Structured Decision Making

A Practical Guide to Environmental Management Choices

Robin Gregory, Lee Failing, Michael Harstone, Graham Long, Tim McDaniels, Dan Ohlson

• Publisher: Wiley-Blackwell
• Year: 2012
• ISBN:
  9781444398533
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

This book outlines the creative process of making environmental management decisions using the approach called Structured Decision Making. It is a short introductory guide to this popular form of decision making and is aimed at environmental managers and scientists.

This is a distinctly pragmatic label given to ways for helping individuals and groups think through tough multidimensional choices characterized by uncertain science, diverse stakeholders, and difficult tradeoffs. This is the everyday reality of environmental management, yet many important decisions currently are made on an ad hoc basis that lacks a solid value-based foundation, ignores key information, and results in selection of an inferior alternative. Making progress – in a way that is rigorous, inclusive, defensible and transparent – requires combining analytical methods drawn from the decision sciences and applied ecology with deliberative insights from cognitive psychology, facilitation and negotiation.

The authorsreview key methods and discuss case-study examples based in their experiences in communities, boardrooms, and stakeholder meetings. The goal of this book is to lay out a compelling guide that will change how you think about making environmental decisions.

Visit www.wiley.com/go/gregory/ to access the figures and tables from the book.

1

Structuring Environmental Management Choices

In late 1999, 24 people representing a variety of interests, perspectives, and agencies signed off on a consensus agreement that fundamentally changed water flows on a disputed stretch of a managed river in British Columbia, Canada. Up until then, hydroelectric facilities on the river had been operated primarily for power production, with limited consideration given to effects on fisheries, wildlife, recreation, and local communities. Relationships among the diverse stakeholders (BC Hydro, which produces electricity from the dam, the federal Department of Fisheries and Oceans, the provincial Ministry of Environment, and community members) were strained. Court actions were threatened by both the local aboriginal community and the federal Fisheries regulator. From the utility’s perspective, water management options were complicated by an unclear regulatory environment that offered little guidance about how to involve other stakeholders, how to address trade-offs affecting water flows, or how to adapt management practices to public values that had changed over time.

Conventional thinking suggested a choice between negotiation and litigation. Instead, the utility, along with provincial and federal regulators, collaboratively developed and adopted a structured decision making (SDM) approach. In addition to achieving consensus agreements at all but one of 23 facilities, the SDM process produced a common understanding among key stakeholders about what could and couldn’t be achieved with different management alternatives, about which trade-offs were acceptable, and about which uncertainties were most important. By focusing on mutual learning, it built trust and stronger working relationships among key stakeholders, and institutionalized a commitment to improving the information available for decision making over time. The process won a range of international awards for sustainability.

Although there are many reasons for the remarkable success of water-use planning in British Columbia, one key factor was the use of SDM methods to guide both analysis and deliberations¹. Over 10 years later, SDM continues to play a prominent role in framing important environmental management decisions in the province. The provincial government regularly requests the use of SDM to help guide environmental assessment and project or program planning efforts. BC Hydro, the government regulated provincial energy utility, uses SDM approaches to assess its electricity generation options and incorporates SDM in its triple bottom-line approach to corporate purchasing policies. In both BC and the adjacent province of Alberta, several indigenous communities are using SDM as part of environmental management and ecosystem restoration initiatives²; one has adopted its own SDM guidelines as the overarching framework for planning and negotiations in its territory with the provincial and federal governments³. The federal fisheries regulator wrote SDM-based procedures into its Wild Salmon Policy⁴ and has used the process to produce an interim agreement on the management of a widely recognized threatened species⁵. Forestry practitioners in western Canada recently used SDM to help assess climate-change vulnerabilities and adaption options for sustainable forest management⁶.

In the United States, the Fish and Wildlife Service (USFWS) has adopted SDM as a standard of practice and is using SDM methods in a variety of environmental management contexts. In its technical guide for the conduct of adaptive management, the US Department of the Interior (USDOI) states that ‘Adaptive management is framed within the context of structured decision making, with an emphasis on uncertainty about resource responses to management actions . . . ’⁷.

This interest in SDM is not limited to North America. In Australia, the Department of Agriculture, Fisheries and Forests has a community of practice in SDM and is using it to develop an approach to the management of agricultural pests and invasive species. SDM approaches have been used in New Zealand to aid recovery of the endangered Hector’s dolphin⁸ and the country is debating use of an SDM approach to help develop a risk framework for management of genetic organisms, with a special emphasis on ways to integrate concerns of the aboriginal Maori culture alongside concerns developed through western scientific studies.

Why all the interest? What’s different about SDM? Fundamentally, SDM reframes management challenges as choices; not science projects, not economic valuation exercises, not consultation processes or relationship builders. You have a decision (or a sequence of decisions) to make. The context is fuzzy. The science is uncertain. Stakeholders are emotional and values are entrenched. Yet you – or someone you are advising – has to make a choice. This decision will be controversial. It needs to be informed, defensible, and transparent. This is the reality of environmental management. It has been said that reality is what we deal with when there are no other options. We think that SDM is a useful way to deal with the realities of everyday environmental management.

1.1 Three typical approaches to environmental decision making

Let’s look first at three dominant paradigms that guide how environmental management decisions are conventionally made: science-based decision making, consensus-based decision making, and analyses based in economics or multi-criteria decision techniques.

1.1.1 Science-based decision making

A file arrives on the desk of a resource manager working in government. A biologist by training, she learns that a species recovery plan for the recently listed split-toed frog will need to be in place in 18 months. This is a priority issue and she has been given lead responsibility. She pulls together an inventory of all the science on split-toed frogs and launches a science review and planning team. Within a few months, work is underway to produce a comprehensive risk assessment and a state of the art habitat model. The modeling is completed within 15 months, an extraordinary accomplishment. Our scientist heaves a big sigh of relief and settles down to develop a plan. Two months later, however, she is disillusioned, attacked by angry environmental activists who reject the recommended captive breeding options on ethical grounds, by local tourism operators who claim that the proposed road closures will ruin their businesses, and by frustrated recreationists who demand that recovery funding be used instead for the protection of more visible species. Faced with the impending deadline, her embattled boss pushes through a band-aid solution that slightly soothes stakeholders’ ruffled feathers but will never protect the frog. Everyone is frustrated and disillusioned with a world where, once again, politics trumps science.

This scenario is (perhaps) a little exaggerated, but it illustrates the deep-rooted reliance on science of many decision makers and resource managers and their desire to produce ‘science-based’ decisions. When a solution supported by scientific experts fails to receive wide support, environmental managers often throw up their hands and decry the vagaries of ‘irrational’ social values and power politics. The problem is not with the science: sound science must underlie good environmental management decisions. The problem is with society’s tendency to ask too much of science in making decisions and to leave out too many of the other things that matter to people. First of all, science is not the only credible or relevant source of knowledge for many environmental management decisions. Secondly, social considerations and ethics and the quality of dialogue play important roles in shaping environmental management choices. Most importantly, rarely is there a single objectively right answer and science provides no basis for dealing with moral or value-based choices. The biologist Jane Lubchenco, in her Presidential Address to the American Association of the Advancement of Science, reminded the audience (in the context of environmental planning) that ‘Many of the choices facing society are moral and ethical ones, and scientific information can inform them. Science does not provide the solutions . . . ’⁹.

There is increasing recognition that when management choices are characterized by a high degree of stakeholder controversy and conflict, the decision process must address the values held by key participants¹⁰. Unfortunately, most resource management agencies have little knowledge about how to deal constructively with value-based questions. Nor is it generally recognized that many so-called environmental initiatives also will have implications for economic, social, and other considerations. If a narrow, environmentally focused agency mandate means that these related concerns have not been identified carefully, then progress in implementation may be blocked. The frequent result is an 11th-hour, behind closed-doors, largely ad-hoc capitulation to vaguely defined ‘social values’ and ‘political’ pressures – as in our scientist scenario.

One of the things we want to do in this book is to help scientists and scientifically trained managers figure out how to contribute usefully to public policy decisions that are as much about values as about science. Making good choices requires the thoughtful integration of science and values – the technical assessment of the consequences of proposed actions and the importance we place on the consequences and our preferences for different kinds of consequences – as part of a transparent approach to examining a range of policy options. While credible environmental management relies on carefully prepared technical analysis, it also relies on creating a deliberative environment in which thoughtful people can express their views in a collaborative yet disciplined way. Science alone will not make good environmental policy choices. But a values free-for-all will not get us there either.

For some types of problems the objectives are simple and clear, the range of alternatives is well understood, and the evaluation of them involves few and relatively uncontroversial value judgments. For example, if a policy decision has been made to reduce waste or emissions by 30%, then the task of deciding how to achieve that target might be quite technical, largely driven by cost-effectiveness or least-cost analysis (implement the lowest cost alternatives up to the point where the target is reached.) Scientific or technical analysis can perhaps provide ‘answers’ in this constrained decision context, with scientists acting as ‘honest brokers’¹¹. For other, morally charged questions – regarding genetically modified foods, the hunting of baby seals, or lethal predator control, for example – beliefs are so deeply entrenched that the influence of scientific or technical information on decisions may be small. These choices often end up in the hands of political leaders who will make a value-based choice with little reference to scientific information.

The problem for environmental managers is that the vast majority of environmental decisions fall into a messy middle ground where science plays a bounded but critical role and values and preferences, often strong and initially polarized, are also critical but not fixed. Research in behavioral decision making emphasizes that, particularly in less-familiar evaluation contexts, preferences are often ‘constructed’ based on information gained during a process¹², rather than uncovered or revealed as fixed pre-existing constructs. Factual information will never, by itself, make a decision, but it informs and shapes values, which do determine choices.

This clearly implies that what is needed is a framework for making environmental management choices that deals effectively with both science and values. Yet when managers and scientists – and most other people as well – talk about values, they find themselves tip-toeing around, more than a little uncertain how to proceed. Most often, efforts to resolve value-based conflicts focus on bargaining and negotiation or on consensus building. Unfortunately, an overemphasis on process, dialogue, and consensus can create its own problems.

1.1.2 Consensus-based decision making

As the name suggests, consensus-based decision-making processes are those that focus on the endpoint of bringing a group to a consensus agreement.

What could possibly be wrong with this? As an outcome, nothing; we’re fans of consensus, just as we’re fans of laughter or happiness. Our criticism arises whenever consensus is a goal of group deliberations, because we’ve often seen an emphasis on consensus take environmental management processes in the wrong direction. The biggest problem is that the group will often push too soon, too hard toward convergence, at the expense of a full exploration of minority views and creative solutions. An approach based on building consensus presumes that people have a good idea at the start of what they want to see happen, and that this reflects a good understanding of what the various alternatives will deliver. When addressing tough environmental management problems, this is rarely the case. Whenever decisions are characterized by multiple and conflicting objectives and a complex array of alternatives with uncertain outcomes – a nearly universal situation in environmental management – people are likely to enter into a decision-making process with plenty of emotions and strong positions but a poor understanding of relationships between actions and consequences. And as we discuss more fully in Chapter 2, it is naïve and misleading to assume that working with people in a group is a simple cure for the shortcomings of individual decision makers.

In addition, insufficient attention typically is given to dealing with uncertainty in the anticipated consequences of actions and to what this means for establishing an effective and robust management strategy. Although in some cases significant reductions in uncertainty are possible, at other times key sources of uncertainty will be irreducible, at least with available resources and within the time scale of management concern. Reaching agreement in these cases necessarily involves tackling directly the thorny issue of risk tolerance – how much risk people are willing to accept and to which of the th...

Table of contents

• Cover
• Series page
• Title page
• Copyright page
• Foreword
• Preface
• 1 Structuring Environmental Management Choices
• 2 Foundations of Structured Decision Making
• 3 Decision Sketching
• 4 Understanding Objectives
• 5 Identifying Performance Measures
• 6 Incorporating Uncertainty
• 7 Creating Alternatives
• 8 Characterizing Consequences
• 9 Making Trade-Offs
• 10 Learning
• 11 Reality Check: Implementation
• 12 Conclusion
• Index