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
Alessandra Cassar, UCSC (email@example.com).
Alessandra is investigating how local interactions among banks
can precipitate financial crises.
In her model, banks receive deposits and underwrite loans to
customers, while simultaneously using transfers among
"neighboring" banks to balance funds. In such a system, insolvent banks can
begin a contagion cycle in which other banks are forced to default.
She finds that different connection topologies
imply different tradeoffs between maintaining
liquidity and avoiding insolvency.
The work suggests that policies designed to enhance the right
kind of connectivity may greatly improve the robustness of financial
Sean Gailmard, Cal Tech (firstname.lastname@example.org).
Sean is looking at adaptive mechanism design for effectively
controlling a common property resource, such as a fishery.
The model has fishery managers attempting to better control the
fishery by adapting a control mechanism,
here consisting of target fish populations, net size restrictions,
and violation penalties.
He finds that adaptive agents can discover productive mechanisms to
control this nonlinear system, though depending on the objective function,
complete degradation can still ensue. The computational approach should
provide a productive laboratory for mechanism design research.
Mike Gibney, University of London (M.A.Gibney@qmw.ac.uk).
Mike is using a market-based control mechanism to design a distributed
control system, such as a telecommunication network. The initial work
explores how such mechanisms can effectively allocate complementary goods.
Such goods naturally arise in telecommunication, for example, the value
of controlling a link between two locations often depends on what other links
in the system the agent currently controls.
The early evidence indicates that a market mechanism can result in
resource allocations that are quite effective at allocating existing resources.
Moreover, this high performance is achieved using either a homogeneous collection
of the best-known trading agent or a more heterogeneous collection of all types
Brit Grosskopf, Universitat Pompeu Fabra (email@example.com).
Brit is exploring models of incomplete information using artificial
adaptive agents. The agents are placed in a simple
coordination game with a Pareto inferior, but risk dominant,
outcome, and they are given different levels of information
about the play of the opponent. This environment matches the one she used
in some previously conducted human experiments.
Finite automata are coevolved in this world using a genetic algorithm. She
finds that these agents tend towards the risk-dominant equilibria---perhaps due
to an inherent preference of evolutionary systems to promote "survival of the
adequate" in such systems.
Serena Guarnaschelli, Cal Tech (firstname.lastname@example.org).
Serena is analyzing information aggregation in a double auction market using a common
value good. She has identified a variety of candidate strategies from the literature,
ranging from highly rational to somewhat irrational. By combining these
strategies into various ecologies, she hopes to better understand the
implications of heterogeneous collections of agents both at the individual
and aggregate level in such markets.
Kelly Lautt, UCLA (Kellyl@ucla.edu).
Kelly is interested in the gifts-for-goods corruption that has recently
arisen in China. In this system, citizens may proffer gifts to officials
in hopes of receiving key goods. Kelly models this system as a replicator
dynamic, and early results indicate that the system may evolve in and out
of corruption depending on the underlying norms of gifting and the penalties
imposed on corrupt officials.
Jim Leady, University of Michigan (email@example.com).
Jim is studying how bounded processing ability impacts the play of adaptive agents
facing multiple games. In the model, agents adapt finite automata using a genetic
algorithm that must play a set of standard two-by-two games: the Prisoner's
Dilemma, Chicken, Battle of the Sexes, and a simple coordination game. Agent's
strategies must be implemented using a limited amount of processing ability,
and thus strategies for the different games may need to share various scarce
Artur Minkin, University of Wisconsin (firstname.lastname@example.org).
Artur is developing new econometric techniques for estimating parameters
in spatially connected systems. His initial focus is on the estimation of
growth models with heterogeneous countries. In his work, he uses
Bayesian inference to identify appropriate groups of countries. To improve the quality
of the estimates, he is using artificial worlds to refine the econometric
techniques before applying them to the real data.
David Robalino, Rand (David_Robalino@rand.org).
David is applying computational techniques to evaluate the implications of
including social interactions in current integrated assessment models. In
a preliminary application, David shows that optimal savings rates derived
from the standard stochastic neoclassical growth model may tend to
underestimate socially optimal savings rates. His results suggest that
policy analysis ought to consider expanding current representative agent
models to incorporate social networks. His approach may also prove useful
to formalize the well known empirical linkage between social capital and
Sonia Schulenburg, University of Edinburgh (email@example.com).
Sonia is using classifier systems to model the behavior of asset traders.
Initial work has focused on creating adaptive traders that can effectively
identify and trade on price series of stocks generated by actual markets. The
insights gained from this work are being used to create artificial market
systems composed of adaptive agents that endogenously determine prices.
Miller , firstname.lastname@example.org.