Nnano.2011.163 a Decision-directed Approach for Prioritizing Mariana Pereira

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A decision-directed approach for prioritizing

research into the impact of nanomaterials on

the environment and human healthIgor Linkov1*, Matthew E. Bates1, Laure J. Canis1, Thomas P. Seager2 and Jeffrey M. Keisler3

The emergence of nanotechnology has coincided with anincreased recognition of the need for new approaches to under-stand and manage the impact of emerging technologies on theenvironment and human health. Important elements in thesenew approaches include life-cycle thinking, public participationand adaptive management of the risks associated with emer-ging technologies and new materials1. However, there is aclear need to develop a framework for linking research on the

risks associated with nanotechnology to the decision-makingneeds of manufacturers, regulators, consumers and other stake-holder groups2,3. Given the very high uncertainties associatedwith nanomaterials and their impact on the environmentand human health, research resources should be directedtowards creating the knowledge that is most meaningful tothese groups. Here, we present a model (based on multi-criteria decision analysis and a value of information approach)for prioritizing research strategies in a way that is responsive tothe recommendations of recent reports on the management ofthe risk4,5 and impact of nanomaterials on the environment andhuman health6.

The considerable uncertainty associated with nanomaterials com-plicates product-development decisions that must be made whenmultiple materials or synthesis technologies are available, but the con-sequences of each technological choice are only incompletely under-stood. Value of information (VoI) explores how decision preferencesmight change if new information becomes available (for example,through research) before a decision is made7. However, the appli-cation of VoI to decisions concerning the environment or humanhealth is further complicated by the need to evaluate multiple (andoften conflicting) criteria. Multi-criteria decision analysis (MCDA)enables us to compare the outcomes of different decisions when itis not possible to reduce the relevant competing criteria to a single cri-terion8,9. Traditional approaches to VoI in MCDA can be straight-forward when the relative satisfaction of all outcomes, also known asthe utility, can be clearly enumerated. However, the high uncertaintyassociated with nanomaterials makes it impossible to obtain detailedutility functions, especially where the views of multiple stakeholders

must be considered simultaneously10

. Here we present a model forthe application of VoI using MCDA outranking methods that allowus to make judgements and trade-offs across different decision criteria,even under conditions of high uncertainty.

Figure 1 presents a flowchart showing how this approach couldbe applied to the selection of a technology for the synthesis ofsingle-walled carbon nanotubes. Although this decision might bemade by a privately owned company in the context of materials

production, the company is nevertheless held accountable toseveral different stakeholder groups (which are hypothetical in ourcase study), including manufacturers who might use nanotubes intheir products, consumers who might buy these products, regulatorsand environmental groups. Consequently, the decision problemmust be understood from the perspectives of these different stake-holders, who all place different weights on the importance ofvarious decision criteria: cost, material efficiency, energy consump-

tion, life-cycle environmental impacts, and risks to human health(Supplementary Fig. S1). Experts must assess the various synthesistechnologies on offerarc discharge (arc), chemical vapour depo-sition (CVD), high-pressure carbon monoxide (HiPCO) and laservaporization (laser)relative to the different decision criteriaselected by the different stakeholders. Because these assessmentsare likely to be uncertain, experts must be permitted to provideprobability distributions that bound a range of possible outcomes.These can be based on testing of material properties, evaluating syn-thesis technologies or on other results of research or experience.

The core of the decision recommendation is a comparative analy-sis of the expert assessments using MCDA outranking methods11

(see Supplementary Information). Here, we first use Monte Carlosimulation to sample point estimates from the probability distri-butions supplied by the experts. Each technology is compared withall others and ranked on each criterion according to which performsbest. The results are then summed across criteria (suitably weightedfor each stakeholder) to determine an overall score (the net flow)for each technology in a given simulation. Finally, all technologiesare ranked in order, from most preferred (that is, highest netflow) to least preferred (lowest net flow) for each stakeholdergroup, and these rankings are compared to a balanced perspectivein which all criteria are weighted equally. To ensure a robustsampling of all possible combinations, the Monte Carlo simulationis carried out thousands of times and the results are reported as aprobabilistic rank ordering of technologies in terms of which ismost often preferred by each stakeholder.

To illustrate the approach, we use the hypothetical weights andtriangular probability distributions previously reported12, with

life-cycle data extracted from published literature1315

. In the caseof health risks, the uncertainty is so high that expert judgementsare represented with a lowmediumhigh ordinal scale. (SeeSupplementary Information for further information, and ref. 16for a discussion of regulatory considerations.) The MCDA results(Fig. 2a) show that HiPCO is most likely to be the preferred syn-thesis technology for most stakeholders, although a stakeholderwith a preference for material efficiency over cost may prefer laser

1US Army Engineer Research and Development Center, US Army Corps of Engineers, 696 Virginia Road, Concord, Massachusetts 01742-2718, USA,

2Global Institute of Sustainability and the School of Sustainable Engineering and the Build Environment, Arizona State University, PO Box 875306, Tempe,

Arizona 85287-5306, USA,3Department of M anagement Science and Information Systems, College of Management, University of Massachusetts Boston,

M-5-249, 100 Morrissey Boulevard, Boston, Massachusetts 02125-3393, USA. *e-mail: [email protected]

LETTERSPUBLISHED ONLINE: 2 OCTOBER 2011 | DOI: 10.1038/NNANO.2011.163

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Stakeholders

Manufacturers

Regulators

Consumers

Environmental groups

Balanced

Value

judgementsDecision

criteria

Cost

Materials

Energy

Life-cycle impacts

Health risks

Experts provide

probabilistic

assessments

Uncertain scores for each

technological alternative

on each decision criterion

Designers and developers

provide technological

alternatives

MCDA to compare

technologies

Experimental

science

Thermodynamic andchemical data

Nanotoxicology

Environmental media

Product and technology

development

Value of information to

prioritize reseach

Manufacturing research

Health research

Probabilistic ranking of

technological alternatives,

given current information

Figure 1 | MCDA/VoI framework for prioritizing research into the impact of nanomaterials on the environment and human health. The decision-making

process involves different stakeholders who place different weights on different decision criteria. We can view the process as starting at manufacturing

companies, where designers and developers need to select a particular technology for a particular task (such as the synthesis of single-walled carbon

nanotubes). Experts assess each proposed technology relative to the decision criteria through probability distributions based on experimental science or

experience. The MCDA model integrates all of this information by comparing the technologies to determine which performs best on each criterion, and

computes an overall preference score across criteria for each technology for each stakeholder group. The VoI investigation explores the uncertainty in

the MCDA results to determine how new information gained through research might impact the selection decision. If the overall score for a particular

stakeholder group can be significantly improved by establishing technological details with certainty, then a research programme that is capable of providing

this information may be highly valuable to those stakeholders.

100

a

80

60

Timerankedfirst(%)

40

20

Man

ufactu

rers

C

onsumers

Regulat

ors

Envir

onmentalgrou

ps

Balan

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0

Stakeholders

HiPCOLaserArcCVDb

Manufac

turers

Consum

ers

Regulat

ors

Envir

onmentalgrou

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ced

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0.8

Averagetotalnetflowof

highest-scoringalternative(s) 0.7

0.6

0.5

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0.3

0.2

0.1

0.0

Manufacturing and health Health research

No additional researchManufacturing research

Figure 2 | Model results showing decision recommendations in the base case and the relative importance of different types of research. a, MCDA results

for the single-walled carbon nanotube case study show the likelihood that a particular synthesis technology (arc, CVD, HiPCO or laser) will be most

preferred by each stakeholder group (manufacturers, consumers, regulators and environmental groups) given current knowledge and uncertainties. A

balanced case that gives equal weight to the five decision criteria (see main text) is shown on the right. Most stakeholders are likely to prefer HiPCO, but

further research may help some stakeholders differentiate between the arc, HiPCO and laser approaches. b, VoI analysis reveals the potential for different

types of research to change the expected confidence that each stakeholder group will have in its choice of technology. The blue portion of each bar

represents the average decision confidence (net flow) in the preferred technology for each stakeholder group in the base case, and also for the balanced

weighting scenario. The red portion indicates the improvement in average decision confidence that occurs when new information on manufacturing becomes

available through research. The green portion indicates the improvement that occurs when new information on health becomes available. The purple portion

indicates the additional synergistic improvement that occurs when new information on both manufacturing and health becomes available simultaneously.

NATURE NANOTECHNOLOGY DOI: 10.1038/NNANO.2011.163 LETTERS

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synthesis. CVD is clearly the least preferred alternative, with almostno chance of ranking first. For all stakeholders, no single technologychoice dominates in all cases, but additional research could helpdifferentiate between technologies and improve confidence in afinal selection. These rank orderings are referred to as the base case.

The purpose of the VoI model is to explore the remaining uncer-tainties to determine which types of new information might influ-ence the likely rank ordering for each stakeholder. To simplify the

analysis, we created two classes of hypothetical research projectsthat could reduce uncertainty: manufacturing research providesnew information on cost, material efficiency, energy consumptionand life-cycle environmental impact; health research providesnew information related to the risks to human health. New infor-mation that improves confidence in the eventual rank ordering isrepresented as a higher net flow, relative to the base case. Theseanticipated increases in decision confidence are useful for prioritiz-ing research most relevant to the decision at hand.

Figure 2b shows how the net flows calculated by the MCDA out-ranking algorithm change as more information on the four differentsynthesis technologies becomes available through research. Thebottom portion (in blue) of each bar in the chart representsaverage net flow for the highest-ranked technology in the basecase, whereas the increases in these net flows that result from new

information produced by the different research programmes areshown in different colours above the base case. As might beexpected, new information related to underweighted criteria leadsto little improvement in net flow. For example, even if it could beestablished through research that the health risks associated withHiPCO are unequivocally higher than those of the alternatives,manufacturers would continue to prefer HiPCO and, moreover,their confidence in their decision would not be undermined.However, both regulators and environmental groups would benefitfrom new information obtained from health research (shown ingreen in Fig. 2b), and to a lesser extent all the stakeholders wouldbenefit from the results of manufacturing research (shown in red).

Although the four stakeholders have different views on whichtype of research is most useful, some stakeholders show an added

value from completing the whole research programme in additionto its separate parts (shown in purple in Figure 2b). That is, infor-mation regarding criteria that are not highly valued will neverthelessmake information relating to criteria that are highly weighted evenmore valuable for improving decision confidence. Even though inthis case the synergetic impact is small relative to the base-casenet flow, and is appreciable only for environmental groups andfor the balanced weighting, this demonstrates the value of aformal stochastic investigation of the performance table from mul-tiple perspectives, as intuition alone is insufficient to understand theinteraction between different uncertainties.

The integrated MCDA/VoI model proposed in this Letter suggeststhat the highest-priority research should relate to questions that maymodify the rank ordering of preferred technologies in a specificdecision context. In the present case study, no single synthesis tech-

nology dominates under all weight scenarios, but some inferior tech-nologies are identifiable (such as CVD). HiPCO is preferable for mostof the hypothetical stakeholders based on the distribution of life-cycleand risk-to-health parameters, but when the uncertainties related tocost, materials efficiency, energy and life-cycle impact are reduced,the laser approach to synthesis may be preferable for some stake-holders. Because it is not realistic to undertake research that wouldreduce all uncertainties, it is useful to know which uncertainties aremost influential to stakeholders that lack confidence in their prefer-ence ordering. Typically, this information will not dictate a singlecorrect research direction. Rather, it will inform decisions aboutwhat type of research should be undertaken first.

The development of a comprehensive decisionanalytic frame-work incorporating both uncertainty and sensitivity to new

information provides an actionable intellectual agenda for furthernanomaterials research. In practice, in addition to informingresearch strategies, this integrated MCDA/VoI approach couldalso facilitate the participation of various stakeholder groups.Although it is unlikely to achieve consensus among differentstakeholders on what research is most important, it may helporient discussion of disagreements towards those issues that havepractical relevance. This is especially urgent given ongoing efforts

to revise federal research strategies to focus attention on both thecommercialization of nanomaterials and research into the impactof nanomaterials on the environment and human health17.Adoption of this type of decisionanalytic approach will requiresubstantial institutional changes and expertise from the socialsciences18. Traditionally, information has flowed from researchscientists to risk managers, but there is growing recognition of theneed for two-way interactions between decision analysis andrisk analysis19.

MethodsDecision model. The information analysis presented in this Letter uses an outrankingdecision model to rank-order technologies for the synthesis of single-walled carbonnanotubes, as reported in ref. 12. Uncertainties in input parameters are represented asprobability distributions, whereas contrasting weight sets are illustrative ofhypothetical stakeholder groups. In total, 10,000 rank orderings were generated

via Monte Carlo sampling of new information from the supplied distributions20,resulting in a probabilistic distribution of net flows for each alternativeand stakeholder.

Preferred alternative evaluation. Mathematically, let i, j, k denote indicescorresponding to the stakeholders, decision criteria and synthesis technologies,respectively, and wij denotes an array expressing stakeholder is weight on criterion j,and xjk denotes the technologyks score on criterion j. For each scenario, we calculatewhat is called a weighted net flow, wi(k) (Sjwij w

ij(k))/(i21) for each technologyusing the PROMETHEE II algorithm11, where wij(k) results from pairwise comparisonofxjk to all other technologies on all criteria. w

ij(k) ranges from 1 to 1, where 1means technologyk is worse than every other technology on criterion j, and1 meansit is better than all other technologies on criterion j. (See Supplemental Informationfor a detailed discussion and example of net flow calculations.) The technology thataverages the greatest net flow over all 10,000 trials is most preferred.

Value of Perfect Information. In classical approaches7, VoI is defined as theincrease in average value (or utility) attained by obtaining information prior to the

decision. This Letter follows21 by adapting classical approaches and mathematicallysubstituting expected net flow for utility. This adapted VoI sets an upper bound forthe confidence a stakeholder can expect to gain from additional research on a topic.

In the base case, with no new information, optimal expected net flow iscalculated as w_Nomaxk(Sn(wi(k))/n), effectively asking, What is the averagenet flow of the one technology that most often ranked first? Here, a stakeholdersconfidence is expressed as the difference in average net flow between the highestand second-highest ranking technologies, over n simulated possible realities fromthe uncertainty distributions.

With perfect information on all criteria, we instead ask, What is the average netflow of all the different technologies that ranked first in each trial? By shifting thedecision point with respect to n samplings, as described by ref. 22, we calculate anew expected net floww_PerfectSn(maxk(wi(k)))/n. With perfect informationavailable only on a subset Cof the criteria (sometimes called partial-perfectinformation; for example, Ccould contain all of the manufacturing criteria but notthe health criterion), we calculate the expected net flow Fij(k) Sm(w

ij(k))/m overm trials for each of the criteria j not in C(where m n), and Fij(k) w

ij(k)otherwise. Finally, we calculate F

i

(k)Sj

wij

Fij

(k) for each iteration ofn and averagethese to obtain the expected net flow with information on C, w_CSn(maxk(Fi(k)))/n. We then calculate the value of information as the increase in expectednet flow from perfect information or information on C.

Received 3 May 2011; accepted 31 July 2011;

published online 2 October 2011

References1. Morris, J. et al. Science policy considerations for responsible nanotechnology

decisions. Nature Nanotech. 6, 7377 (2011).2. Environmental Protection AgencyNanomaterial Research Strategy, EPA 620/K-

09/011 (2009); available at http://www.epa.gov/nanoscience/files/nanotech_research_strategy_final.pdf.

3. National Nanotechnology Initiative Strategy for Nanotechnology-RelatedEnvironmental, Health, and Safety Research (2008); available at http://www.nano.gov/NNI_EHS_Research_Strategy.pdf.

LETTERS NATURE NANOTECHNOLOGY DOI: 10.1038/NNANO.2011.163

NATURE NANOTECHNOLOGY | VOL 6 | DECEMBER 2011 | www.nature.com/naturenanotechnology786
http://www.epa.gov/nanoscience/files/nanotech_research_strategy_final.pdfhttp://www.epa.gov/nanoscience/files/nanotech_research_strategy_final.pdfhttp://www.nano.gov/NNI_EHS_Research_Strategy.pdfhttp://www.nano.gov/NNI_EHS_Research_Strategy.pdfhttp://www.nature.com/doifinder/10.1038/nnano.2011.163http://www.nature.com/naturenanotechnologyhttp://www.nature.com/naturenanotechnologyhttp://www.nature.com/doifinder/10.1038/nnano.2011.163http://www.nano.gov/NNI_EHS_Research_Strategy.pdfhttp://www.nano.gov/NNI_EHS_Research_Strategy.pdfhttp://www.epa.gov/nanoscience/files/nanotech_research_strategy_final.pdfhttp://www.epa.gov/nanoscience/files/nanotech_research_strategy_final.pdf


4/4

4. National Research Council Science and Decisions: Advancing Risk Assessment(National Academies Press, 2009).

5. Presidential/Congressional Commission on Risk Assessment and RiskManagement Framework for Environmental Health Risk Management, FinalReport, Volume 1 (1997); available at http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=36372.

6. National Research Council Review of Federal Strategy for Nanotechnology-Related Environmental, Health, and Safety Research (National AcademiesPress, 2009).

7. Raiffa, H. & Schlaifer, R. Applied Statistical Decision Theory (Harvard Univ.

Press, 1961).8. Belton, V. & Stewart, T. J. Multicriteria Decision Analysis: An Integrated

Approach (Springer, 2001).9. Linkov, I. & Moberg, E. Multi-Criteria Decision Analysis: Environmental

Applications and Case Studies (CRC Press, 2011).10. Tervonen, T. et al. Risk-based classification system of nanomaterials.

J. Nanop art. Res. 11, 757766 (2009).11. Brans, J. P. & Mareschal, B. in Multiple Criteria Decision Analysis: State of the Art

(eds Figueira, J. et al.) 163195 (Springer, 2005).12. Canis, L., Linkov, I. & Seager, T. P. Application of stochastic multiattribute

analysis to assessment of single walled carbon nanotube synthesis processes.Environ. Sci. Technol. 44, 87048711 (2010).

13. Isaacs, J. A., Tanwani, A., Healy, M. L. & Dahlben, L. J. Economicassessment of single-walled carbon nanotube processes. J. Nanopart. Res. 12,551562 (2010).

14. Ganter, M. J., Seager, T. P., Schauerman, C. M., Landi, B. J. & Raffaelle, R. P.A life-cycle energy analysis of single wall carbon nanotubes produced through

laser vaporization. Proc. 2009 IEEE Int. Symp. Sustain. Syst. Technol. 14 (2009).15. Healy, M. L., Dahlben, L. J. & Isaacs, J. A. Environmental assessment of single-walled carbon nanotube processes. J. Ind. Ecol. 12, 376393 (2008).

16. Linkov, I., Satterstrom, F. K., Monica, J. C. Jr, Hansen, S. F. & Davis, T. A. Nanorisk governance: current developments and future perspectives. Nanotech. LawBus. 6, 203220 (2009).

17. Presidents Council of Advisors on Science and Technology Report to thePresident and Congress on the Third Assessment of the National NanotechnologyInitiative (2010); available at http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-nano-report.pdf.

18. Gregory, R. & McDaniels, T. in Decision Making for the Environment (edsBrewer, G. D. & Stern, P. C.) 175199 (National Academies Press, 2005).

19. http://www.nano.gov/events/meetings-workshops/capstone.20. Lemieux, C. Monte Carlo and Quasi-Monte Carlo Sampling (Springer, 2009).21. Azondekon, S. H. & Martel, J. M. Value of additional information in

multicriterion analysis under uncertainty. Eur. J. Oper. Res. 117, 4562 (1999).

22. Brennan, A., Kharroubi, S., OHagan, A. & Chilcott, J. Calculating partialexpected value of perfect information via Monte Carlo sampling algorithms.

Med. Decis. Making 27, 448470 (2007).

AcknowledgementsThis work was supported by the Environmental Quality Research Program of the US Army

Engineer Research and Development Center. The authors thank E. Ferguson, the managerof this programme. J. Steevens and M. Chappell of the US Army Corps of Engineers are

thanked for their editorial comments and suggestions. Permission was granted by the Chief

of Engineers to publish this information.

Author contributionsI.L. developed the overall approach and application framework and guided the preparationof the manuscript. L.J.C. performed background research and developed an initial model.

T.P.S. provided contributions on life cycle assessment and decision analysis. J.M.K. guidedthe VoI analysis. M.E.B. completed the model and performed all calculations. All authors

discussed the results and co-wrote the paper.

Additional informationThe authors declare no competing financial interests. Supplementary informationaccompanies this paper at www.nature.com/naturenanotechnology. Reprints and

permissioninformationis availableonlineat http://www.nature.com/reprints. Correspondence

and requests for materials should be addressed to I.L.

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Nnano.2011.163 a Decision-directed Approach for Prioritizing Mariana Pereira

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