2005 Graduate Workshop in Computational Economics

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.

Kirill Chernomaz, Economics, Ohio State (chernomaz.1@osu.edu).

Kirill is interested in understanding the difficulty of learning in economics. He relies on a model of collusion in independent, private-value first-price auctions as an environment to explore this question, and has agents learning polynomial bidding functions via a genetic algorithm. Agents have the ability to bound the learning task at hand by reducing the complexity of the function they are trying to learn. He finds that agents learn to make tradeoffs between the difficulty of the learning task they give themselves and the environment. In particular, agents tend to simplify the learning task at hand, at a cost of deviating from optimal behavior, as long as the environment is not too complex.

Yen-Sheng Chiang, Sociology, Washington (yen506@u.washington.edu).

Yen is exploring a model of corruption in social and economic systems. Studies have shown that economic development and corruption tend to be negatively correlated. In the first model, he considers agents queuing up for a service. Corruption is introduced by allowing agents to bribe either one another or a central authority to alter their positions in the queue. In the second model he uses a three level model that entails agents bribing an official who is under a supervisor.

Scott Christley, Computer Science, Notre Dame (schristl@nd.edu).

Scott wants to understand how different organizational forms influence software development. He represents software as being composed of interacting modules and uses a NK(C) model to capture this notion. Agents can manipulate the software in various ways, ranging from random modification to more specialized efforts on particular parts of the software, and also vary in their ability to manipulate the software ("expertise"). When the software has few interactions, all agent types appear to quickly develop the optimal software. As software becomes more complex, expertise begins to dominate, though this advantage tends to diminish as complexity increases. The underlying model will serve as a productive testbed for exploring a variety of key questions about software development, both in terms of how to organize programmers and how to structure software.

Sheila Conway, Systems Engineering, NASA and Old Dominion (Sheila.R.Conway@nasa.gov).

Sheila is investigating ways to improve air traffic control systems. Her model considers banks of flights arriving and departing from an air traffic control facility that is operationally constrained. With this model she can identify the key driving forces of the system, and develop new policy prescriptions that can improve performance both under current and anticipated future air traffic conditions.

Adrian de Froment, Ecology and Evolutionary Biology, Princeton (adriande@princeton.edu).

Adrian is modeling niche construction and firms. In the work, firms need to adapt to a landscape of consumer preferences while consumers simultaneously adapt their preferences to firms. Across a variety of model specifications, he found that the system tended to create a similar number, type, and distribution of firms. However, there were important differences, for example in first mover advantage, depending on the degree of niche construction allowed.

Joel Grus, Social Science, Cal Tech (grus@hss.caltech.edu).

Joel considers the problem of innovation, complexity, and patents. Technology is modeled as a landscape that can embrace different levels of nonlinearity. Agents search across this landscape using various heuristics, and are prevented via "patents" from searching neighborhoods of previous identified points. The model allows the analysis of the interaction among search heuristics, underlying complexity of the technology, and the patent system. He finds that along with exploring issues of innovation free riding, the model provides a tractable way to explore how patents alter the dynamics of search.

Kyle Joyce, Political Science, Pennsylvania State (kjoyce@psu.edu).

Kyle's work focuses on why some wars expand to include third parties in the conflict. Using an agent-based model, states must decide whether or not to join an ongoing conflict based on their expectations of the outcome of the war and their connections to the embattled states. He finds that the inclusion of third parties can have a big impact on the dynamics of war, and that as the threshold for joining the conflict increases the system appears to embrace a very different dynamics.

Yasmina Khoury, Economics, Columbia (yek2001@columbia.edu).

Yasmina is analyzing how consumers can trust messages when the source may either be from credible consumers or self-interested merchants. This model reflects some new marketing trends, like those used by the company Bzzagents. A key question is when does such word-of-mouth advertising break down. She considers an evolutionary system driven by a replicator dynamic. She finds that when only firms can evolve, they learn to lie about the quality of their product; when only consumer evolve, they learn to distrust signals from firms. Note that both of these behaviors result in an inferior outcome for the system as a whole. Finally, when both sides of the market can evolve, a similar outcome arises, but consumers learn their strategy much faster.

Andreas Pape, Economics, Michigan (apape@umich.edu).

Andreas is interested in agents and causal inference. In particular, how do agents develop useful causal models of the world. The basis of his work is using recent developments in causal inference techniques developed by statisticians as a way to model agent behavior. Agents need to differentiate among potential causal graphs given a set of observations. The basic framework will be applied to a variety of economic scenarios.

Markus Schneider, Economics, New School (SchnM869@newschool.edu).

Markus employs an agent-based model to analyze urban dynamics, in particular, the impact of home ownership versus renting on neighborhood formation. In the model, agents must decide where to live and whether to buy or rent, based on neighborhood quality, cost, and availability of credit. He finds that high quality neighborhoods form anchored by owners. The model will ultimately serve as a means by which to better understand gentrification processes in urban areas, changes in racial composition, and how social networks and learning dynamics influence neighborhoods.

John H. Miller , miller@santafe.edu.