consensus, informed by the science and the study results, but also affected by their differing valuations, prior probabilities and interpretation, and trust in scientific studies. This often leads to conflict in the decision process or, when one party has the authority or power to impose its will on others, dissatisfaction of the other parties with the decision outcome. What is needed then is a decision-analysis framework that identifies the sources of these differences and provides a rational basis for concrete steps that can overcome them. This leads to a broader and potentially more powerful notion of information value, based on the value of information for conflict resolution.

The idea that better information could help to facilitate conflict resolution is an intuitive one. If part of the failure to reach consensus is due to a different view of the science—a disagreement over the “facts”—then a reduction in the uncertainty concerning these facts should help to eliminate this source of conflict. Scientists often disagree on the facts (Cooke, 1991; Hammitt and Shlyakhter, 1999; Morgan and Keith, 1995). While the source of this disagreement may stem from (“legitimate”) disciplinary or systematic differences in culture, perspective, knowledge, and experience or (“less legitimate,” but just as real) motivational biases associated with research sponsorship and expectation, strong evidence that is collected, peer-reviewed, published, tested and replicated in the open scientific community and literature should lead eventually to a convergence of opinion. The Bayesian framework provides a good model for this process: even very different prior distributions should converge to the same posterior distribution when updated by a very large sample size with accurate and precise data.

Consider now a decision-analytic framework that must translate the implications of changes in assessments resulting from new information for scientists and the “decision support community” into new assessments for decision makers and interested and affected parties. Even were the science to be perfect and all scientists and stakeholders agree that the outcomes associated with each decision option are known with certainty, the different stakeholders to the problem are likely to value these outcomes differently, due to real or perceived differences in allocation of the benefits, costs, and risks associated with them. Measures of VOI for this situation must thus consider the likelihood that the information will convince conflicting participants to reach consensus, a situation of relevance to Department of Homeland Security (DHS). A VOI for conflict resolution has been proposed for this purpose (Small, 2004), and (Adams and Thompson, 2002; Douglas, 1987; Thompson et al., 1990; Verweij, 2006) addresses the underlying problem of policy analysis where stakeholder groups have very diverse worldviews. These differing ways of evaluating the VOI in a risk analysis should be considered by DHS in developing its research and data collections programs.

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