1997
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Program Announcement
Students
- Robert Bernard, Rutgers/C&L
- Diego Comin, Harvard [email]
- Simon Emrich, London School of Economics [email]
- Tomas Klos, U. of Groningen [email] [Web]
- Brian Krauth, Wisconsin [email]
- Sylvain Leduc, U. of Rochester [email]
- Michael Lenox, MIT [email]
- Wei Lin, C&L
- Paolo Lupi, York [email]
- Agostino Manduchi U of London, formerly Columbia [email]
- Salvatore Pitruzzello, Columbia [email]
- Francisco Rodriguez, Harvard [email]
- Bill Watkins, Federal Reserve Board, Washington D.C. (formerly UC Santa Barbara) [email]
- Peter Wurman, U. of Michigan [email]
Schedule
Faculty
- Brian Arthur, Economics, SFI.
- Larry Blume, Economics, Cornell.
- Jim Crutchfield, Physics, SFI.
- Steven Durlauf, Economics, U. of Wisconsin.
- Christopher Langton, Artificial Life, Santa Fe Institute.
- Melanie Mitchel, Adaptive Computation, SFI.
- John H. Miller, Economics, Carnegie Mellon University (co-director).
- Scott E. Page, Economics, Cal Tech (co-director).
- Richard Palmer, Physics, Duke.
Homework
Student Projects
Each student began a research project during the two-week workshop. Below are brief descriptions of these various projects. The expectation is that these projects will form the basis for dissertation chapters and/or journal articles.
Robert Bernard, Rutgers/C&L
Rob has created an adaptive model of an apartment rental market. Agents, based on limited knowledge of the available housing stock, seek utility maximizing apartments, while landlords adaptively adjust their pricing policies so as to best fill their units. Using this model as an "adaptive flight simulator," Rob is investigating the impact of various rent control/decontrol policies. One phenomena that has emerged from the model is a natural tendency towards seasonal rental markets.
Diego Comin, Harvard(dcomin@kuznets.fas.harvard.edu)
Diego is investigating learning and the non-neutrality of money in a neutral economy. Two types of agents coexist in an asset market, one of the types pays attention to market fundamentals while the other also incorporates some outside information, for example, projected rates of inflation. Using both replicator dynamic and least squares learning algorithm, he finds that even a few non-fundamental traders (2%) can seriously impact the behavior of the system.
Simon Emrich, London School of Economics (S.Emrich@lse.ac.uk)
Simon is exploring the dynamics of asset price formation. In the model agents update trading strategies by imitating their more successful neighbors. This spatial neighborhood structure appears to alter the price dynamics in fundamental ways.
Tomas Klos, U. of Groningen (t.b.klos@bdk.rug.nl)
Tomas is concerned with the formation of buyer/supplier relationships. The model allows endogenous trade networks to form among agents based on both potential profitability and past history of interactions (instantiated in a "loyalty" parameter). Tomas is using this model to better understand how such networks can be maintained over time, as well as how various exogenous factors, for example, diversity in technical capabilities, alters the formation of such networks.
Brian Krauth, Wisconsin (bvkrauth@students.wisc.edu)
Brian is using computational methods to enhance his understanding of an analytic model of job networks and social stratification. In the model, social links allow managers to better observe the innate ability of agents in hiring decisions. Analytically, Brian has shown that if the quality of social links exceed a critical value, then stratification will ensue. Computationally, Brian is exploring the behavior of this critical parameter. He has also found that an analytically questionable approximation of the model that lends a variety of new insights into the theory, appears to capture the fundamental behavior of the more complex model.
Sylvain Leduc, U. of Rochester (sl013d@uhura.cc.rochester.edu)
Sylvain is attempting to explain the forward discount bias puzzle arising in international finance via an adaptive agent model. Grounded in a standard expectations model, he allows the expectations parameters to enter adaptively. Early results indicate that the forward discount bias disappears as more learning is allowed in the system.
Michael Lenox, MIT (mlenox@mit.edu)
Michael has created an abstract model of the role of knowledge transfer in the diffusion of innovations. Each agent has a basic core capability to both understand new outside innovations as well as communicate these developments to the the colleagues to whom they are connected. Initial results with standard organizational forms indicate that there is a tradeoff between having an organization that is quickly able to exploit innovations once they become known inside of the firm and being able to discover new innovations as they arise outside of the firm. Michael is also using a genetic algorithm to understand how adaptive systems might be able to better design such organizations.
Wei Lin, C&L
Wei has developed a model of decentralized organizational task solving. In the model, organizations form by incorporating agents with various skills. The model provides a bases from which to explore the dynamics of self-organizing problem-solving organizations. Wei has found that as tasks become more complicated, there is more cooperation among the smaller firms, while medium sized tasks can engender complicated cycles of non-cooperative behavior.
Paolo Lupi, York (paolo@shiva.york.ac.uk)
Paolo is exploring the impact of aspiration-based learning on spatial models of economic systems. In the model, duopoly competition occurs at every point on a lattice. When the aspirations of the firm fall below the average profits of all the firms in the model, the firm alters its strategy by imitating the behavior of a randomly local firm. Notwithstanding the seemingly simple learning dynamics, Paolo has found that strategic ideas are rapidly propagated in this setting, resulting in the emergence of cooperation throughout the lattice.
Agostino Manduchi, Columbia (am195@columbia.edu)
Agostino is trying to understand how learning affects a spatial model of monopolistic competition. Agents are arrayed on a circle and compete with their immediate neighbors for consumers. Learning proceeds by imitating better-performing firms that find themselves in relatively similar situations vis-a-vis competitors. The results suggest that such a learning process leads the producers to quote prices that are, on average, above the level that would prevail if they faced different competitors in each round of the game, and in so doing realize higher average profits.
Salvatore Pitruzzello, Columbia (pitruzz@columbia.edu)
Salvatore wants to understand the impact of globalization on policy convergence. He has created a dynamic model of international interaction that incorporates many current theoretical ideas from international relations research. By incorporating these ideas in a single, dynamic model, he can now begin to unravel the full implications of each of the various theories.
Francisco Rodriguez, Harvard (frodrigu@kuznets.fas.harvard.edu)
Francisco is using computational methods to better understand the relationship between inequality and redistribution in a simple voting model. Different income classes in the electorate are courted by adaptive parties attempting to implement different taxation policies. Although this is a simple model, standard theoretical tools yield few insights. The computational approach has begun to generate a variety of new insights into this problem.
Bill Watkins, UC Santa Barbara (bill@econ.ucsb.edu)
Bill is using adaptive algorithms to create rapid information diffusion networks within an organization. In the model, agents probabilistically learn new information from those colleagues with whom they are connected. The computational models have revealed new organizational forms from which new analytic explorations can begin.
Peter Wurman, U. of Michigan (pete.wurman@umich.edu)
Peter is developing new forms of discrete multi-dimensional auctions which can be used as decentralized solution mechanisms for the knapsack problem. In these auctions, simple adaptive agents submit bids over multi-dimensional goods tempered by information generated by more traditional knapsack algorithms. Initial results indicate that such auctions can indeed lead to good outcomes in this difficult domain.