The effects of periodic and continuous market environments on the performance of trading agents

Simulation experiments are conducted on simple continuous double auction (CDA) markets based on the experimental economics work of Vernon Smith. CDA models within experimental economics usually consist of a sequence of discrete trading periods or “days”, with allocations of stock and currency replenished at the start of each day, a situation we call “periodic” replenishment. In our experiments we look at both periodic and continuous-replenishment versions of the CDA. In this we build on the work of Cliff and Preist (2001) with human subjects, but we replace human traders with Zero Intelligence Plus (ZIP) trading agents, a minimal algorithm that can produce equilibrating market behaviour in CDA models. Our results indicate that continuous-replenishment (CR) CDA markets are similar to conventional periodic CDA markets in their ability to show equilibration dynamics. Secondly we show that although both models produce the same behaviour of price formation, they are different playing fields, as periodic markets are more efficient over time than their continuous counterparts. We also find, however, that the volume of trade in periodic CDA markets is concentrated in the early period of each trading day, and the market is in this sense inefficient. We look at whether ZIP agents require different parameters for optimal behaviour in each market type, and find that this is indeed the case. Overall, our conclusions mirror earlier findings on the robustness of the CDA, but we stress that a CR-CDA marketplace equilibrates in a different way to a periodic one

Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Agent-based Computational Economics: a Methodological Appraisal

This paper is an overview of "Agent-based Computational Economics (ACE)", an emerging approach to the study of decentralized market economies, in methodological perspective. It summarizes similarities and differences with respect to conventional economic models, outlines the unique methodological characteristics of this approach, and discusses its implications for economic methodology as a whole. While ACE rejoins the reflection on the unintended social consequences of purposeful individual action which is constitutive of economics as a discipline, the paper shows that it complements state-of the-art research in experimental and behavioral economics. In particular, the methods and techniques of ACE have reinforced the laboratory finding that fundamental economic results rely less on rational choice theory than is usually assumed, and have provided insight into the importance of market structures and rules in addition to individual choice. In addition, ACE has enlarged the range of inter-individual interactions that are of interest for economists. In this perspective, ACE provides the economist‘s toolbox with valuable supplements to existing economic techniques rather than proposing a radical alternative. Despite some open methodological questions, it has potential for better integration into economics in the future.Agent-based Computational Economics, Economic Methodology, Experimental Economics.

Decoupling Markets and Individuals: Rational Expectations Equilibrium Outcomes from Information Dissemination among Boundedly-Rational Traders

Attainment of rational expectations equilibria in asset markets calls for the price system to disseminate traders’ private information to others. It is known that markets populated by asymmetrically-informed profit-motivated human traders can converge to rational expectations equilibria. This paper reports comparable market outcomes when human traders are replaced by boundedly-rational algorithmic agents who use a simple means-end heuristic. These algorithmic agents lack the capability to optimize; yet outcomes of markets populated by them converge near the equilibrium derived from optimization assumptions. These findings suggest that market structure is an important determinant of efficient aggregate level outcomes, and that care is necessary not to overstate the importance of human cognition and conscious optimization in such contexts