Monday, May 11, 2015

Use and Perception of Science's Results

I asked to Bilal Adem Esmail, one of our Ph.D. students, working on the relation existing between ecosystem services and the water cycle to comment my previous post on ecosystems and hydrology from his point of view. Here below, please find his answer related to the use of models.

He says (my comments in italics):

“If I understood well your thesis, you support the idea that a good understanding and capacity of modelling biophysical phenomena are fundamental for making decisions, for instance about soil (my note: here used with the planners meaning), that are based on solid foundations. Specifically, it is needed to a correct quantification of the various phenomena, avoiding to use approaches too poorly approximate.
I agree with this thesis, which must be pursued with conviction by all, scientists and anyone. However, we do not have to go beyond a reasonable complexity, and fall in the Borges paradox to reproduce the Empire with a map the coincides identically with the Empire itself (see also here).
We can try to set these limits by considering two crucial elements: the use that will be done of the scientific knowledge (to disseminate, decide or, for instance, negotiate), and (not disjoint by the first), the perception of the same knowledge according to the users of the knowledge (see the scheme below). If perception is wrong nothing can be done.
Cash et al. (2013) and Clark et al (2011) suggest to consider the following three fundamental criteria referring to the perception of users of scientific knowledge: the perception of credibility (the scientific rigor perceived), relevance (of the specific problem), legitimacy (i.e. the capacity of inclusion of more points of view) of the sources.

We can take as an example of the Stanford's Natural Capital Project (NCP ( in the Water Fund in Latin America. These support governance mechanism and financial investments that guarantee clean water to downhill communities involving uphill communities. This works, more in general, by favouring collaboration among the stakeholder. NCP started to support scientifically the Water Funds, only after that first phase, by using a series of physical models of increasing complexity, called Tier1, Tier2, and Tier 3.

NCP approach is also transdisciplinary (Jahn et al., 2012, see also below^1) and includes at any stage of the process the stake holder. which is, actually considered the qualifying aspect of the process. In the experience of Water Funds, stakeholders were unable to understand the more complex biophysical models, and NCP had to implement simpler models called Tier0, ceasing the using of the complex models^2^3

This shows that in practice accepted scientific theories can find unespected difficulties, in relation to cognitive perceptions. "


  • Cash, D. W., W. C. Clark, F. Alcock, N. M. Dickson, N. Eckley, D. H. Guston, J. Jäger, and R. B. Mitchell. 2003. Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences of the United States of America 100:8086–8091. 
  • Clark, W.C. et al., 2011. Inaugural Article: Knowledge Systems for Sustainable Development Special Feature Sackler Colloquium: Boundary work for sustainable development: Natural resource management at the Consultative Group on International Agricultural Research (CGIAR). Proceedings of the National Academy of Sciences.
  • Jahn, T., M. Bergmann, and F. Keil. 2012. Transdisciplinarity: Between mainstreaming and marginalization. Ecological Economics 79:1–10.


^1 Transdisciplinarity is a critical and self-reflexive research approach that relates societal with scientific problems; it produces new knowledge by integrating different scientific and extra scientific insights; its aim is to contribute to both societal and scientific progress; integration is the cognitive operation of establishing a novel, hitherto non-existent connection between the distinct epistemic, social–organizational, and communicative entities that make up the given problem context. (Jahn et al 2012)

^2 - This use of the simplest models is also diffuse in science, even if not always justified. Because some aspects of science are very specialistic, even specialists are not able to understand the reasons of complexity which goes beyond their strict domain of expertise, even they tend to favour the use of the simplest model, even when they are too simple (see Einstein citation).  Everyone of us should remember that if what s/he does not understand an explanation of a phenomenon in a snap, not necessarily the explanation is to throw away, but possibly require more application to be understood. Obviously sometimes model complexity was produced just because the Occam’s razor was not applied: this is the case when complexity is not welcomed. 

^3 - In my opinion, there is a basic cognitive limitation. To understand complexity (of even new paradigms) time and domestication is necessary. Aristotelians who looked inside Galileo’s telescope were not really able to see what Galileo saw. Their cognitive attitudes (their brains) were simply not prepared to, and they could not. Even scientists, supposed to be rational,  in face of unwanted results simply refuse to see them, and it is not uncommon the very famous colleagues died without accepting scientific statements against their own views, even if supported by overwhelming facts.

1 comment:

  1. Perception of science: Nice comment Blal, but I always confused by this line of argument. As to me, the controversy between the complex (reductionist thinking) and the transdisciplinary (system thinking) theory of problem solving and process understanding is really clear, which is also fancy, theoretically, but in practice it is meaningful. Take an example, ecosystem process dynamics, I believe every individual science (hydrology and hydraulics, forestry, geomorphology, geology, atmospheric physics) has to understand all the complexities in it and fight for the rigor in the science. Then, based on the massive complexities explored, it could be possible to move backward to less level of complexities. My point here is all, or at least some, scientists need to go as complex as possible to understand and make all the possible models (model complex realities). After having the possible complex models and their relevance in understanding the reality, then it is possible to pursue the “reasonable” complex imagination of the system.

    Moreover, what we think is irrelevant and less recognized in the current public, could be the one thing that solve many problem of tomorrow’s world.

    I understand that managers, public planners, and the politicians do not like complexes, even if they are interested they don’t have time to understand it, and aims to solve problems at hand for the current generation (probably before this term election). Yea, for those people, we need to use our system thinking so s to solve a particular policy issue.