Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/61516
|
Title: | 以代理人基模型模擬的施與受賽局 Agent-Based Model Simulation of Donor-Recipient Game |
Authors: | 曾嘉瑤 Tseng, Chia Yao |
Contributors: | 馬文忠 陳樹衡 Ma, Wen Jong Chen, Shu Heng 曾嘉瑤 Tseng, Chia Yao |
Keywords: | 代理人基模型 施與受賽局 社會規範 非同步 羅吉斯分佈 波茲曼分佈 零度模擬 波茨模型 Agent-based model Donor-Recipient game Social norms Asynchronous logistic distribution Boltzmann distribution zero-temperature simulations Potts Model |
Date: | 2012 |
Issue Date: | 2013-11-01 11:48:42 (UTC+8) |
Abstract: | 人類合作造成的社會影響與個人影響是社會科學的一個重要問題。最近提出了一個動態可調整的合作策略和不同聲譽的衡量規範的個人社會模型 [1]。為了將平均場分析結果作進一步解析,我們以代理人基模型進行電腦計算模擬。在模型中的每一位代理人實施的策略調整,均由社會學習模式來決定,類似在Potts模型下Metropolis能量驅動的狀態轉換。在施與受賽局演化模型中,社會由許多代理人組成,每一個代理人會隨機遇到另一個代理人,雙方共同合作,構成捐贈方及受援方的成對組合。在給定捐贈方的策略及受援方的評價後,捐贈方每一回合遊戲可以採取合作或不合作,與加入懲罰的三種策略。在遊戲試驗進行中,根據各種策略已經給定合作的交易成本與收益以及懲罰的成本與損失,在每一回合遊戲進行結束後,捐贈方將被重新給定評價,並且計算全體代理人的財富變化。在連續進行的遊戲中,代理人會根據每個代理人與社會群體的財富變化,產生知識累積的學習模式,作為策略轉換權數的基準。在以類比於自旋翻換模型於溫度零度的模擬下,我們對此社會模型代理人策略採取的演化模式,得到了一些初步觀察的結果。使用代理人基模型模擬三種社會規範:簡單社會規範(無懲罰的社會規範),弱懲罰社會規範(允許懲罰的社會規範)與強懲罰的社會規範(加強懲罰的社會規範)與平均場理論作初步比較。模擬結果得出與原先平均場理論一致的結論:主要解與第一次要解均相同,懲罰將促進合作,並在強懲罰社會規範下存在第二次要解。 在代理人基模型的各代理人是以社會學習模式後採取更新策略。社會學習與個人學習的差異在於,每一位代理人賽局累積經驗作為學習的樣本來自於社會全體代理人還是只有自己。在賽局中各代理人的所得與財富將依照代理人在每一回合賽局中的身份與策略產生變動,對此變動計數在分別以兩種模式:簡單平均(人數權重法)與權重平均(事件權重法)計算平均法得出,簡單平均法產生唯一主要解,權重平均法將差異保留,主要解與次要解共存。我們發現代理人基模型中最終狀態的次要解,除了可能以其所有成員都為好人的合諧社會的型式出現外,也可能是以極限軌道而非離散點的新型態出現。
[1] Tongkui Yu, Shu-heng Chen, Honggang Li, "Social Norm, Costly Punishment and the Evolution to Cooperation," 17th International Conference on Computing in Economics and Finance. The effects of human cooperation on the societies and on the individuals is an important issue in social science. The dynamics of a model society of individuals with adjustable cooperation strategies and with varying reputations gauged by social norms has been recently proposed [1]. In order to refine the mean-field type analysis, we implement the Agent-based model in computer simulations, where the strategy adjustment of each individual is determined by a social learning procedure. In between consecutive strategy changes, one individual encounters a partner in a Donor-Recipient game, which results in the wealth changes in both parties in form of cost, punishment or benefit and is followed by a reputation re-assignment to the donor, taking into account the strategy of the donor and the reputation of the recipient. The accumulated knowledge of wealth changes from sequences of games for all individuals in the society weighs the strategy change transitions. We obtain some primitive observations on the evolutions of strategies adapted by the individuals of the model society. Using the agent-based models to simulate three kinds of social norms: Simple social norm (punishment-free social norm), Weakly augmented social norm (punishment-optional social norm) and Strongly augmented social norm (punishment-provoking social norm). We try to compare the outcome of the agent-based model with the solutions of mean-field equation. The two methods are found to have unanimous results: they have the same the primary solution and the main secondary solution; punishment would promote cooperation and social norms in strong penalties exist under the second secondary solution. In contrast to the mean-field scenario, the players in the agent based model update their strategies asynchronously, based on the accumulated knowledge of wealth changes for players adapting each strategy. We distinguish the models of two modules of such knowledge, learned either by simple averages (player-weighted method) or by weighted averages (event-weighted method). In carrying out the zero-temperature analogy of spin-flipping simulation, we obtain some primitive observations on the strategy evolution of the agents. While all solutions of the mean field equations are consistently obtained in the latter case, only the primary solution is found for the former case in each social norm. It is found that a minor stable attractor may survive in the time evolution which are ported by harmonious societies, where all agents are reputed as “good”. In the time evolution, the competition between strategies may display the presence of dynamic orbits as the final domain of time evolution.
[1] Tongkui Yu, Shu-heng Chen, Honggang Li, "Social Norm, Costly Punishment and the Evolution to Cooperation," 17th International Conference on Computing in Economics and Finance. |
Reference: | Tongkui Yu, Shu-heng Chen, Honggang Li, "Social Norm, Costly Punishment and the Evolution to Cooperation," 17th International Conference on Computing in Economics and Finance. 2011
R.N. Mantegna, H.E. Stanley, “An Introduction to Econophysics: Correlations and Complexity in Finance”, Cambridge University Press, Cambridge, 1999.
H.E. Stanley, L.A.N. Amaral, D. Canning, P. Gopikrishnan, Y. Lee , Y. Liu, “Econophysics: Can physicists contribute to the science of economics?” Center for Polymer Studies, Department of Physics, Boston University, Boston, MA 02215, USA, Harvard Institute for International Development, Harvard University, Cambridge, MA 02138, USA, Received 3 May 1999
Y. Liu, P. Gopikrishnan, P. Cizeau, M. Meyer, C.-K. Peng, H.E. Stanley, Physical Review E 60 (1999) 1390-1400 (1990), “Statistical properties of the volatility of price fluctuations” Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215. Margret and H. A. Rey Laboratory for Nonlinear Dynamics in Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215. Received 22 February 1999; published in the issue dated August 1999
W.C. Zhou, H.C. Xu, Z.Y. Cai, J.R. Wei, X.Y. Zhu, W. Wang, L. Zhao, J.P. Huang*, “Peculiar statistical properties of Chinese stock indices in bull and bear market phases”. Department of Physics and Surface Physics Laboratory (National Key Laboratory), Fudan University, Shanghai, 200433, China, Physica A 388 (2009) 891-899
B.K. Chakrabarti, A. Chakraborti, and A. Chatterjee (Eds.), “Econophysics and Sociophysics: Trends and Perspectives” (Wiley VCH, Berlin, 2006).
B.K. Chakrabarti, A. Chatterjee, and Y. Sudhakar (Eds.), “Econophysics of Wealth Distributions” (Springer, Milan, 2005).
史建平,韓復齡《改革之路:金融證券與中國經濟》China, 2008.
L.A.N. Amaral, P. Cizeau, P. Gopikrishnan, Y. Liu, M. Meyer, C.-K Peng, H.E. Stanley. “Econophysics: can statistical physics contribute to the science of economics?” Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, USA. Computer Physics Communications 121-122 (1999) 145-152.
W. C. Zhou, H. C. Xu, Z. Y. Cai, J. R. Wei, X. Y. Zhu, W. Wang, L. Zhao, and J. P. Huang “Peculiar statistical properties of Chinese stock indices in bull and bear market phases ”Physica A volume 388, 891-899 (2009).
C. Ye and J. P. Huang “Non-classical oscillator model for persistent fluctuations in stock markets” Physica A volume 387, 1255 (2008).
Anirban Chakraborti, Ioane Muni Toke, Marco Patriarca, Frederic Abergel “Econophysics: Empirical facts and agent-based models” (Submitted on 10 Sep 2009 (v1), last revised 21 Jun 2010 (this version, v2))
John Von Neumann, and Oskar Morgenstern, “Theory of games and economic behavior”, Princeton, Princeton University Press, 1953.
Wei Wang, Yu Chen, and Jiping Huang, “Heterogeneous preferences, decision-making capacity, and phase transitions in a complex adaptive system”, Fudan University, Shanghai, China, 2009.
Robert Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton. Princeton University Press. 1997. ISBN 978-0-691-01567-5
Agent-Based Models of Industrial Ecosystems. Rutgers University, Oct. 2003.
Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences. May 2002.
Aoki, M. and Yoshikawa, H. (2012) Non-self-averaging in macroeconomics models: a criticism of modern micro-founded macroeconomics. Journal of Economic Interaction and Coordination 7(1): 1-22
Ormrod, J.E. (1999). Human learning (3rd ed.). Upper Saddle River, NJ: Prentice-Hall
Artino, A. R. (2007). Bandura, Ross, and Ross: Observational learning and the Bobo doll. (ERIC Document Reproduction Service No. ED499095)
Landau, Lev Davidovich; and Lifshitz, Evgeny Mikhailovich (1980) [1976]. Statistical Physics 5 (3 ed.). Oxford: Pergamon Press. ISBN 0-7506-3372-7.
------------------------------------------------------------------------------------------------------
Andreoni, J., Harbaugh, W., Vesterlund, L., 2003. The carrot or the stick:Rewards, punishments, and cooperation. The American Economic Review93 (3), 893–902.
Axelrod, R., 1984. The Evolution of Cooperation. Basic Books, New York.
Bergstrom, T. C., 2002. Evolution of social behavior: Individual and group selection. The Journal of Economic Perspectives 16 (2), 67–88.
Bochet, O., Page, T., Putterman, L., 2006. Communication and punishment involuntary contribution experiments. Journal of Economic Behavior and Organization 60 (1), 11–26.
Boehm, C., 1993. Egalitarian behavior and reverse dominance hierarchy. Current Anthropology 34 (3), 227.
Bowles, S., Gintis, H., 2004. The evolution of strong reciprocity: Cooperation in heterogeneous populations. Theoretical Population Biology 65 (1), 17–28.
Boyd, R., Gintis, H., Bowles, S., Richerson, P. J., 2003. The evolution of altruistic punishment. Proceedings of the National Academy of Sciences of the United States of America 100 (6), 3531–3535.
Dawid, H., 2007. Evolutionary game dynamics and the analysis of agent-based imitation models: The long run, the medium run and the importance of global analysis. Journal of Economic Dynamics and Control 31 (6), 2108–2133.
de Quervain, D. J.-F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., Fehr, E., 2004. The neural basis of altruistic punishment. Science 305 (5688), 1254–1258.
Dreber, A., Rand, D. G., Fudenberg, D., Nowak, M. A., 2008. Winners don’t punish. Nature 452 (7185), 348–351.
Egas, M., Riedl, A., 2008. The economics of altruistic punishment and the maintenance of cooperation. Proceedings of the Royal Society B: Biological Sciences 275 (1637), 871–878.
Falk, A., Fehr, E., Fischbacher, U., 2005. Driving forces behind informal sanctions. Econometrica 73 (6), 2017–2030. Fehr, E., Fischbacher, U., 2003. The nature of human altruism. Nature425 (6960), 785–791.
Fehr, E., Fischbacher, U., 2004. Social norms and human cooperation. Trends in Cognitive Sciences 8 (4), 185–190.
Fehr, E., Fischbacher, U., Gachter, S., 2002. Strong reciprocity, human cooperation, and the enforcement of social norms. Human Nature 13 (1), 1–25.
Fehr, E., Gachter, S., 2002. Altruistic punishment in humans. Nature415 (6868), 137–140.
Fehr, E., Simon, G., 2000. Cooperation and punishment in public goods experiments. The American Economic Review 90 (4), 980–994.
Fowler, J. H., 2005. Altruistic punishment and the origin of cooperation. Proceedings of the National Academy of Sciences of the United States of America 102 (19), 7047–7049.
Gachter, S., Herrmann, B., 2009. Reciprocity, culture and human cooperation: previous insights and a new cross-cultural experiment. Philosophical Transactions of the Royal Society B: Biological Sciences 364 (1518), 791–806.
Gintis, H., 2000. Strong reciprocity and human sociality. Journal of Theoretical Biology 206 (2), 169–179.
Gurerk, O., Irlenbusch, B., Rockenbach, B., 2006. The competitive advantage of sanctioning institutions. Science 312 (5770), 108–111.
Henrich, J., 2004. Cultural group selection, coevolutionary processes and large-scale cooperation. Journal of Economic Behavior and Organization53 (1), 3–35. Henrich, J., 2006. Cooperation, punishment, and the evolution of human institutions. Science 312 (5770), 60–61.
Henrich, J., Boyd, R., 2001. Why people punish defectors: Weak conformist transmission can stabilize costly enforcement of norms in cooperative dilemmas. Journal of Theoretical Biology 208 (1), 79–89, doi: DOI:10.1006/jtbi.2000.2202.
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., McElreath, R., Alvard, M., Barr, A., Ensminger, J., Henrich, N. S., Hill, K.,Gil-White, F., Gurven, M., Marlowe, F. W., Patton, J. Q., Tracer, D.,2005. “Economic man” in cross-cultural perspective: Behavioral experimentsin 15 small-scale societies. Behavioral and Brain Sciences 28 (06),795–815.
Henrich, J., Ensminger, J., McElreath, R., Barr, A., Barrett, C., Bolyanatz, A., Cardenas, J. C., Gurven, M., Gwako, E., Henrich, N., Lesorogol, C., Marlowe, F., Tracer, D., Ziker, J., 2010. Markets, religion, communitysize, and the evolution of fairness and punishment. Science 327 (5972),1480–1484.
Henrich, J., McElreath, R., Barr, A., Ensminger, J., Barrett, C., Bolyanatz,A., Cardenas, J. C., Gurven, M., Gwako, E., Henrich, N., Lesorogol, C., Marlowe, F., Tracer, D., Ziker, J., 2006. Costly punishment across humansocieties. Science 312 (5781), 1767–1770.
Herrmann, B., Thoeni, C., Gachter, S., 2008. Antisocial punishment across societies. Science 319, 1362–1367.
Hofbauer, J., Sigmund, K., 1998. Evolutionary Games and Population Dynamics. Cambridge University Press, Cambridge.
Knauft, B. M., Abler, T. S., Betzig, L., Boehm, C., Dentan, R. K., Kiefer, T. M., Otterbein, K. F., Paddock, J., Rodseth, L., 1991. Violence and sociality in human evolution [and comments and replies]. Current Anthropology32 (4), 391–428.
Marlowe, F. W., Berbesque, J. C., Barr, A., Barrett, C., Bolyanatz, A., Cardenas,J. C., Ensminger, J., Gurven, M., Gwako, E., Henrich, J., Henrich,N., Lesorogol, C., McElreath, R., Tracer, D., 2008. More ”altruistic” punishment in larger societies. Proceedings of the Royal Society B: Biological Sciences 275 (1634), 587–592.
Milinski, M., Rockenbach, B., 2008. Punisher pays. Nature 452 (7185), 297–298.
Nowak, M. A., 2006. Five rules for the evolution of cooperation. Science314 (5805), 1560–1563.
Nowak, M. A., Sigmund, K., 2005. Evolution of indirect reciprocity. Nature437 (7063), 1291–1298.
Ohtsuki, H., Iwasa, Y., 2007. Global analyses of evolutionary dynamics and exhaustive search for social norms that maintain cooperation by reputation. Journal of Theoretical Biology 244 (3), 518–531.
Ohtsuki, H., Iwasa, Y., Nowak, M. A., 2009. Indirect reciprocity provides only a narrow margin of efficiency for costly punishment. Nature 457 (7225),79–82. Olson, M., 1965. The Logic of Collective Action: Public Goods and te Theory of Groups. Harvard University Press, Cambridge, MA.
Oosterbeek, H., Sloof, R., van de Kuilen, G., 2004. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7 (2), 171–188.
Ostrom, E., 2000. Collective action and the evolution of social norms. The Journal of Economic Perspectives 14 (3), 137–158.
Ostrom, E., Walker, J., Gardner, R., 1992. Covenants with and without as word: Self-governance is possible. The American Political Science Review86 (2), 404–417.
Rockenbach, B., Milinski, M., 2006. The efficient interaction of indirect reciprocity and costly punishment. Nature 444 (7120), 718–723.
Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., Frith, C. D., 2006. Empathic neural responses are modulated by the perceived fairness of others. Nature 439 (7075), 466–469.
Taylor, C., Nowak, M. A., 2007. Transforming the dilemma. Evolution61 (10), 2281–2292.
Tesfatsion, L., 2001. Introduction to the special issue on agent-based computational economics. Journal of Economic Dynamics and Control 25 (3-4), 281–293.
Wu, J.-J., Zhang, B.-Y., Zhou, Z.-X., He, Q.-Q., Zheng, X.-D., Cressman, R.,Tao, Y., 2009. Costly punishment does not always increase cooperation. Proceedings of the National Academy of Sciences 106 (41), 17448–17451.
Young, H. P., 2008. Social Norms. Palgrave Macmillan, Basingstoke. |
Description: | 碩士 國立政治大學 應用物理研究所 99755006 101 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0997550061 |
Data Type: | thesis |
Appears in Collections: | [應用物理研究所 ] 學位論文
|
Files in This Item:
File |
Size | Format | |
006101.pdf | 8827Kb | Adobe PDF2 | 326 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|