Speaker
Description
The last three decades have witnessed two parallel and possibly divergent developments. This season can indeed be described as the spring of post-positivist approaches to planning theory (Allmendinger, 2002) as well as of digital modelling applications intended as decision support systems in planning practice (Geertman and Stillwell, 2004). While these support systems faced the so-called implementation gap (Jiang et al., 2020), the latest experiments in artificial intelligence (AI) have revived expectations that information science may ultimately be trusted to deliver expert-driven technocratic solutions for planning problems. This expectation raises either anxieties or enthusiasm depending on whether one endorses the post-positivist critique of expert knowledge, or whether one assumes that the proven limitations of human expert knowledge were just a matter of inadequate computing abilities. This paper draws on John Friedmann’s classic understanding of scientific based planning, the engineering mindset and of system engineering’s influence on planning thought (Friedmann, 1987), to refresh its insights. As Friedman’s understanding dates to the eve of the above-mentioned season, refreshing encompasses both revival and update. It highlights both its contemporary relevance to question enthusiastic expectations about AI applications in planning practice, as well as the need to integrate it with new insights that may reduce a number of anxieties. The new insights derive from the observation that the above-mentioned spring season raised post-positivist concerns across domains, encompassing information science and urban and regional digital modelling, and establishing the ground for post-positivist pluralism from within. Examples of this pluralism can be found in philosophical and semantic-cognitive approaches to information science (Couclelis, 1983; Guarino, 1998; Borgo et al. 2022), as well as in shifts in system theory on the closure of systems in the light of complexity science (Batty and Torrens, 2001; Portugali, 2021). In this light the paper concludes that engaging with scholarship in information science and system theory akin to post-positivist perspectives is pivotal for planners to confront both anxieties and enthusiasms regarding the adoption of AI or other digital technologies in planning practice.
References
Allmendinger, Ph. (2002) Towards a post-positivist typology of planning theory. Planning Theory, 1(1), pp. 77–99.
Batty. M. and Torrens, P.M. (2001) Modelling complexity: The limits to prediction. UCL Working Papers Series, Paper 36.
Borgo, S., Galton, A., Kutz, O. (2022) Foundational ontologies in action: understanding foundational ontology through examples. Applied Ontology, 17, pp.1-16.
Couclelis, H. (1983) Some second thoughts about theory in the social sciences. Geographical Analysis, 15(1), pp. 28-33.
Friedmann, J. (1987) Two Centuries of Planning Theory. In: Planning in the Public Domain: From Knowledge to Action, Princeton, NJ: Princeton University Press.
Geertman, S., and Stillwell, J. (2004) Planning support systems: an inventory of current practice. Computers, Environment and Urban Systems, 28(4), pp. 291-310.
Guarino N. (1998) Formal Ontology and Information Systems. In: Guarino N. (ed.) Formal Ontology in Information Systems (FOIS’98). Amsterdam, Trento: IOS Press.
Jiang, H., Geertman, S., & Witte, P. (2020) Avoiding the planning support system pitfalls? What smart governance can learn from the planning support system implementation gap. Environment and Planning B: Urban Analytics and City Science, 47(8), pp. 1343-1360.
Portugali, J. ed. (2021) Handbook on Cities and Complexity. Cheltenham, UK and North Hampton, USA, Edward Elgar.
| Keywords | positivism; pluralism; ontology; urban systems; planning support systems; |
|---|---|
| Best Congress Paper Award | Yes |
