Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T03:26:13.026Z Has data issue: false hasContentIssue false

18 - Cities and Entropy: Assessing Urban Sustainability as a Problem of Coordination

from Part IV - Focal Points of Urban Sustainability

Published online by Cambridge University Press:  27 March 2020

Claudia R. Binder
Affiliation:
École Polytechnique Fédérale de Lausanne
Romano Wyss
Affiliation:
École Polytechnique Fédérale de Lausanne
Emanuele Massaro
Affiliation:
École Polytechnique Fédérale de Lausanne
Get access

Summary

Assessing urban sustainability is a crucial step towards solving the challenges we face today. Solutions to these challenges are likely to demand new and impressive levels of coordination: people will need to change their habits and learn to focus their actions in specific sustainable directions. The deeper nature of such challenges may be clarified through a classic concept from information theory and thermodynamics: entropy, both a measure of probability in the face of uncertainty and a measure of disorder. Arguing that the problem of entropy may throw light on issues of sustainability in social and urban systems, we propose in this chapter that sustainability can be stimulated by cities that enable us to coordinate better, reducing the entropy triggered by uncertainties and the unintended consequences of our actions. Investigating the role of cities in social entropy through a new agent-based model (ABM), we show that cities may play a crucial role in our conscious and unconscious efforts to cooperate.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, T. (1977). Managing the Flow of Technology. Cambridge, MA: MIT Press.Google Scholar
Alonso, W. (1964). Location and Land Use: Toward a General Theory of Land Rent. Cambridge, MA: Harvard University Press.Google Scholar
Ayeni, M. A. O. (1976). The city system and the use of entropy in urban analysis. Urban Ecology, 2 (1), 3353.CrossRefGoogle Scholar
Batty, M., Morphet, R., Masucci, P., & Stanilov, K. (2014). Entropy, complexity, and spatial information. Journal of Geographical Systems, 16(4), 363385.Google Scholar
Brooks, R. (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, MA: MIT Press.Google Scholar
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 719.Google Scholar
Clark, A., (1997). Being There: Putting Brain, Body, and World Together Again. Cambridge, MA: MIT Press.Google Scholar
Claudel, M., Massaro, E., Santi, P., Murray, F., & Ratti, C. (2017). An exploration of collaborative scientific production at MIT through spatial organization and institutional affiliation. PLoS ONE, 12(6), e0179334.Google Scholar
Cohen, M. (2017). A systematic review of urban sustainability assessment literature. Sustainability, 9(11), 2048.Google Scholar
dos Santos, R. V., Ribeiro, F. L., & Martinez, A. S. (2015). Models for Allee effect based on physical principles. Journal of Theoretical Biology, 385, 143152.Google Scholar
Farber, S., O’Kelly, M., Miller, H. J., & Neutens, T. (2015). Measuring segregation using patterns of daily travel behavior: A social interaction-based model of exposure. Journal of Transport Geography 49, 2638.Google Scholar
Faria, A., & Krafta, R. (2003). Representing urban cognitive structure through spatial differentiation, in Proceedings of 4th Space Syntax International Symposium, pp. 531–518, London: UCL Press.Google Scholar
Franklin, S. (1995). Artificial Minds. Cambridge, MA: MIT Press.Google Scholar
Gibson, J. (1979). The Ecological Approach to Visual Perception. Boston: Houghton-Mifflin.Google Scholar
Glenberg, A. M., & Robertson, D. A. (1999). Indexical understanding of instructions. Discourse Processes, 28(1), 126.Google Scholar
Haken, H, & Portugali, J. (2015). Information Adaptation: The Interplay between Shannon Information and Semantic Information in Cognition. New York: Springer.Google Scholar
Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of Planners, 25(2), 7376.Google Scholar
Hashemian, B., Massaro, E., Bojic, I., Arias, J. M., Sobolevsky, S., & Ratti, C. (2017). Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE, 12(11), e0187031.Google Scholar
Hillier, B. (1996). Space Is the Machine. Cambridge, UK: Cambridge University Press.Google Scholar
Hodges, B. H., & Baron, R. M. (1992). Values as constraints on affordances: perceiving and acting properly. Journal for the Theory of Social Behaviour, 22(3), 263294.Google Scholar
Iverson, J. M., & Goldin-Meadow, S. (1998). Why people gesture when they speak. Nature, 396(6708), 228.CrossRefGoogle ScholarPubMed
Kintsch, W. (1970). Memory and Cognition. New York: John Wiley and Sons.Google Scholar
Kirsh, D. & Maglio, P. (1994). On distinguishing epistemic from pragmatic action. Cognitive Science, 18(4), 513549.CrossRefGoogle Scholar
Kolchinsky, A., & Wolpert, D. H. (2018). Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus, 8: 20180041. http://dx.doi.org/10.1098/rsfs.2018.0041.Google Scholar
Kosslyn, S. M. (1994). Image and Brain: The Resolution of the Imagery Debate. Cambridge, MA: MIT Press.Google Scholar
Krafta, R., Netto, V. M., & Lima, L. (2011). Urban built form grows critical. Cybergeo: European Journal of Geography, 565. DOI:10.4000/cybergeo.24787.Google Scholar
Krauss, R. M. (1998). Why do we gesture when we speak? Current Directions in Psychological Science, 7 (2), 5460.Google Scholar
Lakoff, G., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. New York: Basic Books.Google Scholar
Lanchier, N., & Scarlatos, S. (2013). Fixation in the one-dimensional Axelrod model. The Annals of Applied Probability, 23(6) 25382559.Google Scholar
Luhmann, N. (1995). Social Systems. Stanford University Press.Google Scholar
Marchettini, N., Pulselli, F. M., & Tiezzi, E. (2006). Entropy and the city. WIT Transactions on Ecology and the Environment, 93. DOI:10.2495/SC060251.Google Scholar
Michaelian, K., & Sutton, J. (2013). Distributed cognition and memory research: History and current directions. Review of Philosophy and Psychology, 4, 124.Google Scholar
Neisser, U. (1994). Multiple systems: A new approach to cognitive theory. European Journal of Cognitive Psychology, 6(3) 225241.CrossRefGoogle Scholar
Netto, V. M. (2008). Practice, space, and the duality of meaning. Environment and Planning D: Society and Space, 26(2), 359379.Google Scholar
Netto, V. M. (2017). The Social Fabric of Cities. New York: Routledge.Google Scholar
Netto, V. M., Brigatti, E., Meirelles, J., et al. (2018) Cities, from information to interaction. Entropy, 20(11), 834. https://doi.org/10.3390/e20110834.Google Scholar
Pantaleone, J. (2002). Synchronization of metronomes. American Journal of Physics, 70(10), 9921000.Google Scholar
Parsons, T. (1968). The Structure of Social Action. New York: The Free Press.Google Scholar
Passini, R. (1992). Wayfinding in Architecture. New York: Van Nostrand Reinhold.Google Scholar
Pfeifer, R., & Scheier, C. (1999). Understanding Intelligence. Cambridge, MA: MIT Press.Google Scholar
Portugali, J. (2011). Complexity, Cognition and the City. New York: Springer.Google Scholar
Prigogine, I., & Stengers, I. (1984). Order out of Chaos: Man’s New Dialogue with Nature. New York: Bantam Books.Google Scholar
Purvis, B., Mao, Y., & Robinson, D. (2017). Thermodynamic entropy as an indicator for urban sustainability? Procedia Engineering, 198, 802812.Google Scholar
Ribeiro, F. L. (2015). A non-phenomenological model of competition and cooperation to explain population growth behaviors. Bulletin of Mathematical Biology, 77(3), 409433.Google Scholar
Ribeiro, F. L., & Ribeiro, K. N. (2015). A one dimensional model of population growth. Physica A. Statistical Mechanics and Its Applications, 434, 201210.Google Scholar
Ribeiro, F. L., Meirelles, J., Ferreira, F. F., & Neto, R. C. (2017). A model of urban scaling laws based on distance-dependent interactions. Royal Society Open Science, 4, Article ID 160926.Google Scholar
Rosch, E. (1978). Principles of categorization. In Rosch, E. and Lloyd, B. (eds.), Cognition and Categorization. Hillsdale: Lawrence Erlbaum, pp. 2849.Google Scholar
Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143186.Google Scholar
Schelling, T. C. (1978). Micromotives and Macrobehavior. New York: Norton.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(4), 623656.Google Scholar
Singh, R., Murty, H., Gupta, S., & Dikshit, A. (2012). An overview of sustainability assessment methodologies. Ecological Indicators, 15(1), 281299.Google Scholar
Strogatz, S. (2012). Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Life. Hachette, UK: Hachette Books.Google Scholar
United Nations. (2011). Cities and Climate Change: Global Report on Human Settlements 2011. London: Earthscan.Google Scholar
Vygotsky, L. (1978). Mind in Society: The Development of Higher Psychological Processes., Cambridge, MA: Harvard University Press.Google Scholar
Weber, M. (1978). Economy and Society, vol. 1. Berkeley: University of California Press.Google Scholar
Wilson, A. G. (2013/1970). Entropy in Urban and Regional Modelling (Routledge Revivals). London: Routledge. [Original: 1970, London: Pion] .Google Scholar
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625636.Google Scholar
Wittgenstein, L. (2001). Philosophical Investigations, 3rd edition. Oxford, London: Blackwell Publishers.Google Scholar
Xuan, W., Jieqiong, S., Shan, S., & Yan, Z. (2012). Urban ecological regulation based on information entropy at the town scale. Procedia Environmental Sciences, 13, 11551164.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×