Wirtualizacja przestrzeni – nowe kierunki aplikacji modelowania wieloagentowego w ekonomii przestrzennej
DOI:
https://doi.org/10.18778/0208-6018.346.01Słowa kluczowe:
modelowanie wieloagentowe, systemy informacji geograficznej, ekonomia miast, ekonomia przestrzennaAbstrakt
W związku z ogromnym postępem technologicznym przed naukami społeczno‑ekonomicznymi otworzyły się nowe płaszczyzny badań złożonych i nie do końca poznanych zjawisk. Jednym z podejść badawczych w tych obszarach jest tzw. modelowanie wieloagentowe (Agent‑Based Modeling) w połączeniu z danymi geograficznymi (GIS). Modelowanie wieloagentowe to metoda, w której budowane są złożone systemy składające się z autonomicznych jednostek (agentów). Między agentami zachodzą interakcje na poziomie mikro, których rezultatem jest ewolucja całego systemu na poziomie makro. Jednym z interesujących trendów modelowania wieloagentowego jest geosymulacja, czyli symulacja wieloagentowa osadzona w świecie wirtualnym, będącym odpowiednikiem realnej, fizycznej przestrzeni. Geosymulacja umożliwia zaawansowane i bardziej realistyczne badania na gruncie ekonomii przestrzennej, socjologii czy psychologii. Niniejszy artykuł pogłębia tę problematykę. Dokonano w nim identyfikacji i porównania dostępnych platform do symulacji wieloagentowej i wybrano trzy, które posiadają wsparcie dla danych geograficznych (GIS). Na tych platformach zaimplementowano dane GIS o zagospodarowaniu przestrzennym dla jednej z dzielnic Poznania. Dokonano również porównania funkcjonalności oprogramowania pod kątem trzech kryteriów: trudności programowania, funkcjonalności i współpracy z danymi GIS oraz dostępności materiałów szkoleniowych. Badania te stanowią wstępny etap opracowania złożonego, społeczno‑ekonomicznego systemu miejskiego, osadzonego w paradygmacie modelowania wieloagentowego.
Pobrania
Bibliografia
Abar S., Theodoropoulos G. K., Lemarinier P., O’Hare G. M. P. (2017), Agent Based Modelling and Simulation tools: A review of the state‑of‑art software, “Computer Science Review”, vol. 24, pp. 13–33.
Adamatti D.F., Dimuro G. P., Coelho H. (2014), Interdisciplinary Applications of Agent‑Based Social Simulation and Modeling, IGI Global, Hershey. DOI: https://doi.org/10.4018/978-1-4666-5954-4
Akerlof G. A., Yellen J. L. (1987), Rational Models of Irrational Behavior, “The American Economic Review”, vol. 77, no. 2, Papers and Proceedings of the Ninety‑Ninth Annual Meeting of the American Economic Association, pp. 137–142.
Ariely D. (2008), Predictably Irrational: The Hidden Forces That Shape Our Decisions, Harper‑Collins, New York.
Axelrod R., Hamilton W. D. (1981), The Evolution of Cooperation Science, “New Series”, vol. 211, no. 4489, pp. 1390–1396.
Axtell R., Epstein J. M. (1996), Growing Artificial Societies. Social Science from the Bottom Up, MIT Press, Cambridge. DOI: https://doi.org/10.7551/mitpress/3374.001.0001
Benenson I., Torrens P. M. (2006), Geosimulation: Automata‐Based Modeling of Urban Phenomena, John Wiley & Sons, Ltd., Sussex.
Berryman M. (2008), Review of Software Platforms for Agent Based Models. Technical report, https://pdfs.semanticscholar.org/a158/181431fbfd01765668dc1d08229072e982aa.pdf [accessed: 6.12.2019].
Blanchard O. (2018), On the future of macroeconomic models, “Oxford Review of Economic Policy”, vol. 34, issue 1–2, pp. 43–54
Borrill P. L., Tesfatsion L. (2010), Agent‑Based Modeling: The Right Mathematics for the Social Sciences?, Staff General Research Papers Archive, Iowa State University, Department of Economics, Ames.
Boyce D., Williams H. (2015), Forecasting Urban Travel: Past, Present and Future, Edward Elgar Publishing, Cheltenham–Northampton. DOI: https://doi.org/10.4337/9781784713591
Brock W. A., Hommes C. H. (1994), Heterogeneous beliefs and routes to chaos in a simple asset pricing model, “Journal of Economic Dynamics and Control”, vol. 22, issues 8–9, pp. 1235–1274.
Brunsdon Ch., Singleton A. (2015), Geocomputation: a practical primer, Sage Publications, Inc., London DOI: https://doi.org/10.4135/9781473916432
Crooks A., Castle C. J. E. (2012), The Integration of Agent‑Based Modelling and Geographical Information for Geospatial Simulation, [in:] A. Heppenstall, A. Crooks, L. See, M. Batty (eds.), Agent‑Based Models of Geographical Systems, Springer, Dordrecht, pp. 219–251. DOI: https://doi.org/10.1007/978-90-481-8927-4_12
Crooks A., Hudson‑Smith A., Patel A. (2011), Advances and Techniques for Building 3D Agent‑Based Models for Urban Systems, [in:] D. Marceau, I. Benenson (eds.), Advanced Geosimulation Models, Bentham Books, Hilversum, pp. 49–65.
Garretsen H., Martin R. (2010), Rethinking (New) Economic Geography Models: Taking Geography and History More Seriously, “Spatial Economic Analysis”, vol. 5, no. 2, pp. 127–160
Gershenson C. (2012), Complexity at large, “Complexity”, no. 18, pp. 1–4
Gilbert N. (2008), Agent‑based models, Sage Publications, Los Angeles–London–Delhi–Singapore.
Gilbert N., Troitzsch K. G. (1999), Simulation for the Social Scientist, Open University Press, Buckingham.
Haklay M., O’Sullivan D., Thurstain‑Goodwin M., Schelhorn T. (2001), “So go downtown”: simulating pedestrian movement in town centres, “Environment and Planning B: Planning and Design”, no. 28, pp. 343–359
Hamblen M. (2015), Just what is a smart city, “Computerworld”, https://www.computerworld.com/article/2986403/just-what-is-a-smart-city.html [accessed: 6.12.2019].
Heppenstall A. J., Crooks A. T., See L. M., Batty M. (2011), Agent‑Based Models of Geographical Systems, Springer, London–New York. DOI: https://doi.org/10.1007/978-90-481-8927-4
Luke S., Cioffi‑Revilla C., Panait L. (2005), MASON: A Multi‑Agent Simulation Environment, Department of Computer Science and Center for Social Complexity George Mason University, Fairfax. DOI: https://doi.org/10.1177/0037549705058073
Lynch K. (1960), The Image of the City, The MIT Press, Cambridge–London.
Lyu X., Han Q., Vries B. de (2016), Towards a Simulation of Mixed Land Use Impacts on Transport: a Procedural Urban Modelling of Urban Layout, Paper presented at 13th international conference on design & descision support systems in architecture and urban planning, Eindhoven.
Macal Ch.M., North M. (2005), Tutorial on agent‑based modeling and simulation, Simulation conference, 2005 proceedings of the winter.
Macy M. W., Willer R. (2002), From Factors to Actors: Computational Sociology and Agent‑Based Modeling, “Annual Review of Sociology”, vol. 28, pp. 143–166.
Marceau D. J., Benenson I. (2011), Advanced Geosimulation Models, Centre for Advanced Spatial Analysis UCL, London.
Palmer R. G., Arthur W. B., Holland J. H., LeBaron B., Tayler P. (1994), Artificial economic life: a simple model of a stockmarket, “Physica D: Nonlinear Phenomena”, vol. 75, issues 1–3, pp. 264–274
Perrons D. (2017), Social theory, economic geography, space and place: reflections on the work of Ray Hudson, “European Urban and Regional Studies”, vol. 24(2), pp. 133–137
Resch B., Sagl G., Törnros T., Bachmaier A., Eggers J.‑B., Herkel S., Narmsara S., Gündra H. (2014), GIS‑Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues, “ISPRS International Journal of Geo‑Information”, no. 3, pp. 662–692.
Reynolds C. (1987), Flocks, herds and schools: A distributed behavioral model, SIGGRAPH ‘87: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. Association for Computing Machinery, pp. 25–34
Rubinstein A. (2017), Comments on Economic Models, Economics, and Economists: Remarks on Economics Rules by Dani Rodrik, “Journal of Economic Literature”, vol. 55(1), pp. 162–172
Rydin Y., Bleahu A., Davies M., Dávila J. D., Friel S. (2015), Shaping cities for health: complexity and the planning of urban environments in the 21st century, “Lancet”, vol. 379, issue 9831, pp. 2079–2108
Schelhorn T., O’Sullivan D., Haklay M., Thurstain‑Goodwin M. (1999), STREETS: an agent‑based pedestrian model, CASA Working Papers 9, Centre for Advanced Spatial Analysis UCL, London.
Schelling T. C. (1971), Dynamic models of segregation, “The Journal of Mathematical Sociology”, vol. 1, no. 2, pp. 143–186
Suh J., Kim S. M., Yi H., Choi Y. (2017), An Overview of GIS‑Based Modeling and Assessment of Mining‑Induced Hazards: Soil, Water, and Forest, “International Journal of Environmental Research and Public Health”, Nov 27, vol. 14(12), pp. 1463.
Tan Y., Xu H., Zhang X. (2016), Sustainable urbanization in China: a comprehensive literaturę review, “Cities”, no. 55, pp. 82–93.
Tesfatsion L. (2017), Modeling Economic Systems as Locally‑Constructive Sequential Games, “Journal of Economic Methodology”, vol. 24, issue 4, pp. 384–409.
Tseng F., Liu F., Furtado B. A. (2017), Humans of Simulated New York (HOSNY): an exploratory comprehensive model of city life, Cornell University Library, https://arxiv.org/abs/1703.05240
Torrens P. M. (2018), A computational sandbox with human automata for exploring perceived egress safety in urban damage scenarios, “International Journal of Digital Earth”, vol. 11, issue 4, pp. 369–396, DOI: https://doi.org/10.1080/17538947.2017.1320594
Wilensky U. (1997), NetLogo Segregation model, Center for Connected Learning and Computer‑Based Modeling, Northwestern University, Evanston, http://ccl.northwestern.edu/netlogo/models/Segregation [accessed: 6.12.2019].
Wilensky U., Rand W. (2015), An Introduction to Agent‑Based Modeling, MIT Press, Cambridge–London.
Yang Y., Zhang S., Yang J., Bu K., Xing X. (2014), A review of historical reconstruction methods of land use/land cover, “Journal of Geographical Sciences”, vol. 24, issue 4, pp. 746–766.
Zia K., Farrahi K., Sharpanskykh A., Ferscha A., Muchnik L. (2013), Parallel and Distributed Simulation of Large‑Scale Cognitive Agents, [in:] Y. Demazeau, T. Ishida, J. M. Corchado, J. Bajo (eds.), Advances on Practical Applications of Agents and Multi‑Agent Systems. PAAMS 2013. Lecture Notes in Computer Science, vol. 7879, Springer, Berlin–Heidelberg. DOI: https://doi.org/10.1007/978-3-642-38073-0_38





