Workshop in Computational Economics
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
Ravinder Bhavnani, University of Michigan (email@example.com).
Ravi would like to understand how institutions
can emerge and promote cooperation among agents, and he is investigating a
variety of pathways for such institutional formation.
For example, entrepreneurs may make investments into "trading clubs" in
which agents, after paying a fee, are allowed to interact with other
members of the club. The model indicates that the emergence of such
institutions can allow high levels of cooperation to develop in
Sylvie Geisendorf, Kassel
Sylvie is exploring the
behavior of adaptive agents who must compete with one another in
acquiring a renewable, but fragile, resource. The model considers a
fishery where adaptive agents decide on the size of the boat
(catching potential) they wish to acquire based on past experience.
Analysis of the behavior of the model should increase
our understanding of tightly coupled
economic and ecological systems,
illuminate the conditions under which agents learn to sustainably
use resources, and provide insight into
the effectiveness of various policy options.
Iris Ginzburg, C&L (firstname.lastname@example.org)
Iris has modeled a system in which firms have the option of sharing
information with other firms in the industry. The flow of such
information may be carried by agents who have knowledge of each firm's
operations, for example consultants. The model implies that the
sharing of information is most likely in environments where
firms are frequently exposed to catastrophic events.
Asim Khwaja, Harvard University
Asim has developed a model that
can be used to understand how explicit and implicit contracts can
emerge in society. In the model, information about each agent's
past behavior flows through the network of friendship relations.
This information allows each agent to form beliefs about the other
agents, and based on these beliefs decide on how to form contracts with
Depending on certian parameters, either contractual form may arise and
support different informational regimes.
Susan Lee, University of Wisconsin
Sue has analyzed a model in which
agents undergo assortative matching before playing a coordination
game---thus allowing interaction networks to form endogenously. The
underlying analytic and computational models allow her to investigate
central issues surrounding income inequality.
The inequality that results from
the model can be linked to a variety of exogenous factors, for
example, the strength of the assortative matching mechanism, the memory
of the agents, etc.
Nienke Oomes, University of Wisconsin
(email@example.com). Nienke has built a dynamic, discrete
choice model with local interactions. Agents, based on information
about their neighbors' and their own past behavior, decide whether or
not to seek work during a given time period. Employed agents in a
particular area earn money and contribute to the local economy. In
general, the model generates classes of persistent, well
defined employment patterns. The actual characteristics of the final
pattern can be linked to just a few key parameters in the model.
Alexander Outkin, Virginia Tech, (firstname.lastname@example.org).
Sasha is attempting to understand how information flow across
local interactions can result in alliance formation. Firms, each with
a particular spatial location, attempt to compete with one another by
offering goods that better match the preferences of their local
customers. Even though neighboring firms compete for the same
customers, they still may find it in their interest to share
information with their joint customers since such information allows them to
better compete against their other neighbors. Links between local and
global processing are emerging as key to understanding this type of
Dawn Parker, University of California---Davis
Dawn is investigating how local
spatial externalities impact land use choices. In the model,
generators (for example, pesticide-based agriculture) impose a
negative externality on their immediate neighbors (for example, organic
farmers). The model allows bargaining between immediate neighbors, and
preliminary results indicate that the dynamic behavior and ultimate
land-use patterns emerging from the model can be tied to
exogenous price and
productivity parameters as well as relative bargaining power.
Moreover, it appears that decentralized markets and
bargaining often imply very inefficient outcomes.
John Patty, Cal Tech (email@example.com).
John wants to understand how political parties and institutions
emerge and evolve. In the model, political actors have limited
information (in the form of agent-specific information partitions)
about the key, underlying characteristics of the system.
If an agent trades this information with another agent, both can
benefit as the information partition becomes more refined. The model
links the likelihood of trade to the similarity of the agents' past
behavior. These information exchange networks are sufficient to
promote party formation.
Jianping Shen, C&L (firstname.lastname@example.org).
Jianping developed a genetic algorithm that is able to sort
arrays of agents attempting to solve a coordination problem. The
algorithm, relying on the spatial structure imposed by the underlying
problem, uses a spatially oriented cross over operator, and
was able to
very quickly find ideal sortings while simultaneously moving all of the
Justin Smith, Oxford
(email@example.com). Justin designed a program
that attempts to beat a human player in a game of matching pennies.
The program exploits patterns in the play of the human to gain an
advantage in the game. Preliminary results show that if
humans have no feedback on the play of the
game, they can be easily exploited by the machine. However, even when the
humans have full
feedback, the machine can often defeat them. The model is
being extended so that issues of human pattern recognition and learning
can be investigated further.
James Thomas, Carnegie Mellon University
(firstname.lastname@example.org). James is analyzing how decentralized
organizations can solve non-decomposable problems. He has developed a
simple framework from which to study how problems of various
difficulties can be solved by both centralized and decentralized
entities. The model indicates that even small levels of noise in the
evaluation function result can imply a big advantage to
decentralization. The basic framework should have ready
applications to both human and artificial systems.
William Vachula, Wharton
(email@example.com). Bill created an abstract model of
contract negotiations. In the model, buyers and sellers must agree on
10 negotiation issues---each of which can be resolved in a way that
will favor either the buyer or the seller in the event of an exogenous
failure on the issue in question. Initial results indicate that as
the cost of issue failure increases, the number of agreed upon
contracts declines as agents take bargaining stances that become more
William Walsh, University of Michigan
(firstname.lastname@example.org). Bill is attempting to solve task
allocation problems that arise in computer systems by creating a
decentralized market mechanism in which computational agents bid
for resources, and in so doing create multi-stage supply chains.
Agents must confront both local knowledge and communication, as well as
resources and final goods that are time dependent. He finds that the
market mechanism appears to be both quicker and more efficient than
techniques that are currently used to solve such problems.
James Warnick, University of Pittsburgh
(email@example.com). Jim uses symbolic regression techniques
(based on a genetic program) to analyze the role of expectation
formation in games. In the model, agents attempt to induct the
behavior of opponents through regression and then, using this information,
best respond. Though agents use deterministic predictors,
behavior that resembles mixed strategy emerges on long time scales. He
also found that when one agent is allowed to use more elaborate
expectation formation mechanisms significant performance advantages
can accrue. However, even small amounts of noise in the system
dramatically mitigate this advantage.
Miller , firstname.lastname@example.org.