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Program Announcement

Program Announcement 2000

Students

  • Josh Anderson, Economics, UCSB [email]
  • Yann Bramoulle, Economics, U of Maryland [email]
  • Elizabeth Bruch, Sociology, UCLA [email]
  • Stephanie Chow, Social Science, Cal Tech [email]
  • Eldar Nigmatullin, Economics, U of Wisconsin [email]
  • Paolo Patelli, Economics, Pisa [email]
  • Daniel Reeves, AI, U of Michigan, [email]
  • Darren Schreiber, Political Scienced, UCLA [email]
  • Troy Tassier, Economics, U of Iowa [email]
  • Charles Williams, Business, U of Michigan [email]

Schedule

Schedule 2000

Faculty

  • David Ackley, Computer Science, UNM.
  • Jim Crutchfield, Physics, SFI.
  • Doyne Farmer, Physics and Economics, SFI.
  • Norman Johnson, Physics, Los Alamos National Labs.
  • Alex Lancaster, Swarm, Santa Fe Institute.
  • John H. Miller, Economics, Carnegie Mellon University (co-director).
  • Mark Newman, Physics, SFI.
  • Scott E. Page, Economics, Iowa (co-director).

Homework

Homework Problem 2000

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.

Josh Anderson, UCSB (josh@econ.ucsb.edu).
Josh is developing a more cognitive-based theory of finance. In his model, two types of agents attempt to buy and sell a security. The first type of agent bases its behavior on reinforcement (associative) learning concepts arising from cognitive psychology. In the initial model, these agents base their behavior on price and volume observations from the previous k time periods (in future models, agents will adaptively identify key features to focus upon). The second type of agent uses the more traditional decision approach of attempting to predict and discount the future value of a security. The model will explore the dynamics of such a world.

Yann Bramoulle, U of Maryland (bramoulle@arec.umd.edu).
Yann has created a simple model of crime and social interactions. In his model, agents must decide whether or not to participate in a life of crime versus joining the legitimate labor market. This choice is influenced by social networks that form among the agents---agents who are connected to other agents pursuing lives of crime tend towards that choice as well (influenced either by norms or lower information costs). By manipulating the level of property rights (that is, the amount of legitimate activity that is immune from crime) and network connections, Yann finds that very different social outcomes result, ranging from stable, low-crime worlds to highly unstable scenarios in which wave after of wave of crime hits society.

Elizabeth Bruch, UCLA (bruch@ucla.edu).
Elizabeth has extend Schelling's basic tipping model to multiple minority groups so that she can investigate key demographic issues surrounding multi-ethnic segregation patterns. The model suggests that additional minority groups can buffer segregation patterns. She is currently refining the model and will eventually link its basic elements (for example, preferences for living with other groups) and outcomes to empirical data.

Stephanie Chow, Cal Tech (steph@hss.caltech.edu).
Stephanie is exploring the dynamics of political party formation. In her model, citizens who cannot find a suitable candidate from among the current slate, form new parties that are tested during the primary elections. Final party positions depend on both the candidate's inherent preferences and a willingness to compromise in order to win. She is currently investigating the party formation dynamics implied by this model. Early results indicate that such a system tends toward good policies and the domination of elections by a small number of parties.

Eldar Nigmatullin, U of Wisconsin (enigmatu@ssc.wisc.edu).
Eldar is focusing his work on how neighborhood interactions can influence socioeconomic dynamics. Specifically, he is developing a theoretical and empirical model of pre-marital births. In the model, the likelihood of having a pre-marital birth is tied to the occurrence of such events in an agent's social network. Early results indicate that such neighborhood interactions can dramatically alter the hazard rate and that the efficacy of policy is closely linked to understanding key interactions.

Paolo Patelli, Pisa (paolo@black.gelso.unitn.it).
Paolo is analyzing the decentralized information processing inherent in large, hierarchical organizations. In his work, organizations are modeled as hierarchical trees with each node being capable of limited information processing. The nodes employ adaptive behavior to try and refine their individual information processing. The model will allow Paolo to investigate issues ranging from how to provide effective feedback to the nodes to ways of duplicating activity that will improve performance.

Daniel Reeves, U of Michigan, (dreeves@umich.edu).
Dan is exploring both optimal and heuristic bidding in the synchronous k-double auction. He has created a "strategy-zoo" of heuristic rules for bidding (for example, shave your true value by a fixed percent), and is currently studying the performance of these strategies in mixed populations. He is also deriving "optimal" strategies for this auction environment. Eventually he hopes to refine current strategic theories about these auctions as well as implement these results in actual web-based auction markets.

Darren Schreiber, UCLA (dschreib@ucla.edu).
Darren is analyzing the dynamics underlying political party formation. In his model, agent behavior is guided by a small set of nested heuristics, for example, form an alliance with agents that have similar preferences, change your position towards winning platforms, etc. The nested heuristics allow various types of parties to form, ranging from pure, preference-based coalitions to ambitious, office-seeking parties. The current analysis of the model is focused on understanding the dynamics of party formation and heuristic interaction, as well as testing some long-standing theoretical ideas (for example, Duverger's law).

Troy Tassier, U of Iowa (troy-tassier@uiowa.edu).
Troy has created a model of referral networks in labor markets. In the model, firms need workers for both low and high-skilled jobs. Workers, who also have different skill levels, learn about job openings either through general advertising or via social networks of knowledgeable friends. The current focus of the analysis is on the dynamics of job acquisition in a market where there is a minority group with an identical distribution of skills as the majority group. By exploring the impact of social networks and segregation, key policy questions such as the speed at which minorities can attain equal job access, etc., can be investigated.

Charles Williams, U of Michigan (fcw@umich.edu).
Charlie is exploring a simple model of market-driven labor specialization. In his model, agents begin to specialize their production activities by forming long-term trading networks with other agents who also specialize. The model can be used to investigate the effectiveness of decentralized versus centralized organizations, the dynamics of adaptive trade network and supply chain formation, the impact of technological shocks, etc.