基于复杂适应系统的我国石油开发政策模拟与仿真
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摘要
2002年来,我国石油对外依存度不断攀升,而国内石油产量难以明显提高,石油行业发展面临诸多方面问题,为了促使石油行业快速发展,需要我国石油开发政策不断调整,以促进我国能源经济结构的快速转型。因此开展石油开发政策研究,对提高国内石油资源开发效率和石油生产供应能力,增强石油安全,具有重大战略意义。
     本论文除了运用传统的宏观定性研究以外,还用了定量分析和系统的观点和方法来讨论石油开发政策体系。在研究复杂适应系统的理论并分析石油开发政策体系与复杂适应系统之间映射关系的基础上,探讨我国石油资源开发政策体系。建立基于复杂适应系统的石油开发系统模型,利用复杂适应系统模拟仿真平台Swarm,对石油开发政策下各类主体的行为以及政策作用效果进行模拟和仿真。研究石油开发政策中的各类主体及其行为规则,观察和研究不同石油开发政策体系下各类主体的微观行为和变化规律,分析我国政府石油开发政策的宏观调控是如何引起石油开发系统中主体的微观行为或规则的变化,探索个体的、局部的行为或规则是如何引起整体的、宏观的涌现现象,探索微观行为或规则与宏观现象或规律之间的纽带关系,为石油行业决策者制定合理的石油开发政策提供一定的参考依据。
     通过分析不同的仿真实验结果发现,政府资源税的改变对生产商开发石油的积极性影响较大,进而影响到经销商和石化公司的经营;而增值税的改变对参与市场的经销商和石化公司影响较大,对生产商影响相对较小,说明经销商和石化公司的“抗竞争力”相对生产商较弱;适当增加石化公司主体数量,增大成品油供给量或储备量,对成品油价格过快上涨有一定调控作用,同时能看出石化公司的利润也比较微博。因此,政府如果能适当放松经销商和石化公司的“准入”条款,让更多的主体参与市场,并根据石油产业中不同业务性质实行区别对待的管制政策,是保证石油供需相对平衡,控制油价过快上涨的一种行之有效的方法,也是建立一个稳定、公平竞争和健康发展石油市场的调控政策之一。
     本论文建立了一个研究石油开发政策体系从微观迈向宏观世界的桥梁,希望此研究能为今后进一步的定量分析提供一种比较新颖的方法和研究基础。
From 2002, China's dependence on import oil is rising, and it is difficult to significantly increase domestic oil production, so oil industry faces many problems. It is needed that China's oil development policies are constantly adjusted in order to promote the rapid development of oil industry and transformation of energy structure. Therefore, researching oil development policy is of great strategic significance to improving the efficiency of developing domestic oil, the capacity of oil production and supply and enhancing oil security.
     The thesis also used a quantitative analysis and system point of view to discuss oil development policy system in addition to the traditional macro-qualitative method. China's oil development policy system is studied on the basis of researching the theory of complex adaptive system and analyzing the mapping relations between China's oil development policy system and complex adaptive system.Then a model of oil development is established using simulation platform Swarm of complex adaptive system to simulate the behavior and rules of different agents and the main effect of oil development policy. It are analyzed and studied how the government's macro-control changes the micro behavior and rules and according to observing the changes of the microscopic behavior under different types of oil development policy. It are explored how the government's macro-control causes the changes of agents' micro behavior or rules and how the individual micro-behavior or rules cause the whole macro emergence phenomena and the ties between the micro behavior or rules and the macro phenomena or the law. All these provide a frame of reference for the oil industry making reasonable oil development policy.
     The analysis of different experimental results showed that the change of government resources tax have large influence on manufacturers’activation of developing oil, thereby affecting the business of dealers and petrochemical companies;And value-added tax have larger influence on manufacturers’activation of developing oil, thereby affecting the business of dealers and petrochemical companies;And tax impacts largely dealers and petrochemical companies in the market participation, and relatively smaller impact on manufacturers.This distribute to the dealers and petrochemical companies’"anti-competitive" weaker capacity compared with the manufacturers’.Increasing the number of petrochemical companies and oil supply or reserves is a certain regulatory role for oil prices rising too fast, and the micro profits of the petrochemical company can be seen at the same time. Therefore, it is an effective method to ensure relative balance of oil supply and demand, to control rapid rise in oil prices and one of regulation policies establish a stable, fair competition and healthy developing oil market that and the government appropriately relaxes "access" provisions for dealers and petrochemical companies, let them participate in the market more, and treats different business regulatory policies according to the nature of the different implementation in oil industry.
     This thesis has established a bridge to study the system of oil development from the micro into the macro world. Wish this research provide a relatively new method and research base for further quantitative analysis in future.
引文
Albert G Huizing, Awel A F Bloemen,1996.An Efficient Scheduling Algorithm for a Multifunction Radar[C]//IEEE
    International Symposium on Phased Array System and Technology.1996.USA: IEEE: 359-364.
    Arthur W. Brian, 1999. Complexity and the Economy. Science. 284(2).
    Arthur W.Brian.The end of certainty in Economics. 1994,http://www.econ.iastate.edu/tesfatsi.
    Arthur W. Brian.Reasoning and Bounded Rationalit,1994.http://www.econ.iastate.edu/tesfatsi.
    Basu N, Pryor R, Quint.T.ASPEN: A microsimulation model of the economy[J].Computational Economics,1998(12):223-241.
    Beroggi, Giampiero E G.Visual-interactive decision modeling (VIDEMO) in policy management: Bridging the gap between analytic and conceptual decision modelling [J].European Journal of Operational Research, 2001,128(2): 338-350.
    Chi-Sheng Shih,Sathish Gopalakrishnan,2003.Scheduling Real-Time Dwell Using Tasks with Synthetic Periods[C]//Proceeding of the 24th IEEE International Real-Time System Symposium.USA:IEEE,2003.
    C.Langton, N.Minar, R.Burkhart.The swarm simulation system: a tool for studying complex systems. Santa Fe Institute Working Paper. 1995.
    DeguchiH.Economics as an Agent-Based Complex System [M].Tokyo, Berlin, Heidelberg&NewYork: Springer-VerlagPress, 2004.
    D.J.Pedregal, O.Dejua′n. Modelling demand for crude oil products in Spain. Energy Policy, 2009, (37):4417-4427.
    Felipe J, Fisher FM,1986.Aggregation in Production Functions: What Applied Economists Should Know [J]. Metroeconomica, 54(2-3): 208-62.
    F.Urban, R.M.J.Benders, H.C.Moll, 2007. Modelling energy systems for developing countries. Energy Policy, (35):3473-3482.
    Gang Li, Hongjiao Yang, Linyan Sun, et al. The evolutionary complexity of complex adaptive supply networks: A simulation and case study [J]. Int. J. Production Economics, 2010, 124:310–330.
    Gilbert GN, Troitzsch KG, 1999.Simulation for the Social Scientist [M].Buckingham: OpenUniversity Press.
    Greenwald B C,Stiglitz JE.Externalities in Economieswith Imperfect Information and Incomplete Markets [J]. Quarterly Journal of Economics, 1986,101(2): 229-264.
    Hertel T W. Global Trade Analysis: Modeling and Applications [M]. Cambridge, New York&Melbourne: Cambridge University Press, 1997.
    Holland J.H. Hidden Order, 1995.How Adaptation Builds Complexity [M]. Reading, MA: Addison-Wesley PublishingCompany, Inc.
    http://www.cinic.org.cn/site951/hgpd/2010-09-26/433171.shtml
    http://www.sinopecnews.com.cn/shnews/content/2010-09/10/content_860417.htm
    Hubbert M K. Energy Resources. National Academy of Sciences. National Research Research Councio.Washionton D. C.1962.
    Hubbert M K. Degree of advancement of petroleum exploration in the United States[J]. AAPG Bulletin, 1967, 52(11):2207-2227.
    Hubbert M K. Energy from fossil fuels[J]. Science, 1949, 109(2823): 103-109.
    http://www.chinamining.com.cn/news/listnews.asp?classid=154&siteid=305043
    Iba T, Hirokane M, Takenaka H, eta1. Boxed economy model:fundamental concepts and perspectives[C].First International Workshop on Computational Intelligence in Economics and Finance(CIEF’2000). Atlantic City, New Jersey, USA: Association for Intelligent Machinery, 2000: 941-944.
    I.Dineer, M.M.Hussain.Energy and exergy utilization in agricultural sector of Sandi Arabia[J]. Energy Policy, 2005, (33):1461-1467.
    I.Dineer, M.M. Hussain, I.AI-Zaharnah.Energy and exergy use in public and private sector of Saudi Arabia [J].Energy Policy, 2004, (32):1615-1624.
    IOANNIS N, ATHANASIADIS R.A hybrid agent-based model for estimating residential water demand [J].Simulation, 2005,81(3):175-187.
    JStephen Lansing. Complex adaptive systems [J]. Annual Review of Anthropology. 2003.32:183-183.
    Kirman A P.Whom or What Does the Representative Individual Represent? [J]. Journal of Economic Perspectives, 1992,6 (2):117-136.
    Larson B A, Scatasta S. Modeling the impacts of environmental policies on agricultural imports[J].Journal of Policy Modeling, 2005, 27(5);565-574.
    Laherrère JH. TheHubbert curve: its strengths and weaknesses[J]. Oil&Gas Journal,2004, 98(16): 63
    Lee D. Hoffer, Georgiy Bobashev,Robert J. MorrisResearching a Local Heroin Market as a Complex Adaptive System[N]. Am J Community Psychol, 2009,44:273–286.
    LempertR.Agent-based Modeling as Organizational and Public Policy Simulators [J]. Proceedings of the National Academy of Sciences, 2002,99(3): 7195-7196.
    Macy M W, R Willer.From Factors toActors: Computational Sociology and Agent-BasedModeling [J]. AnnualReview ofSociol-ogy, 2002,28: 143-166.
    Manson, Steven M. Simplifying Complexity: A Review of Complexity Theory [J]. Geoforum, 2001, 32: 405-414.
    Marcenac.P.Emergence of Behaviors in Natural Phenomena Agent-Simulation[C].in Proceedings of the Third International Conference on Complex Systems:From Local Interactions to Global Phenomena,Eds.R.Stocker,H.Jelinek,Bohdan Durnota and T.Bossomaier,Albury,Australia,1996:284-289.
    McCauley JL.The Futility of Utility: How Market Dynamics MarginalizeAdam Smith [J]. PhysicaA, 2000,285: 506-538.
    Moss. Policy Analysis from First Principles [J]. Proceedings of the National Academy of Sciences, 2002, 99(3): 7267-7274.
    Neil Strachan, Steve Pye, Ramachandran Kannan.The iterative contribution and relevance of modelling to UK energy policy. Energy Policy, 2009 (37):850–860.
    Nijkamp P, Wang Shunli, Kremers H.Modeling the impacts of international climate change policies in a CGE context: The Use of the GTAP-Emodel [J].Economic Mdoeling,2005,22(6):955-974.
    Nwaobi G C. Emission policies and the Nigerian economy: simulations from a dynamic applied general equilibrium model [J].Energy Economics, 2004, 26(5):921-936.
    O' Looney J.Sprawl decisions: A simulation and decision support tool for citizens and policy makers [J].Government Information Quarterly, 2001, 18(4):309-327.
    Othman Natheer, William W. Dougherty, David Von Hippel.Corrigendum to“Environmental benefits of energy effieiency and renewabl eenergy in Saudi Alabia’s electric seetor’’[J].EnergyPoliey, 2006,(34):2-10.
    Paresh Kumar Narayan,Seema Narayan.Modelling oil price volatility. Energy Policy, 2007, (35):6549-6553.
    Paul Johnson, Alex Lancaster. Swarm User Guide [DB/OL]. 1999.2.Erie Bonabeau. The building behavior of lattice swarms. Artificial Life IV. 1995.
    Rahul Pandey.Energy policy modelling: agenda for developing countries.Energy Policy, 2002, (30) :97-106.
    Reynolds.CW. Flocks, Herds, Schools. A Distributed Behavioral Model[A]. Proceeding of SIGGRAPH’87[C]. Computer Graphics, 1987, 14(4): 102-109.
    R.A.DeSantis.Crude oil Price fluetuations and SaudiAiabia’s ehaviour [J].Energy Eeonomies, 2003, (25):155-173.
    S.A.AI-Ajlan, A.M.AI-Ibrahim,M. Abdulkal eq, F.Alghamdi.Developing sustainable energy policies for electric conservation in SandiArabia[J].Energy Policy, 2006, (34):1556-1565.
    Ste′phane De′esa,Pavlos Karadeloglou, 2007.Modelling the world oil market Assessment of a quarterly econometric model[J]. Energy Policy, (35):178-191.
    Tesfatsion L, 2003. Agent-based Computational Economics, ISU Economics working paper. http://www.econ.iastate.edu/tesfatsi.
    The swarm development group.Swarm[DB/OL],http://wwww.swarm.org.Paul Johnson,Alex Lancaster. Swarm User Guide [DB/OL]. 1999.
    VLADIMIR S,KORITAROV N.Modeling the electricity market as a complex adaptive system with an agent-based approach[J].IEEE Power and Energy Magazine, 2004,23(4):39-46.
    白世贞,郑小京.基于三层-回声模型的供应链复杂适应系统资源流研究[J].中国管理科学,2007,15(2):111-120.
    毕贵红,王华,李强,侯燕.基于Agent的居民环境行为动态演化与政策仿真模型[J]. 广西师范大学学报:自然科学版,2008,26(1):198-202.
    陈元千.油气藏工程实用方法[M].北京:石油工业出版社.1996:3-4.
    陈元千,胡建国,张栋杰.Logistic模型的推导及应用[J].新疆石油地质,1996,17(2):150-155.
    陈元千.广义翁氏预测模型的推导与应用[J].天然气工业,1996,16(2):22-26.
    陈元千,胡建国.对翁氏模型原建模的回顾及新的推导[J].中国海上油气(地质),1996,10(5):317-324.
    陈元千,田建国.哈伯特二次函数的推导与应用[J].新疆石油地质, 1998, 19(6): 502-506.
    陈禹.复杂适应系统理论(CAS)及其应用——由来、内容与启示[J].系统辨证学学报, 2001,9(4): 35-39.
    丁煌.发展中的中国政策科学——我国公共政策学科发展的回眸与展望[J].管理世界,2003(2):28—38.
    邓宏钟.基于多智能体的整体建模仿真方法及其应用研究[D],国防科技大学,2002.
    冯连勇,赵林,赵庆飞,王志明.石油峰值理论及世界石油峰值预测[J].石油学报,2006,27(5):139-142.
    高宝俊,戴辉,宣慧玉.应用基于Agent的股票市场仿真:个体行为对市场、政策效果的影响.系统工程理论方法应用,2005,14(6): 497-501.
    高铁梅,赵振全,韩东梅,等.我国宏观经济计量模型及政策模拟分析[J].中国软科学,2000(08):114-120.
    郭鹏,薛惠锋,赵宁.基于复杂适应系统理论与CA模型的城市增长仿真[J].地理与地理信息科学,2004,20(6):69-72.
    郝柏林.复杂性的刻画与复杂性科学[J].科学, 1999,51(3):3-8.
    胡建国,陈元千,张盛宗.预测油气田产量和可采储量的新模型[J].石油学报,1995,16(1):79-86.
    何云景,武杰:构建复杂适应的创业支持系统[J].系统科学学报, 2007, 15 (3):42-46.
    侯合银.复杂适应系统的特征及其可持续发展问题研究[J].系统科学学报,2008,16(4):81-85.
    黄季煜,李宁辉.中国农业政策分析和预测模型——CAPSiM [J].南京农业大学学报(社会科学版),2003(02):33-44.
    黄天辰,韩京才.基于Agent技术的复杂适应系统分析与建模[J].计算机仿真,2005,22(9).
    金艳鸣,雷明,黄涛.环境税收对区域经济环境影响的差异性分析[J].经济科学,2007,3: 104-111.
    李宏亮.基于Agent的复杂系统分布仿真[D].国防科技大学计算机学院博士学位论文,2001.10.
    李允,刘志斌.现代优化技术在油田开发中的应用[M].北京:石油工业出版社, 2000.
    李晓宁,于洪敏,张华才.复杂适应系统理论及军事应用[J].兵工自动化, 2007,26(11):102-103.
    李志刚,傅泽田,郑志安,李玲.基于CGE模型的政策模拟系统的研究[J].中国农业大学学报,2006,11(5),98-102.
    廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方法[J].系统仿真学报,2004,16(1):113-117
    刘聪,曾建潮,王宏刚.基于复杂适应系统的食物链建模与仿真[J]. 系统仿真学报,2009,21(2):538-541.
    刘颖,陈禹.复杂适应系统理论对控制SARS疫情的模拟分析[J].复杂系统与复杂性科学, 2004,1(2):74-79.
    刘兴华.复杂适应系统及证券市场动力学机制[J].山东经济,2006,(5):43-46.
    罗平,耿继进.深圳市房地产系统仿真及政策实验研究.热带地理,2004,24(4).
    廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方[J], 2004,16(1):113-117.
    廖守亿,戴金海.基于多Agent的天战系统建模与仿真方法研究[J].计算机仿真,2003,1.
    米歇尔·沃尔德罗普.复杂:诞生于秩序与混沌边缘的科学[M].陈玲,译.北京:三联书店,1997:115,390-426.
    苗东升.复杂性研究的现状与展望[J].系统辩证学学报, 2001,9(4): 3-9.
    苗东升.复杂性研究的现状与展望[J].系统辩证学学报,2001,9(4).
    彭敏晶,林健.基于遗传Data Farming的公共政策优化仿真模型.系统仿真学报,2008,20(21):5963-5966.
    任昊利,李新明,2008.基于复杂适应系统理论的电子信息[J].装备体系模型研究装备指挥技术学院学报,19(5):89-92.
    石丽,李坚.数据仓库与决策支持[M].北京:国防工业出版社,2003.
    申万万,曾建潮,谭瑛,姜旭.基于复杂适应系统的群体组织形成模型及其模拟[J].复杂系统与复杂性科学,2007,4(3):78-86.
    孙世岩,刘忠,刘健.复杂适应系统理论与计算机作战模拟[J].计算机仿真,2003,20(12).
    尹春华,方福康.复杂适应系统Internet中的网络流量仿真研究.系统工程学报,2005,20(2):139-142.
    吴彤.复杂性范式的兴起[J].科学技术与辩证法, 2001,18(6):20-24.
    王潼.我国积极财政政策简明仿真模型[J].中国软科学,2003,(8):34-37.
    王海燕,刘鲁,杨方廷.基于SD的粮食预测和政策仿真模型研究[J].系统仿真学报,2009,21(10):3079-3083.
    王韬,周建军.我国进口关税减让的宏观经济效应-可计算一般均衡模型分析[J].系统工程,2004,22(2): 38-45.
    王冰.复杂网络的演化机制及若干动力学行为研究[D].大连理工大学博士学位论文, 2006.
    王文芳.复杂适应系统演化探究-基于Agent技术的分析[D],华南师范大学博士学位论文,2003.
    王霞,沈西挺,孙石磊.基于平台的电信客户忠诚度建模与仿真研究.河北工业大学学报,2007,36(6) :53-56.
    王铮.中国国家环境经济安全政策模拟分析[M].北京:科学出版社,2004
    许国志.系统科学[M].上海:上海科技教育出版社,2000: 252.
    许萍,刘洪.复杂适应系统观的组织变革[J].复杂系统与复杂性科学, 2007,4(2):18-24.
    杨城,谢志龙,2009.复杂适应系统的多层级建模研究[J].计算机工程,35(22):244-247.
    姚莉.多Agent计算组织及其建模方法研究[J].广西师范大学学报:自然科学版,2003,21(1):67-73.
    约翰·霍兰.隐秩序:适应性造就复杂性[M].周晓牧,韩晖,译.上海:上海科技教育出版社, 2000
    约翰·霍兰.涌现:从混沌到有序[M].陈禹,译.上海:上海科学出版社, 2001: 3-4
    朱爱平,吴育华.试论复杂适应系统与企业管理研究的创新发展[J].科学管理研究,2003,21(4): 63-66.
    朱杰,柳广弟,刘成林,车长波.基于多峰高斯模型的石油储量与产量预测.中国石油大学学报(自然科学版),2009,33 (3):45-48.
    朱景伟,张世明.电子政务系统的复杂适应系统建模[J].微型电脑应用,2009,25(9):60-65.
    朱诗兵,陈刚等.基于复杂适应理论的电子信息系统模型构建.科学技术与工程,2009,9(23):7044-7049.
    朱晔.复杂适应系统软件平台SWARM在金融体系中的博弈仿真研究:[硕士学位论文].泉州:华侨大学,2002.
    张世伟.基于主体的宏观经济微观模拟模型[J].财经科学,2004(1):74-78.
    张起花.资源税改探路新建开发[J].中国石油石化,2010(12):16-18.
    张抗.油气田生命周期和战术战略接替[M].北京:地质出版社,2000. 163-164.
    郑玉歆,樊明太,马纲.中国CGE模型及政策分析[M].北京:社会科学文献出版社,1999.
    赵志刚,熊熊,张小涛.基于主体建模的金融政策仿真研究及其应用.软科学,2009,23(7).
    赵晓哲,郭锐,杜河建.复杂适应系统理论与信息化战争研究.军事运筹与系统工程,2005,19(2):3-7.

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