Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts

Can islands of effectiveness thrive in difficult governance settings ? the political economy of local-level collaborative governance

Many low-income countries contend with a governance syndrome characterized by a difficult combination of seeming openness, weak institutions, and strong inter-elite contestation for power and resources. In such countries, neither broad-based policy nor public management reforms are likely to be feasible. But are broad-based approaches necessary? Theory and evidence suggest that in such settings progress could be driven by"islands of effectiveness"-- narrowly-focused initiatives that combine high-quality institutional arrangements at the micro-level, plus supportive, narrowly-targeted policy reforms. This paper explores whether and how local-level collaborative governance can provide a platform for these islands of effectiveness. Drawing on the analytical framework developed by the Nobel-prize winning social scientist Elinor Ostrom, the paper reviews the underpinnings of successful collaborative governance. It introduces a simple model for exploring the interactions between collaborative governance and political economy. The model highlights the conditions under which coordination is capable of countering threats from predators seeking to capture the returns from collaborative governance for themselves. The relative strength in the broader environment of two opposing networks emerges as key --"threat networks"to which predators have access, and countervailing"trumping networks"on which protagonists of effective collaborative governance can draw. The paper illustrates the potential practical relevance of the approach with three heuristic examples: the governance of schools, fisheries, and road construction and maintenance. It concludes by laying out an agenda for further empirical research, and suggesting what might be the implications of the approach for future operational practice.Governance Indicators,National Governance,Public Sector Corruption&Anticorruption Measures,Environmental Economics&Policies,Economic Policy, Institutions and Governance

Organic Farming in Europe by 2010: Scenarios for the future

How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector. This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis

Structuring Decisions Under Deep Uncertainty

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature

Food supply chain network robustness : a literature review and research agenda

Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

Adaptation to Climate Change: Do Not Count on Climate Scientists to Do Your Work

Many decisions concerning long-lived investments need to take into account climate change. But doing so is not easy for at least two reasons. First, due to the rate of climate change, new infrastructure will have to be able to cope with a large range of changing climate conditions, which will make design more difficult and construction more expensive. Second, uncertainty in future climate makes it impossible to directly use climate model outputs as inputs for infrastructure design, and there are good reasons to think that the needed climate information will not be available soon. Instead of optimizing based on the climate conditions projected by models, therefore, future infrastructure should be made robust to most possible changes in climate conditions. This aim implies that users of climate information must also change their practices and decision-making frameworks, for instance by adapting the uncertainty-management methods they currently apply to exchange rates or R&D outcomes. Five methods are examined: (i) introducing long-term prospective exercises; (ii) selecting 'no-regret' strategies; (iii) favouring reversible options; (iv) reducing decision time horizons; and (v) promoting soft adaptation strategies. I argue that adaptation strategies should not be assessed in an isolated context. In particular, it is essential to consider both negative and positive side-effects, including possible changes in future energy costs.

Extending the combined use of scenarios and multi-criteria decision analysis for evaluating the robustness of strategic options

Deep uncertainty exists when there is disagreement on how to model inter-relationships between variables in the external/controllable and internal/controllable environment; how to specify probability distributions to represent threats; and/or how to value various consequences. The evaluation of strategic options under deep uncertainty involves structuring the decision problem, specifying options to address that problem, and assessing which options appear to consistently perform well by achieving desirable levels of performance across a range of futures. The integrated use of scenarios and Multi-Criteria Decision Analysis (MCDA) provides a framework for managing these issues, and is an area of growing interest. This thesis aims to explore such integrated use, suggesting a new method for combining MCDA and scenario planning, and to test such proposal through a multi-method research strategy involving case study, behavioural experiment and simulation. The proposal reflects the three key areas of confluence of scenarios and MCDA in the decision making process. The first is based on systematic generation of a larger scenario set, focused on extreme outcomes, for defining the boundaries of the decision problem. The second proposal is based on providing less scenario detail than the traditional narrative, in favour of explicitly considering how uncertainties affect positive and negative outcomes on key objectives. This backward logic seeks to better address the challenge of estimating the consequences of each option and the trade-offs involved. Finally, it is proposed that option selection be based on a concern for robustness through cost-equivalent regret. The empirical findings reflect that the key benefit of integration appears to be a mechanism to improve the efficiency of elicitation and the robustness of options. However, effective application of scenarios and MCDA requires awareness of the desired degree of accuracy required and risk attitude of decision makers

Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation

International audienceThe scientific community is now developing a new set of scenarios, referred to as Shared Socio-economic Pathways (SSPs) that will be contrasted along two axes: challenges to mitigation, and challenges to adaptation. This paper proposes a methodology to develop SSPs with a "backwards" approach based on (i) an a priori identification of potential drivers of mitigation and adaptation challenges; (ii) a modelling exercise to transform these drivers into a large set of scenarios; (iii) an a posteriori selection of a few SSPs among these scenarios using statistical cluster-finding algorithms. This backwards approach could help inform the development of SSPs to ensure the storylines focus on the driving forces most relevant to distinguishing between the SSPs. In this illustrative analysis, we find that energy sobriety, equity and convergence prove most important towards explaining future difference in challenges to adaptation and mitigation. The results also demonstrate the difficulty in finding explanatory drivers for a middle scenario (SSP2). We argue that methodologies such as that used here are useful for broad questions such as the definition of SSPs, and could also be applied to any specific decisions faced by decision-makers in the field of climate change