Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.
We are extending the existing features of Aspen, a powerful economic modeling tool, and introducing new features to simulate the role of confidence in economic activity. The new model is built from a collection of autonomous agents that represent households, firms, and other relevant entities like financial exchanges and governmental authorities. We simultaneously model several interrelated markets, including those for labor, products, stocks, and bonds. We also model economic tradeoffs, such as decisions of households and firms regarding spending, savings, and investment. In this paper, we review some of the basic principles and model components and describe our approach and development strategy for emulating consumer, investor, and business confidence. The model of confidence is explored within the context of economic disruptions, such as those resulting from disasters or terrorist events.
A fundamental challenge for all communication systems, engineered or living, is the problem of achieving efficient, secure, and error-free communication over noisy channels. Information theoretic principals have been used to develop effective coding theory algorithms to successfully transmit information in engineering systems. Living systems also successfully transmit biological information through genetic processes such as replication, transcription, and translation, where the genome of an organism is the contents of the transmission. Decoding of received bit streams is fairly straightforward when the channel encoding algorithms are efficient and known. If the encoding scheme is unknown or part of the data is missing or intercepted, how would one design a viable decoder for the received transmission? For such systems blind reconstruction of the encoding/decoding system would be a vital step in recovering the original message. Communication engineers may not frequently encounter this situation, but for computational biologists and biotechnologist this is an immediate challenge. The goal of this work is to develop methods for detecting and reconstructing the encoder/decoder system for engineered and biological data. Building on Sandia's strengths in discrete mathematics, algorithms, and communication theory, we use linear programming and will use evolutionary computing techniques to construct efficient algorithms for modeling the coding system for minimally errored engineered data stream and genomic regulatory DNA and RNA sequences. The objective for the initial phase of this project is to construct solid parallels between biological literature and fundamental elements of communication theory. In this light, the milestones for FY2003 were focused on defining genetic channel characteristics and providing an initial approximation for key parameters, including coding rate, memory length, and minimum distance values. A secondary objective addressed the question of determining similar parameters for a received, noisy, error-control encoded data set. In addition to these goals, we initiated exploration of algorithmic approaches to determine if a data set could be approximated with an error-control code and performed initial investigations into optimization based methodologies for extracting the encoding algorithm given the coding rate of an encoded noise-free and noisy data stream.
Genetic programming is a powerful methodology for automatically producing solutions to problems in a variety of domains. It has been used successfully to develop behaviors for RoboCup soccer players and simple combat agents. We will attempt to use genetic programming to solve a problem in the domain of strategic combat, keeping in mind the end goal of developing sophisticated behaviors for compound defense and infiltration. The simplified problem at hand is that of two armed agents in a small room, containing obstacles, fighting against each other for survival. The base case and three changes are considered: a memory of positions using stacks, context-dependent genetic programming, and strongly typed genetic programming. Our work demonstrates slight improvements from the first two techniques, and no significant improvement from the last.
Aspen, a powerful economic modeling tool that uses agent modeling and genetic algorithms, can accurately simulate the economy. In it, individuals are hired by firms to produce a good that households then purchase. The firms decide what price to charge for this good, and based on that price, the households determine which firm to purchase from. We will attempt to discover the Nash Equilibrium price found in this model under two different methods of determining how many orders each firm receives. To keep it simple, we will assume there are only two firms in our model, and that these firms compete for the sale of one identical good.