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題名 學習行為與軟體交易策略之比較：個體心智能力對學習行為之影響
作者 戴中擎
Tai, Chung Ching
貢獻者 陳樹衡
Chen, Shu Heng
戴中擎
Tai, Chung Ching
關鍵詞 代理人基計算經濟模型
雙方喊價市場
交易策略
學習
智商
異質性個體
Agent-based Computational Economic Models
Double Auction Markets
Trading Strategies
Learning
IQ
Heterogeneous Agents
日期 2007
上傳時間 18-Sep-2009 16:04:01 (UTC+8)
摘要 因應電子化交易興起而進行的一系列人機互動研究顯示, 縱使人類會透過學習而改善其表現, 電腦化的交易程式獲利能力還是遠勝於真人交易者之表現。本研究遂以遺傳規劃演算法作為學習型交易者之代表, 與一系列電腦化交易策略相競爭, 以探討學習的功效及其限制。

本研究採用離散型雙方喊價機制, 摒除了計算能力所造成之決策時間差異所會帶來的影響, 亦排除掉人類情緒、預期、相關知識不足等可能因子, 在計算能力對等的情況下, 單純地來評估學習與理性設計策略的結果。並且首次嘗試將影響學習至鉅的智商因子帶入模型之中,

實驗結果顯示學習具有相當的能力, 即使是在對環境缺乏認識的情況下, 隨著時間的經過其表現最終可凌駕理性設計的策略之上, 然而學習所需的時間是學習型交易者的一大弱點。同時, 本研究也顯示對於以遺傳規劃建構的學習型交易者而言, 其虛擬智商的參數愈高, 學習的效果也愈佳。此研究因此可作為未來在代理人基經濟學模型中, 更深入地探討智商水準不同所造成之行為差異的基礎。
The study of a series of human-agent interactions as well as computerized trading tournaments in double auction markets has exhibited a general superiority of computerized trading strategies over learning agents. The ineffectiveness
of learning motivates the study of learning versus designed trading agents in this research. We therefore initiates a series of experiments to test the capability of learning GP agents and rationally-designed trading strategies. The results shows that with the cost of time, eventually learning agents can beat all other trading strategies.

At the same time, the notion of intelligence is introduced into the model to investigate the influence of individual intelligence on learning ability. We utilize the population size of the GP trader as the proxy variable of IQ which
is a measure of general intelligence. The results show that individuals with higher intelligence can perform better than those with lower intelligence, which manifests its importance discovered in Psychological research.
參考文獻 Anand, P. (1993). Foundations of Rational Choice Under Risk. Oxford: Oxford University Press.
Brenner, T. (1999). Modelling Learning in Economics. Edward Elgar Publishing.
Brenner, T. (2006). Agent learning representation: Advice on modelling economic learning. In Tesfatsion, L. and Judd, K., editors, Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North Holland.
Chamberlin, E. H. (1948). An experimental imperfect market. Journal of Political Economy, 56(2):95–108.
Chan, N., LeBaron, B., Lo, A., and Poggio, T. (1999). Agent-based models of financial markets: A comparison with experimental markets. MIT Artificial Markets Project, Paper No. 124. Available at
http://people.brandeis.edu/˜blebaron/wps/disagg.pdf.
Chen, S.-H. and Huang, Y.-C. (forthcoming). Risk preference, forecasting accuracy and survival dynamics: Simulations based on a multi-asset agentbased artificial stock market. Journal of Economic Behavior and Organization. Forthcoming.
Cliff, D. and Bruten, J. (1997). Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets. Technical Report HPL-97-141, Hewlett-Packard Laboratories. Available at http://citeseer.ist.psu.edu/cliff97zero.html.
Das, R., Hanson, J. E., Kephart, J. O., and Tesauro, G. (2001). Agenthuman interactions in the continuous double auction. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI). San Francisco, CA: Morgan-Kaufmann.
Duffy, J. (2006). Agent-based models and human subject experiments. In Tesfatsion, L. and Judd, K., editors, Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North Holland.
Easley, D. and Ledyard, J. O. (1993). Theories of price formation and exchange in double oral auction. In Friedman, D. and Rust, J., editors, The Double Auction Market-Institutions, Theories, and Evidence. Addison-Wesley.
Evans, J. S. B. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10):454–459.
Feigenbaum, E. A. and Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8:305–336.
Fink, D. (1997). A compendium of conjugate priors. Technical report, Environmental Statistics Group, Department of Biology, Montana State University, USA.
Fonseca, G. L. (n.d.). Vilfredo Pareto. Retrieved May 6, 2008, from http://cepa.newschool.edu/het/.
Forsythe, R., Rietz, T. A., and Ross, T. W. (1999). Wishes, expectations and actions: A survey on price formation in election stock markets. Journal of Economic Behavior Organization, 39(1):83–110.
Friedman, D. (1991). A simple testable model of double auction markets. Journal of Economic Behavior and Organization, 15:47–70.
Gigerenzer, G. and Selten, R., editors (2001). Bounded Rationality: The Adaptive Toolbox. The MIT Press.
Gjerstad, S. and Dickhaut, J. (1998). Price formation in double auctions. Games and Economic Behavior, 22:1–29.
Gode, D. K. and Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101(1):119–137.
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1):13–23.
Grosjean, P., Spirlet, C., and Jangoux, M. (2003). A functional growth model with intraspecific competition applied to a sea urchin. Canadian Journal of Fisheries and Aquatic Sciences, 60:237–246.
Grossklags, J. and Schmidt, C. (2006). Software agents and market (in)efficiency - a human trader experiment. IEEE Transactions on System, Man, and Cybernetics: Part C, Special Issue on Game-theoretic Analysis & Simulation of Negotiation Agents, 36(1):56–67.
Herrnstein, R. J. and Murray, C. (1994). The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.
Jones, G. and Schneider, W. J. (2006). Intelligence, human capital, and economic growth: A bayesian averaging of classical estimates (BACE) approach. Journal of Economic Growth, 11:71–93.
Lynn, R. and Vanhanen, T. (2002). IQ and the Wealth of Nations. Westport, CT: Praeger.
Lynn, R. and Vanhanen, T. (2006). IQ and Global Inequality. Washington Summit Publishers.
Mandelbrot, B. and Hudson, R. L. (2004). The (Mis)behavior of Markets. Basic Books.
Murray, C. (1998). Income Inequality and IQ. Washington: AEI Press. Available at http://www.aei.org/books/filter.all,bookID.443/book detail.asp.
Murray, C. (2002). IQ and income inequality in a sample of sibling pairs from advantaged family backgrounds. American Economic Review, 92(2):339–343.
Payne, J. W., Bettman, J. R., and Johnson, E. J. (1993). The Adaptive Decision Maker. Cambridge University Press.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
Ram, R. (2007). IQ and economic growth: Further augmentation of Mankiw-Romer-Weil model. Economic Letters, 94:7–11.
Rubinstein, A. (1986). Finite automata play the repeated prisoner`s dilemma. Journal of Economic Theory, 39(1):83–96.
Rust, J., Miller, J., and Palmer, R. (1994). Characterizing effective trading strategies: Insights from a computerized double auction tournament. Journal of Economic Dynamics and Control, 18:61–96.
Rydval, O. and Ortmann, A. (2004). How financial incentives and cognitive abilities affect task performance in laboratory settings: An illustration. Economic Letters, 85:315–320.
Smith, V. L. (1991). Experimental economics: Behavioral lessons for microeconomic theory and policy. 1990 Nancy Schwartz Lecture, KGSM, Northwestern University.
Taniguchi, K., Nakajima, Y., and Hashimoto, F. (2004). A report of U-Mart experiments by human agents. In Shiratori, R., Arai, K., and Kato, F., editors, Gaming, Simulations, and Society: Research Scope and Perspective, pages 49–57. Springer.
Thaler, R. H. (2000). From homo economicus to homo sapiens. Journal of Economic Perspectives, 14(1):133–141.
Wolpert, D. and Macready, W. (1995a). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.
Wolpert, D. and Macready, W. (1995b). No free lunch theorems for search. Technical Report 95-02-010, Santa Fe Institute.
Zhan, W. and Friedman, D. (2007). Markups in double auction markets. Journal of Economic Dynamics and Control, 31:2984–3005.
描述 博士
國立政治大學
經濟研究所
90258503
96
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0902585032
資料類型 thesis
dc.contributor.advisor 陳樹衡zh_TW
dc.contributor.advisor Chen, Shu Hengen_US
dc.contributor.author (Authors) 戴中擎zh_TW
dc.contributor.author (Authors) Tai, Chung Chingen_US
dc.creator (作者) 戴中擎zh_TW
dc.creator (作者) Tai, Chung Chingen_US
dc.date (日期) 2007en_US
dc.date.accessioned 18-Sep-2009 16:04:01 (UTC+8)-
dc.date.available 18-Sep-2009 16:04:01 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 16:04:01 (UTC+8)-
dc.identifier (Other Identifiers) G0902585032en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35796-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟研究所zh_TW
dc.description (描述) 90258503zh_TW
dc.description (描述) 96zh_TW
dc.description.abstract (摘要) 因應電子化交易興起而進行的一系列人機互動研究顯示, 縱使人類會透過學習而改善其表現, 電腦化的交易程式獲利能力還是遠勝於真人交易者之表現。本研究遂以遺傳規劃演算法作為學習型交易者之代表, 與一系列電腦化交易策略相競爭, 以探討學習的功效及其限制。

本研究採用離散型雙方喊價機制, 摒除了計算能力所造成之決策時間差異所會帶來的影響, 亦排除掉人類情緒、預期、相關知識不足等可能因子, 在計算能力對等的情況下, 單純地來評估學習與理性設計策略的結果。並且首次嘗試將影響學習至鉅的智商因子帶入模型之中,

實驗結果顯示學習具有相當的能力, 即使是在對環境缺乏認識的情況下, 隨著時間的經過其表現最終可凌駕理性設計的策略之上, 然而學習所需的時間是學習型交易者的一大弱點。同時, 本研究也顯示對於以遺傳規劃建構的學習型交易者而言, 其虛擬智商的參數愈高, 學習的效果也愈佳。此研究因此可作為未來在代理人基經濟學模型中, 更深入地探討智商水準不同所造成之行為差異的基礎。		zh_TW
dc.description.abstract (摘要) The study of a series of human-agent interactions as well as computerized trading tournaments in double auction markets has exhibited a general superiority of computerized trading strategies over learning agents. The ineffectiveness
of learning motivates the study of learning versus designed trading agents in this research. We therefore initiates a series of experiments to test the capability of learning GP agents and rationally-designed trading strategies. The results shows that with the cost of time, eventually learning agents can beat all other trading strategies.

At the same time, the notion of intelligence is introduced into the model to investigate the influence of individual intelligence on learning ability. We utilize the population size of the GP trader as the proxy variable of IQ which
is a measure of general intelligence. The results show that individuals with higher intelligence can perform better than those with lower intelligence, which manifests its importance discovered in Psychological research.		en_US
dc.description.tableofcontents 誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

1 緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
　1.1 學習行為與軟體代理人. . . . . . . . . . . . . . . . . . . 1
　1.2 智商與學習. . . . . . . . . . . . . . . . . . . . . . . . 3
　1.3 本文架構. . . . . . . . . . . . . . . . . . . . . . . . . 6

2 文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . 9
　2.1 人類與軟體代理人之競賽. . . . . . . . . . . . . . . . . . 9
　　　2.1.1 Das et al. (2001) 的雙方喊價市場研究. . . . . . . . 9
　　　2.1.2 Taniguchi et al. (2004) 的期貨市場交易研究. . . . . 12
　　　2.1.3 Grossklags and Schmidt (2006) 的期貨市場研究. . . . 14
　　　2.1.4 小結. . . . . . . . . . . . . . . . . . . . . . . . 18
　2.2 Santa Fe 雙方喊價市場競賽. . . . . . . . . . . . . . .. . 19
　　　2.2.1 實驗設計. . . . . . . . . . . . . . . . . . . . . . 19
　　　2.2.2 策略分類. . . . . . . . . . . . . . . . . . . . . . 21
　　　2.2.3 結果與探討. . . . . . . . . . . . . . . . . . . . . 24
　2.3 智商與代理人基建模. . . . . . . . . . . . . . . . . . . . 26
　　　2.3.1 經濟建模: 由經濟人邁向智人. . . . . . . . . . . . . 26
　　　2.3.2 智商與學習行為的關聯. . . . . . . . . . . . . . . . 28
　2.4 研究議題. . . . . . . . . . . . . . . . . . . . . . . . . 31

3 研究方法與實驗設計. . . . . . . . . . . . . . . . . . . . . . 33
　3.1 AIE-DA 雙方喊價市場平台. . . . . . .. . . . . . . . . . . 33
　　　3.1.1 市場結構. . . . . . . . . . . . . . . . . . . . . . 33
　　　3.1.2 保留價格與籌碼. . . . . . . . . . . . . . . . . . . 34
　　　3.1.3 喊價活動流程. . . . . . . . . . . . . . . . . . . . 36
　3.2 研究問題與假設. . . . . . . . . . . . . . . . . . . . . . 39
　　　3.2.1 學習與設計. . . . . . . . . . . . . . . . . . . . . 39
　　　3.2.2 智商與學習. . . . . . . . . . . . . . . . . . . . . 43
　　　3.2.3 研究假設. . . . . . . . . . . . . . . . . . . . . . 44
　3.3 實驗設計. . . . . . . . . . . . . . . . . . . . . . . . . 47
　　　3.3.1 GP 交易者參數. . . . . . . . .. . . . . . . . . . . 47
　　　3.3.2 實驗參數. . . . . . . . . . . . . . . . . . . . . . 48

4 交易策略. . . . . . . . . . . . . . . . . . . . . . . . . . . 53
　4.1 基本名詞介紹. . . . . . . . . . . . . . . . . . . . . . . 53
　4.2 文獻策略. . . . . . . . . . . . . . . . . . . . . . . . . 56
　　　4.2.1 Truth Teller 交易策略. . . . . . .. . . . . . . . . 56
　　　4.2.2 Skeleton 交易策略. . . . . . . . .. . . . . . . . . 56
　　　4.2.3 Kaplan 交易策略. . . . . . . . . .. . . . . . . . . 58
　　　4.2.4 Ringuette 交易策略. . . . . . . . . . . . . . . . . 59
　　　4.2.5 ZIC 交易策略. . . . . . . . . . . . . . . . . . . . 61
　　　4.2.6 Markup 交易策略. . . . . . . . . .. . . . . . . . . 62
　　　4.2.7 ZIP 交易策略. . . . . . . . . . . . . . . . . . . . 63
　　　4.2.8 Easley-Ledyard 交易策略. . . . . .. . . . . . . . . 65
　　　4.2.9 Gjerstad-Dickhaut 交易策略. . . . . . . . . . . . . 67
　　　4.2.10 BGAN 交易策略. . . . . . . . . . . . . . . . . . . 68
　　　4.2.11 Empirical 交易策略. . . . . . . . . . . . . . . . 71
　　　4.2.12 文獻策略之比較. . . . . . . . . . . . . . . . . . 72
　4.3 Genetic Programming 交易者. . . . . . . . . . . . . . . . 74
　　　4.3.1 基本概念. . . . . . . . . . . . . . . . . . . . . . 74
　　　4.3.2 AIE-DA GP 交易者. . . . . . . . . . . . . . . . . . 79
　4.4 策略分類. . . . . . . . . . . . . . . . . . . . . . . . . 82

5 策略表現分析. . . . . . . . . . . . . . . . . . . . . . . . . 85
　5.1 衡量標準. . . . . . . . . . . . . . . . . . . . . . . . . 85
　5.2 文獻策略之基本表現與特性. . . . . . . . . . . . . . . . . 88
　　　5.2.1 文獻策略的獲利排名. . . . . . . . . . . . . . . . . 89
　　　5.2.2 固定型策略與調適型策略之比較. . . . . . . . . . . . 92
　　　5.2.3 獲利波動程度. . . . . . . . . . . . . . . . . . . . 93
　　　5.2.4 平均財富與財富變異. . . . . . . . . . . . . . . . . 95
　　　5.2.5 效率前緣. . . . . . . . . . . . . . . . . . . . . . 98
　5.3 學習性個體. . . . . . . . . . . . . . . . . . . . . . . . 101
　　　5.3.1 GP 之學習能力. . . . . . . . . . . . . . . . . . . 104
　　　5.3.2 No Free Lunch 檢驗. . . . . . . . . . . . . . . . . 108
　　　5.3.3 策略複雜度. . . . . . . . . . . . . . . . . . . . . 114
　5.4 動態市場. . . . . . . . . . . . . . . . . . . . . . . . . 118
　5.5 總結. . . . . . . . . . . . . . . . . . . . . . . . . . . 123

6 智商與學習效果. . . . . . . . . . . . . . . . . . . . . . . . 127
　6.1 智商與學習成果. . . . . . . . . . . . . . . . . . . . . . 127
　　　6.1.1 更為完整的智商抽樣. . . . . . . . . . . . . . . . . 130
　　　6.1.2 智商優勢與學習努力. . . . . . . . . . . . . . . . . 136
　6.2 動態環境中的學習能力. . . . . . . . . . . . . . . . . . . 140
　6.3 總結. . . . . . . . . . . . . . . . . . . . . . . . . . . 145

7 結論與未來研究方向. . . . . . . . . . . . . . . . . . . . . . 147
　7.1 未來研究方向. . . . . . . . . . . . . . . . . . . . . . . 151

參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . 153		zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0902585032en_US
dc.subject (關鍵詞) 代理人基計算經濟模型zh_TW
dc.subject (關鍵詞) 雙方喊價市場zh_TW
dc.subject (關鍵詞) 交易策略zh_TW
dc.subject (關鍵詞) 學習zh_TW
dc.subject (關鍵詞) 智商zh_TW
dc.subject (關鍵詞) 異質性個體zh_TW
dc.subject (關鍵詞) Agent-based Computational Economic Modelsen_US
dc.subject (關鍵詞) Double Auction Marketsen_US
dc.subject (關鍵詞) Trading Strategiesen_US
dc.subject (關鍵詞) Learningen_US
dc.subject (關鍵詞) IQen_US
dc.subject (關鍵詞) Heterogeneous Agentsen_US
dc.title (題名) 學習行為與軟體交易策略之比較：個體心智能力對學習行為之影響zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Anand, P. (1993). Foundations of Rational Choice Under Risk. Oxford: Oxford University Press.zh_TW
dc.relation.reference (參考文獻) Brenner, T. (1999). Modelling Learning in Economics. Edward Elgar Publishing.zh_TW
dc.relation.reference (參考文獻) Brenner, T. (2006). Agent learning representation: Advice on modelling economic learning. In Tesfatsion, L. and Judd, K., editors, Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North Holland.zh_TW
dc.relation.reference (參考文獻) Chamberlin, E. H. (1948). An experimental imperfect market. Journal of Political Economy, 56(2):95–108.zh_TW
dc.relation.reference (參考文獻) Chan, N., LeBaron, B., Lo, A., and Poggio, T. (1999). Agent-based models of financial markets: A comparison with experimental markets. MIT Artificial Markets Project, Paper No. 124. Available atzh_TW
dc.relation.reference (參考文獻) http://people.brandeis.edu/˜blebaron/wps/disagg.pdf.zh_TW
dc.relation.reference (參考文獻) Chen, S.-H. and Huang, Y.-C. (forthcoming). Risk preference, forecasting accuracy and survival dynamics: Simulations based on a multi-asset agentbased artificial stock market. Journal of Economic Behavior and Organization. Forthcoming.zh_TW
dc.relation.reference (參考文獻) Cliff, D. and Bruten, J. (1997). Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets. Technical Report HPL-97-141, Hewlett-Packard Laboratories. Available at http://citeseer.ist.psu.edu/cliff97zero.html.zh_TW
dc.relation.reference (參考文獻) Das, R., Hanson, J. E., Kephart, J. O., and Tesauro, G. (2001). Agenthuman interactions in the continuous double auction. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI). San Francisco, CA: Morgan-Kaufmann.zh_TW
dc.relation.reference (參考文獻) Duffy, J. (2006). Agent-based models and human subject experiments. In Tesfatsion, L. and Judd, K., editors, Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North Holland.zh_TW
dc.relation.reference (參考文獻) Easley, D. and Ledyard, J. O. (1993). Theories of price formation and exchange in double oral auction. In Friedman, D. and Rust, J., editors, The Double Auction Market-Institutions, Theories, and Evidence. Addison-Wesley.zh_TW
dc.relation.reference (參考文獻) Evans, J. S. B. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 7(10):454–459.zh_TW
dc.relation.reference (參考文獻) Feigenbaum, E. A. and Simon, H. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8:305–336.zh_TW
dc.relation.reference (參考文獻) Fink, D. (1997). A compendium of conjugate priors. Technical report, Environmental Statistics Group, Department of Biology, Montana State University, USA.zh_TW
dc.relation.reference (參考文獻) Fonseca, G. L. (n.d.). Vilfredo Pareto. Retrieved May 6, 2008, from http://cepa.newschool.edu/het/.zh_TW
dc.relation.reference (參考文獻) Forsythe, R., Rietz, T. A., and Ross, T. W. (1999). Wishes, expectations and actions: A survey on price formation in election stock markets. Journal of Economic Behavior Organization, 39(1):83–110.zh_TW
dc.relation.reference (參考文獻) Friedman, D. (1991). A simple testable model of double auction markets. Journal of Economic Behavior and Organization, 15:47–70.zh_TW
dc.relation.reference (參考文獻) Gigerenzer, G. and Selten, R., editors (2001). Bounded Rationality: The Adaptive Toolbox. The MIT Press.zh_TW
dc.relation.reference (參考文獻) Gjerstad, S. and Dickhaut, J. (1998). Price formation in double auctions. Games and Economic Behavior, 22:1–29.zh_TW
dc.relation.reference (參考文獻) Gode, D. K. and Sunder, S. (1993). Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy, 101(1):119–137.zh_TW
dc.relation.reference (參考文獻) Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1):13–23.zh_TW
dc.relation.reference (參考文獻) Grosjean, P., Spirlet, C., and Jangoux, M. (2003). A functional growth model with intraspecific competition applied to a sea urchin. Canadian Journal of Fisheries and Aquatic Sciences, 60:237–246.zh_TW
dc.relation.reference (參考文獻) Grossklags, J. and Schmidt, C. (2006). Software agents and market (in)efficiency - a human trader experiment. IEEE Transactions on System, Man, and Cybernetics: Part C, Special Issue on Game-theoretic Analysis & Simulation of Negotiation Agents, 36(1):56–67.zh_TW
dc.relation.reference (參考文獻) Herrnstein, R. J. and Murray, C. (1994). The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.zh_TW
dc.relation.reference (參考文獻) Jones, G. and Schneider, W. J. (2006). Intelligence, human capital, and economic growth: A bayesian averaging of classical estimates (BACE) approach. Journal of Economic Growth, 11:71–93.zh_TW
dc.relation.reference (參考文獻) Lynn, R. and Vanhanen, T. (2002). IQ and the Wealth of Nations. Westport, CT: Praeger.zh_TW
dc.relation.reference (參考文獻) Lynn, R. and Vanhanen, T. (2006). IQ and Global Inequality. Washington Summit Publishers.zh_TW
dc.relation.reference (參考文獻) Mandelbrot, B. and Hudson, R. L. (2004). The (Mis)behavior of Markets. Basic Books.zh_TW
dc.relation.reference (參考文獻) Murray, C. (1998). Income Inequality and IQ. Washington: AEI Press. Available at http://www.aei.org/books/filter.all,bookID.443/book detail.asp.zh_TW
dc.relation.reference (參考文獻) Murray, C. (2002). IQ and income inequality in a sample of sibling pairs from advantaged family backgrounds. American Economic Review, 92(2):339–343.zh_TW
dc.relation.reference (參考文獻) Payne, J. W., Bettman, J. R., and Johnson, E. J. (1993). The Adaptive Decision Maker. Cambridge University Press.zh_TW
dc.relation.reference (參考文獻) R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.zh_TW
dc.relation.reference (參考文獻) Ram, R. (2007). IQ and economic growth: Further augmentation of Mankiw-Romer-Weil model. Economic Letters, 94:7–11.zh_TW
dc.relation.reference (參考文獻) Rubinstein, A. (1986). Finite automata play the repeated prisoner`s dilemma. Journal of Economic Theory, 39(1):83–96.zh_TW
dc.relation.reference (參考文獻) Rust, J., Miller, J., and Palmer, R. (1994). Characterizing effective trading strategies: Insights from a computerized double auction tournament. Journal of Economic Dynamics and Control, 18:61–96.zh_TW
dc.relation.reference (參考文獻) Rydval, O. and Ortmann, A. (2004). How financial incentives and cognitive abilities affect task performance in laboratory settings: An illustration. Economic Letters, 85:315–320.zh_TW
dc.relation.reference (參考文獻) Smith, V. L. (1991). Experimental economics: Behavioral lessons for microeconomic theory and policy. 1990 Nancy Schwartz Lecture, KGSM, Northwestern University.zh_TW
dc.relation.reference (參考文獻) Taniguchi, K., Nakajima, Y., and Hashimoto, F. (2004). A report of U-Mart experiments by human agents. In Shiratori, R., Arai, K., and Kato, F., editors, Gaming, Simulations, and Society: Research Scope and Perspective, pages 49–57. Springer.zh_TW
dc.relation.reference (參考文獻) Thaler, R. H. (2000). From homo economicus to homo sapiens. Journal of Economic Perspectives, 14(1):133–141.zh_TW
dc.relation.reference (參考文獻) Wolpert, D. and Macready, W. (1995a). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.zh_TW
dc.relation.reference (參考文獻) Wolpert, D. and Macready, W. (1995b). No free lunch theorems for search. Technical Report 95-02-010, Santa Fe Institute.zh_TW
dc.relation.reference (參考文獻) Zhan, W. and Friedman, D. (2007). Markups in double auction markets. Journal of Economic Dynamics and Control, 31:2984–3005.zh_TW