Virtualising Space – New Directions for Applications of Agent-Based Modelling in Spatial Economics

Authors

DOI:

https://doi.org/10.18778/0208-6018.346.01

Keywords:

agent‑based modelling, geographical information systems, urban economics, spatial economics

Abstract

Due to enormous technological progress, socio‑economic science has gained new possibilities of investigating complex and not well‑known socio‑economic phenomena. One of the recent promising research approaches is agent‑based modelling (ABM) with connection to geographical (GIS) data. ABM is a bottom‑up research method concerning individuals that live and interact in the artificial environment. In this type of simulation, evolution of the whole system and macro‑level patterns results from individual behaviour of autonomous entities. Combining ABM with GIS data moves the simulation into the real geographical space. Applying this approach provides powerful possibilities of more realistic socio‑economic simulations concerning urban and spatial economics, sociology and psychology. Geosimulation also helps to answer questions about dependencies between geographical space and economic performances of modern cities. In this paper, a closer look at this topic is presented. We deal with the problem of implementation of GIS data into agent‑based modelling software. In the first step of our research procedure, we compare ABM programming platforms, then we chose three of them which provide GIS data support. In the second step, we implement OpenStreetMap GIS data for one of the districts of Poznań into these programming platforms. Finally, we compare the performance of ABM platforms regarding three major criteria: difficulty of programming, GIS data compatibility and available technical support. Our research is the first step in developing a comple Xsocio‑economic urban system under the ABM paradigm.

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References

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.
Google Scholar

Adamatti D.F., Dimuro G. P., Coelho H. (2014), Interdisciplinary Applications of Agent‑Based Social Simulation and Modeling, IGI Global, Hershey.
Google Scholar 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.
Google Scholar

Ariely D. (2008), Predictably Irrational: The Hidden Forces That Shape Our Decisions, Harper‑Collins, New York.
Google Scholar

Axelrod R., Hamilton W. D. (1981), The Evolution of Cooperation Science, “New Series”, vol. 211, no. 4489, pp. 1390–1396.
Google Scholar

Axtell R., Epstein J. M. (1996), Growing Artificial Societies. Social Science from the Bottom Up, MIT Press, Cambridge.
Google Scholar 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.
Google Scholar

Berryman M. (2008), Review of Software Platforms for Agent Based Models. Technical report, https://pdfs.semanticscholar.org/a158/181431fbfd01765668dc1d08229072e982aa.pdf [accessed: 6.12.2019].
Google Scholar

Blanchard O. (2018), On the future of macroeconomic models, “Oxford Review of Economic Policy”, vol. 34, issue 1–2, pp. 43–54
Google Scholar

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.
Google Scholar

Boyce D., Williams H. (2015), Forecasting Urban Travel: Past, Present and Future, Edward Elgar Publishing, Cheltenham–Northampton.
Google Scholar 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.
Google Scholar

Brunsdon Ch., Singleton A. (2015), Geocomputation: a practical primer, Sage Publications, Inc., London
Google Scholar 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.
Google Scholar 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.
Google Scholar

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
Google Scholar

Gershenson C. (2012), Complexity at large, “Complexity”, no. 18, pp. 1–4
Google Scholar

Gilbert N. (2008), Agent‑based models, Sage Publications, Los Angeles–London–Delhi–Singapore.
Google Scholar

Gilbert N., Troitzsch K. G. (1999), Simulation for the Social Scientist, Open University Press, Buckingham.
Google Scholar

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
Google Scholar

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].
Google Scholar

Heppenstall A. J., Crooks A. T., See L. M., Batty M. (2011), Agent‑Based Models of Geographical Systems, Springer, London–New York.
Google Scholar 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.
Google Scholar DOI: https://doi.org/10.1177/0037549705058073

Lynch K. (1960), The Image of the City, The MIT Press, Cambridge–London.
Google Scholar

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.
Google Scholar

Macal Ch.M., North M. (2005), Tutorial on agent‑based modeling and simulation, Simulation conference, 2005 proceedings of the winter.
Google Scholar

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.
Google Scholar

Marceau D. J., Benenson I. (2011), Advanced Geosimulation Models, Centre for Advanced Spatial Analysis UCL, London.
Google Scholar

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
Google Scholar

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
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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
Google Scholar

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.
Google Scholar

Schelling T. C. (1971), Dynamic models of segregation, “The Journal of Mathematical Sociology”, vol. 1, no. 2, pp. 143–186
Google Scholar

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.
Google Scholar

Tan Y., Xu H., Zhang X. (2016), Sustainable urbanization in China: a comprehensive literaturę review, “Cities”, no. 55, pp. 82–93.
Google Scholar

Tesfatsion L. (2017), Modeling Economic Systems as Locally‑Constructive Sequential Games, “Journal of Economic Methodology”, vol. 24, issue 4, pp. 384–409.
Google Scholar

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
Google Scholar

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,
Google Scholar 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].
Google Scholar

Wilensky U., Rand W. (2015), An Introduction to Agent‑Based Modeling, MIT Press, Cambridge–London.
Google Scholar

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.
Google Scholar

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.
Google Scholar DOI: https://doi.org/10.1007/978-3-642-38073-0_38

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Published

2020-02-03

How to Cite

Wozniak, M. (2020). Virtualising Space – New Directions for Applications of Agent-Based Modelling in Spatial Economics. Acta Universitatis Lodziensis. Folia Oeconomica, 1(346), 7–26. https://doi.org/10.18778/0208-6018.346.01

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