Agents learned, but do we? Knowledge discovery using the agent-based double auction markets
نویسندگان
چکیده
This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies, discovered by them, and from there, an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.
منابع مشابه
An agent-based simulation of double-auction markets
An agent-based simulation of double-auction markets Ted Xiao Guo Master of Science Graduate Department of Computer Science University of Toronto 2005 Agent-based simulation has emerged as a promising research alternative to the traditional analytical approach in studying financial markets. The idea behind agent-based simulation is to constructively model a market in a bottom-up fashion using ar...
متن کاملAutomated trading agents verses virtual humans: An evolutionary game-theoretic comparison of two double-auction market designs
In this paper we describe an analysis, using evolutionary game theory, of two double auction markets—the clearing house auction and the continuous double auction. We use heuristic-strategy approximation to analyse two broad classes of traders. One heuristic strategy approximates human behavior, and the other is a simple automated strategy, so our analysis permits us to predict the evolution of ...
متن کاملA Comparison of Effective Trading Agents in Double Auction Markets
In this paper, we conducted agent-based simulations in double auction markets with strategies gathered from the literature. The goal is to compare various styles of strategies based on their effectiveness. In shows that adopting heuristics, adaptive strategies, or even more innovative algorithms such as Genetic Programming, can yield different advantages in a profit-variation exchange setup. Th...
متن کاملAutomated Bidding Strategy Adaption using Learning Agents in Many-to-Many e-Markets
In this paper the issue of bidding strategy learning in electronic markets is addressed. The primary aim is to identify machine learning techniques which are best suited to learn bidding behaviour in electronic markets. The developed methodologies are applied within a structured market engineering process to improve the qualtiy of market designs. Market simulations are carried out based on a di...
متن کاملArtificial Software Agents on Thin Double Auction Markets – A Human Trader Experiment
This paper studies how software agents influence the market behavior of human traders. Programmed traders with a passive arbitrage seeking strategy are introduced in a double auction market experiment with human subjects in the laboratory. As a treatment variable, the influence of information on the existence of software agents is investigated. We found that common knowledge about the presence ...
متن کامل