by Jonatan Godinez Madrigal, Rozemarijn ter Horst, Bich Tran, Rossella Alba

We are a group of young scholars working together in the Constructive Advanced Thinking Programme framework. We asked and received funding to unpack and discuss 'Controversial tools: researching modelling practices in water governance'.

Models do not think. But they easily become substitutes for thinking. In water science, computer-based models are used as intellectual tools that estimate knowledge about coupled human-water systems. But what is the relationship between numerical models and other human mental faculties like thinking? Should numerical models also have the explicit role of eliciting thinking? Is that even desirable? We have encountered many instances where models are fetishized and expected to provide clear and unproblematic solutions to complex problems, leading humans to foreclose their thinking faculty. This obscures imbalanced power relations, erases people and the natural world, and ignores uncertainties. As a result, it is difficult to achieve a deeper understanding of the social and natural world in decision-making.

In a world that prioritizes STEM disciplines (Science, Technology, Engineering, and Mathematics) in tackling global issues, there are risks and unintended consequences of using models exclusively as intellectual tools. Martin Heidegger's axiom, "science does not think" (1993), challenges the assumption that numerical modelling engenders a profound understanding. Computer-based models in water management provide data-driven solutions, often prioritizing efficiency and optimization. However, Heidegger's assertion prompts us to critically examine the depth of thought inherent in models. Can they genuinely engage with the plurality of meanings and valuations of water, or do they merely provide utilitarian solutions divorced from a more profound understanding?This is not an idle question as "Thinking, no doubt, plays an enormous role in every scientific enterprise, but it is the role of a means to an end; the end is determined by a decision about what is worthwhile knowing, and this decision cannot be scientific" (Arendt, 1981).

What does this mean for the way we engage and develop models? The dominant understanding is that science and engineering are enough to develop good models. However, for some scientists, the modelling process "requires imagination, inspiration, creativity, ingenuity, experience and, skill…Hydrology is an art as much as it is science and engineering" (Savenije, 2009). Therefore, we claim that water modelling as a process needs to change, lest its huge potential be wasted by foreclosing thinking.

Models are useful for computing complex processes that are difficult for humans to calculate. They can help explore the past, present, and future and consolidate hypotheses about the world. However, using them as thinking tools can be challenging because practitioners often confuse them with reality when, in fact, they are only simplifications of reality. Reductionist modelling practices view water as a unidimensional (economic) resource, a 'standing reserve', to be optimized for efficiency divorced from its cultural, ecological, and existential significance, and excluding people and alternative views. For example, in the Zapotillo conflict in Mexico, a high-level international consultancy was hired to develop a hydrological model using WEAP software to settle knowledge controversies around a controversial urban water supply dam. They embarked on a two-year modelling process that focused on the government's vision of dam optimization. They disregarded less impactful alternatives while ostracizing key stakeholders from the modelling process, including the affected communities. The result was a model that lacked legitimacy and did not help transform the conflict (Godinez Madrigal et al., 2020). However, no one seemed accountable for this fiasco. The responsibility was diffused between the model itself, the modellers, and the model commissioners.

To understand this diffusion of accountability, it is necessary to unpack the problematic traits of models as intellectual tools. These include:

  • Humans have delegated to models the intellectual faculty to compute and calculate with great capacity. The role of thinking is also present in the modelling process, but it is often obscured when using off-the-shelf, one-size-fits-all established modelling software. This is troublesome because the thinking faculty is expansive and driven by imagination and a deeper engagement with the meaning and significance of phenomena (Arendt, 1971). Established software may not allow for a deeper understanding of reality. Therefore, a strong argument exists for developing tailored and situated models that force the modellers to think of reality beyond pre-established calculations.
  • Modelling has an often-unrecognized tension between searching for truth and meaning. Relying solely on models can limit discussions between different types of knowledge with non-modelling actors and foreclose thinking while implementing controversial solutions derived from models. It can also impose a particular worldview on water. "The model hath spoken" has often been used to justify implementing controversial decisions or imposing a particular worldview on water. Arendt warns us that knowledge is a world-building enterprise, as material as building houses.
  • Proprietary software can be problematic as it may change its capacities, restrict access, become obsolete, or degrade in quality due to profit-seeking (see 'Enshittification'). This limits human intervention in discussions about the software and its results, particularly with the emergence of AI and machine learning. These models can predict future outcomes without disclosing their inner workings, leading to a loss of knowledge and independent thinking.


If thinking relates to memory as a function to make sense of the past and the will to imagine our common future, then could computer-based models, instead of foreclosing thought, be capable of fostering, as thinking processes, the potentiality of humans to enrich (re)-interpretation of the past, make sense of the present through a plurality of perspectives, and open up the decision space to foster a creative imagination for the future?

There are great examples of using models in ways that potentiate human thinking. In a non-exhaustive list, we can identify alternative modelling processes focusing on thinking (ter Horst et al., 2023): counter-modelling; exposing black boxing; explicitly showcasing the development process of modelling and how modelling decisions affect outcomes; openly questioning modelling decisions and assumptions behind them; foregrounding power relations; calling for particular ethics; and focusing on the process instead of the tool (i.e., companion modelling). So, if there are so many alternative manners of modelling, then why are we still not doing it differently?

We have highlighted the pitfalls and risks of many current modelling undertakings. We are not arguing for doing away with numerical and computer-based models in water management but to rethink their development and use. We claim that an answer lies especially with fostering thinking. We aim to stir and join an ongoing conversation about what models do, their potentiality (all possible ways they can be used), and their affordance (the restrictions inherent in how they are used) in a world where thinking is increasingly rare but the search for truth and meaning is inescapable. However, this search cannot be done without simultaneously considering the uncertainties inherent in models and the power asymmetries intrinsic to different value systems involved in water systems when developing and using models. We must reform the currently dominant development pathway dependent on numerical and computer-based models that are foreclosing thought and instead use models as pathways to expansive human thinking and its collective flourishment.


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Rossella Alba studies socio-ecological transformations and inequalities taking as a point of reference infrastructural relations and the governance of resources, and more particularly water. She works in an interdisciplinary manner by combining critical social science research with natural science approaches. ITHESys, Humboldt University, Germany

Rozemarijn ter Horst has been working as a lecturer and doctoral student in the Water Resources Management Group at Wageningen University since October 2020. She studies how quantitative models influence water management and governance. To show how models are political, Rozemarijn ter Horst focuses on case studies in which models are introduced in the hope of reducing or resolving conflicts over shared water resources. In these case studies, she explores how data and technologies play a role in identifying generally accepted development options and how and when contestations (can) take place in this process. The case studies include the federal Kaveri (or Cauvery) River, shared by Kerala, Karnata, Tamil Nadu and the Union Territory of Puducherri, as well as the aquifers shared between Israel and Palestine. Her research draws on science and technology studies and constructivist theories, and she seeks to work closely with those who develop and implement the models. Before working with Wageningen University, Rozemarijn ter Horst worked at IHE Delft on water diplomacy, and remains affiliated as a visiting researcher on cross-border water governance.

Tran Bich is currently a doctoral student at IHE - Delft Institute for Water Education, and at the Technical University of Delft since September 2021. She studies uncertainties in evapotranspiration derived from satellite data and how the implication of these uncertainties in the evaluation of water resources. Prior to her PhD, Tran Bich worked as a research assistant at IHE Delft on the Water Accounting Plus (WA+) framework, which uses open access earth observation data and spatially distributed hydrological models to the assessment of water resources at the basin level. She has conducted several studies on water management and is interested in the uncertainties of these data, models and assessments of water resources.

Jonatan Godinez-Madrigal is a postdoctoral researcher on global transitions in water distribution regimes at IHE Delft. In his research, Jonatan bridges the dichotomy between objective, technical expertise and the more subjective socio-political expertise needed to understand complex socio-ecological issues. By combining mixed methods, such as longitudinal, interdisciplinary, and transdisciplinary research, he is able to simultaneously study the historical, social, and biophysical dimensions of water-related conflicts and socio-technical transitions.


References

Arendt, H. (1971). The life of the mind. New York: HMW.

Godinez-Madrigal, J., Van Cauwenbergh, N., & van der Zaag, P. (2020). Unraveling intractable water conflicts: the entanglement of science and politics in decision-making on large hydraulic infrastructure. Hydrology and Earth System Sciences, 24(10), 4903-4921.

Heidegger, M. (1993). Basic Writings. San Francisco: HarperCollins Publishers.

Savenije, H. H. (2009). HESS Opinions "The art of hydrology". Hydrology and Earth System Sciences 13(2), 157-161, https://doi.org/10.5194/hess-13-157-2009, 2009.

ter Horst, R., Alba, R., Vos, J., Rusca, M., Godinez-Madrigal, J., Babel, L. V., Veldwisch, G., Venot, J., Bonté, B., Walker, D.W., & Krueger, T. (2023). Making a case for power-sensitive water modelling: a literature review. Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2023-164 in review.