Decision analysis and political processes

Decision analysis has been with us for at least half a century. Over that time it has developed from a theoretical paradigm for individual rational choice to a practical tool for individuals, small groups and ‘unitary’ organisations, which helps them towards a sound decision-making mindful of the behavioural characteristics of individuals and group dynamics. Decision analysis has also shown its worth in the context of stakeholder engagement and public participation. The time is right for it to be more widely used in making societal decisions. However, to achieve that we need to realise that in many circumstances it will only be one input to the political process that leads to the actual decision. Recognising that suggests that our community of decision analysts needs to deconstruct our paradigm and attend more to communicating the result of the analysis in comparison with other inputs to the societal decision

Capturing preferences for inequality aversion in decision support

We investigate the situation where there is interest in ranking distributions (of income, of wealth, of health, of service levels) across a population, in which individuals are considered preferentially indistinguishable and where there is some limited information about social preferences. We use a natural dominance relation, generalized Lorenz dominance, used in welfare comparisons in economic theory. In some settings there may be additional information about preferences (for example, if there is policy statement that one distribution is preferred to another) and any dominance relation should respect such preferences. However, characterising this sort of conditional dominance relation (specifically, dominance with respect to the set of all symmetric increasing quasiconcave functions in line with given preference information) turns out to be computationally challenging. This challenge comes about because, through the assumption of symmetry, any one preference statement (“I prefer giving $100 to Jane and$110 to John over giving $150 to Jane and$90 to John”) implies a large number of other preference statements (“I prefer giving $110 to Jane and$100 to John over giving $150 to Jane and$90 to John”; “I prefer giving $100 to Jane and$110 to John over giving $90 to Jane and$150 to John”). We present theoretical results that help deal with these challenges and present tractable linear programming formulations for testing whether dominance holds between any given pair of distributions. We also propose an interactive decision support procedure for ranking a given set of distributions and demonstrate its performance through computational testing

CUT: a multicriteria approach for concavifiable preferences

We consider the problem of helping a decision maker (DM) choose from a set of multiattributed objects when her preferences are "concavifiable," i.e. representable by a concave value function. We establish conditions under which preferences or preference intensities are concavifiable. We also derive a characterization for the family of concave value functions compatible with a set of such preference statements expressed by the DM. This can be used to validate dominance relations over discrete sets of alternatives and forms the basis of an interactive procedure. We report on the practical use of this procedure with several DMs for a flat-choice problem and its computational performance on a set of project-portfolio selection problem instances. The use of preference intensities is found to provide significant improvements to the performance of the procedure

Communicating geographical risks in crisis management: The need for research

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatio-temporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatio-temporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatio-temporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis the uncertainties involved may be deep, i.e. difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios

Uncertainty handling during nuclear accidents.

In the years following Chernobyl, many reports and projects reflected on how to improve emergency management processes in dealing with an accidental offsite release of radiation at a nuclear facility. A common observation was the need to address the inevitable uncertainties. Various suggestions were made and some of these were researched in some depth. The Fukushima Daiichi Disaster has led to further reflections. However, many of the uncertainties inherent in responding to a threatened or actual release remain unaddressed in the analyses and model runs that are conducted to support the emergency managers in their decision making. They are often left to factor in allowances for the uncertainty through informal discussion and unsupported judgement, and the full range of sources of uncertainty may not be addressed. In this paper, we summarise the issues and report on a project which has investigated the handling of uncertainty in the UK’s national crisis cell. We suggest the R&D programmes needed to provide emergency managers with better guidance on uncertainty and how it may affect the consequences of taking different countermeasures

Production trade-offs in models of data envelopment analysis with ratio inputs and outputs: An application to schools in England

In applications of data envelopment analysis (DEA), the inputs and outputs representing environmental and quality characteristics of the production process are often stated in the form of percentages, ratios and averages, collectively referred to as ratio measures. It is known that the conventional variable and constant returns-to-scale (VRS and CRS) DEA models cannot correctly incorporate such ratio inputs and outputs. This problem has been addressed by the development of Ratio-VRS and Ratio-CRS (R-VRS and R-CRS) models suitable for the incorporation of both volume and ratio inputs and outputs. Such models may, however, depending on the application, lack sufficient discriminatory power. In this paper we address this issue by developing a further extension of the R-VRS and R-CRS models (the latter with the most common fixed type of ratio inputs and outputs) by allowing the specification of production trade-offs between volume inputs and outputs, and, similarly, between ratio measures. As in the case of conventional VRS and CRS models in which the role of production trade-offs is well understood, the specification of such trade-offs in the R-VRS and R-CRS production technologies leads to their controlled expansion and results in improved efficiency discrimination of the resulting DEA models. We illustrate the application of the proposed methodology by the assessment of efficiency of a large sample of secondary schools in England.</p

Fair resource allocation: using welfare-based dominance constraints

In this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem

Incomplete risk-preference information in portfolio decision analysis

Portfolio decision analysis models support decisions on the allocation of resources among assets with uncertain outcomes (e.g., investments, projects or stocks). These models require information on the decision maker’s risk-preferences which can be difficult to obtain in practice. Stochastic dominance criteria show promise in this regard as they can compare portfolios without exact specification of risk-preferences, but the current literature lacks practical approaches for generating the efficient frontier, i.e., the set of those portfolios that are not stochastically dominated by any other portfolio. We address this gap by developing models to identify sets of portfolios that are efficient in the sense of second- or third-order stochastic dominance (SSD, TSD). These models provide novel insights into the composition of portfolios belonging to the efficient frontier by, e.g., identifying those assets that are included in all efficient portfolios. Moreover, the identification of the efficient frontier makes it possible to utilize additional information on the decision maker’s risk preferences to further reduce the set of admissible portfolio alternatives, and to analyze the implications this information has on the amount of capital that should be allocated to each individual asset. We illustrate the usefulness of these models with applications in project portfolio selection and financial portfolio diversification.</p

Advances in spatial risk analysis [Editorial]

Advances in spatial risk analysis [Editorial