Jeroen P. van der Sluijs and Kjellrun Hiis Hauge j.p.vandersluijs@uu.nl

1. People and the Sea V: Living with Uncertainty and Adapting to Change ECOST PANEL: SCIENCE, STATE AND SOCIETYAmsterdam 9 July 2009Plurality of views in fishery science for policy Jeroen P. van der Sluijs and Kjellrun Hiis Hauge j.p.vandersluijs@uu.nl Copernicus Institute for Sustainable Development and InnovationUtrecht University

2. Diagnosis Two strategies dominate, uncertainties are either: • downplayed to promote political decisions (enforced consensus), or • overemphasised to prevent political action • Both promote decision strategies that are not fit for meeting the challenges posed by the uncertainties and complexities faced. • This delays a transition to sustainability. • Post normal science is crucial to better cope with uncertainty, scientific dissent & plurality in sustainability science.

3. Complex sustainability issues Typical characteristics (Funtowicz & Ravetz): • Decisions urgent • High decision stakes • Values in dispute • Deep uncertainties • More research  less uncertainty • Knowledge produced through models, scenarios, assumptions, extrapolations • (hidden) value loadings Knowledge Quality Assessment is essential

4. A practical problem: Protecting a strategic fresh-water resource 5 scientific consultants addressed same question: “which parts of this area are most vulnerable to nitrate pollution and need to be protected?” (Refsgaard, Van der Sluijs et al, 2006)

5. 3 framings of uncertainty (Van der Sluijs, 2006) 'deficit view' • Uncertainty is provisional • Reduce uncertainty, make ever more complex models • Tools: quantification, Monte Carlo, Bayesian belief networks 'evidence evaluation view' • Comparative evaluations of research results • Tools: Scientific consensus building; multi disciplinary expert panels • focus on robust findings 'complex systems view' • Uncertainty is intrinsic to complex systems: permanent • Uncertainty can be result of new ways of knowledge production • Acknowledge that not all uncertainties can be quantified • Openly deal with deeper dimensions of uncertainty • Tools: Knowledge Quality Assessment “speaking truth to power” vs “working deliberatively within imperfections”

6. How to act upon such uncertainty? • Bayesian approach: 5 priors. Average and update likelihood of each grid-cell being red with data (but oooops, there is no data and we need decisions now) • IPCC approach: Lock the 5 consultants up in a room and don’t release them before they have consensus • Nihilist approach: Dump the science and decide on an other basis • Precautionary robustness approach: protect all grid-cells • Academic bureaucrat approach: Weigh by citation index (or H-index) of consultant. • Select the consultant that you trust most • Real life approach: Select the consultant that best fits your policy agenda • Post normal: explore the relevance of our ignorance: working deliberatively within imperfections

7. Different perspectives on fisheries facts • Fishermen vs scientists • Geographical scale units • Day-by-day experiences vs estimated averages • Mixed catches vs stock by stock analyses • Predictability of stocks (Degnbol 2003)

8. Fish stock prediction Alan Christopher Finlayson (1996) Fishing for Truth: A Sociological Analysis of Northern Cod Stock Assessments from 1977 to 1990 Main message: Failures to predict fish stocks is closely related to the failure to recognize how scientists' interpretations of natural reality are themselves socially constructed to a crucial degree.

9. Stock is a problematic concept • Stocks are not independent, predator-prey etc. • Sub-populations exist within one species (demonstrated with genetic studies of North Sea cod, sandeel) • Mismatch between biological stock and management stock • Fishing pattern may be influenced by how a stock is defined • Hyper-precision of stock predictions • three significant digits used by ICES where only one is warranted: 2008 quota blue whiting North East Atlantic < 835000 tons • one significant digit could give 10% to 100% fluctuations in quota from year to year, which is politically undesirable

10. Mathematical fishing Two categories of Models in fish management 1. Modeling blindfolded: non-biologists or biologists deeply ensconced in the political system -> politically acceptable optimistic answer -> Uncertainties are hidden 2. Models as “Fig Leaves, Shields and Clubs” -> something to hide behind -> device to create unchallengeable authority -> insulator: protecting agency scientists and fishery managers from attack by politicians who want to please unhappy fisherman ‘The use of a model to reduce fishing pressure on a species – even if model is wrong – is better than the alternative of just an expert opinion that can be refuted by an other expert’ Pilkey & Pilkey, 2007 book

11. Models of Science and Policy ‘Modern’ model: Perfection and perfectibility • Facts determine correct policy • The true entails the good • No limits to progress of control over environment • No limits to material & moral progress • Technocratic view • Science informs policy by producing objective, valid and reliable knowledge: “Speaking truth to power” (Funtowicz, 2006; Funtowicz & Strand, 2007)

12. Fishery science for fishery policy • EC calls for objective and impartial scientific advice; Clear division of labour between science and management. • International Council of the Exploration of the Sea (ICES): Gathering facts about marine ecosystem; “unbiased, non-political advice”, objectivity, integrity. -> S-P-Interface based on “Modern Model”

13. limitations of modern model • Objective, valid and reliable, but... - is information really objective? [values, interests] • is it valid? [assumptions, models, scenarios] • is it reliable? [uncertainty, ignorance] Modern model assumes that: • Uncertainty can be eliminated or controlled • Only one correct system description • as if system and problem are not complex (Funtowicz, 2006; Funtowicz & Strand, 2007)

14. Responses to limitations of Modern Model • Denial • Accommodations • Rethinking (Funtowicz, 2006; Funtowicz & Strand, 2007)

15. Rescuing Modern Model from uncertainty: The Precautionary Model • Imperfection in science: • “lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation” (Rio Declaration 1992) • EU: proportionality • not PP but extended cost-benefit analysis • Normative principle still in terms of quantitative science and modern rationality (CBA) • What if we can not know what kind of surprises a new technology may lead to (Funtowicz, 2006; Funtowicz & Strand, 2007)

16. Rescuing the modern model from indeterminacy The Framing Model • In absence of conclusive facts, science is one of many inputs in policy, functioning as evidence in the discourse. • Conflicting certainties, multitude of alternative framings defendable • Rescue: Dialogue, participation, inter-subjective knowledge, consensus formation, robustness, upstream engagement • Works if framing problem is one of bias and bounded rationality (“blinkers”) • Retains the modern ideal of certain scientific knowledge But... it is a matter of necessary choices, not of unnecessary biases. (Funtowicz, 2006; Funtowicz & Strand, 2007)

17. Rescuing the modern model from conflict of interests Model of science/policy demarcation • Acknowledges expert disagreement and bias, but diagnoses and prescription differ from framing model • Framing: make values explicit; demarcation: values = politics, facts = science, keep separated! • Ensure that political accountability is not shifted to scientist, keep science objective and value free • Call for independent studies, sound science, strict separation of risk assessment and risk management • But... Complexity, Indeterminacy, fundamental impossibility of value free science (Funtowicz, 2006; Funtowicz & Strand, 2007)

18. Shifts in fishery science for policySeeking the true Total Allowable Catch (TAC) • 1972 Maximum Yield • 1977 Maximum Sustainable Yield (MSY) • 1991 Minimum Biological Accepted Level • 1995 Precautionary approach • precautionary reference points interpreted as target in stead of limit: optimising fish quota on the edge of precaution • 2001 Ecosystem Approach to Fisheries (EAF); multi-species management • 2002 MSY extended to include stock production: Johannesburg UN Summit: restore stocks to MSY levels by 2015 • 2006 Harvest Control Rules (HCR) • Trend to incorporate economy and fisherman into HCR evaluation tools

19. Summary of responses to problems of modern model • Imperfection • Policy modified by precaution • Misuse • Problems (co-)framed by multiple disciplines and stakeholders • Abuse • Protect science from political pressures and interests (Funtowicz, 2006; Funtowicz & Strand, 2007)

20. In case of complex problems, all modifications of modern models fail because: • Truth cannot be known and is thus not a substantial aspect of the issue • “... good scientific work has a product, which should ... correspond to Nature as closely as possible... But the working judgements on the product are of its quality, and not of its logical truth.” (Funtowicz and Ravetz 1990, p. 30)

21. The alternative model: PNS Extended participation: working deliberatively within imperfections • Science is only one part of relevant evidence • Critical dialogue on strength and relevance of evidence • Interpretation of evidence and attribution of policy meaning to knowledge is democratized • Tools for Knowledge Quality Assessment empower all stakeholders to engage in this deliberative process (Funtowicz, 2006; Funtowicz & Strand, 2007)

22. Elements of Post Normal Science • Appropriate management of uncertainty quality and value-ladenness • Plurality of commitments and perspectives • Internal extension of peer community (involvement of other disciplines) • External extension of peer community (involvement of stakeholders in environmental assessment & quality control)

23. PNS in practice: Tools & checklists for Knowledge Quality Assessment SCIENCE VOL 316 13 APRIL 2007 “Today, eight years on from the Dutch scandal, no one makes more strenuous efforts than does the Netherlands’ RIVM to accommodate and cope with the uncertainties of environmental data and models, hence to achieve the greatest possible quality in generating environmental foresight.” (Bruce Beck)

24. NL Environmental Assessment Agency (RIVM/MNP) Guidance: Systematic reflection on uncertainty & quality in:

25. Dimensions of uncertainty • Technical (inexactness) • Methodological (unreliability) • Epistemological (ignorance) • Societal (limited social robustness)

26. NUSAP: Qualified Quantities Classic scientific notational system: • Numeral Unit Spread For problems in the post-normal domain, add two qualifiers: • Assessment & Pedigree “Assessment” expresses expert judgement on reliability of numeral + spread “Pedigree” expresses multi-criteria evaluation of the strength of a number by looking at: • Background history by which the number was produced • Underpinning and scientific status of the number

27. Pedigree matrix for evaluating the tenability of a conceptual model

28. Model Quality Assessment • Models are tools, not truths • A model is not good or badbut there are ‘better’ and ‘worse’ forms of modelling practice • Models are ‘more’ or ‘less’ useful when applied to a particular problem. Model Quality Assessment can provide: • insurance against pitfalls in process • insurance against irrelevance in applicationrefs: www.mnp.nl/guidanceRisbey, J., J. van der Sluijs, et al. (2005): Application of a Checklist for Quality Assistance in Environmental Modelling to an Energy Model. Environmental Modeling & Assessment10 (1), 63-79.

29. A model can have higher fitness for function (=quality) by being a less true representation of reality! models as “stylized facts”

30. Uncertainty and model complexity

31. Books Websites: http://www.jvds.nl http:// www.postnormaltimes.net http:// www.nusap.net http://alba.jrc.it/ibss

32. References Post Normal Science: Funtowicz SO, Ravetz JR. Uncertainty and quality in science for policy. Dordrecht: Kluwer; 1990. 229 pp. Funtowicz SO, Ravetz JR. Science for the post-normal age. Futures 1993;25(7):735–55. Ravetz, J. 2006, The no-nonsense guide to science, New Internationalist. van der Sluijs, JP, 2002, A way out of the credibility crisis around model-use in Integrated Environmental Assessment, Futures, 34 133-146. Science Policy Interface Van der Sluijs JP. 2005. Uncertainty as a monster in the science policy interface: four coping strategies. Water Sci Technol 52:87–92. Funtowicz S, Strand R. Models of science and policy. in: Traavik, T. and Lim, L.C. (eds.) Biosafety First: Holistic Approaches to Risk and Uncertainty in Genetic Engineering and Genetically Modified Organisms (forthcoming) Tapir Academic Press, Trondheim Funtowicz SO. 2006. Why knowledge assessment? In: Interfaces between Science and Society (Guimarães Pereira Â, Guedes Vaz S, Tognetti, S, eds). Sheffield: Greenleaf Publishers, 138–145. Guimarães Pereira Â, Guedes Vaz S, Tognetti, S, (eds), 2006. Interfaces between Science and Society. Sheffield: Greenleaf Publishers van der Sluijs JP, 2007, Uncertainty and Precaution in Environmental Management: Insights from the UPEM conference, Environmental Modelling and Software, 22, (5), 590-598.

33. Relfective approaches to uncertainty J.P. van der Sluijs, A.C. Petersen, P.H.M. Janssen, James S Risbey and Jerome R. Ravetz (2008). Exploring the quality of evidence for complex and contested policy decisions, Environmental Research Letters, 3 024008 (9pp) Janssen PHM, Petersen AC, van der Sluijs JP, Risbey JS, Ravetz JR. 2005. A guidance for assessing and communicating uncertainties. Water Sci Technol 52:125–131. Risbey, J., J. van der Sluijs, P. Kloprogge, J. Ravetz, S. Funtowicz, and S. Corral Quintana (2005): Application of a Checklist for Quality Assistance in Environmental Modelling to an Energy Model. Environmental Modeling & Assessment Vol. 10 (1), 63-79. Uncertainty tools Jens Christian Refsgaard; Jeroen P van der Sluijs; Anker Lajer Højberg; Peter A Vanrolleghem (2007), Uncertainty in the Environmental Modelling Process: A Review, Environmental Modelling & Software. 22 (11), 1543-1556. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M and Tarantola S 2008 Global Sensitivity Analysis: The Primer (Chichester: Wiley) Saltelli A, Chan K, Scott M. 2000, Sensitivity analysis. Probability and statistics series. John Wiley & Sons Publishers; 475 pp. Saltelli A, Tarantola S, Campolongo F, Ratto M. 2004, Sensitivity analysis in practice: a guide to assessing scientific models. JohnWiley & Sons Publishers; 219 pp. van der Sluijs JP, Craye M, Funtowicz SO, Kloprogge P, Ravetz JR, Risbey JS. 2005. Combining quantitative and qualitative measures of uncertainty in model based environmental assessment: the NUSAP system. Risk Anal. 25:481–492. van der Sluijs, J.P. 2006, Uncertainty, assumptions, and value commitments in the knowledge-base of complex environmental problems, in: Ângela Guimarães Pereira, Sofia Guedes Vaz and Sylvia Tognetti, Interfaces between Science and Society, Green Leaf Publishing, 2006, p.67-84.