Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.

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Abstract:The integrated use of Scenario Planning and Multi-Criteria Decision Analysis (MCDA) has been advocated as a powerful combination for providing decision support in strategic decisions. Scenario Planning helps decision makers in devising strategies and thinking about possible future scenarios; while MCDA can support an indepth performance evaluation of each strategy, as well as in the design of more robust and better options. One of the frameworks proposed recently, by Goodwin & Wright, suggests the use of scenario planning with multi-attribute value theory, a mathematically simple, yet extensively researched and widely employed multi-criteria method.However, so far, such framework has been presented only using hypothetical problems. In this paper we describe two case-studies where this approach was used to support real-world strategic decisions. We discuss the challenges and limitations we encountered in applying it; and suggest some possible improvements that could be made to such framework.Key-words: scenario planning, multi-criteria decision analysis, strategic decision making, risk and uncertainty." [Under uncertainty] there is no scientific basis on which to form any calculable probability whatever. We simply do not know. Nevertheless, the necessity for action and for decision compels us as practical men to do our best to overlook this awkward fact (…)." John Maynard Keynes (1937)

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This paper proposes a tool for multi-criteria decision aid to be referred to as a reasoning map. It is motivated by a desire to provide an integrated approach to problem structuring and evaluation, and in particular, to make the transition between these two processes a natural and seamless progression. The approach has two phases. In the first one, the building of a reasoning map supports problem structuring, capturing a decision maker's reasoning as a network of means and ends concepts. In the second phase, this map is enhanced, employing a user-defined qualitative scale to measure both performances of decision options and strengths of influence for each means-end link. This latter phase supports the decision maker in evaluating the positive and negative impacts of an action through synthesis of the qualitative information. A case study, which investigates the use of the method in practice, is also presented

The growing impact of the ''analytics'' perspective in recent years, which integrates advanced data-mining and learning methods, is often associated with increasing access to large databases and with decision support systems. Since its origin, the field of analytics has been strongly business-oriented, with a typical focus on data-driven decision processes. In public decisions, however, issues such as individual and social values, culture and public engagement are more important and, to a large extent, characterise the policy cycle of design, testing, implementation, evaluation and review of public policies. Therefore public policy making seems to be a much more socially complex process than has hitherto been considered by most analytics methods and applications. In this paper, we thus suggest a framework for the use of analytics in supporting the policy cycle-and conceptualise it as ''Policy Analytics''.Keywords Analytics Á Policy analysis Á Decision analysis Á Policy cycle Á Decision support

Multi-criteria decision analysis (MCDA) is well equipped to deal with conflicting, qualitative objectives when evaluating strategic options. Scenario planning provides a framework for confronting uncertainty, which MCDA lacks. Integration of these methods offers various advantages, yet its effective application in evaluating strategic options would benefit from scenarios that reflect a larger number of wide-ranging scenarios developed in a time-efficient manner, as well as incorporation of MCDA measures that inform within and across scenario comparison of options. The main contribution of this paper is to illustrate how a more diverse set of scenarios could be developed quickly, and to investigate how regret could be used to facilitate comparison of options. First, the reasons for these two areas of development are elaborated with respect to existing techniques. The impacts of applying the proposed method in practice are then assessed through a case study involving food security in Trinidad and Tobago. The paper concludes with a discussion of findings and areas for further research.

Additional Information:• This paper was accepted for publication in the journal Preventive Vet- Del Rio, V., Voller, F., Montibeller, G., Franco, L. A., Sribhashyam, S., Watson, E., Hartley, M., & Gibbens, J.2013. An integrated process and management tools for ranking multiple emerging threats to animal health. Abstract: The UK's Department for Environment, Food and Rural Affairs supports the use of systematic tools for the prioritisation of known and well defined animal diseases to facilitate long and medium term planning of surveillance and disease control activities. The recognition that emerging events were not covered by the existing disease-specific approaches led to the establishment of the Veterinary Risk Group (VRG), constituted of government officials and supporting structures, the risk management cycle and the emerging threat highlight report (ETHiR), to facilitate the identification, reporting and assessment of emerging threats to UK's animal health. Since its inception in November 2009 to the end of February 2011, the VRG reviewed 111 threats and vulnerabilities (T&V) reported through ETHiR. In July 2010 a decision support system based on multi-criteria-decision-analysis (MCDA) improved ETHiR to allow the systematic prioritisation of emerging T&V. The DSS, known as e-THiR, allows the regular ranking of emerging T&V by calculating a set of measurement indices related to the actual impact, possible impact on public perception, and level of available capabilities associated with every T&V. The systematic characterisation of the processes leading to the assessment of T&V by the VRG has led to a consistent, auditable and transparent approach to the identification and assessment of emerging risks. The use of MCDA to manage a portfolio of emerging risks represents a different and novel application of MCDA in a health related context. This paper describes and discusses the characterisation and management of emerging risks by the VRG since its inception, and results from a pilot application of the e-THiR system to a reduced set of emerging threats.