Michigan
http://www-personal.umich.edu/~axe/complexity_syllabus.htm
Scott Page et al.
http://www.pscs.umich.edu/lab/documentation/ICPSR-00/icpsr-Nonlinear-00.txt
Douglas R. White, UC Irvine
http://eclectic.ss.uci.edu/~drwhite/Anthro179a/syllabus.htm
James Hughes, Chicago
http://www.changesurfer.com/Acad/SocEco.html
Repast:
http://repast.sourceforge.net/
AgentBuilder: (commercial)
http://www.agentbuilder.com/
Selected Philosophy Books
1.
Archer, M. et al. (1997), Critical Realism—Essential Readings, London: Routledge.
2.
Aronson, J. L., Harré, R., and Way, E. C. (1994), Realism Rescued, London: Duckworth.
3.
Ayer, A. J. (ed.) (1959), Logical Positivism, Glencoe, IL: Free Press.
4.
Azevedo, J. (1997), Mapping Reality: An Evolutionary Realist Methodology for the Natural and Social Sciences, Albany, NY: State
University of New York Press.
5.
Bechtel, W. and R. C. Richardson (1993), Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research,
Princeton, NJ: Princeton University Press.
6.
Bhaskar, R. (1975), A Realist Theory of Science, London: Leeds Books. [2nd ed., London, Verso, 1997].
7.
Bhaskar, R. (1979), The Possibility of Naturalism, London: Routledge.
8.
Boden, M. A. (ed.) (1996), The Philosophy of Artificial Life, New York: Oxford University Press.
9.
Callebaut, W., and Pinxten, R. eds. (1987) Evolutionary Epistemology: A Multiparadigm Program, Dordrecht, The Netherlands: Reidel.
10. Cartwright, N. (1999), The Dappled World: A Study of the Boundaries of Science, Cambridge, UK: Cambridge University Press.
11. Collier, A. (1994), Critical Realism: An Introduction to Roy Bhaskar’s Philosophy, London: Verso.
12. Curd, M. and J. A. Cover (1998), Philosophy of Science: The Central Issues, New York: Norton.
13. Devitt, M. (1984), Realism and Truth, Oxford, UK: Oxford University Press.
14. Friedman, M. (1999), Reconsidering Logical Positivism, New York: Cambridge University Press.
15. Fuller, S. (2000), Thomas Kuhn: A Philosophical History of Our Times, Chicago, IL: University of Chicago Press.
16. Gross, P. R. and N. Levitt (1998), Higher Superstition: The Academic Left and Its Quarrels with Science, Baltimore, MD: Johns Hopkins
Press.
17. Gross, P. R., ed. (1996), The Flight from Science and Reason, Baltimore, MD: Johns Hopkins Press.
18. Hacking, I. (1999), The Social Construction of What, Cambridge, MA: Harvard University Press.
19. Hahlweg, K., and Hooker, C. A. (eds.) (1989), Issues in Evolutionary Epistemology, New York: State University of New York Press.
20. Halpern, P. (2000), The Pursuit of Destiny: A History of Prediction, Cambridge, MA: Perseus.
21. Hooker, C. A. (1987), A Realistic Theory of Science, Albany, NY: State University of New York Press.
9
22. Hooker, C. A. (1995), Reason, Regulation, and Realism: Toward a Regulatory Systems Theory of Reason and Evolutionary Epistemology,
Albany, NY: State University of New York Press.
23. Hunt, S. D. (1991), Modern Marketing Theory: Critical Issues in the Philosophy of Marketing Science, Cincinnati, OH: South-Western.
24. King, G., R. O Keohane and S. Verba (1994), Designing Social Inquiry: Scientific Inference in Qualitative Research, Princeton, NJ:
Princeton University Press.
25. Klee, R. (1997), Introduction to the Philosophy of Science, New York: Oxford University Press.
26. Koertge, N., ed. (1998), A House Build on Sand: Exposing Postmodernist Myths About Science, New York: Oxford University Press.
27. Lawson, T. (1997), Economics & Reality, New York: Routledge.
28. Leplin, J. (Ed.) (1984), Scientific Realism, Berkeley, CA: University of California Press.
29. McKim, V. R. and Stephen P. Turner (1997), Causality in Crisis: Statistical Methods and the Search for Causal Knowledge in the Social
Sciences, Notre Dame, IN: University of Notre Dame Press.
30. Miller, D. (1994), Critical Rationalism: A Restatement and Defence, Peru, IL: Open Court.
31. Morgan, M. S. and Morrison, M. (eds.) (2000), Models as Mediators: Perspectives on Natural and Social Science, Cambridge, UK:
Cambridge University Press.
32. Nola, R. (1988), Relativism and Realism in Science, Dordrecht, The Netherlands: Kluwer.
33. Norris, C. (1997), Against Relativism: Philosophy of Science, Deconstruction, and Critical Theory, Oxford, UK: Blackwell.
34. Outhwaite, W. (1997), New Philosophies of Social Science, London: Macmillan
35. Pearl, J. (2000), Causality: Models, Reasoning, and Inference, New York: Cambridge University Press.
36. Radnitzky, G. and W. W. Bartley, III (Eds.) (1987), Evolutionary Epistemology, Rationality, and the Sociology of Knowledge, La Salle, IL:
Open Court.
37. Rescher, N. (1987), Scientific Realism: A Critical Reappraisal, Dordrecht, The Netherlands: Reidel.
38. Salmon, M. H, J. Earman, et al. (1999), Introduction to the Philosophy of Science, Indianapolis, IN: Hackett.
39. Salmon, W. C. (1998), Causality and Explanation, New York: Oxford University Press.
40. Sarup, M. (1993), An Introductory Guide to Post-Structuralism and Postmodernism, Athens, GA: University of Georgia Press.
41. Sayer, A. (1992), Method in Social Science: A Realist Approach, London: Routledge.
42. Sokal, A. D. and J. Bricmont (1998), Fashionable Nonsense: Postmodern Intellectuals’ Abuse of Science, New York: Picador USA.
43. Suppe, F. (1977), The Structure of Scientific Theories (2nd ed.), Chicago: University of Chicago Press.
44. Suppe, F. (1989), The Semantic Conception of Theories & Scientific Realism, Urbana-Champaign, IL: University of Illinois Press.
45. Thompson, P. (1989), The Structure of Biological Theories (Albany, NY: State University of New York Press,.
46. Van de Vijver, G., S. N. Salthe and M. Delpos (1998). Evolutionary Systems: Biological and Epistemological Perspectives on Selection and
Self-Organization. Dordrecht, The Netherlands: Kluwer Academic.
47. Williams, M. and T. May (1996), Introduction to the Philosophy of Social Research, London: UCL Press.
More Complete Bibliography on Basic Complexity Theory
1.
Abu-Mostafa, Y. S., B. LeBaron, A. W. Lo and A. S. Weigend (eds.) (2000), Computational Finance, Cambridge, MA: MIT Press.
2.
Albert, A. (ed.) (1993), Chaos and Society Frontiers in Artificial Intelligence and Applications, Amsterdam, The Netherlands: IOS Press.
3.
Albin, P. S. (1998), Barriers and Bounds to Rationality: Essays on Economic Complexity and Dynamics in Interactive Systems (ed. with
Introduction by D. Foley), Princeton, NJ: Princeton University Press.
4.
Anderson, P. W., K. J. Arrow and D. Pines (eds.) (1988), The Economy as an Evolving Complex System I, Proceedings of the Santa Fe
Institute, Vol. V, Reading, MA: Addison-Wesley.
5.
Andrews, S. B. and D. Knoke (eds.) (1999), Networks In and Around Organizations, Stamford, CT: JAI Press.
6.
Arthur, W. B., S. N. Durlauf and D. A. Lane (eds. ) (1997), The Economy as an Evolving Complex System II, Proceedings Vol. XXVII,
Reading, MA: Addison-Wesley.
7.
Atkins, P. W. (1994), The 2nd Law: Energy, Chaos, and Form, New York: Scientific American Books.
8.
Auyang, S. Y. (1998), Foundations of Complex-system Theorie: In Economics, Evolutionary Biology, and Statistical Physics, Cambridge,
UK: Cambridge University Press.
10
9.
Axelrod, R. (1997), The Complexity of Cooperation, Princeton, NJ: Princeton University Press.
10. Axlerod, R. and M. D. Cohen (1999), Harnessing Complexity, New York: Free Press.
11. Axlerod, R. and M. D. Cohen (2000). Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: Free Press.
12. Bak, P. (1996), How Nature Works: The Science of Self-Organized Criticality, New York: Springer-Verlag.
13. Barnett, W. A., C. Chiarella, S. Keen, R. Marks and H. Schnabl (eds.) (2000), Commerce, Complexity, and Evolution, Cambridge, UK:
Cambridge University Press.
14. Barnett, W. A., J. Geweke and K. Shell (eds.) (1989), Commerce, Complexity, and Evolution, Cambridge, UK: Cambridge University Press.
15. Bar-Yam, Y. (1997), Dynamics of Complex Systems, Reading, MA: Addison-Wesley.
16. Baskin, K. (1998), Corporate DNA: Learning from Life, Boston, MA: Butterworth Heinemann.
17. Beck, C. and F. Schlögl (1993), Thermodynamics of Chaotic Systems: An Introduction, Cambridge, UK: Cambridge University Press.
18. Bonner, J. T. (1988), The Evolution of Complexity, Princeton, NJ: Princeton University Press.
19. Brenner, T. (ed.) (1999), Computational Techniques for Modelling Learning in Economics (Advances in Computational Economics, Vol.
11), Dordrecht, The Netherlands: Kluwer.
20. Briggs, J. and F. D. Peat (1989), Turbulent Mirror: An Illustrated Guide to Chaos Theory and the Science of Wholeness, New York: Harper
and Row.
21. Brown, S. and K. Eisenhardt (1997), Competing on the Edge, Boston, MA: Harvard Business School Press.
22. Bushev, M. (1994), Synergetics: Chaos, Order, Self-Organization, Singapore: World Scientific.
23. Camazine, S., J-L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz and E. Bonabeau (eds.) (2001), Self-Organization in Biological
Systems, Princeton, NJ: Princeton University Press.
24. Carley, K. M., and M. J. Prietula (eds.)(1994). Computational Organization Theory. Hillsdale, NJ: Erlbaum.
25. Casti, J. L. (1995), Complexification, New York: HarperPerennial.
26. Casti, J. L. (1997), Would-Be Worlds: How Simulation is Changing the Frontiers of Science, New York: Wiley.
27. Chaisson, E. J. (2001), Cosmic Evolution: The Rise of Complexity in Nature, Cambridge, MA: Harvard University Press.
28. Cohen, J. and I. Stewart (1994), The Collapse of Chaos, New York: Viking.
29. Cohen, J. and I. Stewart (1997). Figments of Reality: The Evolution of the Curious Mind, Cambridge, UK: Cambridge University Press.
30. Colander, D. (2000), The Complexity Vision and the Teaching of Economics, Cheltenham, UK: Elgar.
31. Coveney, P. and R. Highfield (1995), Frontiers of Complexity: The Search for Order in a Chaotic World, New York: Fawcett Columbine.
32. Cowan, G. A., D. Pines and D. Meltzer (eds.) (1994), Complexity: Metaphors, Models, and Reality, Proceedings of the Santa Fe Institute,
Vol. XIX, Reading, MA: Addison-Wesley.
33. Cramer, F. (1993), Chaos and Order: The Complex Structure of Living Things (trans. D. L. Loewus), New York: VCH.
34. Deutsch, D. (1997). The Fabric of Reality. New York: Penguin.
35. Devaney, R. L. (1992), A First Course in Chaotic Dynamical Systems, Reading, MA: Addison-Wesley.
36. Dyke, C. (1988), The Evolutionary Dynamics of Complex Systems: A Study of Biosocial Complexity, New York: Oxford University Press.
37. Epstein, J. M. and R. Axtell (1996), Growing Artificial Societies: Social Science from the Bottom Up, Cambridge, MA: MIT Press.
38. Eve, R. A., S. Horsfall, and M. E. Lee (eds.) (1997), Chaos, Complexity, and Sociology, London: Sage.
39. Favre, A., H. Guitton, J. Guitton, A. Lichnerowicz and E. Wolff (1995), Chaos and Determinism (trans. B. E. Schwarzbach), Baltimore:
Johns Hopkins University Press.
40. Ferber, J. (1999), Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Reading, MA: Addison-Wesley.
41. Gilbert, N. and K. G. Troitzsch (1999), Simulation for the Social Scientist, Buckingham, UK: Open University Press.
42. Gleick, J. (1987), Chaos: Making a New Science, New York: Penguin.
43. Goldstein, Jeffrey (1994), The Unshackled Organization, Portland, OR: Productivity Press.
44. Goodwin, B. (2001), How the Leopard Changed Its Spots: The Evolution of Complexity, Princeton, NJ: Princeton University Press.
45. Goodwin, R. (1990), Chaotic Dynamics, Oxford, UK: Clarendon/Oxford.
11
46. Gumerman G. J. and M. Gell-Mann (eds.) (1994), Understanding Complexity in the Prehistoric Southwest, Proceedings Vol. XVI, Reading,
MA: Addison-Wesley.
47. Guastello, S. J. (1995). Chaos, Catastrophe, and Human Affairs. Mahwah NJ: Lawrence Erlbaum.
48. Haken, H. (1977). Synergetics, An Introduction: Non-Equilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and
Biology. Berlin: Springer-Verlag.
49. Haken, H. (1983). Synergetics, An Introduction: Non-Equilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and
Biology, 3rd ed. Berlin: Springer-Verlag.
50. Haken, H. and A. Mikhailov (eds.) (1993), Interdisciplinary Approaches to Nonlinear Complex Systems,
51. Hales, N. K. (1991), Chaos and Order, Chicago, University of Chicago Press.
52. Halpern, P. (2000), The Pursuit of Destiny: A History of Prediction, Cambridge, MA: Perseus.
53. Herrmann, H. (1998), From Biology to Sociopolitics: Conceptual Continuity in Complex Systems, New Haven, CT: Yale University Press.
54. Holland, J. H. (1995), Emergence: From Chaos to Order, Reading, MA: Addison-Wesley.
55. Holland, J. H. (1995). Hidden Order, Reading, MA: Addison-Wesley.
56. Ilgen, D. R. and C. L. Hulin (eds.) (2000), Computational Modeling of Behavior in Organizations, Washington, DC: American
Psychological Association.
57. Jantsch, E. (1980), The Self-Organizing Universe, New York: Pergamon.
58. Johnson, S. (2001), Emergence: The Connected Lives of Ants, Brains, Cities, and Software, New York: Scribner.
59. Kauffman, S. A. (1993), The Origins of Order, New York: Oxford University Press.
60. Kauffman, S. A. (1995), At Home in the Universe, New York: Oxford University Press.
61. Kauffman, S. A. (2000), Investigations, New York: Oxford University Press.
62. Kaye, B. (1993), Chaos and Complexity, New York: VCH.
63. Kelly, K. (1994), Out of Control, Reading, MA: Perseus.
64. Kelly, S. and M. A. Allison (1999), The Complexity Advantage: How the Science of Complexity Can Help Your Business Achieve Peak
Performance, New York: BusinessWeek/McGraw-Hill.
65. Kelso, J. A. S (1995), Dynamic Patterns: Self-Organization of Brain and Behavior, Cambridge, MA: MIT Press.
66. Khalil, E. L. and K. Boulding (eds.) (1996), Evolution, Order, and Complexity, London, Routledge.
67. Kiel, D. (1994), Managing Chaos and Complexity in Government: A New Paradigm for Managing Change, Innovation, and
Organizational Renewal, San Francisco, CA: Jossey-Bass.
68. Kiel, L. D. and E. Elliot (eds.) (1996), Chaos Theory in the Social Sciences, Ann Arbor, MI: University of Michigan Press.
69. Krugman, P. (1996), The Self-Organizing Economy, Oxford, UK: Blackwell.
70. Levy, S. (1992), Artificial Life: A Report from the Frontier where Computers Meet Biology, New York: Vintage/Random House.
71. Lewin, R. (1993), Complexity: Life at the Edge of Chaos, Chicago, IL: University of Chicago Press.
72. Lomi, A. and E. R. Larsen (eds.) (2001), Dynamics of Organizations, Cambridge, MA: MIT Press.
73. Marion, R. (1999), The Edge of Organization: Chaos and Complexity Theories of Formal Social Systems, Thousand Oaks, CA: Sage.
74. Masuch, M., and Warglien, M. eds.: Artificial Intelligence in Organization and Management Theory: Models of Distributed Activity
(Amsterdam, The Netherlands: North Holland, 1992).
75. Maturana, R. and F. H. Verela (1980), Autopoiesis and Cognition, Dordrecht, The Netherlands: Reidel.
76. McCain, R. A. (2000), Agent-Based Computer Simulation of Dichotomous Economic Growth, Boston, MA: Kluwer.
77. Merry, U. (1995), Coping with Uncertainty: Insights from the New Sciences of Chaos, Self-Organization, and Complexity, London:
Praeger.
78. Mirowski, P. (1989), More Heat than Light: Economics as Social Physics, Physics as Nature’s Economics, Cambridge, UK: Cambridge
University Press.
79. Morowitz, H. J. and J. L. Singer (1995), The Mind, The Brain, and Complex Adaptive Systems, Proceedings of the Santa Fe Institute, Vol.
XXII, Reading, MA: Addison-Wesley.
80. Nicolis, G. and I. Prigogine (1989), Exploring Complexity: An Introduction, New York: Freeman.
12
81. Nijhout, H. F., L. Nadel and D. L. Stein, eds. (1997), Pattern Formation in the Physical and Biological Sciences, Reading, MA: AddisonWesley.
82. Ormerod The Death of Economics, New York: Wiley.
83. Ormerod, P. Butterfly Economics, New York: Pantheon.
84. Pascale, R. T., M. Millemann and L. Gioja (2000), Surfing the Edge of Chaos: The Laws of Nature and the New Laws of Business, New
York: Three Rivers.
85. Pines, D. (1988), Emerging Syntheses in Science, Proceedings of the Santa Fe Institute, Vol. I, Reading, MA: Addison-Wesley.
86. Prietula, M. J., K. M. Carley and L. Gasser (eds.) (1998), Simulating Organizations: Computational Models of Institutions and Groups,
Cambridge, MA: MIT Press.
87. Prigogine, I. and I. Stengers (1984), Order Out of Chaos, New York: Bantam.
88. Read, S. J. and L. C. Miller (eds.) (1998), Connectionist Models of Social Reasoning and Social Behavior, : Hillsdale, NJ: Erlbaum.
89. Salthe, S. N. (1993), Development and Evolution: Complexity and Change in Biology, Cambridge, MA: MIT Press.
90. Sanders, T. I. (1998), Strategic Thinking and the New Science, New York: Free Press.
91. Schweitzer, F. (ed.) (1997), Self-Organization of Complex Structures: From Individual to Collective Dynamics, Amsterdam, The
Netherlands: Gordon and Breach.
92. Schweitzer. F. and G. Silverberg (eds.) (1998), Evolution and Self-Organization in Economics, ???
93. Sherman, H. and R. Schultz (1998), Open Boundaries: Creating Business Innovation through Complexity, New York: Perseus.
94. Solé, R. and B. Goodwin (2000). Signs of Life: How Complexity Pervades Biology. New York: Basic Books.
95. Stacey, R. D. (1992), Managing the Unknowable: Strategic Boundaries between Order and Chaos in Organizations, San Francisco, CA:
Jossey-Bass.
96. Stacey, R. D. (1993), Strategic Management and Organizational Dynamics, London: Pitman.
97. Stacey, R. D. (1996), Complexity and Creativity in Organizations, San Francisco, CA: Barrett-Koehler.
98. Stewart, I. (1989), Does God Play Dice, Oxford, UK: Blackwell.
48. Van de Vijver, G., S. N. Salthe and M. Delpos (1998). Evolutionary Systems: Biological and Epistemological Perspectives on Selection and
Self-Organization. Dordrecht, The Netherlands: Kluwer Academic.
99. Velupillai, K. (2000), Computable Economics, Oxford, UK: Oxford University Press.
100. Waldrop, M (1992), Complexity: The Emerging Science at the Edge of Order and Chaos, New York: Touchstone.
101. Weiss, G. (ed.) (1999), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Cambridge, MA: MIT Press.
102. Wood, R. (2000), Managing Complexity, London: Profile Books.
103. Yates, E. (ed.) (1987), Self-Organizing Systems: The Emergence of Order, New York: Plenum.
104. Zohar, D. (1997), ReWiring the Corporate Brain: Using the New Science to Rethink How We Structure and Lead Organizations, San
Francisco, CA: Berrett-Koehler.
SOME BASIC BOOKS ON AGENT-BASED MODELING
The following are frequently referenced books—mostly—on agent-based models. I have put a * after those that are introductory texts. Prices
circa 1997.
COMPUTER SIMULATION IN GENERAL
1.
Bárdossy, A. and L. Duckstein (1995), Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems,
Boca Raton, FL: CRC Press. ($75.95).
2.
Casti, J. L. (1997). Would-Be Worlds. New York, Wiley.
3.
Elithorn, A. and D. Jones (eds.) (1973), Artificial and Human Thinking, San Francisco, CA: Jossey-Bass.
4.
Forrest, S. (Ed.) (1991), Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial
Computing Networks, Cambridge, MA: MIT Press.
5.
Gaylord, R. J. and P. R. Wellin (1995), Computer Simulations with Mathematica: Explorations in Complex Physical and Biological
Systems, New York: Springer-Verlag. ($54.95)*
6.
Gould, H. and J. Tobochnik (1996), An Introduction to Computer Simulation Methods, Reading, MA: Addison-Wesley. ($59.25).
7.
Huberman, B. A. (1988), The Ecology of Computation, New York: North Holland.
8.
Langley, P. (1996), Elements of Machine Learning, San Francisco: Morgan Kaufmann. $54.95)*
13
9.
Madala, H. R. and A. G. Ivakhnenko (1993), Inductive Learning Algorithms for Complex Systems Modeling, Boca Raton, FL: CRC Press.
10. Maes, P. (Ed) (1990), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back, Cambridge, MA: MIT
Press.
11. Rao, V. and Rao H. (1995), C++ Neural Networks and Fuzzy Logic, New York: MIS Press. ($39.95)
12. Steeb, W. H. (1999), The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Fuzzy Logic
with C++, Java, Symbolic C++, and Reduce Program, Singapore: World Scientific.
13. Van den Bosch, P. P. J. and A. C. Van der Klauw (1994), Modeling, Identification and Simulation of Dynamical systems, Boca Raton, FL:
CRC.
14. Wooldridge, M. (2000), Reasoning about Rational Agents, Cambridge, MA: MIT Press.
15. Michalewicz, Z. and D. B. Fogel (1999), How To Solve It: Modern Heuristics, Berlin: Springer-Verlag.
ARTIFICIAL LIFE
16. Brooks, R. A. and P. Maes (Eds.) (1995), Artificial Life IV, Cambridge, MA: MIT Press. ($60.00)
17. Epstein, J. M. and R. Axtell (1996), Growing Artificial Societies, Washington, DC: Brookings Institution Press. ($19.95)
18. Gilbert, N. and R. Conte (Eds.) (1995), Simulating Societies: The Computer Simulation of Social Phenomena, London: UCL Press.
19. Nolfi, S. and D. Floreano (2000), Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines,
Cambridge, MA: MIT Press.
20. Sigmund, K. (1993), Games of Life: Explorations in Ecology, Evolution, and Behavior, Oxford, UK: Oxford University Press.
21. Weiss, G. (1996), Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments, Berlin: SpringerVerlag.
22. Weiss, G., ed. (1999), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Berlin: Springer-Verlag.
23. Weiss, G. and S. Sen (eds.) (1996), Adaptation and Learning in Multi-Agent Systems, Berlin: Springer-Verlag.
SPIN-GLASS
24. Fischer, K. H. and J. A. Hertz (1993), Spin Glasses, New York: Cambridge University Press.
25. Mézard, M., G. Parisi, and M. A. Virasoro (1987), Spin Glass Theory and Beyond, Singapore: World Scientific. ($28.00)
26. Stein, D. L. (Ed.) (1992), Spin Glasses and Biology, Singapore: World Scientific.
27. Theumann, W. K. and R. Köberle (Eds.) (1990), Neural Networks and Spin Glasses, Singapore: World Scientific. ($113.00)
CELLULAR AUTOMATA
28. Carroll, J. and D. Long (1989), Theory of Finite Automata, Englewood Cliffs, NJ: Prentice-Hall.
29. Gaylord, R. J. and K. Nishidate (1996), Modeling Nature: Cellular Automata Simulations with Mathematica, New York: Springer-Verlag.
($39.95)*
30. Griffeath, D. and C. Moore (2002), New Constructions in Cellular Automata, Santa Fe Institute Studies on The Sciences of Complexity,
New York: Oxford University Press.
31. Gutowitz, H. (Ed) (1991), Cellular Automata: Theory and Experiment, Cambridge, MA: MIT Press.
32. Hopcroft, J. E. and J. D. Ullman (1979), Introduction to Automata Theory, Languages, and Computation, Reading, MA: Addison-Wesley.
($50.50)*
33. Resnick, M. (1997), Turtles, Termites, and Traffic Jams, Cambridge, MA: MIT Press.
34. Toffoli, T. and N. Margolus (1987), Cellular Automata Machines: A New Environment for Modeling, Cambridge, MA: MIT Press.
($40.00)*
35. von Neumann, J. (1966), Theory of Self-Reproducing Automata, Edited by A. W. Burks. Urbana, IL: University of Illinois Press.
36. Weisbuch, G. (1991), Complex Systems Dynamics: An Introduction to Automata Networks, (trans. S. Ryckebusch), Lecture Notes Vol. II,
Santa Fe Institute, Reading MA: Addison-Wesley.*
37. Wolfram, S. (ed.) (1994), Cellular Automata and Complexity: Collected Papers, Reading, MA: Addison-Wesley.
38. Wolfram, S. (1994), Cellular Automata and Complexity, Reading, MA: Addison-Wesley.
SIMULATED ANNEALING
39. Aarts, E. and J. Korst (1989), Simulated Annealing and Boltzmann Machines, New York: Wiley. ($115.00)
40. Azencott, R. (1992), Simulated Annealing: Parallelization Techniques, New York: Wiley. ($75.95)
41. Davis, L. (Ed.) (1989), Genetic Algorithms and Simulated Annealing, Los Altos, CA: Morgan Kaufmann. ($175.00)
42. Garzon, M. (1995), Model of Massive Parallelism: Analysis of Cellular Automata and Neural Networks, Berlin: Springer-Verlag. ($49.50)
43. Resnick, M. (1994), Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds, Cambridge, MA: MIT Press.
44. Rumelhart, D. E. and J. L. McClelland (1986), Parallel Processing: Exploration in the Microstructure of Cognition, Vol. I, Cambridge,
MA: MIT Press.
45. van Laarhoven, P. J. M. and E. H. L. Aarts (1987), Simulated Annealing: Theory and Applications, Dordrecht, Netherlands: Reidel.
GENETIC ALGORITHMS
14
46. Chambers, L. (1995), Practical Handbook of Genetic Algorithms, Vols. I & II, Boca Raton, FL: CRC Press.
47. Forrest, S. (Ed.) (1993), Genetic Algorithms (Proceedings volume), San Mataeo, CA: Morgan Kauffmann. ($195.00)
48. Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison-Wesley. ($48.50)*
49. Haupt, R. L. and S. E. Haupt (1998), Practical Genetic Algorithms, New York: Wiley-Interscience.
50. Holland, J. H. (1992), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and
Artificial Intelligence, Cambridge, MA: MIT Press.*
51. Holland, J. H. (1995), The Hidden Order: How Adaptation Builds Complexity, Reading MA: Addison-Wesley/Helix.*
52. Koza, J. R. (1992), Genetic Programming: On the Programming of Machines by Means of Natural Selection, Cambridge, MA: MIT Press.
53. Koza, J. R. (1994), Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, MA: MIT Press.
54. Michalewicz, Z. (1992), Genetic Algorithms + Data Structures = Evolution Programs (3rd ed.), New York: Springer-Verlag. ($44.95)
55. Mitchell, M. (1996), An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press. ($30.00)*
56. Pal, S. K. and P. P. Wang (1996), Genetic Algorithms for Pattern Recognition, Boca Raton, FL: CRC Press. ($79.95)
NEURAL NETWORKS
57. Bartlett, P. L. and M. M. Anthony (1999), Neural Network Learning: Theoretical Foundations, Cambridge, UK: Cambridge University
Press.
58. Bishop, C. M. (1995), Neural Networks for Pattern Recognition, New York: Oxford University Press.
59. Davalo, E. and P. Naim (1991), Neural Networks, New York: Macmillan.*
60. Deco, G. and D. Obradovic (1996), An Information-Theoretic Approach to Neural Computing, New York: Springer-Verlag. ($49.95)
61. Domany, E., J. L. van Hemmen and K. Schulten (Eds.) (1995), Models of Neural Networks I (2nd ed.), Berlin: Springer-Verlag. ($69.00)
62. Fausett, L. V. (1994), Fundamentals of Neural Networks, Englewood Cliffs, NJ: Prentice-Hall.
63. Freeman, J. A., D. M. Skapura (1992, Neural Networks: Algorithms, Applications, and Programming Techniques, Reading MA: AddisonWesley. ($50.50)*
64. Hassoun, M. H. (1995), Fundamentals of Artificial Neural Networks, Cambridge, MA: MIT Press. ($60.00)*
65. Haykin, S. (1998), Neural Networks: A Comprehensive Foundation, (2nd ed.), New York: Macmillan. ($71.00)*
66. Müller, B., J. Reinhardt and M. T. Strickland (1995), Neural Networks (2nd ed.), Berlin: Springer-Verlag. ($39.95)*
67. Nadel, L., L. A. Cooper, P. Culicover and R. M. Harnish (Eds.) (1989), Neural Connections, Mental Computation, Cambridge, MA: MIT
Press. ($24.95)
68. Principe, J. C., N. R. Euliano and W. C. Lefebvre (1999), Neural and Adaptive systems: Fundamentals through Simulations. New York:
Wiley.
69. Reed, R. D. and R. J. Marks II (1999), Neural Smithing, Boston, MA: MIT Press.
70. Smith, M. (1996), Neural Networks for Statistical Modeling, London: International Thomson Computer Press. ($41.95)
71. Swingler, K. (1996), Applying Neural Networks: A Practical Guide, San Francisco, CA: Academic Press.
72. Wasserman, P. D. (1989), Neural Computing Theory and Practice, New York: Van Nostrand Reinhold. ($38.00)*
73. Wasserman, P. D. (1993), Advanced Methods in Neural Computing, New York: Van Nostrand Reinhold. $(39.95)
MISCELLANEOUS
74. Mishra, R. K., D. Maa, and E. Zwierlein (1994), On Self-Organization An Interdisciplinary Search for a Unifying Principle, Berlin:
Springer-Verlag.
75. Schelling, T. C. (1978), Micromotives and Macrobehavior, New York: Norton.
76. Weidlich, W. and G. Haag (1983), Concepts and Models of a Quantitative Sociology, Berlin: Springer-Verlag.
77. Beck, C. and F. Schlögl (1993), Thermodynamics of Chaotic Systems: An Introduction, New York: Cambridge University Press.
EVOLUTION
78. Axelrod, R. (1984), The Evolution of Cooperation, New York: Basic Books.
79. Cavalli-Sforza, L. L. and M. W. Feldman (1981), Cultural Transmission and Evolution: A Quantitative Approach, Princeton, NJ: Princeton
University Press.
80. Depew, D. J. and B. H. Weber (1995), Systems Dynamics and the Genealogy of Natural Selection, Cambridge, MA: MIT Press.
81. Edelstein-Keshet, L. (1988), Mathematical Models in Biology, Birkhaüser Mathematics Series, New York: Random House.
82. Feldman, M. W. (Ed.) (1989), Mathematical Evolutionary Theory, Princeton, NJ: Princeton University Press.
83. Hofbauer, J. and K. Sigmund (1988), The Theory of Evolution and Dynamical Systems, Cambridge, UK: Cambridge University Press.
($27.95)
84. Kauffman, S. A. (1993), The Origin of Order, New York: Oxford University Press.
85. Murray, J. D. (1989), Mathematical Biology, New York: Springer-Verlag.
86. Perelson, A. S. and S. A. Kauffman (Eds.) (1991), Molecular Evolution on Rugged Landscapes: Proteins, RNA, and the Immune System,
Redwood City, CA: Addison-Wesley.
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ECONOMICS
87. Aoki, M. (1996), New Approaches to Macroeconomic Modeling: Evolutionary Stochastic Dynamics, Multiple Equilibria, and Externalities
as Field Effects, New York: Cambridge University Press.
88. Aoki, M. (2002)., Modeling Aggregate Behavior and Fluctuations in Economics Stochastic Views of Interacting Agents. Cambridge, UK:
Cambridge University Press.
89. Fisher, F. M. (1983), Disequilibrium Foundations of Equilibrium Economics, New York: Cambridge University Press.
90. Gilli, M. (1996), Computational Economic Systems: Models, Methods & Econometrics (Advances in Computational Economics, Vol. 5),
Dordrecht, The Netherlands, Kluwer.
91. Masters, T. (1995), Neural, Novel & Hybrid Algorithms for Time Series Prediction, New York: Wiley. ($49.95)
92. McCarney, B. J. and V. L. Owen (1972), Economics: A Synergetic Approach, Hinsdale, IL: Dryden.
93. Sargent, T. (1993), Bounded Rationality in Macroeconomics, New York: Clarendon/Oxford.
94. Zhang, W. B. (1991), Synergetic Economics: Time and Change in Nonlinear Economics, Berlin: Springer-Verlag. ($98.00)
SOCIAL NETWORK
Since the foregoing are “connectionist” in one way or another, you might want to compare with sociologists’ use of social network models in
modeling organizations, hence:
95. Pattison, P. (1993), Algebraic Models for Social Networks, New York: Cambridge University Press. ($39.95)
96. Scott, J. (1992), Social Network Analysis: A Handbook, New York: Sage.
97. *Wasserman, S. and K. Faust (1994), Social Network Analysis: Methods and Applications, New York: Cambridge University Press.
($29.95)