From Santa Fe Institute Events Wiki
- Simon Angus, Economics, U. of New South Wales [email]
- Jeremy Dalletezze, Economics, Brandeis [email]
- Matt Grossmann, Political Science, Berkeley [email]
- Kristen Hassmiller, Public Health, Michigan [email]
- Robert Letzler, Policy, Berkeley [email]
- Dan Li, Finance, Carnegie Mellon University [email]
- Rodolfo Sousa, Policy, Manchester Metropolitan U. [email]
- Horacio Trujillo, Policy, RAND [email]
- Leanne Ussher, Economics, New School U. [email]
- Xing Zhong, Sociology, U. of Chicago [email]
- Samuel Bowles, Economics, U. Massachusetts and SFI.
- Michelle Girvan, Physics, SFI.
- David Krakauer, SFI.
- Seth Lloyd, MIT.
- John H. Miller (co- director), Economics, Carnegie Mellon University and SFI.
- Scott E. Page (co-director), Economics, U. of Michigan and SFI.
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.
Simon Angus, Economics, U. of New South Wales (email@example.com).
Simon is modeling the emergence of networks in worlds composed of strategic agents. Each agent in the model evolves a strategy, captured by a finite automata, that not only dictates how the agent plays the game (here a repeated Prisoner's Dilemma), but also whether to strengthen or weaken the tie to the particular opponent. His initial analysis indicates that the system leads to the emergence of interesting network dynamics as the agents learn to reciprocate ties with other cooperative agents.
Jeremy Dalletezze, Economics, Brandeis (firstname.lastname@example.org).
Jeremy considers a model of competition and alliance-formation dynamics in innovation-rich industries. Firms must strategize about partnerships and allocate resources to develop core expertise via research and development. More successful firms continue in the industry and can appropriate parts of innovations from other firms. He calibrates this agent-based model to econometric estimates of market share, research and development expenditures, patents, and observed alliances, and finds that his model is able to capture some of the key patterns in the data.
Matt Grossmann, Political Science, Berkeley (email@example.com).
Matt is analyzing the mobilization of social groups, with a focus on how the individual-level attributes of social groups aggregate into broader mobilization patterns. In the model, innate agent characteristics and social networks initiate a desire to mobilize that feeds into various dynamics relating to the overall level of interest group activity and the amount of policy-maker attentiveness. He uses survey data on five American ethnic groups to calibrate the model and finds important differences in estimated parameters across the various groups.
Kristen Hassmiller, Public Health, Michigan (firstname.lastname@example.org).
Kristen has implemented both differential-equations-based and agent-based approaches to modeling the spread of tuberculosis. She then "docks" the two approaches in an attempt to identify key similarities and differences. She finds that modeling choices concerning granularity and contact networks lead to important differences in predictions and policy prescriptions.
Robert Letzler, Policy, Berkeley (email@example.com).
Rob is looking at heterogeneous preferences and learning in a public goods game. He models agents with preferences for both altruism and spite, and then explores the behavioral dynamics implied by the collection of preferences under various public goods institutions. He then introduces learning by allowing agents to incorporate predictions from past trends into their choice calculations, and finds that the system can be very sensitive to initial conditions.
Dan Li, Finance, Carnegie Mellon University (firstname.lastname@example.org).
Dan is exploring cooperation in problem solving, in particular, market behavior. The work is based on some ideas from machine learning focused on the efficacy of discrimination and diversity in problem solving. Agents attempt to make predictions in a market by using algorithms chosen from a pool of candidate learning rules. She finds that the ability of the system to effectively aggregate the predictions is closely tied to the underlying market mechanism and problem difficulty.
Rodolfo Sousa, Policy, Manchester Metropolitan U. (email@example.com).
Rodolfo's work focuses on the the political economy of redistribution. In the model agents of various incomes vote, using majority rule, on redistribution policies proposed by adaptive parties. He finds that the basic system results in an alternation of tax rates between the parties as they exchange the incumbent position, resulting in a greater than optimal tax rate. He also considers the linkages between the model and the median voter theorem, as well as the impact of simple parameter changes on the underlying dynamics.
Horacio Trujillo, Policy, RAND (firstname.lastname@example.org).
Horacio employs an agent-based model to analyze gentrification dynamics and segregation. In the work, agents attempt to acquire the best locations on a landscape. An agent's preference for a particular location is tied to the location's inherent quality, rent, and various externalities induced by the locations and types of neighboring agents. He finds that the patterns of gentrification are tightly coupled to the externalities induced by other agents.
Leanne Ussher, Economics, New School U. (email@example.com).
Leanne is studying the dynamics of a speculative futures market. The focus of the model is on the impact of various regulatory regimes on key market outcomes such as price volatility. The model is composed of two representative hedgers (which largely influence the spot prices) and groups of speculative agents (that rely on three different mechanisms to predict future prices). She finds that various regulatory requirements across transaction taxes and margin requirements directly impact price volatility and other key outcomes in the market.
Xing Zhong, Sociology, U. of Chicago (firstname.lastname@example.org)
Xing is investigating social structure and innovation dynamics. In her model collaborations lead to either positive or negative externalities, and agents must learn how to search for appropriate partners across an exogenously-adjustable innovation landscape. The model will be tested using empirical data on U.S. patent activity across major metropolitan statistical areas.