Navigating the field of decision analysis

Helping decision makers frame, analyze, and implement decisions

The field of environmental decision analysis has come a long way in recent decades. So much so that the richness of decisionmaking approaches can sometimes seem overwhelming. Underlying that richness are a few basic elements and processes. In this article we present some key elements to help point the way.

The field of environmental decision analysis has come a long way in recent decades. So much so that the richness of decision making approaches can sometimes seem overwhelming. Underlying that richness are a few basic elements and processes. In this article we present some key elements to help point the way.


Key messages:
  • 1. All decisions have the same recognizable elements. Context, objectives, alternatives, consequences, and deliberation. Decision makers and analysts familiar with these elements can quickly see the underlying structure of a decision.
  • 2. There are only a small number of classes of decisions. These classes differ in the cognitive and scientific challenge they present to the decision maker; the ability to recognize the class of decision leads a decision maker to tools to aid in the analysis.
  • 3. Sometimes we need more information, sometimes we don’t. The role of science in a decision-making process is to provide the predictions that link the alternative actions to the desired outcomes. Investing in more science is only valuable if it helps to choose a better action.
  • 4. Implementation. The successful integration of decision analysis into environmental decisions requires careful attention to the decision, the people, and the institutions involved.

Managers, policy makers, and decision makers with responsibility for environmental decisions have an extraordinarily difficult job. The systems they manage are
complex (coupled human-natural systems), with many dimensions and complicated dynamics. Our knowledge of how those systems respond to management actions is limited, so many of the decisions have to be made in the face of uncertainty.

The field of decision analysis provides a comprehensive set of tools for structuring, analyzing, and making decisions. Arising initially as a way to understand and manage risk, the field has expanded in the last 80 years to cover such topics as multipleobjective trade-offs, time-dependent linked decisions, value of information, and competition among multiple decision makers. At the same time, cognitive psychologists have studied human decisions to understand when our innate processes work and when they fail.

Formal decision analysis is increasingly being used in many sectors, including economic, industrial, manufacturing, agricultural, transportation, and medical sectors, by individuals, corporations, non-profits, and government agencies. Occasional application of decision analysis to environmental decisions began in the mid-1970s, but the environmental science and management world was largely unaware of decision analysis until the late 1990s and early 2000s. (A major aim of the ARC Centre of Excellence for Environmental Decisions – CEED – was to better connect decision analysis and environmental
management).

Today, the field of environmental decision analysis is coming of age with the maturation of a rich set of tools to help decision makers frame, analyze, and implement decisions. Sometimes this ‘richness’ itself is overwhelming (especially when considered with the complexity of the systems we want to make decisions about), but there are core principles that tie decision analysis together. To help the reader navigate this complexity, we summarize the field of decision analysis in four key messages.

1. All decisions have the same recognizable elements

The decisions we have to make are normally surrounded by a fog of complexity. One of the most valuable contributions of the field of decision analysis is the recognition that all decisions have a common set of elements: context, objectives, alternatives, consequences, and deliberation. These elements form a useful guide for the decision maker.

Context: Albert Einstein once said that if he had 1 hour to save the world he would spend 55 minutes defining the problem and 5 minutes finding the solution. Clarity about the decision context leads to an efficient search for solutions.

Understanding the decision context begins with some simple questions (simple to ask, anyway). What is the problem that is being faced and what triggered it? Who will make the decision and what authority do they have to act? Who else cares about and can influence the decision? What is the geographic and temporal scope of the problem? What ecological, social, and legal background is relevant?

Objectives: The decisions that we make are driven not by science but by the values we hold. Understanding the value sets of the decision makers, the organisations responsible for decisions, and the key stakeholders is a crucial step in a structured approach to decision-making.

The values at the heart of any decision are the fundamental objectives—the long-term outcomes we are trying to achieve by implementing a course of action. There are often many of these objectives, and they might conflict. Thus, we might care about the long-term conservation of wetland birds at the same time we care about maximizing agricultural production and minimizing flooding risk.

We often lose track of fundamental objectives by focusing instead on means objectives. These are potential ways by which we might achieve our fundamental objectives. For example, we might focus on target levels for irrigation withdrawal and argue over those, forgetting that what we really care about are the birds, the crops, and the flood protection.

Alternatives: Once we know what we want to achieve with a decision, it’s possible to start thinking about what we could do to achieve these outcomes. Unfortunately, most people reverse this process, assessing what they could do before understanding why they are doing it.

While working out what actions we can take to achieve our objectives seems simple, the process is hampered by psychological biases. Often we anchor on the decisions we have made in the past or small variations on those decisions (see Decision Point #93). But to achieve a good decision, we need to be creative, to think beyond our current set of actions and our means objectives, and instead focus on developing actions that may help us achieve our fundamental objectives and expose trade-offs within these objectives. Indeed, given the challenges we face in environmental management, creative solutions are needed more than ever, and sticking with business as usual is unlikely to solve the problems we have.

A creative search for alternative actions should challenge constraints. Many constraints to a decision are only perceived, not real. We may be told, for example, that a decision has a firm budget constraint, but creative consideration of leveraged funding might open up new alternatives.

Prediction of consequences: The role of science in decision making is to make predictions. There are no decisions without prediction. If you decide what bus to take, you think about what time you need to get somewhere and predict which bus will get you there in time. We make predictions daily for every decision we make.
What are these predictions? They are a mapping of the actions we are choosing among (the alternatives) to the things we want to achieve (the objectives). The challenge for the scientist is to use the best available information to make these predictions, while comprehensively accounting for uncertainty.

Selection of preferred alternatives: By this point of the process we have evaluated each action or set of actions against each objective. But no decision has been made, yet, and indeed, the best choice might not be obvious. In the deliberations to find a preferred alternative, the decision maker might need to weigh the multiple objectives to find the right balance, or consider the risk posed by uncertainty, or consider how this decision will affect future decisions. These are challenges at the interface of science and values, and the field of decision analysis provides a large set of tools to help.

2. There are only a small number of classes of decisions

Many decisions resemble each other, and this recognition is one of the ways that people become better decision makers and analysts, because they realize they have made a similar decision in the past. For a decision analyst, each class of decisions has a common structure, poses a similar challenge to the decision maker, and benefits from a similar set of analytical tools. Although the circumstances and details of each environmental decision are unique, recognizing the class of decision accelerates the process of analysis. Here we discuss five classes of decisions that arise commonly in environmental contexts.

1. Risk analysis: Coupled socio-ecological systems are complex, our knowledge of how they respond to management actions is limited, and some aspects of their dynamics are out of our control. Thus, many environmental decisions are made in the face of uncertainty and are open to risk. A grassland manager, for example, in an effort to hold back succession, can choose between mowing and burning. The effects of mowing are better understood and more controllable, but the disturbance does not return the same nutrients to the soil nor does it induce germination. On the other hand, even with careful preparation, it is hard to fully control a burn. What are the risk trade-offs in choosing among these actions?

Risk decisions are ones in which the precise outcomes of the alternative actions are not known prior to implementation. The challenge for the scientist is to estimate the probabilities of the various outcomes under the different alternatives. The challenge for the decision makers is to articulate their degree of risk tolerance. The methods for risk analysis are well developed and have been applied in a number of environmental contexts, including endangered-species management, biosecurity, and pesticide regulation.

2. Project prioritization: Consider an agency that has $300,000 to spend to restore instream, riparian, and upland habitat for the benefit of a suite of threatened endemic species. The agency has received several dozen proposed projects, ranging in cost from $10,000 to $175,000. Taken together the projects cost $1.4 million, far in excess of the available funds. Which of the projects should be funded to assure the most benefit for the species in question?

This common class of problem goes by many names including portfolio allocation, the knapsack problem, or combinatorial optimization. In the environmental world it is frequently called project prioritization (see Decision Point #29, p8-10; and Decision Point #103). The challenge is to select a subset of a large number of possible projects, subject to a budget or resource constraint, that maximizes the combined benefit.

The number of possible portfolios to select is typically very large (for example, with 24 projects to select from, the possible number of combinations is 224 = 16.8 million). The benefit of adding a particular project may depend on what other projects are in the portfolio, because there could be synergies, conflicts, or redundancies among projects. The analytical challenges are to calculate the net benefit of a candidate set of projects, then search among the large set of possible portfolios for the optimal one.

Note that a broad set of decisions is included in this class. The ‘projects’ could be species to save, invasive species to combat, research projects to fund, land parcels to acquire, varieties of actions to implement, etc. The resource constraints could be money, staff time, the total number of projects to undertake, or even the number of individuals of a threatened species available for translocation.

3. Spatial planning: Spatial planning is a special case of project prioritization. The individual ‘projects’ are spatial elements (eg, land parcels, grid squares), and the challenge is to identify the best set of spatial units to include in the reserve to maximize some measure of environmental benefit, while either staying within budget or minimizing the area required. The nature of the interactions among the units can be important, reflecting the benefits of connectivity but possibly also the value of spatial independence.

In the past two decades, a set of tools has been developed to aid governments or other management agencies in spatial planning: identifying where to locate and how to configure areas that should receive some sort of protection to conserve their natural processes. Related tools are used in contexts like urban planning, electoral districting, and school zoning.

4. Recurrent decisions: Many natural resource management decisions are made repeatedly; the same, or a similar, decision is revisited on a recurrent basis. For example, hunting seasons, fishing catch limits, or quotas for subsistence take are often set on an annual basis. Likewise, timber management decisions within a large forest are made recurrently. In some parts of the world, the inundation of wetlands is managed on a seasonal or annual basis to provide habitat for migrating species, encourage primary productivity, or set back succession.

Recurrent decisions are challenging because the systems are dynamic. An action taken in one cycle, which might generate a short-term benefit, changes the trajectory of the system, affecting the actions that can be taken subsequently as well as the benefits that will arise from them. To calculate the long-term benefit of taking an action today, we have to anticipate all the subsequent actions that will be taken over the timeframe of management.

5. Multiple objective trade-off decisions: One of the emblematic environmental issues of the late 20th Century in the United States was the development of the Northwest Forest Plan, which exposed conflicts between conservation of the northern spotted owl and maintenance of an old-growth timber industry. These challenges are ubiquitous in environmental decisions—humans have multiple objectives they would like to achieve from an ecological system, like biodiversity conservation, economic growth, recreation, provision of ecosystem services, and maintenance of subsistence harvest, but it might not be possible to achieve them all to their highest degree. How should these trade-offs be managed?

Multi-criteria decision analysis provides a set of tools for structuring, analyzing, and negotiating multiple objective decisions. The core challenge for the decision maker is a values question, not a scientific one—what is the relative value of the multiple objectives?

3. Sometimes we need more information, sometimes we don’t

Uncertainty surrounds all the decisions we make. It’s something we must capture in our predictions and consider in our final decision. Sometimes, reducing that uncertainty can improve our ability to make a decision. However, sometimes additional learning won’t change our choice of action. Recent advances provide tools for understanding when new science is needed.

Monitoring: Monitoring is generally viewed as a ‘smart’ activity in the pursuit of improved conservation outcomes. By explicitly asking the question, “is spending money on monitoring justified?”, we must be prepared to not monitor in some cases.

Good monitoring rests fundamentally on a clear justification for acquiring information in the first place. That is, what we strive to know should be driven by what we need to know (see Decision Point #52, p4-6, and see the box on analysis and monitoring).

Value of information: Most information gathered about the natural world does not help inform decision-making. This is the crux of value-of-information analysis (see Decision Point #67). It is about asking whether money and time spent collecting data might change our decision. It recognizes that gathering information costs money, delays actions, and takes resources away from management. Formal value-of-information analysis can lead to more efficient allocation of resources.

Adaptive management: Adaptive management, sometimes called ‘learning whilst doing’, is a formal approach to making linked decisions in the face of uncertainty. More than just trial and error, adaptive management embeds the idea of value of information into monitoring design and experimental actions, so that management actions can improve over time. (See Decision Point #102).

4. Implementation: requires careful attention to the decision, the people, and the institutions involved

Environmental decisions are hard, not only because their elements are complex and multi-faceted, but because the political and institutional settings in which they are made are filled with passionate, contentious and diverse people. Faced with this complexity, decision-analysis tools are very attractive. They give us the hope of structured, rational deliberation. But these tools don’t actually make the decisions. They are only aids for the decision makers, not prescriptions. Further, we are as distrustful of other people’s processes as we are of their policies, so the proposal to even undertake decision analysis is itself often contentious.

If these reservations can be overcome, however, decision analysis does offer a rich set of tools to enhance the deliberation behind environmental decisions. Overcoming those reservations requires attention to the interpersonal, institutional, and political dynamics surrounding the decision.

Here are three suggestions that may help overcome some of these reservations?

  1. Use rapid prototyping to sketch an initial decision analysis. Minimal initial investment with the opportunity for substantive input may invite decision makers and stakeholders into the process.
  2. Pay attention to who the decision maker is. In complex institutions (like government agencies), it may not be immediately clear who will end up making the decision.
  3. Recognise the importance of stakeholders and the possibility of multiple decision makers. Deeply political settings involve multiple decision makers, who may not agree to cooperate. Recognition of this dynamic can lead to strategies for negotiation, compromise, and forward progress.

Making good decisions about and for the environment is enormously challenging. Rather than throwing our hands up and saying it’s all too hard, decision makers stand to make considerable gains if they can better engage with the rich and growing field of decision analysis.

More info: Eve McDonald-Madden e.mcdonaldmadden@uq.edu.au


Decision analysis and monitoring

Decision analysis is a procedure for discriminating between suitable courses of action; in the case of monitoring, it helps to select the most appropriate regime for monitoring (Decision 6, Question 13) or management (Decisions 10, 14, 15). Decision analysis involves a structured enquiry into the different options available to manage or monitor, along with their costs, benefits and constraints. A simple form of decision analysis ranks options according to their expected cost effectiveness, or expected benefit divided by expected cost.

When deciding which management option is best, one must consider the benefit of each possible action in terms of reaching the overall program objective, the probability of success of that action, and of course the cost of implementing that action.

To select the best monitoring regime we include the same components, but the benefits now relate to the quality of information needed to make a decision based on the reason for monitoring (eg, track system state to guide state-dependent management, or track performance to guide adaptive management). Furthermore, adequate monitoring must consider the ability of the strategy to detect changes in the system.

Acquiring information on benefits and costs for a decision analysis can be achieved through expert elicitation or through more detailed scenario modelling. Options for implementing decision analysis range from a simple calculation of the combined benefits relative to the total costs incurred (eg, Benefit x Probability of success / Cost) to a more complex optimisation (eg, stochastic dynamic programming or reinforcement learning). Methods of obtaining data and implementing decision analysis vary in their cost and their ability to provide rigorous results.

Figure 1: A decision tree for deciding when to implement monitoring to improve conservation management (and when not to). From Macdonald-Madden et al, 2010.

Figure 1: A decision tree for deciding when to implement monitoring to improve conservation management (and when not to). From Macdonald-Madden et al, 2010.

Reference

McDonald-Madden E, PWJ Baxter, RA Fuller, TG Martin, ET Game, J Montambault & HP Possingham (2010). Monitoring does not always count. Trends in Ecology and Evolution 25: 547-550.

And see Decision Point #52, p4-6.

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