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2002

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Program Announcement

Program Announcement 2002

Students

  • Margo Bergman, Economics, U. of Houston [email]
  • Matthew Cronin, Organizations, Carnegie Mellon U. [email]
  • Ahlam Fakhar, Economics, Claremont Graduate University [email]
  • Nicolas Garrido, Economics, U. de Alcala de Henares [email]
  • Robert Gazzale, Economics, U. of Michigan [email]
  • A. Joseph Guse, Economics, U. of Wisconsin [email]
  • Nobuyuki Hanaki, Economics, Colubia U. [email]
  • Marc Reimann, Center for Business Studies, U. of Vienna [email]
  • Michael Rimler, Economics, U. of Michigan [email]
  • Sibel Sirakaya, Economics, U. of Wisconsin [email]

Schedule

Schedule 2002

Faculty

  • Samuel Bowles, Economics, U. Massachusetts and SFI.
  • Josh Epstein, Political Science, Brookings and SFI.
  • Stuart Kauffman, Biology, Bios Group and SFI.
  • John H. Miller (co- director), Economics, Carnegie Mellon University and SFI.
  • Scott E. Page (co-director), Economics, U. of Michigan and SFI.
  • Cosma Shalizi, Physics, SFI.

Homework

Homework Problem 2002

Student Projects

Each student began a research project during the two-week workshop. Below are brief descriptions of these various projects. These projects will form the basis for dissertation chapters and/or journal articles.

Margo Bergman, Economics, U. of Houston (margotanne@email.com).
Margo is exploring the evolution of risk preferences. Agents, with degrees of risk aversion ranging from risk loving to risk averse, must make a series of choices over various lotteries. Agents who receive low payoffs during the lotteries, imitate the risk preferences of more successful agents. If agents are allowed to accumulate wealth over time, then the system evolves toward risk loving behavior; if wealth is not allowed to accumulate, then risk averse behavior arises. When agent evolution is more tightly coupled to neighborhoods, important skews in the distribution of final risk aversion appear to arise.

Matthew Cronin, Organizations, Carnegie Mellon U. (mcronin@andrew.cmu.edu).
Matt is investigating how groups of individuals can form productive, problem-solving organizations. He assumes that his agents face various bounds to rationality and he also incorporates ideas from psychology such as insight and incremental learning processes. The model has tasks represented by bit strings, with different configurations of the bits being mapped into outcomes. Agents are able to "know" only a few of the bits initially; with time, the agents can begin to investigate other bits, as well as share their knowledge with others through processes that capture notions of trust, respect, and liking. He finds that trust improves information discovery but slows down progress, while the respect and liking operators both increase communication and insight.

Ahlam Fakhar, Economics, Claremont Graduate University (ahlam.fakhar@cgu.edu).
Ahlam is studying how norms and laws interact in social taboos, such as drug use or prostitution. Agents have different beliefs about the appropriateness of the taboo and perceptions about the current degree of enforcement via the law and social norms. The beliefs of the agents get modified based on their experiences with the law and the influence of friendship networks. She finds that there are some important asymmetries in the belief dynamics, and that effective policy options need to be tailored to these various dynamics.

Nicolas Garrido, Economics, U. de Alcala de Henares (Nicolas_Garrido@brown.edu).
Nico linked an equation-based market model to an agent-based one. In this work, he first derived an equation-based approach and solved it using some new methodological techniques more typically used in the analysis of solid state physical systems. He then used a closely-tied agent-based model to test the efficacy of the new methodological techniques. Next, he expanded the agent-based model by allowing more elaborate forms of behavior. This research strategy allowed him to compare the various approaches and identify key similarities and differences among them. One result is that while the ultimate demand patterns did not differ across the ensemble of models, adaptive firms appeared resulted in higher prices.

Robert Gazzale, Economics, U. of Michigan (rgazzale@umich.edu).
Bob is analyzing product differentiation with "experience" goods, that is, with goods that must first be consumed by an agent before the good's value can be known fully. Goods have various attributes, and these attributes may interact with one another in a nonlinear way to produce value. The transparency of a good is the number of attributes that an agent can know a priori. Firms attempt to adaptively increase their profits by developing different bundles of attributes in response to consumer demand. He finds that as firms use more sophisticated adaptive algorithms aggregate consumer utility increases as the firms are able to better differentiate their products from one another in meeting the demand. Moreover, there appears to be an important nonlinearity at lower levels of transparency.

A. Joseph Guse, Economics, U. of Wisconsin (ajguse@students.wisc.edu).
Joseph is studying group formation in the absence of a state. Agents, with the possibility of forming into groups, need access to a common, and scarce, resource to produce an output. Groups meeting at the common resource play a hawk/dove game biased by the total fitness of each group. Individuals enter into new groups by bargaining over the ultimate division of total group output, while attempting to maximize their own fitness. Their group formation strategies are based on the agent's access to the common resource last period and the relative size of the other group. It appears as though the model can produce a variety of group formation and dissolution dynamics, including a driving force for fairness in the bargaining outcomes driven by maintaining overall group fitness.

Nobuyuki Hanaki, Economics, Columbia U. (nh85@columbia.edu).
Nobi is using adaptive models to understand the emergence of efficient and fair outcomes in laboratory experiments. In games like the battle of the sexes, human subjects appear to easily find the efficient and fair outcome. Typical learning models, however, have a difficult time replicating this particular result. In his model, he uses individual learning (over a constrained set of strategies) and evolutionary selection (including the possibility of a rematch if they are unhappy with their current partner). Using such an algorithm, he finds that an efficient and fair outcome emerges relatively quickly. Moreover, this result appears to be quite robust to variations in the algorithm.

Marc Reimann, Center for Business Studies, U. of Vienna (marc.reimann@univie.ac.at).
Marc is modeling disruptive technologies and the evolution of market shares. The fundamental question he focuses on is the innovators dilemma discussed by Christensen: namely why do well managed companies fail to heed the emergence of potentially disruptive technologies. The model looks at entry dynamics over a two-dimensional product space, where new entrants are capable of improving the product on one dimension but suffering on the other. In the simple model, he can link characteristics of the entering technology to key aspects of the resulting market dynamics.

Michael S. Rimler, Economics, U. of Michigan (msrimler@umich.edu).
Michael is looking at evolving strategic behavior in a game among a group of agents where information is slowly revealed over time, such as stud poker. Such a game has a variety of economic applications, ranging from patent races to labor negotiations. A genetic algorithm is used to model the strategic behavior of the agents. He finds that interdependencies across the elements of a given strategy can cause interesting internal dynamics whereby a given agent refines and trade offs various aspects of the strategic decision.

Sibel Sirakaya, Economics, U. of Wisconsin (ssirakay@ssc.wisc.edu).
Sibel, incorporating ideas from non-cooperative game theory, created a new way to train neural networks using genetic algorithms. In her work, "competing" genetic algorithms are assigned to different parts of the neural network, and then adapt based on the current best efforts of the other algorithms. She finds that this process results in both improved speed and fit over an ensemble of problems. Furthermore, she plans to use the above system to gain insight into broader themes surrounding organizational problem solving.