Gilberto Montibeller

Facilitated modelling in operational research

The traditional way of employing operational research in organisational interventions has been the expert mode. In this mode, the problem situation faced by the client is given to the operational research consultant, who then builds a model of the situation, solves the model to arrive at an optimal (or quasi-optimal) solution, and then provides a recommendation to the client based on the obtained solution. An alternative mode of engagement is to conduct the whole intervention together with the client: from structuring and defining the nature of the problem situation of interest, to supporting the evaluation of priorities and development of plans for subsequent implementation. In this latter mode, the operational researcher works throughout the intervention not only as an analyst, but also as a facilitator to the client. This paper discusses this latter mode of engagement with clients, with particular emphasis on the use of facilitated modelling as the intervention tool. Drawing on research scattered across a range of publications and domains, the review presented here provides a formal definition of facilitated modelling, together with a general framework that allows the conceptualisation of a wide variety of facilitated modelling approaches to organisational intervention. Design issues in facilitated modelling and their practical implication are discussed, and directions for future research identified.

Cognitive and motivational biases in decision and risk analysis

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.

Reasoning maps for decision aid: an integrated approach for problem-structuring and multi-criteria evaluation

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 makers 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.

Extending the use of scenario planning and MCDA for the evaluation of strategic options

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.

Multi-Criteria Decision Analysis for Strategic Decision Making

In this chapter we discuss the use of MCDA for supporting strategic deci- sion making, particularly within strategy workshops. The chapter begins by explor- ing the nature of strategic decisions and the characteristics of the strategic decision making process. Specifically, we examine the technical issues associated with the content of strategic decisions, and the social aspects that characterise the processes within which they are created. These features lead us to propose a number of adapta- tions to the standard MCDA approach if it were to be used at a more strategic level. We make suggestions on how to implement these proposals, and illustrate them with examples drawn from real-world interventions in which we have participated as strategic decision support analysts.

Structuring resource allocation decisions: a framework for building multi-criteria portfolio models with area-grouped options

Multi-criteria portfolio modelling has been extensively employed as an effective means to allocate scarce resources for investment in projects when considering costs, benefits and risks. Some of these modelling approaches allow the grouping of projects into organisational areas, thus also supporting the decision of resource allocation among organisational units in a way that is collectively efficient for the organisation. However, structuring in practice a portfolio model using this latter type of approach is not a trivial task. How should areas be defined? Where should new projects be included? How should one define the criteria to evaluate performance? As far as we know, there is very little indication in the operational research and decision sciences literatures on how to structure this type of model. This paper suggests different ways to structuring portfolio models where projects are divided into areas and evaluated by multiple criteria, and illustrates their use in two action-research projects. Drawing on these experiences it then suggests a general framework for the structuring of such models in practice. Directions for future research are also identified.

Raising the Bar: Strategic Multi-Criteria Decision Analysis

This paper discusses the use of multi-criteria decision analysis for supporting strategic decision making in organisations. It begins by exploring the notions of strategic decisions and the strategic decision-making process. We suggest that structuring strategic objectives, dealing with high levels of uncertainty about the future, as well as considering the interconnectedness of strategic options and their long-term consequences are key aspects of strategic decision making support. We then consider the discursive nature of the processes within which strategic decisions are created and negotiated. Our exploration of these concepts leads us to propose a number of adaptations to the standard multi-criteria decision analysis approach, if it were to provide effective strategic decision support, particularly in strategy workshops. We make suggestions on how to implement these proposals, and illustrate their potential with examples drawn from real-world interventions in which we have provided strategic decision support.

An integrated process and management tools for ranking multiple emerging threats to animal health

The UKs 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 such as the Risk Management Cycle and the Emerging Threat Highlight Report (ETHiR), to facilitate the identification, reporting and assessment of emerging threats to UKs 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 (DSS) based on multi-criteria-decision-analysis (MCDA) improved ETHiR to allow the systematic prioritisation of emerging T&V. The DSS 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 regular use of MCDA to manage a portfolio of emerging risks represents a different and novel application of MCDA in a health related context.

Key challenges and meta-choices in designing and applying multi-criteria spatial decision support systems

There is an increasing use of multi-criteria spatial decision support systems in recent years for dealing with problems that have a spatial distribution of consequences. This growth might be explained by the widespread recognition that there are multiple and conflicting objectives to be considered in spatial planning (e.g. minimizing pollution to air, water and soil, increasing the acceptance of the projects, reducing implementation costs), by new requirements to consider societal values in the evaluation and to increase participation in decision processes, as well as by the crucial role that the spatial dimension plays in such problems. However, we argue in this paper that there are key challenges confronted by DSS designers who are developing such systems and by DSS practitioners who are employing them to support decision making. These challenges impose important meta-choices to designers and practitioners, which may lead to different contents of the evaluation model and to distinctive outcomes of the analysis. In this paper, we present and discuss these key challenges and the associated meta-choices. The contribution that we aim to provide to both researchers and practitioners can be summarized as follows: (i) an increased awareness about choices to be made in the design and implementation of these decision support systems; (ii) a better understanding about the available alternatives for each choice, based on recent developments in the literature; and (iii) a clearer appraisal about the inherent trade-offs between advantages and disadvantages of each alternative. The focus is on multi-criteria spatial decision support systems (MC-SDSS).The paper identifies the key challenges in designing and applying MC-SDSS.For each challenge, the associated meta-choices are presented.Guidelines and insights for future applications are also discussed in the paper.

Supporting factoring transactions in Brazil using reasoning maps: a language-based DSS for evaluating accounts receivable

Factoring companies are a widespread way of providing working capital to small enterprises in Brazil. This type of financial transaction has higher risks when performed in developing countries, due to unreliable financial information on firms, an unstable environment, and particular managerial practices. This paper describes a case study in which a language-based DSS was developed for a Brazilian factoring company to evaluate the perceived risk of buying accounts receivable; and discusses the suitability of different approaches to decision support for this type of decision in Brazil-which may be relevant for similar situations in other developing countries.