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Simulating Interacting Agents and Social Phenomena pp 119–136Cite as

The Agent-Based Double Auction Markets: 15 Years On

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Part of the book series: Agent-Based Social Systems ((ABSS,volume 7))

Abstract

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.

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Notes

  1. 1.

    See [22], p. 28–30.

  2. 2.

    Genetic algorithms and learning classifier systems can be other alternatives. However, to the best of our knowledge, most agent-based economic applications of genetic algorithms do not manifest this capability, and, for some reason not exactly known, there are almost no agent-based economic applications of learning classifier systems.

  3. 3.

    The reason why we choose the agent-based double auction market as the main pursuit of this paper is because this is one of the few economic models in which human agents, programmed agents and autonomous agents have been involved. See Sect. 2 for the details.

  4. 4.

    As we shall see below, zero-intelligence agents or slightly modified zero-intelligence agents cannot compete with some well-thought human-written programs.

  5. 5.

    The first DA tournaments were held by the Santa Fe Institute in 1990. A share of $10,000 was offered to the writers of algorithms that could perform well in a double auction competition. The tournament attracted around 25 different and well thought-out strategies.

  6. 6.

    Submitted by Todd Kaplan, then a student at the University of Minnesota. See Sect. 3.2.

  7. 7.

    More details will be given in Sect. 3.3. In brief, all randomly generated programs can be regarded as samples from the span of some bases. These bases, as listed in Table 1, are all from human-written programs.

  8. 8.

    Please refer to [20] for the random token-generation process.

  9. 9.

    Named by or after their original designers, these strategies were modified to accommodate our discrete double auction mechanism in various ways. They were modified according to their original design concepts as much as possible. As a result, they might not be 100% the same as their original forms.

  10. 10.

    We choose 0.1 because [24]’s simulations shows that the market efficiency will be maximized when traders all have 0.1 markup rates.

  11. 11.

    The elements in the terminal and function sets are extracted from the Skeleton, Kaplan, and Ringuette strategies, which are human-designed trading rules and are proved to be quite efficient in gaining profits. Please refer to [19, 20] for the structure and the performance of these strategies.

  12. 12.

    For a more detailed explanation about how GP can be used to construct trading strategies in double auction markets, i.e., how strategies are generated and renovated with crossover and mutation, please refer to [4].

  13. 13.

    The fitness value of GP traders is defined as the achievement of the individual efficiency, which will be explained later in Sect. 4.

  14. 14.

    To avoid the flaw that a strategy is deserted simply because it is not selected, we set N as twice the size of the population, so that theoretically each strategy has the chance to be selected twice.

  15. 15.

    The tournament size and the mutation rate are two important parameters which may influence GP traders’ performance. On the one hand, the larger the tournament size, the earlier that the convergence of strategies can be expected. On the other hand, the larger the mutation rate, the more diverse the genotypes of the strategies are. When facing a dynamic problem such as making bids/asks in a double auction market, the impact of different tournament sizes together with different mutation rates on GP performance can only be accessed with a comprehensive experimentation of different combinations. Generally speaking, in many studies the size of the tournament ranges from 2 to 5, while the mutation rate ranges from 1 to 10%.

  16. 16.

    As mentioned in Sect. 1, we can use GP to model the learning process of an expert possessing intelligence based on, say, a 10-year experience and 50,000 “chunks”. In this article, each GP trader has to develop strategies to be used in the markets. These strategies consist of building blocks comprising market variables, and therefore can be viewed as combinations of “chunks”. Since we cannot predict how many chunks our GP traders will use, we did not parameterize this variable. Instead, the size of the population of strategies is utilized to characterize this capacity. In a similar vein, we did not model the “10-year experience” directly. 7,000 trading days are available for our GP traders to make their strategies as good as possible.

  17. 17.

    In our results, the best strategy of GP traders with a population size of 100 in the 34th generation is the selling strategy–Max(PMinBid, PAvg, PAvgAsk, LT), a rather simple rule which adjusts to the market situations by simply choosing whichever is bigger among several types of market and private information. For a more thorough investigation of the kinds of strategies our GP traders are capable of evolving, please see [7].

  18. 18.

    However, the correlation between the population size and the generations needed to defeat other strategies may not prevail in all circumstances. A GP trader in our double auction tournament is a specific-purpose machine which seeks to discover efficient trading strategies. In such a specific problem where the number of potentially efficient strategies is finite, employing too many strategies (say, 10,000 strategies) may not be coupled with a corresponding increase in the learning speed. In fact, a closer look at our data suggests a decreasing correlation between the population size and the generations needed to defeat the rivals when the population sizes become larger and larger.

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Acknowledgements

The authors are grateful to the two anonymous referees for their suggestions in regard to the former version of this paper submitted to the WCSS 2008 post-conference proceedings. National Science Council Research Grant No. NSC 95-2415-H-004-002-MY3 and National Chengchi University Top University Program No. 98H432 are also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Economics, AI-ECON Research Center, National Chengchi University, No.64, Sec.2, ZhiNan Rd., Wenshan District, Taipei City, 11605, Taiwan (R.O.C)

    Shu-Heng Chen

  2. Department of Economics, Tunghai University, No.181, Sec.3, Taichung Harbor Road, Taichung, 40704, Taiwan (R.O.C.)

    Chung-Ching Tai

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Correspondence to Shu-Heng Chen .

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Editors and Affiliations

  1. Department of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    Keiki Takadama (Associate Professor) (Associate Professor)

  2. Center for Social Complexity, George Mason University, 4400 University Drive, MSN 6B2, Fairfax, Virginia, 22030, USA

    Claudio Cioffi-Revilla (Professor) (Professor)

  3. Head of LISC (Laboratoire d’Ingénierie des Systèmes Complexes), Cemagref 24 avenue des Landais, 50085, 63172, Aubière Cedex 1, France

    Guillaume Deffuant

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Chen, SH., Tai, CC. (2010). The Agent-Based Double Auction Markets: 15 Years On. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol 7. Springer, Tokyo. https://doi.org/10.1007/978-4-431-99781-8_9

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  • DOI: https://doi.org/10.1007/978-4-431-99781-8_9

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