Numerical models are omnipresent in climate research. Constructed to understand the past, to forecast future climate and to gain new knowledge on natural processes and interactions, they enable the simulation of experiments at otherwise unreachable time and spatial scales. These instruments have long been considered to be fed – let even determined – by either theories or observations alone. But are they really? Sociological factors are at play too. It is precisely these influences that the present blog entry attempts at presenting through the review of cornerstone sociological studies and new anthropological insights on decision-making of model builders.
What we here define as numerical models are computer simulations of processes occurring within a system. Global Circulation Models (GCMs), the climate models used to simulate the Earth’s climate and its response to changes of greenhouse gas concentrations in the atmosphere, are certainly the most widely known representatives of this category of scientific instruments. However, numerical models of all types abound in research fields associated to the study of climate change. Some require to be run on national supercomputing centres, while others run on a laptop. Some might be the product of entire research teams, while others result from individual endeavour. Some simulate climate processes at a global scale, while others focus on gas bubbles in a cubic meter of peat. The term “numerical model” employed hereafter embraces this diversity.
Models have long been considered by philosophers to be logically deduced from theory and observations alone. In the late 1990s, Morgan and Morrison (1999) initiated a new turn by insisting on the partial autonomy of models from theory and observations, arising from the diversity of their ingredients. It is this partial autonomy, the authors claim, which enables us to gain knowledge through models about theories and the world. Simultaneously, several studies of the domain of Science and Technology Studies (STS) attempted to address the actual activity of modelling through immersion within climate research institutes, hereby unveiling complex interactions between actors, institutions and stake-holders.
Why does it matter?
Model outputs are widely used as a basis for decision-making, at local up to international level. In a context of political and public defiance, much attention has been devoted to increasing the credibility of numerical models. Model intercomparison projects, uncertainty assessments and “best practice” guidelines have flourished. Yet improving modelling activity also necessarily goes through understanding and analysing current practices. Empirical studies addressing actual practices are a requisite to improve reflexivity – and thereby control – over implicit mechanisms likely to impact model outputs. The question ought hence to be how models are actually constructed, before addressing how they should be.
What we already know…
Current practices can be analysed at different levels, from individual to institutional. Most empirical sociological studies have so far investigated the interface between decision-making spheres and modelling, as well as institutional and disciplinary cultures of modelling. Shackley (1999) notably identified different “epistemic lifestyles” within global circulation modelling, which appeared to be influenced among others by the role, location, objectives and funding of the organisation within which the modelling project was conducted. He further highlighted with fellow researchers the influence of the modeller’s own perception of the policy process on his or her modelling practices (Shackley et al., 1999). Similar assertions have been made in neighbouring fields (such as hydrology, land use change and integrative assessment modelling), but rarely based on empirical material.
… and what we know less
The majority of existing studies considered the epistemic stance of modellers – their perception of the modelling activity, of its objectives and of the modellers’ own roles, as well as the modellers’ positioning with respect to particular issues encountered in climate modelling. However, how modellers really make choices during the construction of their model – and which factors influence these – has barely been examined. This is exactly what we aimed to scrutinize through our study presented at the EGU General assembly 2018.
Decisions in model construction
Studying decision-making within the process of model construction implies to assume that choices have to be made. As straightforward as this claim might seem, the very existence of choices has been largely absent from philosophical reflexion upon modelling during the 20th century and remains yet to be granted appropriate attention. Modellers however make, sometimes in an iterative manner, a plethora of choices during the model building activity. The temporal and spatial scales need to be selected and along with them the natural processes at play, their interactions, their representation through physical equations or parameterization, their numerical implementation, the source of data, the hardware and software at use, etc. (Babel et al., 2019).
The following video summarizes the rationale behind our research and the approach we used.
Choosing how to represent a natural process
We decided to focus on one particular type of choice: the representation of natural processes through equations (time transfer functions) and their numerical implementation. Even when modellers have selected to simulate a particular natural process (evapotranspiration, for example), several representations of one and the same process can generally be contemplated (Guillemot, 2010). We then asked ourselves on which basis modellers choose one representation and not another. We expected mostly technical aspects to come forefront, such as the required data or software and hardware limitations. These indeed take a prominent place in specialized literature.
Interviewing modellers
We adopted a well-established methodology from social sciences based on semi-directed interviews, which were conducted with researchers who developed a model from its earliest stage on. The interviewees were not aware of the exact subject of the interview. Prior to the interviews, we identified in the literature accompanying the presentation of the models one or several processes for which no justification was given on the reasons leading to the choice of the employed representation. After introductive, general questions on the modelling project, the researchers were invited to explain the use of this particular representation.
All interviews were recorded and transcribed. The interviewees came from five universities or research institutes located in four different countries in Europe and Northern America. All but one were senior scientists. A diversity was sought among the types of models (from highly complex ones to models openly described as simplistic) and scientific disciplines, ranging from ecology to geochemistry. With the exception of astrophysics, which we included in order to test a hypothesis not detailed in the present blog entry, all models were devoted to research questions associated to climate change. A total of 14 interviews were conducted.
The role of actors – or what we did not expect
As stated above, we expected the modellers to justify the choice of representations with mostly technical constraints. They did not. Rather, the narratives granted particular emphasis to actors – colleagues, professors, PhD directors – who belonged to the modeller’s network during the construction of his or her model. Many of the interviewees had started building their model (which they nowadays continue developing) as doctorate students. The use of a certain process representation was often explained as having been transferred by the (PhD) research director or colleagues. Two decades later, the representation was still part of the model – and modelling practices of these actors played a paramount role in the modellers’ justification of its use, even in competitive and controversy-laden contexts (Babel et al., 2019).
From transfer to habit
Many of the interviewees were surprised to be asked about an equation or a numerical scheme they did not perceive to be a distinctive, novel feature of their model. Even if other alternatives to the process representation existed in all the analysed cases, they did not necessarily consider to have made a choice. The choice had often been made by others at the very beginning of their career and transferred to them by their PhD directors or colleagues. They incorporated it in their own practices, a process one of the interviewees described as a “natural evolution”.
(…) during my PhD, my PhD director was only working with [this process representation]. And so I was educated with it. And so I couldn’t imagine doing something else (…) And so after my thesis, I naturally evolved with this approach because it was what I knew. It was a natural evolution, it is as… yes, when we can speak a language, we evolve with this language. So here it is a bit the same, I knew how to speak [this process representation] and so I naturally kept on evolving with this approach. But it is true that… yes, it is the main reason, I believe “ (interview quoted in Babel et al., 2019).
The natural evolution this interviewee referred to can be equated to a path dependence. The modeller developed skills and expertise through the repeated use of the representation, which rendered its implementation increasingly evident in the course of his career. We employed the sociological concept of habit, notably analysed by Latour (2013) to describe the progressive incorporation of choices becoming self-evident practices.
Figure 1. Illustration of the transfer of natural processes representations and incorporation within modelling practices.
Habits are required – but self-reflexivity over them too
While the term has often a negative overtone in everyday language, habits can be considered as deeply necessary. As stated by Latour (2013), these smooth out the course of actions: a modeller who would constantly re-consider, on a daily basis, the use of a programming language, a database or a certain variable would lose herself in perpetual decision-making requiring both attention and time. By repeating actions without engaging on new paths – by evolving with the same language, as the interviewee quoted above put it – we gain in efficiency and expertise. Yet, a danger is looming: that of falling into automatisms, losing sight and control over the initial crossroad and hence the ability to reverse, whenever necessary, our paths of actions. Questioning modelling habits and tracking them back to their roots – both on an individual and a collective basis – appears an unavoidable step to gain a better understanding over existing modelling practices.
Collectives may reinforce path dependence
The modellers interviewed during our study displayed striking consciousness of their process representation being often particular to a certain collective (a “school” or “field”) they nowadays identified with. By transferring them with a process representation, their director or colleague had also anchored them within a network: that of scientists using the same representation. This anchoring, which was often unconscious at the beginning of their career, could act as reinforcing the path dependence. Changing of process representation would not only often necessitate considerable effort and time to reach the same level of efficiency and expertise gained over the years, but also imply to turn away from a collective within which the modellers had established themselves (Babel et al., 2019).
A word of caution
Our study does not describe modellers as being determined as habits. Rather, we aimed at shedding light on inter-individual and collective influences within the modelling process often disregarded in field-specific literature. We assume habits to play a role among other factors. The fact that these other (computational, cost-related) factors were rarely mentioned by the modeller during the interviews could be explained by them being perceived as evident or self-speaking; additional studies would be required to explore the intertwinement of other triggers of model decision with inter-individual and collective influences.
Finally, this study was based on a limited number of interviews: we did not seek for exhaustivity or generalizations, but for case studies enabling a first glance on rarely studied processes.
This post has been edited by Janina Bösken and Carole Nehme.
REFERENCES Babel, L., Vinck, D., Karssenberg, D. (2019). Decision-making in model construction: unveiling habits. Environmental Modelling and Software, 120, in press. DOI: https://doi.org/10.1016/j.envsoft.2019.07.015 Guillemot, H. (2010). Connections between simulations and observation in climate computer modeling. Scientist’s practices and “bottom-up epistemology” lessons. Stud. Hist. Philos. Sci. Part B Stud. Hist. Philos. Mod. Phys., Special Issue: Modelling and Simulation in the Atmospheric and Climate Sciences 41, 242–252. Latour, B. (2013). An Inquiry into Modes of Existence. An Anthropology of the Moderns. Harvard University Press, Cambridge, Massachusetts. Morgan, M.S., Morrison, M. (1999). Models as Mediators: Perspective On Natural and Social Science. Cambridge University Press, Cambridge. Shackley, S. (2001). Epistemic Lifestyles in Climate Change Modelling, in: Edwards, P.N. (Ed.), Changing the Atmosphere: Expert Knowledge and Environmental Governance. MIT Press, Cambridge, Massachusetts. Shackley, S., Risbey, J., Stone, P., Wynne, B. (1999). Adjusting to Policy Expectations in Climate Change Modeling. Clim. Change 43, 413–454.
