Chung-Ching Tai

TRADING RESTRICTIONS, PRICE DYNAMICS AND ALLOCATIVE EFFICIENCY IN DOUBLE AUCTION MARKETS: ANALYSIS BASED ON AGENT-BASED MODELING AND SIMULATIONS

In this paper we conduct two experiments within an agent-based double auction market. These two experiments allow us to see the effect of learning and smartness on price dynamics and allocative efficiency. Our results are largely consistent with the stylized facts observed in experimental economics with human subjects. From the amelioration of price deviation and allocative efficiency, the effect of learning is vividly seen. However, smartness does not enhance market performance. In fact, the experiment with smarter agents (agents without a quote limit) results in a less stable price dynamics and lower allocative efficiency.

Computational Economics: A Perspective from Computational Intelligence

Sample of Contents: FINANCIAL MODELING OF INVESTMENT AND FORECASTING MARKET MAKING AND AGENT-BASED MODELING OF MARKETS GAMES COST ESTIMATION POLICY APPRAISAL.

The Agent-Based Double Auction Markets: 15 Years On

Novelties discovering as a source of constant change is the essence of economics. However, most economic models do not have the kind of novelties-discovering agents required for constant changes. This silence was broken by Andrews and Prager 15 years ago when they placed GP (genetic programming)-driven agents in the double auction market. The work was, however, neither economically well interpreted nor complete; hence the silence remains in economics. In this article, we revisit their model and systematically conduct a series of simulations to better document the results. Our simulations show that human-written programs, including some reputable ones, are eventually outperformed by GP. The significance of this finding is not that GP is alchemy. Instead, it shows that novelties-discovering agents can be introduced into economic models, and their appearance inevitably presents threats to other agents who then have to react accordingly. Hence, a potentially indefinite cycle of change is triggered.

Statistical properties of an experimental political futures market

A 24-hour exchange market was created on the Web to trade political futures contracts using fictitious money. In this online market, a political futures contract is a futures contract which matures on the election day with a liquidation price determined by the percentage of votes a candidate receives on the election day. Continuous double auctions were implemented as the system for order storage and price discovery. We drew market participants in the form of tournaments in which top traders won cash awards. Such a market was run, with about 400 registered traders, during the U.S. presidential election in November 2004 and Taiwan parliamentary election in December 2004. The experiments recorded transaction price, highest bid, lowest ask, and trading volume of each contract as a function of time. Despite the relatively small scale of the exchange, in terms of the number of participants and duration of the tournament, we report evidence for asymptotic power-law behaviors of the distributions of price returns, trading volumes, inter-transaction time intervals, and accumulated wealth that were found universal in real financial markets.

Advances in Computational Social Science

This volume is a post-conference publication of the 4th World Congress on Social Simulation (WCSS), with contents selected from among the 80 papers originally presented at the conference. WCSS is a biennial event, jointly organized by three scientific communities in computational social science, namely, the Pacific-Asian Association for Agent-Based Approach in Social Systems Sciences (PAAA), the European Social Simulation Association (ESSA), and the Computational Social Science Society of the Americas (CSSSA). It is, therefore, currently the most prominent conference in the area of agent-based social simulation. The papers selected for this volume give a holistic view of the current development of social simulation, indicating the directions for future research and creating an important archival document and milestone in the history of computational social science. Specifically, the papers included here cover substantial progress in artificial financial markets, macroeconomic forecasting, supply chain management, bank networks, social networks, urban planning, social norms and group formation, cross-cultural studies, political party competition, voting behavior, computational demography, computational anthropology, evolution of languages, public health and epidemics, AIDS, security and terrorism, methodological and epistemological issues, empirical-based agent-based modeling, modeling of experimental social science, gaming simulation, cognitive agents, and participatory simulation. Furthermore, pioneering studies in some new research areas, such as the theoretical foundations of social simulation and categorical social science, also are included in the volume.

Can Artificial Traders Learn and Err Like Human Traders? A New Direction for Computational Intelligence in Behavioral Finance

The microstructure of markets involves not only human traders’ learning and erring processes but also their heterogeneity. Much of this part has not been taken into account in the agent-based artificial markets, despite the fact that various computational intelligence tools have been applied to artificial-agent modeling. One possible reason for this little progress is due to the lack of good-quality data by which the learning and erring patterns of human traders can be easily archived and analyzed. In this chapter, we take a pioneering step in this direction by, first, conducting double auction market experiments and obtaining a dataset involving about 165 human traders. The controlled laboratory setting then enables us to anchor the observing trading behavior of human traders to a benchmark (a global optimum) and to develop a learning index by which the learning and erring patterns can be better studied, in particular, in light of traders’ personal attributes, such as their cognitive capacity and personality. The behavior of artificial traders driven by genetic programming (GP) is also studied in parallel to human traders; however, how to represent the observed heterogeneity using GP remains a challenging issue.

Individual Rationality as a Partial Impediment to Market Efficiency

In this chapter we conduct two experiments within an agent-based double auction market. These two experiments allow us to see the effect of learning and smartness on price dynamics and allocative efficiency. Our results are largely consistent with the stylized facts observed in experimental economics with human subjects. From the amelioration of price deviation and allocative efficiency, the effect of learning is vividly seen. However, smartness does not enhance market performance. In fact, the experiment with smarter agents (agents without a quote limit) results in a less stable price dynamics and lower allocative efficiency.

Modeling Social Heterogeneity with Genetic Programming in an Artificial Double Auction Market

Individual differences in intellectual abilities can be observed across time and everywhere in the world, and this fact has been well studied by psychologists for a long time. To capture the innate heterogeneity of human intellectual abilities, this paper employs genetic programming as the algorithm of the learning agents, and then proposes the possibility of using population size as a proxy parameter of individual intelligence. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.

Modeling intelligence of learning agents in an artificial double auction market

In psychological as well as socioeconomic studies, individual intelligence has been found decisive in many domains. In this paper, we employ genetic programming as the algorithm of our learning agents who compete with other designed strategies extracted from the literature.We then discuss the possibility of using population size as a proxy parameter of individual intelligence of software agents. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.