Values in sustainability decision making

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Journal of Cleaner Production xxx (2015) 1e11

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Journal of Cleaner Production

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Incorporating values into sustainability decision-making

Lawrence MartinErasmus University, Rotterdam, Netherlands

a r t i c l e i n f o

Article history:Received 17 September 2014Received in revised form2 February 2015Accepted 6 April 2015Available online xxx

Keywords:ValuesDecision scienceSustainabilityMulti-criteriaPhronesis

E-mail address: [email protected].

http://dx.doi.org/10.1016/j.jclepro.2015.04.0140959-6526/Published by Elsevier Ltd.

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a b s t r a c t

This paper explores rigorous methods to transparently incorporate values in sustainability decision-making. Empirical, normative and other decision-making methods are discussed using a conceptualarchitecture borrowed from the Aristotelian ideas of Episteme, Techne and Phronesis. The applicationand limits to positivist reasoning for decision-making is explored through discussions of wicked andtame problems (where the introduction of values is discussed), the analytic-deliberative framework (thatcharacterizes most assessment methods), and postenormal science. An example examining air qualityregulation and enforcement is used to explore concepts. Recognizing the continuum of quantitative toqualitative decision-making calculus, and how to apply it constructively to decision-making is an area ofneeded inquiry for scientists, policy-makers, consultants and corporate leaders concerned about helpingto effect the transition to more sustainable societal patterns. This necessitates researchers and decisionmakers acknowledge that sustainability preferences are driven by values. This author concludes thatdecision-making methods that provide a transparent means to value outcomes and to integrate disparateinformation and perceptions (and values) have been demonstrated to be the most useful in settings witha variety of stakeholders that value different outcomes. Such conditions are typical in natural resourceand sustainability problems where trade-offs are often necessary.

Published by Elsevier Ltd.

1. Introduction

This paper is a theoretical andmethodological exploration of theincorporation of values in sustainability decision-making. In gen-eral, the incorporation of value-based judgment occurs on a con-tinuum from analytical and objective to biased and subjective.Science has an interesting history of grappling with where to drawthe line on what value-judgments will be validated and what willbe dismissed as unsubstantiated. Sustainability, in contrast to fluiddynamics, for example, is subject to greater subjectivity by theresearchere from problem formulation and the selection of data, tointerpretation of results. Sustainability and sustainable develop-ment follow from policy and judgments very much informed byvalues. Sustainability decisions are contextual, value laden, andoften focused on social action. In the quest for relevance andpersuasive power, researchers seek to design studies and to explainresults and recommendations with as great a rigor as possible.Understanding the utility and productive use of values in thecontext of the science of decision making and sustainability sciencecan aid the practice of sustainability decision-making through the

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deliberate, judicious and transparent use of informed value-basedjudgment.

This paper is organized as a selective review of decision scienceand sustainability science literature, highlighting features of boththat are relevant to the use of value judgment in sustainabilitydecision-making. By weaving together elucidation of key conceptsand the use of an example, systematic methods are described foranchoring judgment based on values into sustainability decision-making with rigor and transparency.

The science of decision making and sustainability science eachhave rich literatures, decision science in particular having mush-roomed with applications throughout business, research and thesocial sciences. Sustainability science has also grown tremendouslyin recent years as governments and other institutions have workedto incorporate sustainability objectives into their decision-making.This paper is focused on how to incorporate the normative, valuesdimension of sustainability into decision-making for sustainableoutcomes. It explores the Aristotelian concept of phronesis, theincorporation of values into judgments. The author acknowledges anormative framework that advances environmental resource andecosystem management as primary to sustainability decision-making, predicated on the belief that ecosystems are the primarysource for all resulting social and economic conditions. This idea

ustainability decision-making, Journal of Cleaner Production (2015),

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L. Martin / Journal of Cleaner Production xxx (2015) 1e112

was explored in the book, For the Common Good, by Daly and Cobb(1994).

This author examined decision-making method, which isdistinguished from decision support or “problem analysis” (Kepnerand Tregoe, 1965). Good decision-making begins with the properframing of the problem and selection of decision support tools toinform the analysis (NRC, 2009). This is typically a recursive anddeliberative process between framing the problem, consideringdecision support studies or methods to inform the analysis, andcriteria by which the decision is made.

In contrast, decision support is less a process, and more adiscrete tool, model, or data set. Consider the difference in use ofenvironmental indicators and environmental accounting.

The System of Integrated Environmental and Economic Ac-counting (SEEA) was introduced in 2003 to standardize environ-mental indicators and accounting methods for national accounts. Itis available as the Handbook of National Accounting: IntegratedEnvironmental and Economic Accounting e An OperationalManual.1 Ziegler and Ott (2011) observed that the SEEA covers awide range of conceptual and empirical issues relevant to sus-tainability; and the use of indicators can be useful to measure weakand strong sustainability. Indicators provide useful measurement ofdata, and are thus valuable as decision support tools. A decision-making method is then used to place the data measurements (orother information) into a context, such as an accounting frame-work, to inform a decision. Ideally, such a framework providestransparency on what criteria were used to make the decision. TheSEEA provides both a library of decision support indicators, as wellas an accounting framework to evaluate the data in the situationunder study. A decision-making method is still required to use theinformation productively to inform a decision.

Both decision and sustainability science share an investigationof the proper role for (or balance of) a positivist, scientific processversus purposeful inclusion of subjective values into decision-making. Considered on a spectrum, the information consideredcan range from fully reproducible physical science to a time andplace-specific opinion survey. The means to incorporate values intothe decision-making process while preserving rigor constitutes theprimary dimension of this review. It was not intended that thisreview should provide a survey of the full range of theories ormethods employed in either decision or sustainability science. Itprovides grounding in both fields, with a particular focus on howinformation can be used to advance sustainability in environmentaldecision-making and resource management.

1.1. An introduction to decision science

Seminal works in decision science are considered to include vonNeumann and Morgenstern's Theory of Games and EconomicBehavior (1944), Savage's The Foundations of Statistics (1954), andLuce and Raiffa's Games and Decisions (1957). Other importantworks include De Groot's Optimal Statistical Decisions (1970) andBerger's Statistical decision theory and Bayesian Analysis (1985).Decision Sciences: An Integrative Perspective by Kleindorfer et al.(1993), offers a comprehensive survey of the numerous disci-plines contributing to the formation of a decision science (e.g.economics, political science, sociology). They observed that thescience is focused on descriptive and prescriptive attributes ofdecision-making that is distinguishing between understandinghow humans typically make decisions, in contrast to developingand refining rational models of choice (e.g. utility theory). Theauthors noted that these two areas of research are integrated,

1 http://unstats.un.org/unsd/pubs/gesgrid.asp?id¼235 Accessed 3/7/2014.

Please cite this article in press as: Martin, L., Incorporating values into shttp://dx.doi.org/10.1016/j.jclepro.2015.04.014

largely through the descriptive studies informing prescriptivedecision-making methods.

The focus in this inquiry is less on understanding how peoplemake decisions, and accordingly, more on the theory and methodsavailable to make decisions (prescriptive decision-makingmethods). Rational Choice Theory has been the most prominentand influential approach for shaping the social sciences, whichevolved from the naturalist-positivist tradition (Hausman, 2013).The fundamental theory holds that patterns of behavior developwithin society that reflect individuals' choices as they maximizebenefits and minimize costs (Hausman, 2013). The theory has beenwidely translated into predictive models, most significantly andsuccessfully in economics to describe markets.

An Introduction to Decision Theory by Peterson (2009) is notablefor the author's attention to theory, and for his philosophicalgrounding which is not widely found emphasized in other textsthat discuss methods. Peterson observed that decision theory iscommonly understood to be comprised of three largely separabletopics: individual decision-makingwhere the theory of maximizingexpected utility is the dominant paradigm, game theory with itscharacteristic concernwith concepts such as equilibrium strategies,and social-choice theory, which is largely the theme focused uponin this literature review.

A social choice decision-making method of used for addressingenvironmental problems that may have multiple (and sometimescompeting) variables for optimization is multi attribute utilitytheory (MAUT). A useful survey of this approach was written byFigueira et al. (2005) in Multiple Criteria Decision Analysis: State ofthe Art Surveys. Because authors of this book explored various di-mensions of MAUT, the reader receives a broad understanding ofissues such as decision-maker's strength of preference, judgingriskiness, and additive and multiplicative forms of MAUT.

Hossein Arsham, in his web-based matrix of decision sciencecompanion sites,2 described how quantitative models can incor-porate values by positing them as quantifiable problems (e.g. sus-tainable fishery ¼ recruitment > or ¼ to harvest (þmortality). Thevalues must be reflected in construction of the model itself.Arsham's discussions on decision science are organized on-line,searchable, and include an inventory of quantitative decision-making methods with notes on their applicability.

1.2. An introduction to sustainability science

Kates et al. (2001) and twenty-two colleagues published a policyforum piece in Science that outlined sustainability science in broadstrokes as: “A new field … that seeks to understand the funda-mental character of interactions between nature and society and toencourage those interactions along more sustainable trajectories.”Seven core questions were proposed by Kates et al. to guide thestudy in sustainability science with an emphasis on understandingthe systems complexities associated with sustainability. Sustain-ability science was presented as studying and representing theinteractions, behaviors and emergent properties of natural andsocial systems, and providing decision-makers with improved in-formation on the effects of various forms of behaviors or in-terventions (Swart et al., 2004). Of the seven questions, two are keyfor this inquiry e “what are the principle tradeoffs between humanwell-being and the natural environment,” and “can there bemeaningful limits that would provide “warning” for human-environment systems?” The other questions are second orderpertaining to matters of measurement, model development,

2 http://home.ubalt.edu/ntsbarsh/business-stat/opre504.htm Accessed on1.25.2015.


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L. Martin / Journal of Cleaner Production xxx (2015) 1e11 3

guidance, trends and evaluation. These two questions are basedupon values used to characterize human well-being, and how it isbest served.

The Proceedings of the National Academy of Sciences (PNAS, theAcademy's weekly news publication3) editorial board announcedthe creation of a Sustainability Sciences section in 2007 and itcontinues to maintain a current literature website.4 In that sameyear, Harvard's Initiative on Science and Technology for Sustain-ability5 ceased operation and support for a sustainability sciencewebsite,6 and transferred it to the American Association for theAdvancement of Science (AAAS).7 This hub provided a refereedsource for key literature through 2011, when it ceased to update itssources. This foment of scholarship was struggling to create a sci-entific research paradigm. Bettencourt and Kaur (2011) charted theevolution of the paradigm by performing bibliographic analyses ofpapers written between 1974 and 2010. They compiled an extensivedatabase of approximately 20,000 papers authored by about 37,000authors. Bettencourt and Kaur noted that by using network analysisof co-authorship, sustainability science unified around the year2000, with most scholars and places connected with links ofauthorship. They assert that the scholarship created a new field,judged by the emergence of extensive scientific collaboration.

Kates (2011), in a subsequent analysis of the field's growth andstatus, observed that the choice of search terms used by Betten-court and Kaur to build their publication database “is probably notequivalent to sustainability science.” None-the-less, based on Bet-tencourt and Kaur's paper, he observed that the number of articlesbegan to grow rapidly in the 90s and had continued to double every8 years since then. In Kates (2011) the author characterized himselfas a sustainability science “insider” and indeed he was a principaldriver along with William C. Clarke, in building the HarvardInitiative on Science and Technology for Sustainability. Kates andClarke were also editors for the PNAS Sustainability Sciences sec-tion. In his 2011 paper, published in PNAS, Kates concluded thatsustainability science is a “different kind of science, primarily useinspired … with significant fundamental and applied knowledgecomponents, and commitment to moving such knowledge intosocietal action.” Ziegler and Ott (2011) concurred that sustainabilityscience does not fit easily within established criteria of the qualityof science. They noted that four features of sustainability scien-cednormativity, inclusion of nonscientists, urgency, and coopera-tion of natural and social scientists result in the explication andarticulation of values and principles. They observe that sustain-ability science appears to “rest on shaky ground” when examinedusing “customary disciplinary approaches” because of the inclusionof normative consideration of values and principles.

A thorough overview was published by Kates as: Readings inSustainability Science and Technology (2010). This reader iscomprised of three parts. Part 1 is an overview of the dual goals ofsustainable developmentdthe promotion of human developmentand well-being while protecting the earth's life support systems. Itconcludes with discussion of the interactions of human society andEarth's life support systems. Part 2 covered the emerging scienceand technology of sustainability. Part 3 discussed the innovative

3 http://www.pnas.org Accessed 7/27/2014.4 http://sustainability.pnas.org/Accessed 2/28/2014.5 The ISTS It was initiated in 2001 to help channel perspectives to the 2002World

Summit on Sustainable Development (WSSD) and hosted a series of followeupactivities during the five years after WSSD. The ISTS was based in the HarvardKennedy School's Sustainability Science Program. The Program continues to sup-ports initiatives in policy-relevant research, teaching, and outreach.

6 http://sustainabilityscience.org/document.html Accessed 2/28/2014.7 http://www.aaas.org/page/about-center-science-technology-and-sustainability

Accessed 7/27/2014.


solutions and challenges of moving sustainability science into ac-tion. The reader is provided a guided tour through the sustainabilityliteraturewith links to 93 articles or book chapters. The readings onthe science and technology of sustainability focus on its utility formanaging human-environment systems, and the goal of integrated,value-driven understanding. The “science of identifying andanalyzing values and attitudes” is particularly relevant to the focusof this author's review. Readings on the linking of knowledge sys-tems and action to address three critical needs: poverty, climatechange, and peace and security round out Kate's reader, providingboth a solid scientific treatment and a principled orientation to thesustainability challenge.

2. Methods

A selected review of decision science and sustainability scienceliterature was undertaken to identify key issues relevant to sus-tainability decision making. Elsevier identified decision scienceamong its headings for journals in the area of social science. Forty-two journals were listed under this heading, and range in scopefrom number theory to special applications in transportationmanagement.8 The review was focused on key words, initially“sustainability” and “decision science”, and introduced other termsas key concepts became illuminated, including “values”, “methods”,and “rational choice theory”.

The rigorous and transparent incorporation of values into sus-tainability decision-making was prioritized based on its relevancefor sustainability, and the dispute it engenders in the field of de-cision science. Secondary topics important for elucidating the pri-mary theme of values in sustainability decision making were thenprioritized for inclusion into the outline. A narrative describing howsystematic methods for anchoring judgment in values can beincorporated into sustainability decision-making with rigor andtransparency was created by weaving together elucidation of keyconcepts and the use of an example.

3. Empirical, normative and other approaches to decision-making

3.1. Positivism and scientific method

The basic premise in all decisions is that the best informationavailable, under the circumstances, was employed to deliberate andresolve the problem or choice. There are philosophically differentapproaches to decision-making within which different types ofinformation may be available or preferred. The strict positivistposition is that the scientific method allows science to growthrough a process of hypotheses followed by statements of testableempirical predictions and experiments that either support or refutethem.9 Karl Popper in his influential publication “Conjectures andRefutations. The Growth of Scientific Knowledge” (1963) described aprocess of conjectures and refutations that lies at the core of thescientific method. A proposition is only scientific if it is possible totest and disprove it. Much of decision science and particularly thatrelying on quantitative analyses fit into that positivist tradition.

In A General View of Positivism, Comte and Bridges (1865)established a hierarchy of sciences based upon the degree towhich the phenomena can be exactly measured and described.Mathematics is the metric employed to determine the position ofevery science in the hierarchy. Thus, it is the degree to which a

8 https://www.elsevier.com/social-sciences/decision-sciences/journals Accessed2/24/14.

9 http://en.wikipedia.org/wiki/Positivism Accessed 1/28/15.


http://www.pnas.org/

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http://sustainabilityscience.org/document.html

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science can be subjected to mathematical demonstration that it's“positivity” is ranked. Today, Comte's philosophy is the foundationof our current scientific approach for understanding the relation-ships between theory, practice, and comprehension of the world.His emphasis on a quantitative, mathematical basis for decision-making is the foundation of quantitative statistical analysis, anddecision science (Lodahl and Gordon, 1972). Many view this tech-nical rationality as the highest form of knowledge, and place greatfaith in science and the importance of leaving decisions to experts(Miller, 1993).

3.2. Normative science

Championing normative science is Thomas Kuhn, who in hisbook, The Structure of Scientific Revolutions (1962) describes thesocial structure of science as one of scientific communities that areconstituted by a shared faith in a paradigm. The brief introductionto sustainability science, in section 1.1, most assuredly fits thatdescription (although Kates (2011) disagreed, stating a preferencefor “post-paradigm”). Paradigms offer theory articulation, empiricalexperimentation, and measurement units; and thereby, provide aframework for deciding what scientific work is worth performing.Paradigms are structured by an ontological understanding of con-cepts, and a belief that the paradigm provides insight into somebasic reality. In Kuhn's view, scientific claims are adopted andrejected according to criteria that stem from the paradigm itself. Anormative science becomes established through scientific litera-ture, which leads to basic axioms, concepts, and mindsets, as wellas to conferences and peer-reviewed journals that make it possibleto assure the quality of research done within the scientific com-munity. Kuhn emphasized that positivist science is essentiallyshaped through the social processes occurring through its adher-ents, and thus becomes normative by virtue of being definedthrough a lens of values and principles (Ziegler and Ott, 2011).Sustainability science and decision science both comfortably residewithin this system. Sustainability (or sustainable development)essentially defines a set of normative values for the evaluation ofdecision options. That evaluation can be more or less positivistic.

3.3. Quantitative vs. qualitative methods

In examining decision-making methods, it is useful to include abrief treatment of the concept of “hard and soft science”. Sciencehas been characterized as on a continuum from hard to soft, withthe hardest employing a more rigorous scientific method, andsupported by quantifiable data and mathematical models, accuracyand objectivity (Lemons, 1996). According to Popper (1963) hardscience methods favor testable predictions that can be tested incontrolled experiments. In contrast, soft sciences either do notpossess that feature, or their predictions have a higher degree ofuncertainty. The origins of this distinction can be traced to AugusteComte's positivist philosophy of science. Interestingly, Comte'sgrand project was to apply the principles of positivism to what heviewed as the most complex of sciences, sociology (a term attrib-uted to him in many sources); and in contrast to the physical andnatural sciences, is considered by some a soft science.

The social sciences have seen substantive quantitative researchcontributions. Economics, in particular, has evolved from a highlyqualitative and philosophical “political economy” to a sciencelargely dominated by quantitative descriptions. However, theextent to which this has succeeded in a functional set of theoriesto accurately describe and predict economic activity has beenchallenged (Daly, 1996). Development of the social sciences fol-lowed a naturalistic model in America, seeking to emulatemethods used in the natural sciences to understand causality and


predict outcomes (Ross, 1992). This was accentuated by theemergence of behavioralism in the mid-20th century, with itsemphasis on predictive causal models to explain political behavior(Caterino and Schram, 2006). This is significant to this author'sdiscussion because of the fault line between decision methodsthat incorporate non-quantified or subjective information, andthose that do not.

In their introduction to Making Political Science Matter, Caterinoand Schram (2006) provided a lucid description of the positivistenterprise that arose from naturalism, and what they referred to asthe pluralism of post-positivism, which led to a variety of inter-pretive approaches to the social sciences (e.g. Critical Theory, Her-meneutics, Post-structuralism). The pluralism they wrote aboutreferred primarily to scientific methods; but they noted that thesocial sciences remain “constrained by” positivist hegemony.Nonetheless, social science is still widely considered to be softscience (within the framework of the hard sciences at least); and itis reasonable to state that economics, despite its quantitativeanalytical rigor is several steps removed from the hard sciences ofphysics and chemistry in its predictive capability. Quantificationworks reliably in deterministic systems, and has been proven to bevaluable in characterizing social systems, but its methods have notbeen found to be capable of achieving the same reliable degree ofpredictive ability as demonstrated in the physical (i.e. “hard”) sci-ences (e.g. physics, chemistry).

Return to Reason by Toulmin (2001) described the enchantmentof western thought with “universal rationality”. Universal ratio-nality was held as the gold standard for objective knowledge oftruth. Schram and Caterino (2006) discussed Toulmin's descriptionof universal rationality by underscoring that there was an idea thata distinctive scientific method existed that all sciences shouldshare, and that all other forms of knowledge were inferior to thedegree that they failed to conform to the dictates of the scientificmethod. Social scientists sought to emulate the precision andmathematical rigor of the physical scientists e “Physics envymorphed into science envy” Schram and Caterino (2006). Toulmin'smain point was that epistemological theory in the social scienceswas decontextualized from experience and observations, and wasabstracted in increasingly mathematical terms such that its utilityand fit for purposewere often challenged. He asserted that differentsorts of knowledge should emphasize different ways of knowing.He held that between absolutism and relativism lay “reasonable-ness” as a methodology for understanding and using information.Importantly, Toulmin's prescription was for a social theory basedupon practice.

3.4. Episteme, techne and phronesis

The distinctions between hard and soft science were firstidentified in the literature by Aristotle, not as such, but in terms ofknowledge types rather than a hierarchy of quantitative rigor. In theNicomachean Ethics, Aristotle described three approaches toknowledge and named them episteme, techne and phronesis. Epis-teme most closely approximated facts derived scientifically. Aris-totle likened episteme to what we describe as empirical science,arguing that it was based on observations and was useful to explainwhy things are as they appear. It can be loosely associated withtheory insofar as the concept was tied to the notion that epistemeexisted with or without our conscious attention to it. Techne, incontrast, was characterized as a productive state, associated withthe art of craftsmanship or technology; the practice of an art beingthe study of how to bring something into being. This can beinterpreted to mean the introduction a more subjective under-standing of knowledge through practice or contextual under-standing (Dunne,1997). InMaking Social Science Matter (2001) Bent



Flyvbjerg explained that whereas episteme concerns theoretical“knowwhy,” techne denotes technical “know how.”Whereas knowwhy unvarying, use of the knowledge, or know how, can vary byculture, introducing a normative feature into knowledge.

Aristotle observed that we might grasp the nature of phronesisif we consider the sort of people we call prudent. A prudent per-son is able to deliberate correctly about what action is good andadvantageous … but does not deliberate about things that areinvariable (episteme). They may deliberate about how to dosomething (techne), but whether to do it becomes a decisionabout action. Ultimately, the decision maker must decide if theoutcome is good e and who/what benefits; and this is based uponvalue judgment. The distinguishing quality of phronesis is areasoned decision about action with regard to whether theoutcome is thought to be advantageous (Aristotle considered thatthis quality belongs to those who understand the management ofhouseholds or states). Flyvbjerg (2001) characterized phronesis asemphasizing practical knowledge and practical ethics in areasoned deliberation about values with reference to praxis. Forthis reason, the concept of phronesis is of particular interest andconsequential for this inquiry.

The physical sciences have been highly successful in establishingscientific laws, and in so doing created a scientific standardembodied in the scientific method. Social scientists have tried tomimic themwith varying success, as expressed in Comte's notion ofscientific hierarchy and the deprecating concept of soft sciences.Because phronesis explicitly introduces values into judgment it ishighly subjective. In a scientific culture that values objectivity as thevirtual end in itself, subjective science is heavily discounted assubverting episteme with highly normative prescriptions, andlosing sight of the prize e immutable scientific laws. As Ziegler andOtt (2011) observe, sustainability science, thus, does not fit easilywithin the established criteria for quality science. Put simply, ourscientific culture has a bias against the incorporation of values insustainability science.

On occasion the dispute over the value of data collected usingqualitative methods, much less methods informed by a valueproposition breaks the surface. The most famous example of this inrecent times was the Socol hoax10 (B�erub�e, 2011), which was amajor battle in what was referred to as the science wars (Brown,2001). The “wars” were consequential because they contributed toshaping our beliefs in what information is valid for decision-making. The wars also helped to explicate and refine our under-standing of what information is appropriate for what types ofdecisions.

One of the most radical and important outcomes from the sci-ence wars was the publication of Bent Flyvbjerg's Making SocialScience Matter: Why Social Inquiry Fails and How It Can Succeed(2001). Flyvbjerg was critical of social science's pursuit of episteme(in contrast to Comte, who expected sociology to follow thequantitative methods of the physical sciences). Similar to Toulmin(2001), he asserted that sociology's pursuit of episteme is not itsstrongestmeans to advance, and that phronesis is the propermodelfor social science scholarship. Flyvbjerg argued that to be relevant,social science must inform praxis and that this should be under-taken with a focus on values. This is important because of therelevance of values to sustainability decision-making, which isdiscussed in section 4.3.

It is useful to recall that sustainability (and thus sustainabilityscience) is widely acknowledged as incorporating three principleareas of inquiry, and seeking to integrate them into a praxis fordecision-making: ecology, economics and social welfare

10 http://en.wikipedia.org/wiki/Sokal_affair Accessed on 1/29/15.


(Millennium Ecosystem Assessment, 2005). In each of these areasof inquiry the objectivity of the science may be challenged, andboth economics and social welfare are strongly normative, eachwith competing paradigms predicated on different philosophicalunderstanding (e.g. “welfare” vs. “laissez-faire” economics). Ecol-ogy, rooted in the natural sciences, is still none-the-less widelyviewed as normative due to the conservation bias in prescriptionsdrawn from the study of structure and function (de Laplante et al.,2011).

The principal objective for phronetic social science is toformulate problems and to conduct analyses that incorporate arange of methods that are both informed and motivated by valuesin society and aimed at social actione for which decision-making isimplicit. Among the core questions identified for sustainabilityscience by Kates (2011) are:

� “How can society most effectively guide or manage humanenvironment systems toward a sustainability transition?”

� “What are the principal tradeoffs between human well-beingand the natural environment?”

These areas of inquiry are problem driven, value laden, andfocused on social action that require suitable decision-makingmethods capable of incorporating the decision support informa-tion being generated. Phronetic decision-making methods are themost compelling, as is clearly implied by these sustainability sci-ence core questions.

4. Decision making methods and approaches

4.1. Wicked and tame problems

An important contribution to understanding the proper appli-cation of positivist reasoning for decision-making or other moresubjective (or value directed) strategies was provided in the liter-ature on wicked problems. Rittel and Webber (1973) coined theterm to describe a particular sort of problem that they describedwith ten characteristics, the first being the most consequential:“There is no definitive formulation of a wicked problem.” Animportant dimension of this is recognition that multiple stake-holders may bring different perspectives on the nature of theproblem informed by different values. The authors contrast wickedproblems with those considered “tame.” Tame problems, such asmathematics or optimization problems lend themselves well totechniques such as those widely practiced in quantitative ap-proaches to decision science.

Wicked problems are typical of the sort of problems associatedwith sustainability decision-making. Take for example Kate'sformulation: “What are the principal tradeoffs between humanwell-being and the natural environment?” Many indicators havebeen identified to provide a scientific bases for human well-being,but their selection entails social policy and are likely to be con-tested, because in a pluralistic society there is no incontestablepublic good, and no objective definition of equity. Moreover, soci-eties vary through time and space, and so at the very least anormative approach to such a problem, grounded within a socialcontext, would be necessary. Understanding that problems of thisnature, wicked problems, may require non-quantitative methods ishelpful as a criterion for considering preferred decision-makingmethods and thus the type of decision-making likely to occur.Recognizing the continuum of hard to soft, quantitative to quali-tative decision-making calculus, and how to apply it constructivelyto decision-making is an area of needed inquiry for sustainabilityscientists.


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4.2. A risk assessment case study of decision-making

Risk management, built upon risk assessment is a common typeof decision-making. An air pollution risk management case study ispresented to illustrate how a problem can be misunderstood astame, leading to a problematic outcome; but successfully resolvedonce its wicked qualities are properly understood. As is clarified insubsequent paragraphs, even the tame problems can be difficult,beginning with setting an ambient air pollution standard for par-ticulate matter.

This author uses a U.S. example of criteria air pollutant regula-tion for particulatematter (PM) pollution by the U.S. EnvironmentalProtection Agency (EPA). The law, process, current science and riskcharacterization for the regulation of PM are to be found in two EPApublications:

1. Integrated Science Assessment for Particulate Matter (U.S. EPA,2009)

2. Quantitative Health Risk Assessment for Particulate Matter (U.S.EPA, 2010)

In this context, a decision to list criteria air pollutants forregulation is made by the EPA Administrator when they mayreasonably be anticipated to endanger public health and welfare.EPA's listing and regulation of criteria air pollutants is required bythe U.S. Clean Air Act, and the pollutants are collectively referred toas the National Ambient Air Quality Standards (NAAQS). Theirstatus is updated every five years (U.S. EPA, 2009). Particulatematter is among the six NAAQS. The EPA prepares the IntegratedScience Assessment for Particulate Matter assembling all relevantinformation, including health effects, ambient air concentrations,exposure data, exposure pathways and mode of action. Then thesedata are organized into a Quantitative Human Health Risk Assess-ment. The risk assessment provides estimates of premature mor-tality and/or selected morbidity associated with levels of PM (both10 ug/m3 and 2.5 ug/m3), consideration of susceptible populations;and provides insights into the distribution of risks and patterns ofrisk reductions and the variability and uncertainties in those riskestimates (U.S. EPA, 2010).

The risk assessment strongly relies on quantitative data tomake a determination of what levels of PM are acceptable toprotect public health, which will in turn drive the regulation of PMsources (U.S. EPA, 2009). The process is straight forward, because itrelies on health science data, and provides hard, quantified in-formation to support the EPA Administrator's decision. None-the-less the data must be interpreted in context and scientists maydisagree with the final determination, as EPA's Clean Air ScientificAdvisory Committee (established under statute11) has on occasion(U.S. EPA, 2010). Regardless, a scientifically based decision isargued and then made using a method prescribed by law.Currently, the annual primary standard (averaged over threeyears) for PM2.5 is 12 ug/m3 (U.S. EPA, 2014a). A decision of thiskind that is defined by law, is explicitly based upon best availablescience, and which relies entirely on empirical data derivedthrough strict adherence to the scientific method (with only aminimum of normative context) e is on a continuum of wicked totame decisions, a very tame problem.

11 The Clean Air Scientific Advisory Committee (CASAC) was established undersection 109(d) (2) of the Clean Air Act (CAA) (42 U.S.C. 7409) as an independentscientific advisory committee. CASAC provides advice, information and recom-mendations on the scientific and technical aspects of air quality criteria and NAAQSunder sections 108 and 109 of the CAA. The CASAC is a Federal advisory committeechartered under the Federal Advisory Committee Act (FACA).


The means for compelling compliance with NAAQS is beyondthe scope of this review (but may be explored on the EPA web-site12). EPA provides a Menu of Control Measures to assist statesand metropolitan areas in meeting compliance goals; and howoptimal control measures are selected is the next step in this pro-cess. A metropolitan area seeking to comply with the NAAQS PM2.5standard may consult the EPA's Menu of Control Measures to assessoptions for a decision for how tomanage PM2.5 in their jurisdiction/s (U.S. EPA, 2014b). From this and other sources a jurisdiction pre-pares a State Implementation Plan that either demonstratescompliance or shows steps designed to achieve compliance withthe NAAQS standards.

Presume for discussion purposes, a jurisdiction of the U.S. that isnot in compliance with the PM2.5 standard, is the home to in-dustries emitting PM2.5 from electricity production, ferrous metalproduction, cement production and vehicular transportation. Toachieve compliance the jurisdiction must prevent the release of Xtons of PM2.5 annually. This decision is an optimization problemwith economic parameters. The decision must select control mea-sures that will limit emissions to the set level. Optimization resultsfrom the set of control measures that succeeds in meeting the setlevel at the loweste or “optimal” price. This is also a tame problem.The Menu of Control Measures and other sources provide data onthe costs of pollution control equipment, and its effectiveness. Theformula to optimize PM2.5 reductions is to meet the standard atminimum cost, and to maximize reductions in those industries thatprovide the greatest cost-effectiveness. This is a tame problembecause the value to be protected, or optimized, is established bylaw in the form of an ambient air standard for a specified pollutant;and the means to achieve compliance with that standard is a menuof options for which prices are known. With all factors known andthe solution established by law this is a tame, deterministicproblem.

Decisions of this kind have simple decision-making rules, and abest answer. They are entirely fit for standard quantitative riskassessment methods. A good overview and easy read on the use ofrisk assessment for decision-making is the book authored byCharles Yo: the Primer on Risk Analysis: Decision Making UnderUncertainty (2011a). This book has a solid treatment of riskassessment as a tool for decision-makers. It is especially valuablebecause of its focus on uncertainty e a fundamental tenet of riskassessment, but a pretty standard part of decision-making in gen-eral. Professor Yo also published a more rigorous treatment of riskassessment in the book, Principles of Risk Analysis: Decision MakingUnder Uncertainty (2011b).

Our air problem can be made more complex if other variables orcriteria are introduced, such as industrial profitability, subsidies,potential loss/gain of jobs, or vulnerable populations. Then themodel for the problem would become multivariate. However, itwould not change the suitability of quantification for identifyingthe best answer. The use of multi-criteria decision analysis (MCDA)is a structured approach to such problems that has been adoptedwidely. In Multi-Criteria Decision Analysis: Environmental Applica-tions and Case Studies, Linkov and Moberg (2012) provided a veryunderstandable treatment of the subject with applied examples ofthe methods employed. The book contains important referencesuseful to comprehend this quantitative approach to decision-making and includes step-by-step examples of how this methodcan be used.

Risk management problems, illustrated by our PM2.5 exampleare typically “by the book” calculations, but they can turn wicked.

12 http://www.epa.gov/oar/urbanair/sipstatus/overview.html Accessed March 7,2014.


http://www.epa.gov/oar/urbanair/sipstatus/overview.html


Green and Berkes (2011, 2013) described a community's experiencewith carbon black PM2.5 pollution. Initially the issue was investi-gated by the state environmental agency as a straight-forward riskassessment. The data within the established analytical contextwere insufficient to resolve the problem to the community'ssatisfaction. Essentially, the community held a different view of theissue than the state agency's statutory culture. Unsatisfied with thestate's response, the community pursued legal means to change thedecision context in order to resolve their complaint. Civil courtsroutinely deal with competing values leading to a choice of decisionoutcomes.While legal reasoning is typically constrained by law andprecedent, in the cases where different values drive competinginterests the problem is one of the wicked variety.

In Ponka City, Oklahoma citizens lodged 726 complaints con-cerning a fine black dust between 1993 and 2011. Largely attributedto a carbon black plant, the community e including the local gov-ernment, sought an appropriate control action from the stateDepartment of Environmental Quality (DEQ). The DEQ investigated,seeking evidence of fugitive PM emissions crossing the propertyline from the suspected facility. In the absence of such evidence,nearly all the cases ended inconclusively. Unsatisfied with theDEQ's response, various plaintiffs brought four lawsuits between2005 and 2009, which alleged PM pollution from the plant was inviolation of permits and resulted in settlements of over $20M.Reportedly, the PM “dust” in Ponca was subsequently reduced(Green and Berkes, 2011, 2013).

Residents pointed to the sizable settlements as having driventhe PM abatement. The residents maintained that paltry state finesof $25,437, and required “environmental improvements” of$127,631 levied by DEQ since 1995 e had no effect. Following thesuccessful legal intervention by Ponka City the OK DEQ changed itspolicy on fugitive dust “from having to see it cross the propertyline,” as DEQ spokeswoman McElhaney put it, “to if there is clearevidence of fugitive dust crossing the property line, such as dust oncars.” In this example reliance on data and quantitative methodswas insufficient to achieve a decision agreed to by all stakeholders,with the result being legal action by the aggrieved parties (Greenand Berkes, 2011, 2013). This is an example of a wicked problemwhere stakeholders held differing perceptions of the issue. A de-cision amidst circumstances where there is not an agreed uponproblem definition make effective use of quantitative methodsdifficult simply because parties to the decision disagree on therelevance of the information.

4.3. Using qualitative methods for decision-making

In such instances as the Ponka City example, non-quantitativedecision-making methods can offer both insights and strategiesfor resolution that quantitative methods cannot (Rittel andWebber1973). Disagreement on the nature of the problem is not an un-common occurrence in risk assessments characterized by scientific“expert” assessors conducting assessments using objective analysismethods in communities with strong but often unstated valuepreferences. Bryan Norton in his 2005 book titled Sustainability: APhilosophy of Adaptive Ecosystem Management, discussed the con-sequences of decision-making predicated on value-neutral problemformulation and analysis, and contrasted that with the advantagesof careful incorporation of values into the decision-making process.

Norton noted that the risk assessment e risk management (RA/RM) model developed by the U.S. Environmental Protection Agency(US EPA) for environmental decision-making (beginning in the1980s) arose from the positivist tradition that best answers shouldbe derived scientifically and objectively. He characterized early EPArisk assessors as guided by science, independent of values or policypredilections, and they were purposely segregated from the


decision-makers to ensure unbiased, objective science. The authordescribed the process of law and policy that evolved alongside RA/RM as effectively detached from ecological science, as it waseffectively focused on single chemical pollutants targeting singlehuman receptors. He argued that the “serial” approach to firstexploring the science in isolation from a value context, followed bypiece-meal interpretation through environmental laws, frag-mented by differing media, obscured system level ecologicalfunctions. Ecological functions are critical dimensions of sustain-ability (Millennium Ecosystem Assessment, 2005), and thus, theproblem identified by Norton offers insight to sustainability deci-sion-making.

In The role of analytical science in natural resource decision-making, Miller (1993) made the same assertions as Norton, citing“a continuing debate about the proper role of analytical (positivist)science in natural resource decision-making.” Miller recognizedthat certain kinds of problems, to which he referred to as wicked,or “trans-science,” problems, might not be amenable to the stan-dard scientific method analytical processes. He argued thatmistaken application of analytical methods to wicked problemsmight serve to “hinder policy development.” He advocated a more“holistic” approach to the problem by balancing empirical infor-mation with professional judgment, intuition and a broaderproblem context. To illustrate the idea of a broader problemcontext, Miller posed pollution as conventionally viewed to be awaste management problem. Within a broader context, however,the pollution could be addressed as a production process problem,and rather than waste management, the solution could be wasteprevention. This insight also formed the basis for Miller's argu-ment for systems thinking as an important element in the holisticapproach to sustainability.

In Environmental Modelling, Software and Decision Support: Stateof the art and New Perspectives, edited by Jakeman et al. (2008), theeditors appear to have internalized the argument for holism. Theyasserted that integrated assessment is a holistic method withinwhich to examine issues and to inform decisions. IntegratedAssessment was described as pulling on expertise from multipledisciplines to understand complex systems of interest and iden-tifying options for decision-makers. Features included a trans-parent, iterative, and adaptive process open to stakeholders. Themethod is designed to inform decisions about complex societalproblems that arise from the interactions between humans andthe environment. Integrated assessment is about understandingthe system of interest and assessing options for decision-makingabout what to do, where, when and with whom (Jakeman andLetcher, 2003). Jakeman et al. (2008) observed that the sustain-ability of one system may compromise that of others, and thatthere will always be tradeoffs and policies across different sectorsthat need to be integrated. They proposed sustainability as thecontext within which to frame problems for integratedassessment.

Norton (2005) observed that the movement away from dog-matic scientific objectivism toward integration of context intoanalysis, a position also advanced in Jakeman et al. (2008), andWinterfeldt and Edwards (1986), represent an important advance.Norton asserted that Winterfeldt and Edwards were instrumentalin building a connection between the descriptive empirical work ofbehavioralists with the “formal and theoretical” work of decisionscientists. Similarly, Jakeman et al. (2008) actively sought to inte-grate value based positions of stakeholders with modeling rigor.Norton underscored that the National Research Council's (NRC)2005 report, Decision-making for the Environment: Social andBehavioral Science Research Priorities, provided the direction foreffective environmental decision-making. The NRC panel observedthat Risk characterization was the outcome of an analytic-


13 The journal of the Society of Environmental Toxicology and Chemistry (SETAC)http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1551-3793Accessed on 3/12/14.


deliberative process. Its success depends critically on systematicanalysis that is appropriate to the problem, responds to the needs ofthe interested and affected parties, and addresses uncertainties ofimportance to the decision-maker. Analysis and deliberation arecomplementary approaches to gaining knowledge about the world,forming understandings on the basis of knowledge, and reachingagreement among people. Analysis uses rigorous, replicablemethods, evaluated under the agreed upon protocols of anauthoritative discipline such as the natural, social, or decision sci-ences, as well as mathematics, and logic to provide factual answers.Deliberation is a process for communication and collectiveconsideration of issues and answers. Participants discuss, exchangeviews, and reflect upon information in the effort to persuade oneanother (NRC, 2005).

In Risk management frameworks for human health and environ-mental risks Jardine et al. (2003) provided a comprehensiveanalytical review of the risk assessment, risk communication, andrisk management approaches currently being undertaken byvarious North American and international agencies. “The informa-tion acquired for review was used to identify the differences,commonalities, strengths, and weaknesses among the various ap-proaches, and to identify elements that should be included in aneffective, current, and comprehensive approach applicable toenvironmental, human health and occupational health risks.”Among inventories of best practices is a list of ten principles toguide risk management decision-making. It is significant that theauthors stated, without reservation, that “the principles are basedon fundamental ethical principles and values.” Further, theyobserved that the application of the principles “requires flexibilityand practical judgment.” One of the principles (identified as theGolden Rule) is to “Impose no more risk than you would tolerateyourself.” These statements are notable for the close similarity tothe concept of phronesis whereby, decisions are made based onjudgments informed by values and an ethical orientation to theoutcome.

Deliberative tools to complement analysis are many. Mentalmaps are described as useful in understanding participants'cognitive value structure (Linkov, 2008). Scenario development isalso a well-established strategy for exploring shared and differentunderstanding of prospective outcome options from a decision.Scenario analysis, including new participatory and problem-oriented approaches provides is a tool for integrating knowledge,and internalizing human choice into sustainability science (Swartet al., 2004). These methods provide sustainability decision-makers a means to examine conceivable outcomes for social sys-tems as they interact with ecosystems under conditions of uncer-tainty and complexity.

Decision support tools, risk assessment, environmental impactassessments and the full complement of data collection and anal-ysis in support of decision-making are appropriately tied to thedecision-making process itself. Selection of the analytical tools, justas definition of the problem, will bracket the information availableto the decision-maker for the options presented. In revisiting rec-ommendations for environmental and human health risk assess-ment, the NRC (2005) proposed that risk managers and riskassessors should work together closely to initiate the assessment tobetter enable the assessor to provide themanager with informationtargeted to the decisions to be made. This is considered, by some, tobe a departure from earlier recommendations that the assessmentshould be carefully segregated from risk managers who might seekto steer the assessment in support of a preferred outcome (Norton,2005).

Increasingly, authors of recent literature on this topicacknowledge that decisions cannot be divorced from the formula-tion of the problem and the choice of analytical tools, and also


recognition of the importance of stakeholder values. The Journal ofIntegrated Environmental Assessment and Management13 hasfunctioned to integrate domain-specific knowledge in ecologicalrisk assessment to support decision-making with multi-criteriaanalysis methods for trade-offs among sociopolitical, environ-mental, ecological, and economic factors. The ultimate purpose ofmodeling is to inform the process of making good decisions. Bartonet al. (2012) asserted that models should promote social learning,that is learning that helps managers and decision-makers pulltogether stakeholders to support decisions that, without thebenefit of models, might seem unacceptable. Journal authors callfor models that allow environmental and resource managers toconsider social values in decision-making and how to promotesocial learning by requiring stakeholders to articulate their values(Barton et al., 2012). In Bayesian networks in environmental andresource management, Barton et al. provided an overview of aspecial series on probabilistic modeling, and discussed advances inthe last decade in the use of BNs as applied to environmental andresource management. Bayesian networks (BNs) are models thatgraphically and probabilistically represent relationships amongvariables (Barton et al., 2012). As a highly mathematical model thathas been harnessed to explore and explain social variables withvalue foundations, BNs represent state-of-the-art in the integrationof rigorous scientific methods with expressed value-basedobjectives.

For those who would explicitly seek to incorporate phronesisinto their decisions, and to use the process to drive sustainableoutcomes there is action research. Action research proponentsmake no claims to objectivity, and differ in their methods fromother theoretical approaches primarily because the institution andor people studied have some degree of control over the design andmethodology of the research (Kathryn and Anderson, 2005). Intheir guide to the action research Herr and Anderson highlightedthe active quality of the research by noting that through engage-ment with the studied population(s) a shared exploration of boththesis andmethod occurs in connectionwithmutually agreed uponobjectives. Action research is responsive to assertions by scholarssuch as Habermas that knowledge and human interests areinseparable, and who emphasized the social nature of all experi-ence and action (Habermas, 1971). The action researcher seeks toforge closer bonds between knowledge generation and knowledgeapplication (read: “decisions”), bypassing the traditional academicseparation between research and application, discountingneutrality and objectivity in formulation of the research thesis andmethods. “Action research is therefore, an inherently valueeladenactivity, usually practiced by scholar-practitioners who care deeplyabout making a positive change in the world.” (Reason andBradbury, 2001). This is also consistent with the strategies forphronetic social science espoused by Fryvbjerg. Proponents of ac-tion research characterize their methods as appropriate to the sit-uations and circumstances they study, and argue that positivisticmethods are poorly suited to their work. Practitioners of actionresearch have sought to establish recognized methods of scholar-ship and quality measures to create a core of standardized schol-arship (Reason and Bradbury, 2001).

The value of information (VoI) is a decision analytic method forquantifying the potential benefit of additional information in theface of uncertainty (Keisler et al., 2014). A decision-maker mightconsider use of VoI if available information does not provide apersuasive direction, and there is a question whether additionalinformation e that comes with a cost, can be expected to be worth


http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1551-3793


the effort of obtaining it. VOI analyses can provide useful insights inrisk management and other similarly deliberative decisions. VoI isnot widely used because of the complexity in modeling and solvingVOI problems, with most applications currently being made inenvironmental health risk management (Yakota and Thompson,2004). The technique is highly quantitative and can be resourceintensive (Hoomans et al., 2012). The complexity of solving VOIproblems with continuous probability distributions as inputs hasemerged as the main barrier to greater use of VOI (Yakota andThompson, 2004). The comprehensive review of methods formodeling and solving VOI problems for applications related toenvironmental health by Yakota et al. provided the first synthesis ofVOI methodological advances for environmental health. Their in-sights provided decision scientists with guidance on how tostructure and to solve VOI problems focused on environmentalhealth decisions.

In Systematizing the Use of Value of Information Analysis inPrioritizing Systematic Reviews, the authors at the U.S. Agency forHealthcare Research and Quality (Hoomans et al., 2012) reportedon newer approaches to VOI that are less burdensome. One, theminimal modeling approach to VOI is useful when data oncomprehensive outcome measures, such as quality-adjusted life-years or net benefit, are already available from existing research.VOI can then be estimated without constructing a complexmodel.

Decision oversight is important tomention, although it is largelybeyond the scope of this paper. In the book Scientific knowledge,controversy, and public decision-making, Martin and Richards (1995)explored decisions made (and unmade) in public controversyanalysis. Decisions between choices concerning a sustainableoutcome can result in public disagreements among scientific andtechnical experts. Martin and Richards describe four distinctiveapproaches to controversy analysis, labeled as: 1. Positivist, 2.Constructivist, 3. Group politics, and 4. Social structural. Theessence of the positivist approach is that the social scientist acceptsthe orthodox scientific view and proceeds to analyze the issue fromthat standpoint. In contrast, the constructivists challenge the pos-itivist's approach by seeking to explain adherence to all scientificbeliefs on both sides of the controversy, whether they're perceivedto be rational or irrational, or successful or failed. The construc-tivists' approach has opened up the content of disputed scientificknowledge to sociological analysis. The group politics approachconcentrates on the activities of various groups, such as govern-mental bodies, corporations, and citizens' organizations, and isessentially the study of the social controversy, with only passingattention to the scientific issues. Arie Rip argued that controversiesprovide societies with an informal means of technology assessmentthat is often superior to any of the institutionalized methods ofassessing the risks and benefits of new technologies (Rip, 1987).Thesemethodsmight be of use to the sustainability decision-makerembroiled real-time in a protracted public controversy or seekingto draw insights for optimal decision outcomes from other similarcircumstances.

One book stands out for addressing concerns over what role isappropriate for normative science and values in decision-making.The book is titled: Structured Decision-making: A Practical Guide toEnvironmental Management Choices by Gregory et al. (2012). Theauthors observed that decision science as applied to environmentaldecision-making is moving beyond the debate of the positivist-naturalist scientific method vs. normative value-informed socialscience. The authors outlined the “Structured Decision Making”approach to developing environmental management decisions. It isa guide to a process for helping stakeholders and decision-makersthink through tough multidimensional choices characterized byuncertainty, diversity in opinions and values, and the need for


tradeoffs. Thus, it is largely transferable to sustainability decisions,which have been similarly characterized (Kates, 2011).

Structured decision-making is designed to be rigorous, defen-sible, transparent and inclusive. It combines analytical methodsdrawn from decision sciences and applied ecology with delibera-tive methods from cognitive psychology and facilitation. Casestudies are used to illustrate how structured decision-making wasapplied to a wide range of situations, ranging from those wherethere was only a small amount of data, to those where there werelarge quantities of information.

5. Discussion

Efficient and effective decision-making begins with a clear andunambiguous statement of the problem requiring resolution(Kleindorfer et al., 1993). Articulating the correct problem is chal-lenging because what is typically identified as the problem willoften be an element of a larger system of which the problem is only acharacteristic, condition, symptom or element. Sustainabilityexpressly addresses this through a planning and assessment pro-cess that scopes the linkages between issues and relevance to theinitial problem statement in an effort to establish logical bound-aries on the problem to both include relevant elements but also tokeep it tractable. The final problem definition and scoping includespreliminary options for the analysis, stakeholder involvement, andidentification of opportunities for collaboration (NRC, 2011).Decision-making methods are only as good as the characterizationof the problem to which they are applied.

Jakeman et al. (2008) proposed that data on indicators of sus-tainability are valid for supporting good decision-making. If theproblem for which a decision is required is grounded in the systemscontext of sustainability, the analysis in support of the decision isappropriately drawn from the tool box of assessment tools, and thedecision method appropriate for the analytical findings, then thedecision-makers are as well-equipped as possible to make theirdecisions.

Decision support tools discussed in this inquiry included alimited set of well-known approaches including StructuredDecision-making, Multi-Criteria Decision Analysis, Risk Assess-ment, Bayesian Networks and Action Research. There are manyother methods this author has not touched such as material flowanalysis, life cycle analysis, benefit-cost analysis or environmentalfootprint. These tools are considered important sustainability de-cision support tools, and many are discussed in the context ofinforming sustainability in EPA's Sustainability Analytics: Assess-ment Tools & Approaches (U.S. EPA, 2013). Aristotle would havetermed these tools “techne.” Instances where decision-makers relyon the analysis provided by decision support tools e without anyfurther reliance on a framework or other decision-making methodsimply demonstrate that some decisions can be relatively easilymade. Sustainability decisions as discussed previously tend towardgreater complexity and thus a decision framework or method canbe helpful for integrating results from multiple decision supporttools.

Investigations of the tools used for sustainability decision-making were conducted on supply chain management. A sizableliterature has been published over the last 15 years on the topic ofgreen or sustainable forward supply chain management. Decisionmaking on supply chain typically balances risks against desirablefactors (Seuring, 2013). Seuring and Müller (2008) conducted aliterature review on sustainable supply chain management thatexamined 191 papers published from 1994 to 2007. It characterizedinitiating factors for decision making as either supplier manage-ment for risks and performance, or supply chain management forsustainable products. Seuring (2013) summarizes research on



quantitative models and determined that on the environmentalside, life-cycle assessment based approaches and impact criteriadominate. Equilibrium models, multi-criteria decision making andanalytical hierarchy are three dominant modeling approaches. Hereported the social side of sustainability is generally not taken intoaccount. Brandenburg et al. (2014) report that a content analysis of134 carefully identified papers on quantitative models that addresssupply chain sustainability showed most favored multiple criteriadecision making methods such as the analytical hierarchy process,the analytical network process, and life cycle analysis.

The supply chain research highlights that quantitative modelseasily lend themselves to the incorporation of data reflecting sus-tainable values. The conclusion that environmental sustainabilityappeared more often in the analysis than did social welfare isindicative of the degree towhich different values can be reflected inchoice of data. This demonstrates that phronetic reasoning is inevidence not only in the final decision-making phase of problemsolving, but also throughout the design and analysis of the problem.

An important interpretation and assignment of phroneticreasoning was presented by Funtowicz and Ravetz (1991) in A NewScientific Methodology for Global Environmental Issues. Theydescribed postenormal science (PNS), as having the characteristicsof uncertain facts; disputed values; high stakes and urgent de-cisions. They argued that PNS was needed to guide society-scaleddecisions when uncertainty and disagreement created a road-block (or grid-lock) for traditional science, and suggested a pro-cess to advance decisions under such circumstances. Stakeholdersare construed to be the “extended peer community.” The discussionprocess among the stakeholders introduces “extended facts,”including local knowledge (teche). Funtowicz and Ravetz arguedthat this extended discussion process is necessary for improvingthe quality of applied science. The features of PNS are consistentwith other methods discussed previously.

A political case for PNS has also been made that is germane to athorough understanding of the decision-making dominion of sus-tainability. According to Hulme (2007), the limits of normal scienceto inform decisions are reached once scientific “knowledge” in-teracts with other ways people understand and make decisions,such as politics, ethics and spirituality. Hulme stated that “scientificknowledge is always provisional knowledge, and that it can bemodified through its interaction with society.” To appreciate thevalue of this insight one must consider that a “normal” reading ofscience presumes science will first find truth, then it will persuadethe social nexus of power, and then finally policy consistent withthe science will be developed and implemented. Hulme observedthat most scientists function on this level of objective process, as ifthe battle of science, once won, assures the war of values will bewon. However, when science turns “postenormal” disputes “focusas often on the process of sciences dwho gets funded, who eval-uates quality, who has the ear of policy makers das on the facts ofscience” (Hulme, 2007). This is probably an accurate description ofmost efforts to use science to inform and advance a socioeconomicagenda such as sustainability. Thus, an understanding of post-enormal science decision-making is valuable to those who seek tounderstand and influence sustainability decision-making.

Emblematic of the difficulty in resolving sustainability de-cisions, and the requirement for judgment (phronesis), is the classicdebate between weak and strong sustainability paths. Eric Neu-mayer in Weak Versus Strong Sustainability: Exploring the Limits ofTwo Opposing Paradigms (2013) opined that the central debate onsustainable development is the question of whether natural capitalcan be substituted by other forms of capital e termed “weaksustainability.”

Proponents of strong sustainability regard natural capital asnon-substitutable. Neumayer wrote: “It will be argued here that


both paradigms are non-falsifiable under scientific standards.Therefore, there can be no unambiguous support for either weaksustainability or strong sustainability.” Neumayer invoked Popper'sseminal contribution to the philosophy of science (1963): a prop-osition is only scientific if it is possible [to test] to disprove. How-ever, he also described weak and strong sustainability as paradigmse that is, according to Kuhn (1962) a normative science whereclaims are adopted and rejected according to criteria that stem fromthe paradigm itself. If, as Neumayer asserted, weak and strongsustainability are “paradigms” they cannot be refuted throughresearch arising out of their own normative science. As Ziegler andOtt (2011) observed: “Paradigms are not falsifiable according toKuhn's rich account of the history of science and arguably also forconceptual reasons (for example, the holism of paradigms makes itunclear what would have to be rejected if an experiment is to befalsified).” Neumayer stated at the end of his discussion: “thecontest between Weak and Strong cannot be settled by theoreticalinquiry. Nor can it be settled by empirical inquiry.”

Such decisions depend heavily on the reasoned judgment of thedecision-maker, as well as on the circumstances in which the de-cision must be made. This is the distinctive freedom of sustain-ability science and decision-making, as well as the greatestchallenge/responsibility to applying decision-making methods.Because the value of what is to be sustained is weighed on thesubjective scale of the decision-maker/s, positivist decision-makingmethods cannot fully inform the decision because they eschewvalues, as such.

6. Conclusions

Sustainability decisions are contextual, value laden, and focusedon social actions. The evaluation of a decision must also provide ameans to value outcomes. Decision-making methods that provide atransparent means to integrate disparate information and percep-tions (and values), and outcomes have been demonstrated to be themost useful in settings with a variety of stakeholders that valuedifferent outcomes. Such conditions are typical in natural resourceand sustainability problems where trade-offs are often necessi-tated. The act of decision-making, in the terminology of Aristotle, is“techne,” and it is very much a craft executed with subjectivejudgment by any practitioner. Consistent with development of acraft is recognition that the practitioner is guided by internalizedvalues e normative and paradigmatic, mathematical or otherwise.The concept of phronesis is appropriate to describe this, and is alsoconsistent with sustainability decision-making because it carriesthe essential element of value-based judgment that is key toresolving the tradeoffs that are often needed when consideringcomplex systems. With the identification of values transparentlyincluded in decision making, it follows that the values served byoutcomes will be similarly transparent.

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Arsham, Hossein, undated. Statistical Thinking for Managerial Decisions, on line athttp://home.ubalt.edu/ntsbarsh/business-stat/opre504.htm. (accessed25.01.15.) University of Baltimore, Baltimore, Maryland.

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http://www.publicintegrity.org/2011/11/07/7297/community-coated-black-mist-until-citizens-fought-back






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http://plato.stanford.edu/archives/win2013/entries/economics/





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http://www.epa.gov/air/criteria.html

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http://www.epa.gov/air/pdfs/MenuOfControlMeasures.pdf
















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Values in sustainability decision making