Values in Worst-Case Scenarios Per Wikman-Svahn, Ph.D. Researcher, - PowerPoint PPT Presentation

Values in Worst-Case Scenarios Per Wikman-Svahn, Ph.D. Researcher, Department of Philosophy and History Royal InsAtute of Technology KTH, Stockholm

Background • IniAal phase of a 2-year research project on values in worst case scenarios. • Research grant (post-doc) funded by the Swedish Civil ConAngencies Agency MSB. • Based on own experience on assessing worst case scenarios, especially for climate change adaptaAon and impact assessments.

What is a “worst case scenario”? 1. Informal use of the term: ”the most unpleasant or serious thing that could happen in a situaAon” (Cambridge DicAonary) • Such worst-case scenarios are typically very vague and highly value-laden. • E.g. one expert’s worst case scenario could differ a lot from another’s.

What is a “worst case scenario” (2) 2. Technical use of the term: – “Credible worst-case”, “plausible worst-case”, “pracAcal worst-case”, ”plausible upper- bounds” (Paté Cornell 1996, p 100). • Also, very value-laded concept – Why “credible”, “plausible” or “pracAcal”?

My interest here: the role of values in assessing and managing extreme outcomes • Not only worst-case scenarios, but the general realm of very bad outcomes. My interest: 1. ScienAfic assessments of extreme outcomes 2. Decision-making for managing the risks of extreme outcomes

2. CASE STUDY: ASSESSMENTS OF FUTURE SEA LEVEL RISE BY 2100?

Case study: sea level rise by year 2100 (maximal number in selected studies) 300 250 200 cm 150 100 50 0 Wikman-Svahn forthcoming

IPCC 2007 sea level projecAons IPCC 2007, Table 3.1 “The sea level projecAons do not include uncertainAes in climate-carbon cycle feedbacks nor do they include the full effects of changes in ice sheet flow.” (IPCC 2007 , p 46 ).

And how was this interpreted in pracAce? Examples from Sweden: • Swedish Meterological and Hydrological InsAtute (SMHI 2007): 0.18-0.59 m • County AdministraAve Boards: 0.18-0.59 m (Länsstyrelserna i Skåne och Blekinge län 2008) • Municipality Kävlinge: 0.6 m (planning document) von Oelreich, J., Carlsson-Kanyama, A., Svenfelt, Å., & Wikman-Svahn, P. (2013). Planning for future sea-level rise in Swedish municipaliAes. Local Environment, 1–15.

IPCC 2013 sea level projecAons Table SPM.2 | Projected change in global mean surface air temperature and global mean sea level rise for the mid- and late 21st century relative to the reference period of 1986–2005. {12.4; Table 12.2, Table 13.5} 2046–2065 2081–2100 Scenario Mean Likely range c Mean Likely range c RCP2.6 1.0 0.4 to 1.6 1.0 0.3 to 1.7 Global Mean Surface RCP4.5 1.4 0.9 to 2.0 1.8 1.1 to 2.6 Temperature Change (°C) a RCP6.0 1.3 0.8 to 1.8 2.2 1.4 to 3.1 RCP8.5 2.0 1.4 to 2.6 3.7 2.6 to 4.8 Scenario Mean Likely range d Mean Likely range d RCP2.6 0.24 0.17 to 0.32 0.40 0.26 to 0.55 RCP4.5 0.26 0.19 to 0.33 0.47 0.32 to 0.63 Global Mean Sea Level Rise (m) b RCP6.0 0.25 0.18 to 0.32 0.48 0.33 to 0.63 RCP8.5 0.30 0.22 to 0.38 0.63 0.45 to 0.82 IPCC (2013) AR5 WGI SPM SLR ranges are “ likely ” with “ medium confidence ”

How was this interpreted? The authors of the IPCC sea level chapter had to clarifiy what they meant in a leker published in Science: • “The upper boundary of the AR5 “likely” range should not be misconstrued as a worst-case upper limit, as was done in Kerr’s story as well as elsewhere in the media and blogosphere.” (Church et al. 2013b, p 1445). • “roughly a one-third probability that sea-level rise by 2100 may lie outside the ‘likely’ range” (Church et al. 2013b, p 1445).

What can we learn from this? • IPCC assesses & communicates only a limited number of outcomes. • IPCC essenAally silent on “worst-case scenarios”. • Media, naAonal reviews, and local decision- makers in many cases just take the IPCC numbers at face value.

How to communicate knowledge is a choice, which reflects values Other choices could have been made. IPCC could perhaps have said the following of the global mean sea Table 1. Likelihood Scale level rise by year 2100: Term* Likelihood of the Outcome “It will be less than 80 meters.” Virtually certain 99-100% probability • Very likely 90-100% probability “it is virtually certain to be less • than 20 meters (high Likely 66-100% probability confidence)”? About as likely as not 33 to 66% probability Unlikely 0-33% probability “it is very likely to be less than 2 • meters”? Very unlikely 0-10% probability Exceptionally unlikely 0-1% probability “it is about as likely as not to be • more than 0.5 meters” Guidance Note for Lead Authors of the IPCC FiEh Assessment Report on Consistent Treatment of UncertainIes .

PracAcal problem for the value-free ideal of science • The Bayesian response to the challenge from inducAve risk (Jeffrey 1956) does not work in pracAce, as hearers may foreseeable interpret statements, which introduces moral responsibility (a point made by many).

3. THE MODEL

A model of the informaAon flow in science for policy Hansson & Aven (2014)

• Examples: – Individual studies published in scienAfic journals. • Values influence for worst-case scenarios: 1. Research quesAons asked 2. Types of methods, models used. 3. Exploring full uncertainty range in parameters. 4. Communicate uncertainty. • Bias against publishing on worst-case scenarios? – ”Erring on the side of the least drama” (Brysse et al 2012). – ” ScienAfic reAcence and sea level rise” (Hansen 2007)

Example: comparing two studies 300 250 200 cm 150 100 50 0 Wikman-Svahn forthcoming

Pfeffer et al 2008: Max 200 cm Table 3. SLR projections based on kinematic sce- narios. Thermal expansion numbers are from ( 22 ). • “conclude that SLR equivalent (mm) Low 1 Low 2 High 1 increases in excess of 2 Greenland Dynamics 93 93 467 meters are physically SMB 71 71 71 Greenland total 165 165 538 untenable.” (p 1340) Antarctica PIG/Thwaites dynamics 108 394 Lambert/Amery dynamics 16 158 Antarctic Peninsula 12 59 dynamics SMB 10 10 Antarctica total 146 128 619 Glaciers/ice caps Dynamics 94 471 SMB 80 80 GIC total 174 240 551 Thermal expansion 300 300 300 Total SLR to 2100 785 833 2008 Pfeffer, W. T., Harper, J. T., & O’Neel, S. (2008). KinemaAc constraints on glacier contribuAons to 21st-century sea-level rise. Science (New York, N.Y.), 321(5894), 1340–3.

Sriver et al 2011: Max 225 cm Thermal expansion from Pfeffer et al 2008 30 cm Sriver et al 2012 IPCC 2007 Max 55 cm Max 45 cm Sriver, R. L., Urban, N. M., Olson, R., & Keller, K. (2012). Toward a physically plausible upper bound of sea-level rise projecAons. ClimaAc Change, 115(3–4), 893–902

• Examples: – General assessments (e.g. IPCC), Meta-studies (e.g. Review papers), Textbooks • Values influence for worst-case scenarios: 1. When reviewing the literature. 2. Communicate uncertainty. • Even stronger bias against worst-case scenarios?

• Examples: – NaAonal governmental climate assessments, Regional/local governmental climate assessments, Private company assessments • Values influence for worst-case scenarios: 1. ”This evaluaAon has to take the values of the decisionmakers into account” (Hansson & Aven, p 1177). 2. But risk that experts use values they have learnt from working in Box 1?

• Examples: – Deciding on building a railway tunnel, naAonal building standards, insurance policies etc. • Values influence for worst-case scenarios: 1. Highly influenced by non-epistemic values. 2. Risk that worst-case scenarios are downplayed, because decision maker cannot handle the consequences of them.

Photo: Lasse Modin, SKB

What values are jusAfied? • I will use John’s (2015) proposal that we should focus on the difference between “private” and “public” communicaAon. • “private” communicaAon – “speakers aim to communicate to ex-ante known individuals.” • “public” communicaAon – “speakers communicate to ex-ante unknown audiences.”

Arguments for fixed&high epistemic standards for public com. (John 2015) 1. Cannot use floaAng standards because cannot know the audience’s needs ex ante. 2. Fixed epistemic standards are more efficient – For scienAfic community. – For wider society 3. High epistemic standards more efficient – Everybody can agree on the claims based on high fixed epistemic standards.

But for private communicaAon… 1. Experts can know “their audience’s proper epistemic standards for acceptance“ 2. Efficiency gains of fixed standards not as important. 3. Efficiency gains of high standards not as a important. • The argument from inducAve risk is much stronger for private communicaAon! – See also ”The scienAst qua policy advisor”(Steele 2012).

Private communicaAon: FloaAng epistemic standards Public communicaAon: Fixed & high epistemic standards

IPCC 2013 vs US NaAonal Climate Assessment (Parris et al 2012) 300 250 200 cm 150 100 50 0