EPA’s Approach to Decision Support is in need of a Sea Change

In the past few decades, the USEPA has widely recognized the importance of economic analysis to the EPA mission. As a consequence, EPA has hired environmental economists and supported research on benefits assessment. This has greatly enhanced EPA’s knowledge base for decision support. EPA should now make a further significant improvement to their decision support by establishing prescriptive decision analysis as the best way to present uncertain scientific knowledge for informed decision making.

Decision analysis, based on the normative model of decision theory, is a well-established discipline that is taught in many university public policy and business programs. There are two fundamental elements in a decision analysis:

•      A utility function that characterizes the values, or perhaps net benefits, associated with outcomes of interest that result from a management action,
•    A probability model that quantifies the uncertainty in the outcomes of interest that result from a management action.

The economic analysis that is now embraced by EPA may be used to provide the first element of the decision analysis quantifying value. An uncertainty analysis can provide the second essential element.

Why has EPA recognized the importance of economic benefits assessment to inform decision making, yet seems oblivious to the need to follow the decision analysis model that is so well-established as an academic discipline? I think that a major reason for this situation is that the environmental engineering and ecology programs that have provided the academic training for many scientists in EPA and in state environmental agencies do not include a course in decision analysis, nor do they recommend a curriculum that includes decision analysis taught in another academic department.

Perhaps to better appreciate the role of this decision analytic framework, consider the following example from everyday life. All of us have made decisions on outdoor activities in consideration of the forecast for rain. In deciding whether to hold or postpone an outdoor activity, we typically seek (scientific) information on such things as the probability (reflecting uncertainty) of rain. Further, it is not uncommon  to hear the weather forecast on the evening news, but still defer a final decision on the activity until an updated weather prediction in the morning (in other words, get more sample information).

Beyond consideration of the scientific assessment in the weather forecast, we also think about how important the activity is to us. Do we really want to participate in the activity, such that a little rain will not greatly reduce our enjoyment? Or, is the activity of only limited value, such that a small probability of rain may be enough so that we choose not participate?

Every day, we make decisions based on an interplay, or mix, of uncertainty in an event (e.g., rain) and value (enjoyment) of an activity. We are used to weighing these considerations in our minds and deciding. These same considerations--getting new information on the weather (which is analogous to supporting new scientific research, as in adaptive management), and deciding how valuable the activity is to us (which is what we determine through cost/benefit analysis)--are key features of decision analysis.

Public sector decisions involving uncertain knowledge and uncertain forecasts should follow this same decision analytic paradigm. Given the consequences of most public sector decisions and the uncertainties in environmental modeling, it is essential that this happen. Failure on the part of EPA to use decision analysis as their prescriptive model for decision support means that many of EPA’s assessments and models will continue to ignore uncertainty in model predictions, resulting in many unexpected management outcomes because stakeholders are unaware of the large uncertainties in predictions from the deterministic models that EPA provides in its decision support. In my view, this situation is inexcusable.