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Embedding (some) benefit-cost concepts into decision support processes with deep uncertainty

Published online by Cambridge University Press:  17 April 2015

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Abstract:

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Benefit-cost analysis (BCA) aims to help people make better decisions. But BCA does not always serve this role as well as intended. In particular, BCA’s aim of aggregating all attributes of concern to decision makers into a single, best-estimate metric can conflict with the differing world views and values that may be an inherent characteristic of many climate-related decisions. This paper argues that new approaches exist that can help reduce the tension between the benefits of providing useful, scientifically based information to decision makers and the costs of aggregating uncertainty and differing values into single best estimates. Enabled by new information technology, these approaches can summarize decision-relevant information in new ways. Viewed in this light, many limitations of BCA lie not in the approach itself, but with the way it is used. In particular, this paper will argue that the problem lies in a process that begins by first assigning agreed-upon values to all the relevant inputs and then using BCA to rank the desirability of alternative decision options. In contrast, BCA can be used as part of a process that begins by acknowledging a wide range of ethical and epistemological views, examines which combinations of views are most important in affecting the ranking among proposed decision options, and uses this information to identify and seek consensus on actions that are robust over a wide range of such views.

Type
Research Article
Copyright
Copyright © Society for Benefit-Cost Analysis 2014

References

Alley, R. B., Clark, P. U., Huybrechts, P. & Joughin, I. (2005). Ice-sheet and sea-level changes. Science, 310(5747), 456460.Google Scholar
Bankes, S. C. (1993). Exploratory modeling for policy analysis. Operations Research, 41(3), 435449.Google Scholar
Bromirski, P. D., Flick, R. E. & Cayan, D. R. (2003). Storminess variability along the California coast: 1858–2000. Journal of Climate, 16(6), 982993.Google Scholar
Brown, C. (2010). The end of reliability. Journal of Water Resources Planning and Management, 136(2), 143145.Google Scholar
Brown, C. & Wilby, R. (2012). An alternate approach to assessing climate risks. Eos, Transations American Geophysical Union, 93(41), 401.Google Scholar
Brown, C., Werick, W., Leger, W. & Fay, D. (2011). A decision analytic approach to managing climate risks – Application to the Upper Great Lakes. Journal of the American Water Resources Association 47(3), 524534.Google Scholar
Bryant, B. P. & Lempert, R. J. (2010). Thinking inside the box: A participatory, computer-assisted approach to scenario discovery. Technological Forecasting and Social Change, 77, 3449.CrossRefGoogle Scholar
Budescu, D. V., Lempert, R., Broomell, S. & Keller, K. (2013). Aided and unaided decisions with imprecise probabilities. European Journal of Operational Research, 2(12), 3162.Google Scholar
Carter, T. R., Jones, R. N., Lu, S. B. X., Conde, C., Mearns, L. O., O’Neill, B. C., … Zurek, M. B. (2007). New Assessment Methods and the Characterisation of Future Conditions. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Parry, M. L., Canziani, O. F., Palutikof, J. P., Linden, P. J. v. d. and Hanson, C. E. (Eds.). Cambridge, UK: Cambridge University Press, 1, 33171.Google Scholar
Cayan, D. R., Bromirski, P. D., Hayhoe, K., Tyree, M., Dettinger, M. D. & Flick, R. E. (2008). Climate change projections of sea level extremes along the California coast. Climatic Change, 87(1), 5773.Google Scholar
CO-CAT. (2010). “State of California Sea-Level Rise Interim Report.” Retrieved December 11, 2014, from http://www.opc.ca.gov/webmaster/ftp/project_pages/Climate/SLR_Guidance_Document.pdf.Google Scholar
Collins, M. (2007). Ensembles and probabilities: A new era in the prediction of climate change. Philosophical Transactions of the Royal Society A, 365, 19571970.Google Scholar
Cox, J., Louis Anthony (Tony), . (2012). Confronting deep uncertainties in risk assessment. Risk Analysis, 32(10), 16071629.Google Scholar
Dalal, S., Han, B., Lempert, R. J., Jaycocks, A. & Hackbarth, A. (2013). Improving scenario discovery using orthogonol rotations. Environmental Modeling and Software, 48, 116.Google Scholar
Dessai, S. & Hulme, M. (2007). Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the east of England. Global Environmental Change, 17(1), 5972.Google Scholar
Fischbach, J. R., Johnson, D. R., Ortiz, D. S., Bryant, B. P., Hoover, M. & Ostwald, J. (2012). Coastal Louisiana risk assessment model. Santa Monica, CA: RAND Gulf States Policy Institute.Google Scholar
Groves, D. G. & Lempert, R. J. (2007). A New Analytic Method for Finding Policy-Relevant Scenarios. Global Environmental Change, 17, 7385.Google Scholar
Groves, D. G., Sharon, C. & Knopman, D. (2012). Planning tool to support Louisiana’s decisionmaking on coastal protection and restoration. Santa Monica, CA: RAND Gulf States Policy Institute.Google Scholar
Groves, D. G., Fischbach, J. R., Bloom, E., Knopman, D. & Keefe, R. (2013). Adapting to a changing Colorado River: Making future water deliveries more reliable through robust management strategies. Santa Monica, CA: RAND Corporation.Google Scholar
Haasnoot, M., Kwakkel, J. H., Walker, W. E. & ter Maat, J. (2013). Dynamic adaptive policy pathways: A new method for crafting robust decisions for a deeply uncertain world. Global Environmental Change, 23(2), 485498.Google Scholar
Hall, J. M., Lempert, R., Keller, K., Hackbarth, A., Mijere, C. & McInerney, D. (2012). Robust climate policies under uncertainty: A comparison of Info-Gap and RDM methods. Risk Analysis, 32(10), 16571672.Google Scholar
Hallegatte, S., Shah, A., Lempert, R. J., Brown, C. & Gill, S. (2012). Investment decision making under deep uncertainty: Application to climate change. Washington, DC: World Bank.Google Scholar
Hosking, J. R. M. (1990). L-moments – Analysis and estimation of distribution using linear- combinations of order statistics. Journal of the Royal Statistical Society Series B-Methodological 52(1), 105124.Google Scholar
IPCC. (2007). Climate change 2007: Impacts, adaptation and vulnerability. Working Group II contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Carter, T. R., Jones, R. N., Lu, S. B. X., Conde, C., Mearns, L. O. & O’Neill, B. C. (Eds.). Cambridge, UK: Cambridge University Press.Google Scholar
IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E.White, L. L. (Eds.). Cambridge, UK and New York, NY: Cambridge University Press.Google Scholar
Jones, R. N., Patwardhan, A., Cohen, S., Dessai, S., Lammel, A., Lempert, R. J., … van Storch, H. (2014). Chapter 2. Foundations for decision making. In Intergovernmental Panel on Climate Change (IPCC) (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Cambridge, UK and New York, NY: Cambridge University Press.Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.Google Scholar
Kalra, N., Hallegatte, S., Lempert, R. J., Brown, C., Fozzard, A., Gill, S. & Shah, A. (2014). Agreeing on robust decisions: A new process for decision making under deep uncertainty. Washington, DC: Policy Research Working paper, World Bank.Google Scholar
Kasprzyk, J. R., Nataraj, S., Reed, P. M. & Lempert, R. J. (2013). Many-objective robust decision making for complex environmental systems undergoing change. Environmental Modeling and Software, 42, 5571.Google Scholar
Keeney, R. L. & Raiffa, H. (1993). Decisions with multiple objectives. Cambridge, UK: Cambridge University Press.Google Scholar
Kwakkel, J., Walker, W. & Marchau, V. (2010). Classifying and communicating uncertainties in model-based policy analysis. International Journal of Technology Management, 10(4/2010), 14684322.Google Scholar
Lee, K. (1993). Compass and gyroscope: Integrating science and politics for the environment. Washington, DC: Island Press.Google Scholar
Lempert, R. J. (2013). Scenarios that illuminate vulnerabilities and robust responses. Climatic Change, 117, 627646.Google Scholar
Lempert, R. J. & Popper, S. W. (2005). High-performance government in an uncertain world. In Klitgaard, R. & Light, P. (Eds.), High Performance Government: Structure, Leadership, and Incentives. Santa Monica, CA: RAND Corporation.Google Scholar
Lempert, R. J. & Collins, M. (2007). Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, 27(4), 10091026.Google Scholar
Lempert, R. J. & Groves, D. G. (2010). Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American West. Technological Forecasting and Social Change, 77, 960974.Google Scholar
Lempert, R. J., Schlesinger, M. E. & Bankes, S. C. (1996). When we don’t know the costs or the benefits: Adaptive strategies for abating climate change. Climatic Change, 33(2), 235274.Google Scholar
Lempert, R. J., Popper, S. W. & Bankes, S. C. (2003). Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. Santa Monica, CA: RAND Corporation.Google Scholar
Lempert, R. J., Nakicenovic, N., Sarewitz, D. & Schlesinger, M. (2004). Characterizing climate-change uncertainties for decision-makers – An editorial essay. Climatic Change, 65(12), 19.Google Scholar
Lempert, R. J., Groves, D. G., Popper, S. W. & Bankes, S. C. (2006). A general, analytic method for generating Robust strategies and narrative scenarios. Management Science, 52(4), 514528.Google Scholar
Lempert, R. J., Sriver, R. L. & Keller, K. (2012). Characterizing uncertain sea level rise projections to support investment decisions. Sacramento, CA: California Energy Commission.Google Scholar
Lempert, R. J., Groves, D. G. & Fischbach, J. R. (2013a). Is it ethical to use a single probability density function? Santa Monica, CA: RAND Corporation.Google Scholar
Lempert, R. J., Kalra, N., Peyraud, S., Mao, Z., Tan, S. B., Cira, D. & Lotsch, A. (2013b). Ensuring robust flood risk management in Ho Chi Minh City: A robust decision making demonstration. Washington, DC: World Bank.Google Scholar
Lempert, R. J., Popper, S. W., Groves, D. G., Kalra, N., Fischbach, J. R., Bankes, S. C., … McInerney, D. J. (2013c). Making good decisions without predictions: Robust decision making for planning under deep uncertainty. Santa Monica, CA: RAND Corporation.Google Scholar
March, J. & Simon, H. (1958). Organizations. Oxford, England: John Wiley.Google Scholar
McInerney, D., Lempert, R.J. & Keller, K. (2012). What are robust strategies in the face of uncertain climate threshold responses? Robust Climate Strategies. Climate Change, 112(3–4), 547568.Google Scholar
Méndez, F. J., Menéndez, M., Luceño, A. & Losada, I. J. (2007). Analyzing monthly extreme sea levels with a time-dependent GEV model. Journal of Atmospheric and Oceanic Technology, 24(5), 894911. doi:10.1175/JTECH2009.1.Google Scholar
Menendez, M. & Woodworth, P. L. (2010). Changes in extreme high water levels based on a quasi-global tide-gauge data set. Journal of Geophysical Research-Oceans 115.Google Scholar
Mishan, E. J. (1994). Cost-benefit analysis. London, UK: Routledge.Google Scholar
Morgan, M. G. & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, UK: Cambridge University Press.Google Scholar
Morgan, M. G., Kandilikar, M., Risbey, J. & Dowlatabadi, H. (1999). Why conventional tools for policy analysis are often inadequate for problems of global change. Climatic Change, 41, 271281.Google Scholar
Morgan, M. G., Dowlatabadi, H., Henrion, M., Keith, D., Lempert, R. J., McBride, S., … Wilbanks, T. (2009). Best practice approaches for characterizing, communicating, and incorporating scientific uncertainty in decisionmaking. Synthesis and Assessment Product 5.2.Google Scholar
Moss, R., Scarlett, P. L., Kenney, M. A., Kunreuther, H., Lempert, R., Manning, J. & Williams, B.K. (2014). Decision support: Connecting science, risk perception, and decisions. Washington DC: US Global Change Research Program.Google Scholar
National Research Council. (2009). Informing decisions in a changing climate. Washington, DC: The National Academies Press.Google Scholar
Neumann, J. E. & Strzepek, K. (2014). State of the literature on the economic impacts of climate change in the United States. Journal of Benefit-Cost Analysis, 5(3), 411443.Google Scholar
Parker, A. M., Srinivasan, S., Lempert, R. J. & Berry, S. (2013). Evaluating simulation-derived scenarios for effective decision support. Technological Forecasting & Social Change, in press.Google Scholar
Pfeffer, W. T., Harper, J. T. & O’Neel, S. (2008). Kinematic constraints on glacier contributions to 21st-century sea-level rise. Science, 321(5894), 13401343.Google Scholar
Pindyck, R. (2013). Climate change policy: What do the models tell us? Journal of Economic Literature, 51(3), 860872.Google Scholar
Ranger, N., Millner, A., Dietz, S., Fankhauser, S., Lopez, A. & Ruta, G. (2010). Adaptation in the UK: A decision making process. London: Granthan/CCEP Policy Brief.Google Scholar
Rittel, H. & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155169.Google Scholar
Rosen, R. A. & Guenther, E. (2014). The economics of mitigating climate change: What can we know? Technological Forecasting & Social Change. In press.Google Scholar
Rosenhead, J. (2001). Robustness analysis: Keeping your options open. In Rosenhead, Johnathan & Mingers, J. (Eds.), Rational Analysis for a Problematic World Revisited. Chichester, UK: John WIley and Sons.Google Scholar
Sarewitz, D., & Pielke, R. A.J. (2000). Science, prediction: Decisionmaking, and the future of nature. Washington, DC: Island Press.Google Scholar
Schoemaker, P. J. H. (1993). Multiple scenario development: Its conceptual and behavioral foundation. [Abstract]. Strategic Management Journal, 14(3), 193213.Google Scholar
Sussman, F., Grambsch, A., Li, J. & Weaver, C. P. (2014). Introduction to a special issue entitled Perspectives on Implementing Benefit-Cost Analysis in Climate Assessment. Journal of Benefit-Cost Analysis,5(3), 333346.Google Scholar
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.Google Scholar
Toman, M. (2014). The need for multiple types of information to inform climate change assessment. Journal of Benefit-Cost Analysis, 5(3), 469485.Google Scholar
Toman, M. A., Griffin, J. & Lempert, R. J. (2008). Impacts on U.S. energy expenditures and greenhouse-gas emissions of increasing renewable-energy use: Technical report (No. 9780833044976 (pbk. alk. paper)). Santa Monica, CA: RAND Corporation.Google Scholar
United Nations Development Programme (UNDP). (1990). Human development report. Oxford, UK: Oxford University Press.Google Scholar
Walker, W., Marchau, V. & Swanson, D. (2010). Addressing deep uncertainty using adaptive policies. Technology Forecasting and Social Change, 77, 917923.Google Scholar
Walker, W. E., Rahman, S. A. & Cave, J. (2001). Adaptive policies, policy analysis, and policy-making. European Journal of Operational Research, 128, 282289.Google Scholar
Walley, P. (1991). Statistical reasoning with imprecise probabilities. London: Chapman and Hall.Google Scholar
Weaver, C. P., Lempert, R. J., Brown, C., Hall, J. A., Revell, D. & Sarewitz, D. (2013). Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks. WIREs Climate Change, 4, 3960.Google Scholar
Weyant, J. (2014). Integrated assessment of climate change: state of the literature. Journal of Benefit-Cost Analysis, 5(3), 377409.Google Scholar
Woodworth, P. L. & Blackman, D. L. (2004). Evidence for systematic changes in extreme high waters since the mid-1970s. J Climate 17(6), 11901197.Google Scholar