Sciences of complexity and studies of evolutional simulation of regional spatial structure
Received date: 2001-09-28
Revised date: 2001-11-12
Online published: 2011-01-27
During the last two decades, a lot of innovations have appeared in the field of urban and regional research. New paradigms and approaches such as dynamics of complex systems, self-organization, evolution theory, have been recognized for better understanding the evolutional process of regional spatial structure. It can be seen as a cumulative and aggregated order which results from numerous locally made decisions. Therefore the basic force driving the evolution of regional system is inherently microscopic. Regional system is an evolving complex system which grows from simple to intricacy. Inspired by the concept of biology, regional system also evolves into a complex, multiplex and vitality state by certain natural selection and adaptation. The understanding that the region is a complex adaptive system (CAS) means that microscopic simulation emphasizing the way in which locally made decisions and interaction between all kinds of local agents such as households and enterprises give rise to global patterns is highly appropriate. The methodology of CAS model is a part of theory of CAS. The CAS such as urban and regional system is conceived as societies of autonomous agents who are able to act both on themselves and on their environments. The general behavior of the regional spatial evolution is produced by the combination of actions of the households and enterprises. The determinants of an agent's behavior have a local character and there is no global constraint on the system's evolution. These agents can adapt to other agents and environment continuously by learning from their own experience. The classifier system is a good learning algorithm for representation of the agent's adaptation. Therefore, it is a good alternative way of simulating the evolutional process of the regional spatial structure by modeling behaviors of these local active agents and their interactions. It is easy to build and understand the CAS model. The CAS model can overcome the limit of perfect rationality by introducing learning algorithm and integrate any qualitative or quantitative description of an agent, whose behavior may be very complicated. The flexible modeling method allows for a much more detailed representation of spatial interactions and of some local properties and also makes it possible to introduce new agents or new rules in the model without changing the other parts. This paper basically reviews the simulating ideas and methodology aiming at two types of traditionally modeling strategy on the study of regional spatial evolution, in addition, primarily introduces the theory of complex adaptive system, one of the most important achievements of studies of complexity, and besides, discusses the general characteristics of the region as a complex adaptive system, expounds the technical problem of regional simulation based on the CAS and the original idea of the CAS model.
Key words： region; CAS; agent; model; simulation
XUE Ling, YANG Kai-zhong . Sciences of complexity and studies of evolutional simulation of regional spatial structure[J]. GEOGRAPHICAL RESEARCH, 2002 , 21(1) : 79 -88 . DOI: 10.11821/yj2002010010
[1] Allen P M.Cities and regions as evolutionary,complex systems.Geographical S ystems,1997,4:103～130.
[2] Harris B,Wilson A G.Equilibrium values and dynamics of attractiveness terms in production-constrained spatial interac-tion models.Envi ron ment and Planni ng A,1978,10:371～388.
[3] Haag G,Weidlich W.A stochastic t heory of interregional migration.Geographical A nalysis,1984,16:331～357.
[4] Harris B,Wilson A G.Equilibrium values and dynamics of attractiveness terms in production-constrained spatial interaction models.Envi ron ment and Planni ng A,1978,10:371～388.
[5] Clarke M,Wilson A G.The dynamics of urban spatial structure:progress and problem.Journal of Regional Science,1983,23:1～18.
[6] Beaumont J R,Clarke M,Wilson A G.The dynamics of urban spatial structure:some exploratory results using difference equations and bifurcation t heory.Envi ron ment and Planni ng A,1981,13:1473～1483.
[7] Camagni R,Diappi L,Leonardi G.Urban growt h and decline in a hierarchical system:a supply2oriented dynamic ap-proach.Regional Science and U rban Economics,1986,16:145～160.
[8] Krugman P.The Self-organizing Economy.New York:Blackwell Oxford,1996.
[9] Fujita M,Krugman P,Venables A J.The spatial economy:cities,regions,and international trade.Cambridge,Mas-sachusetts:The MIT Press,1999.
[10] Bura S etc.Multiagent systems and t he dynamics of a settlement system.Geographical A nalysis,1996,28:161～178.
[11] Benenson I.Muti-agent simulation of residential dynamics in t he cit y.Com puter,Envi ron ment and U rban S ystems,1998,22:25～42.
[12] Haag G,Dendrinos D S.Toward a stochastic dynamical t heory of location:a non-linear migration process.GeographicalA nalysis,1983,15:269～286.
[13] Dendrinos D S,Haag G.Toward a stochastic dynamical t heory of location:empirical evidence.Geographical A nalysis,1984,16:287～300.
[14] Dendrinos D S,Mullally H.Urban evolution:studies in t he mat hematical ecology of cities.Oxford:Oxford Universit yPress,1984.
[15] Orishimo I.An approach to urban dynamics.Geographical A nalysis,1987,3:200～210.
[16] Zhang W B.Stabilit y versus instabilit y in urban pattern formation.Socio2S patial Dy namics,1990,1:41～56.
[17] Papageorgiou Y Y.On sudden urban growt h.Envi ron ment and Planni ng A,1980,12:1035～1050.
[18] Mees A I.The review of cities in Medieval Europe:a application of catastrophe t heory.Regional Science and U rban Economics,1975,5:403～425.
[19] Batt y M,Xie Y.From cells to cities.Envi ron ment and Planni ng B,1997,21:531～548.
[20] Batt y M.New ways of looking at cities.N at ure,1995,377:574.
[21] Couclelis H.Cellular worlds:a framework for modeling micro2macro dynamics.Envi ron ment and Planni ng A,1985,17:585～596.
[22] Couclelis H.From cellular automata to urban models:new principles for model development and implementation.Environ ment and Planni ng B,1997,24:165～174.
[23] White R,Engelen G.Cellular automata as t he basis of integrated dynamic regional modeling.Envi ron ment and Planni ng B,1997,24:235～246.
[24] 周成虎,孙战利.地理元胞自动机研究.北京:科学出版社,1999.
[25] Ward D P,Murray A T,Phinn S R.A stochastically constrained cellular model of urban growt h.Com puters,Environ ment and U rban S ystems,2000,24:539～558.
[26] White R,Engelen G.Cellular automata and fractal urban form:a cellular modeling approach to t he evolution of urbanland2use patterns.Environment and Planning A,1993,25:1175～1189.
[27] Mandelbrot B B.The Fractal Geometry of Nature.San Francisco:Freeman,1982.
[28] Arlinghaus S L.The fractal t heory of central place geometry.Geographical A nalysis,1989,21:104～121.
[29] 陈彦光,等.土地利用结构和形态的定量描述:从信息熵到分数维.地理研究,2001,20(2):146～152.
[30] Batt y M,Longley P.Fractal cities,a geometry of form and function.London:Academic Press,1994.
[31] Holland J H.Adaptation in natural and artificial systems:an introductory analysis wit h applications to biology,control,and artificial intelligence.Cambridge Massachusetts:The MIT Press,1992.
[32] Holland J H.Complex adaptive system.Boston:Winter,1992.
[33] Holland J H.Hidden order:how adaptation builds complexity.Massachusetts:Addison2Wesley,1995.
[34] 冯珊,唐超,闵君,等.用于复杂系统建模与仿真的面向智能体技术.管理科学学报,1999,2(2):71～76.
[35] 史忠植.高级人工智能.北京:科学出版社,1998.212～248.
[36] Miller J H,Holland J H.Artificial adaptive agents in economic t heory.The A merican Economic Review,1991,81:265～271.
[37] Booker L B,Goldberg D E,Holland J H.Classifier system and genetic algorit hms.A rtif icial I ntelli gence,1989,40:235～282.
[38] 陈述彭,等.地理系统科学与地理信息科学.地理研究,1996,15(2):1～5.
[39] 杨开忠,沈体雁.试论地理信息科学.地理研究,1999,18(3):260～266.
/
| 〈 |
|
〉 |