J.H. Miller
miller@santafe.edu
Research

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Background

Some Recent Projects

Complex Adaptive Social Systems: The Book

The Ghost in the Machine

Neutral Networks and Technology

Discovering Novel Chemotherapies

Giving According to GARP

Computational Modeling

Selected Research Documents

Working Papers

Books

Published Papers

Computational Economics

Computational Political Economy

Experiments in Cooperation and Altruism

Other






Institutional Affiliations:

Carnegie Mellon
   SDS
Santa Fe Institute


Background

My research focuses on understanding the behavior that emerges in complex adaptive social systems (CASS). Understanding the behavior of CASS, composed of interacting, thoughtful (but perhaps not brilliant) agents, is one of the great challenges of science. Such systems capture pervasive and important phenomena, arising in biological, chemical, environmental, economic, organizational, and political systems. Traditional scientific approaches to understanding these types of systems have shown only a limited ability to pry away their secrets. Part of this failure is attributable to a fundamental limitation of the reductionist approach to science: reducing systems to components does not imply the ability to reconstruct them (as anyone who has scrambled an egg innately understands). While it may seem that because we understand one we must also understand two, because one and one make two, this is only true if we also understand and.

Colleagues and I have applied CASS models to the analysis of a variety of key social phenomena. For example, we have explored the dynamics of political platforms in spatial elections, how decentralized institutional mechanisms can be used to sort agents into coherent groups, the emergence of cooperation in the iterated Prisoner's Dilemma, strategic choice in simple two-person games, the development of strategic communication, and bidding behavior in auction markets. To understand the fundamental dynamics of price formation in simple markets, we designed and organized an international computerized double auction tournament in 1988-90 (which was also the first internet-based auction market---here is the DAT Participant's Manual). This tournament allowed us to create an "artificial world" of trading agents in which we explored a variety of theoretical and practical issues. All of the above work indicates the remarkably potential for CASS modeling to advance our understanding about some of the most fundamental questions in the social sciences.

For CASS modeling, traditional tools of scientific theory building, such as mathematical modeling, need to be supplemented with computational models that take advantage of recent technological progress. As the mathematician Stanislaw Ulam pointed out, ``[T]he use of computers seems thus not merely convenient, but absolutely essential...I believe that the experience gained as a result of following the behavior of such processes will have a fundamental influence on whatever may ultimately generalize or perhaps even replace in mathematics our present exclusive immersion in the formal axiomatic method.'' Using computational methods, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis.

Complementing the above work, I have also pursued experimental approaches to understanding human cooperation and altruism. In some recent experiments we have shown that the altruistic choices of human subjects are economically rationalizable in the sense that they could be accounted for by a standard model of economic choice. We also found that while the altruistic preferences of subjects fall into three distinct classes, there is a large degree of heterogeneity in their preferences.

At the conclusion of Origins of the Species, Darwin remarked that ``[T]here is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one...from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.'' Through a remarkable convergence of ideas, technology, and scientific and engineering imperative, we now stand ready to exploit this ``view of life'' in a quest to understand and control complex adaptive social systems.

There is grandeur in this view of social life.

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Some Recent Projects

Complex Adaptive Systems: An Introduction to Computational Models of Social Life

Book Cover Scott Page (Michigan) and I have written a book on complex adaptive social systems (Complex Adaptive Systems, Princeton University Press, 2007). In the book, we derive some fundamental examples and principles of complex adaptive social systems using a simple set of related models. Models composed of thoughtful agents fundamentally differ from the typical interacting particle systems that have been widely discussed in the past. The simple models we develop allow us to to illuminate, and often correct, some of the key principles that have been discussed over the past two decades. Finally, we take the opportunity to outline more fully the foundations necessary for successful computational modeling in this area.

The Ghost in the Machine

James Engle-Warnick (McGill) and I are developing sound statistical methods for divining the underlying "strategic programs" employed by human subjects in experimental games. The goal here is to have a succinct characterization of actual human strategic choice, and then use this knowledge to better understand and predict behavior in such situations.

Neutral Technological Networks: The Value of Muddling Through

This research applies ideas about the impact of neutral networks on evolutionary dynamics in biology to the problem of technological innovation. Theoretically, we find that there may be considerable value in firms "muddling through" technological spaces, as such muddling allows them to achieve superior outcomes that under more traditional models would not have been attainable. One example of this process is in software design, where recent notions of "refactoring" implicitly attempt to exploit the value of neutral networks. [abstract][pdf]

Discovering Novel Chemotherapies

There is good reason to think that cocktails composed of a variety of chemotherapy drugs might be a key means by which to fight diseases like cancer. Unfortunately, discovering effective cocktails is difficult given the underlying combinatorics. If you have, say, twenty drugs that you could include in the mix, you can create over one million different cocktails. Alas, you can only screen around twenty or so cocktails every few days (laboratory biology is slow and hard), so there is no way to exhaustively search all of the possible combinations. To surmount this problem, we employ a nonlinear search algorithm to direct the discovery of the cocktails. Our preliminary results, focusing on lung carcinomas, are quite promising---the algorithm quickly found a very effective cocktail that is promising enough to begin more elaborate trials. [abstract][pdf]

Giving According to GARP

This work uses laboratory studies of human subjects to show that their altruistic choices are economically rational, in the sense that such choices could result from the rational maximization of a well-behaved (in the economic sense) utility function over giving to self and to others. Here is a variant of the basic experiment...are you a rational altruist? [abstract][pdf]

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Computational Modeling

One of the most powerful tools arising from complex systems research is a set of computational techniques that allow a much wider range of models to be explored. Using these tools, worlds of heterogeneous, adaptive agents can interact in a dynamic environment subjected to various limits of time and space. Having the ability to investigate new theoretical worlds obviously does not imply any kind of scientific necessity or validity---these must be earned by carefully considering the ability of the new models to help us understand and predict the questions that we hold most dear.

Over the last decade or so, computational modeling is beginning to gain a foothold in theoretical social science. Like any theoretical tool, great care and effort is required to create high quality models using computational methods. Here are some links focused on computational modeling in the social sciences (including our graduate workshop). Part of our book on complex adaptive social systems will also be devoted to this topic.

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Selected Research Documents

Working Papers

John H. Miller, Ralph Zinner, and Brittany Barrett, ``Directed Discovery of Novel Drug Cocktails,'' Santa Fe Institute Working Paper 05-07-031, (2005). [abstract][pdf]

Jose Lobo, John H. Miller, and Walter Fontana, ``Neutrality in Technological Landscapes,'' Working Paper, 2004. [abstract][pdf]

Ken Kollman, John H. Miller, and Scott E. Page, ``A Simplified Framework for Analyzing the Behavior of Political Institutions,'' 1999.

John H. Miller, ``A Genetic Model of Adaptive Economic Behavior,'' University of Michigan working paper, 1986. This was, to my knowledge, the first paper in economics to use genetic algorithms. [abstract][pdf]

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Books

John H. Miller and Scott E. Page, Complex Adaptive Social Systems: An Introduction to Computational Models of Social Life, Princeton University Press, 2007.

Ken Kollman, John H. Miller, and Scott E. Page (eds.), Computational Models in Political Economy, MIT Press, 2003.

Theodore Bergstrom and John H. Miller, Experiments with Economic Principles: Microeconomics 2nd ed, McGraw Hill, 2000 (1997 1st ed).

Theodore Bergstrom and John H. Miller, Instructor's Manual for Experiments with Economic Principles: Microeconomics 2nd ed, McGraw Hill, 2000 (1997 1st ed).

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Selected Published Papers

   Computational Economics

John H. Miller and Scott E. Page, ``The Standing Ovation Problem,'' Complexity, 9 (2004):8-16. [abstract][pdf]

John H. Miller and Scott Moser, ``Communication and Coordination,'' Complexity, 9 (2004):31-40. [abstract][pdf]

John H. Miller, Carter Butts, and David Rode, ``Communication and Cooperation,'' Journal of Economic Behavior and Organization, 47 (2002):179--95. [abstract][pdf]

John H. Miller, ``The Coevolution of Automata in the Repeated Prisoner's Dilemma,'' Journal of Economic Behavior and Organization 29:1 (January, 1996):87--112. [abstract][pdf]

James Andreoni and John H. Miller, ``Auctions with Adaptive Artificial Agents,'' Journal of Games and Economic Behavior 10 (1995):39--64. [abstract][pdf]

John Rust, John H. Miller, and Richard Palmer, ``Characterizing Effective Trading Strategies: Insights from a Computerized Double Auction Tournament,'' Journal of Economic Dynamics and Control 18 (1994):61--96. [abstract][pdf]

John Rust, John H. Miller, and Richard Palmer, ``Behavior of Trading Automata in a Computerized Double Auction Market,'' in D. Friedman and J. Rust (eds), The Double Auction Market: Institutions, Theories, and Evidence, Addison Wesley (1992):155--98. [abstract][pdf]

John H. Holland and John H. Miller, ``Artificial Adaptive Agents in Economic Theory,'' American Economic Review, Papers and Proceedings 81 (May, 1991):365--70. [abstract][pdf]

John H. Miller, ``The Evolution of Automata in the Repeated Prisoner's Dilemma,'' in Two Essays on the Economics of Imperfect Information, Ph.D. Dissertation, University of Michigan, 1988.

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   Computational Political Economy

Kollman, John H. Miller, and Scott Page, ``Consequences of Nonlinear Preferences in a Federal Political System,'' in Diana Richards (ed.), Political Complexity: Nonlinear Models of Politics, University of Michigan Press, Ann Arbor, MI (2000):23--45.

Ken Kollman, John H. Miller, and Scott E. Page, ``Decentralization and the Search for Policy Solutions,'' Journal of Law, Economics, and Organization, 16 (2000):102--28.

Ken Kollman, John H. Miller, and Scott Page, ``Political Parties and Electoral Landscapes,'' British Journal of Political Science 28 (1998):139--158. [abstract][pdf]

John H. Miller and Peter Stadler, ``The Dynamics of Adaptive Parties under Spatial Voting,'' Journal of Economic Dynamics and Control, 23 (1998):171--189.

Ken Kollman, John H. Miller, and Scott Page, ``Political Institutions and Sorting in a Tiebout Model,'' American Economic Review 87:5 (December, 1997):977--992. [abstract][pdf]

Ken Kollman, John H. Miller, and Scott E. Page, ``Computational Political Economy,'' in Brian Arthur, Steven Durlauf, and David Lane (eds.), The Economy as an Evolving Complex System II, Addison Wesley, 1997.

Ken Kollman, John H. Miller, and Scott Page, ``Adaptive Parties in Spatial Elections,'' American Political Science Review 86 (December, 1992):929--37. [abstract][pdf]

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   Experiments in Cooperation and Altruism

James Andreoni and John H. Miller, ``Giving According to GARP: An Experimental Study of Rationality and Altruism,'' Econometrica, 70 (2002):737--753. [abstract][pdf]

James Andreoni and John H. Miller, ``Rational Cooperation in the Finitely Repeated Prisoner's Dilemma: Experimental Evidence,'' Economic Journal 103:418 (May, 1993):570--85. [abstract][pdf]

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   Other

Stephen Lansing and John H. Miller, ``Cooperation, Games, and Ecological Feedback: Some Insights from Bali,'' Current Anthropology, 46 (2005). [abstract][pdf]

John H. Miller, ``Evolving Information Processing Organizations,'' in Alessandro Lomi and Erik R. Larsen (eds.), Dynamics of Organizations: Computational Modeling and Organization Theories, MIT Press, Cambridge, Massachusetts (2001):307--27. [abstract][pdf]

John H. Miller, ``Active Nonlinear Tests (ANTs) of Complex Simulations Models,'' Management Science 44:6 (June, 1998):820--30. [abstract][pdf]

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