Robert J. Lempert

A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios

Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, identifies clusters of future states of the world to which they are vulnerable, and then evaluates the trade-offs in hedging against these vulnerabilities. This approach can help decision makers design robust strategies while also systematically generating clusters of key futures interpretable as narrative scenarios. Our study demonstrates the approach by identifying robust, adaptive, near-term pollution-control strategies to help ensure economic growth and environmental quality throughout the 21st century.

Robust Strategies for Abating Climate Change

Robert J. LempertRAND1700 Main St.Santa Monica, CA 90407-2138Michael E. SchlesingerDepartment of Atmospheric Sciences, University ofIllinois at Urbana-Champaign, Urbana, IL 61801People are of two minds about uncertainty. On the one hand we crave predictionsabout the future. Virtually every newspaper, magazine, and trade publication has articlespredicting some aspect of the future and many people make good livings foretelling whatthe future will bring. The quest for prediction probably fills some deep human need. Eventhough the accuracy of most predictions has proven to be poor (Sherden 1998), the futurebecomes less scary once given a name and a shape.On the other hand we usually recognize that predictions are inherently unreliableand have been extraordinarily creative in managing our affairs without them. From thefolkways of farmers, to the basic tenets of most religions, to the rules of thumb that we use tonavigate late-20th century life, few provide predictions and most are designed to help theindividual achieve some measure of success and fulfillment no matter what the futurebrings. The currently triumphant forms of social organization, democracy and the freemarket, are both explicitly designed to be self-correcting of errors and robust againstunforeseen circumstances. Both assume people cannot predict the future and will makemistakes. Both probably owe much of their success to the truth of this view.In recent years researchers examining alternative policies to address the threat ofclimate change have become increasingly concerned about uncertainty. This is clearlyappropriate, for few policy problems are dependent on such significant unknowns.Ascertaining the potential impacts of human activities on the immensely complex climatesystem and its related ecosystems is daunting enough. But climate-change policy mustalso concern itself with the state of society fifty and a hundred years hence. Willinnovation drive the cost of non-emitting energy technologies below that of fossil fuels?Will new means of extraction and processing make coal the low-cost fuel of the future?Will society treasure the preservation of the present state of nature or will our descendants

Do we need better predictions to adapt to a changing climate

Many scientists have called for a substantial new investment in climate modeling to increase the accuracy, precision, and reliability of climate predictions. Such investments are often justified by asserting that failure to improve predictions will prevent society from adapting successfully to changing climate. This Forum questions these claims, suggests limits to predictability, and argues that society can (and indeed must) make effective adaptation decisions in the absence of accurate and precise climate predictions.

Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info-Gap Methods

This study compares two widely used approaches for robustness analysis of decision problems: the info-gap method originally developed by Ben-Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate-altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info-gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.

When we don't know the costs or the benefits: Adaptive strategies for abating climate change

Most quantitative studies of climate-change policy attempt to predict the greenhouse-gas reduction plan that will have the optimum balance of long-term costs and benefits. We find that the large uncertainties associated with the climate-change problem can make the policy prescriptions of this traditional approach unreliable. In this study, we construct a large uncertainty space that includes the possibility of large and/or abrupt climate changes and/or of technology breakthroughs that radically reduce projected abatement costs. We use computational experiments on a linked system of climate and economic models to compare the performance of a simple adaptive strategy - one that can make midcourse corrections based on observations of the climate and economic systems - and two commonly advocated ‘best-estimate’ policies based on different expectations about the longterm consequences of climate change. We find that the ‘Do-a-Little’ and ‘Emissions-Stabilization’ best-estimate policies perform well in the respective regions of the uncertainty space where their estimates are valid, but can fail severely in those regions where their estimates are wrong. In contrast, the adaptive strategy can make midcourse corrections and avoid significant errors. While its success is no surprise, the adaptive-strategy approach provides an analytic framework to examine important policy and research issues that will likely arise as society adapts to climate change, which cannot be easily addressed in studies using best-estimate approaches.

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience

Executive Summary Disaster signifies extreme impacts suffered when hazardous physical events interact with vulnerable social conditions to severely alter the normal functioning of a community or a society (high confidence) . Social vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not. Extreme impacts on human, ecological, or physical systems derive from individual extreme or non-extreme events, or a compounding of events or their impacts (for example, drought creating the conditions for wildfire, followed by heavy rain leading to landslides and soil erosion). [1.1.2.1, 1.1.2.3, 1.2.3.1, 1.3] Management strategies based on the reduction of everyday or chronic risk factors and on the reduction of risk associated with non-extreme events, as opposed to strategies based solely on the exceptional or extreme, provide a mechanism that facilitates the reduction of disaster risk and the preparation for and response to extremes and disasters (high confidence) . Effective adaptation to climate change requires an understanding of the diverse ways in which social processes and development pathways shape disaster risk. Disaster risk is often causally related to ongoing, chronic, or persistent environmental, economic, or social risk factors. [1.1.2.2, 1.1.3, 1.1.4.1, 1.3.2] Development practice, policy, and outcomes are critical to shaping disaster risk (high confidence) . Disaster risk may be increased by shortcomings in development. Reductions in the rate of depletion of ecosystem services, improvements in urban land use and territorial organization processes, the strengthening of rural livelihoods, and general and specific advances in urban and rural governance advance the composite agenda of poverty reduction, disaster risk reduction, and adaptation to climate change. [1.1.2.1, 1.1.2.2, 1.1.3, 1.3.2, 1.3.3]

What are robust strategies in the face of uncertain climate threshold responses

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy’s expected performance for a significant increase in a strategy’s performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM.

Confronting surprise

Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surprise. The authors propose employing computer-assisted reasoning methods in conjunction with simulation models to create large ensembles of plausible future scenarios. This framework supports a robust adaptive planning (RAP) approach to reasoning under the conditions of complexity and deep uncertainty that normally defeat analytic approaches. The authors demonstrate, using the example of planning for long-term global sustainability, how RAP methods may offer greater insight into the vulnerabilities inherent in several types of surprises and enhance decision makers’ ability to construct strategies that will mitigate or minimize the effects of surprise.

Adapting to Climate Change: Climate prediction: a limit to adaptation?

Introduction Projections of future climate and its impacts on society and the environment have been crucial for the emergence of climate change as a global problem for public policy and decision-making. Climate projections are based on a variety of scenarios, models and simulations which contain a number of embedded assumptions. Central to much of the discussion surrounding adaptation to climate change is the claim – explicit or implicit – that decision-makers need accurate, and increasingly precise, assessments of the future impacts of climate change in order to adapt successfully. According to Fussel (2007), ‘the effectiveness of pro-active adaptation to climate change often depends on the accuracy of regional climate and impact projections, which are subject to substantial uncertainty’. Similarly, Gagnon-Lebrun and Agrawala (2006) note that the level of certainty associated with climate change and impact projections is often key to determining the extent to which such information can be used to formulate appropriate adaptation responses. If true, these claims place a high premium on accurate and precise climate predictions at a range of geographical and temporal scales. But is effective adaptation tied to the ability of the scientific enterprise to predict future climate with accuracy and precision? This chapter addresses this important question by investigating whether or not the lack of accurate climate predictions represents a limit – or perceived limit – to adaptation.

Making computational social science effective: epistemology, methodology, and technology

There has been significant recent interest in Agent Based Modeling in many social sciences including economics, sociology, anthropology, political science, and game theory. This article describes three problems that need to be addressed in order for such models to become effective tools for formulating new social theory and informing policy debates and suggests approaches to meeting them. These issues are computational epistemology, research methodology, and software technology. These innovations augment Agent Based Modeling to create an effective new tool base to help better understand complex social systems.