Speakers
Description
The adoption of innovative technologies, such as Artificial Intelligence (AI), is redefining Problem Structuring Methods (PSMs), expanding their capacity to address complex and unstructured problems (Sacco & Poli, 2024). AI, understood as a set of computational techniques capable of analyzing vast amounts of data, simulating scenarios, and constructing multidimensional narratives, enhances PSMs' ability to manage uncertainty and engage stakeholders. Traditionally, PSMs have been used to structure decision-making in uncertain and multi-actor contexts, facilitating the representation of different perspectives and addressing complex, ill-structured problems resistant to conventional solutions (Mingers & Rosenhead, 2004). Their integration with AI combines PSMs' strengths in navigating complexity with AI's advantages in processing and interpreting data, opening new possibilities for more comprehensive and nuanced decision-making frameworks. This synergy emerges as an interesting perspective to enrich landscape planning strategies, where the integration of PSMs and AI could enable a more inclusive and articulate representation of ecological, social, and technological components.
In this contribution, the concept of landscape is examined starting from the framework of Ecosystem Services (Hermann et al., 2011), one of the most widely used approaches in landscape studies. In this framework, landscapes are understood as complex systems that deliver essential services—such as provisioning, regulating, supporting and cultural services—to communities, emphasising the landscape’s values for their role in offering benefits to human well-being. Viewed as a tool for community benefits, the landscape loses its subjectivity, turning into an object from which value is extracted. From this perspective, unfortunately, the use of PSMs and AI in this context only reinforces this mindset. While PSMs effectively capture landscape complexity, their predominant focus on human interests limits their ability to account for the interconnections between human and non-human entities. This anthropocentric bias presents a challenge, as it does not fully align with the intrinsic complexity of landscapes, where multiple agents—both human and non-human—interact in dynamic and often unpredictable ways. Conversely, AI offers unparalleled opportunities to reinterpret landscapes, enabling new ways of narrating their complexity. However, an overly predictive approach risks oversimplifying issues, potentially overlooking the nuances and the inherent poetic nature of landscapes and their intrinsic value (Cerreta, 2010). This paper explores the advantages and limitations of adopting PSMs and AI in landscape planning strategies, aiming to identify opportunities from both approaches to develop an integrated vision for landscape values and evaluation.
A compelling alternative perspective emerges from the concept of More-Than-Human (Haraway, 1985), which challenges the traditional anthropocentric paradigm by acknowledging the agency of non-human entities, including ecosystems, biodiversity, and technological infrastructures. Adopting a More-Than-Human approach in landscape planning invites a fundamental rethinking of landscape decision-making processes, encouraging the inclusion of multiple voices—both human and non-human (O’Connor & Kenter, 2019). Rather than providing definitive answers, this contribution aims to outline a landscape-centered framework that could foster a shift in the way the landscape is narrated.
Through the lens of a More-Than-Human approach, the paper investigates how it is possible to integrate PSMs and AI in moving beyond an anthropocentric vision, fostering a deeper understanding of landscape complexity. This shift has the potential to create more inclusive, resilient, and sustainable landscape narratives, better equipped to address contemporary challenges where complexity and uncertainty play a central role. By embracing this perspective, landscape planning can transition towards a more adaptive and ethically grounded practice, capable of responding to the evolving needs of both human and non-human actors in a rapidly changing world.
References
Cerreta, M. (2010) ‘Thinking Through Complex Values’, in M. Cerreta, G. Concilio, and V. Monno (eds) Making Strategies in Spatial Planning. Dordrecht: Springer Netherlands, pp. 381–404. Available at: https://doi.org/10.1007/978-90-481-3106-8_21.
Haraway, D. J. (1985). A manifesto for cyborgs: Science, technology, and socialist-feminism in the 1980s. In Simians, cyborgs, and women: The reinvention of nature (pp. 149-181). Routledge.
Hermann, A., Schleifer, S. and Wrbka, T. (2011) ‘The Concept of Ecosystem Services Regarding Landscape Research: A Review’, Living Reviews in Landscape Research, 5. Available at: https://doi.org/10.12942/lrlr-2011-1.
Mingers, J. and Rosenhead, J. (2004) ‘Problem structuring methods in action’, European Journal of Operational Research, 152(3), pp. 530–554. Available at: https://doi.org/10.1016/S0377-2217(03)00056-0.
O’Connor, S. and Kenter, J.O. (2019) ‘Making intrinsic values work; integrating intrinsic values of the more-than-human world through the Life Framework of Values’, Sustainability Science, 14(5), pp. 1247–1265. Available at: https://doi.org/10.1007/s11625-019-00715-7.
Sacco, S. and Poli, G. (2024) ‘How Can Participatory AI Implement Problem Structuring Methods for Urban Sustainability Enhancement?’, in F. Calabrò et al. (eds) Networks, Markets & People. Cham: Springer Nature Switzerland (Lecture Notes in Networks and Systems), pp. 101–110. Available at: https://doi.org/10.1007/978-3-031-74679-6_10.
| Keywords | Problem Structuring Methods; Artificial Intelligence; Landscape Planning; More-Than-Human |
|---|---|
| Best Congress Paper Award | No |
