Bridging the gap between science and decision making

Abstract

All decisions, whether they are personal, public, or business-related, are based on the decision maker's beliefs and values. Science can and should help decision makers by shaping their beliefs. Unfortunately, science is not easily accessible to decision makers, and scientists often do not understand decision makers' information needs. This article presents a framework for bridging the gap between science and decision making and illustrates it with two examples. The first example is a personal health decision. It shows how a formal representation of the beliefs and values can reflect scientific inputs by a physician to combine with the values held by the decision maker to inform a medical choice. The second example is a public policy decision about managing a potential environmental hazard. It illustrates how controversial beliefs can be reflected as uncertainties and informed by science to make better decisions. Both examples use decision analysis to bridge science and decisions. The conclusions suggest that this can be a helpful process that requires skills in both science and decision making.

Keywords: decision analysis; risk analysis; risk communication; science communication.

Fig. 1.

Framework for linking decisions to scientific knowledge through models of beliefs and values.

Fig. 2.

Decision tree for the decision on whether or not to turn the baby. Squares denote decision nodes, circles denote chance nodes, and triangles denote end nodes. At the end nodes, the outcomes are either a normal birth, a normal Cesarean delivery, or an emergency Cesarean operation.

Fig. 3.

EMF exposure profiles for different mitigation options. The figure shows exposures in milliGauss (TWA, time-weighted averages) as a function of the distance from the powerline. The red profile is for the no-change alternative, the blue profile is for the moderate mitigation alternative (optimal phasing), and the green profile is for undergrounding. Note that undergrounding has a high exposure near the centerline, but low exposures at a distance. Modified from (22).

Fig. 4.

Total expected equivalent costs for three alternatives to mitigate EMF exposure from a 115-kV powerline. Equivalent costs are the product of the consequences in each objective and the costs per unit of consequences. Expected equivalent costs are the product of the probability of obtaining consequences and their equivalent costs. The graph shows the expected equivalent costs broken down by the four mitigation objectives. Modified from (22).

Fig. 5.

User interface to interact with the EMF mitigation model. Users can control all model inputs by moving sliders between a range of numbers. As a result, the total expected equivalent costs and the associated graph will change. Users can also select which criteria they want to use for the analysis (right side of the figure). Modified from (22).