Systemic evaluation of a community environmental management programme

Community environmental management (CEM) involves achieving environmental objectives through the facilitation of community partnerships, local dialogues, consultations and participative decision making. This is increasingly seen as a solution to some of the more complex environmental issues facing regulatory authorities. However, little has been written about how CEM programmes should be evaluated, and this is particularly concerning given that the establishment of a causal relationship between community participation and environmental outcomes is not straight forward. Anecdotal evidence suggests that CEM programmes have much potential, but the lack of a robust evidence-base for their effectiveness means that their role in resource management is often not well understood or well integrated with other environmental management tools and processes. This paper reports on a project that developed a novel, systemic CEM evaluation methodology and trialled it in an intervention with a regional council in New Zealand. The methodology has the potential to be adapted for other contexts where there is a need for more robust evidence of the value (or otherwise) of CEM

Behavioral challenges in policy analysis with conflicting objectives

Public policy problems are rife with conflicting objectives: efficiency versus fairness, technical criteria versus political goals, costs versus multiple benefits. Multi-Criteria Decision Analysis provides robust methodologies to support policy makers in making tough choices and in designing better policy options when considering these conflicting objectives. However, important behavioral challenges exist in developing these models: the use of expert judgments, whenever evidence is not available; the elicitation of preferences and priorities from policy makers and communities; and the effective management of group decision processes. The extensive developments in behavioral decision research, social psychology, facilitated decision modeling, and incomplete preference models shed light on how decision analysts should address these issues, so we can provide better decision support and develop high quality decision models. In this tutorial I discuss the main findings of these extensive, but rather fragmented, literatures providing a coherent and practical framework for managing behavioral issues, minimizing behavioral biases, and optimizing the quality of human judgments in policy analysis models with conflicting objectives. I illustrate these guidelines with policy analysis interventions that we have conducted over the last decade for several organizations, such as the World Health Organization (WHO), the Food and Agriculture Organization of the United Nations (FAO), the UK Department of Environment Food and Rural Affairs (DEFRA), the Malaria Consortium/USAID, the UK National Audit Office, among others

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

Development of a conceptual framework for integrated analysis and assessment of agricultural systems in SEAMLESS-IF

Production Economics,

Livestock farming with care : summaries of essays

Wageningen UR is the country’s major research organisation in the field of livestock farming, providing the knowledge base for innovative livestock farming in our country and beyond and, as such, keen to play a role in the above mentioned debate. To this end an interdisciplinary task force was formed embodying a range of expertise, from livestock technology to system analysis and from economics to public administration. As one of the task force activities, Wageningen UR colleagues were invited to write an essay with their vision on specific aspects of this debate, based on their views and expertise. The result was a series of 30 essays, providing a wide overview of relevant issues with possible directions for solutions

Scenario analysis to support decision making in addressing wicked problems: pitfalls and potential

This paper provides a review and evaluation of the use of scenario methods from the Intuitive Logics (IL) school to address so-called ‘wicked problems’. Scenario planning has been widely advocated by its practitioners and its popularity has increased in the practice arena since the Millennium. However, some academics have described the technique as an ‘art’ that lacks theoretical and methodological rigour. Over recent years, academics have responded to this critique, drawing on both empirical and conceptual studies. This has led to a multiplicity of augmented IL scenario methods. Here, we review these developments and compare them to soft OR methods as a means of tackling wicked problems, drawing, in particular, on Churchman’s moral imperative that we must address the whole problem, not merely ‘carve off’ one part. We conclude that IL scenario planning can be a useful tool in the OR practitioner’s tool kit and that it can complement many of the established soft OR method

A research review of quality assessment for software

Measures were recommended to assess the quality of software submitted to the AdaNet program. The quality factors that are important to software reuse are explored and methods of evaluating those factors are discussed. Quality factors important to software reuse are: correctness, reliability, verifiability, understandability, modifiability, and certifiability. Certifiability is included because the documentation of many factors about a software component such as its efficiency, portability, and development history, constitute a class for factors important to some users, not important at all to other, and impossible for AdaNet to distinguish between a priori. The quality factors may be assessed in different ways. There are a few quantitative measures which have been shown to indicate software quality. However, it is believed that there exists many factors that indicate quality and have not been empirically validated due to their subjective nature. These subjective factors are characterized by the way in which they support the software engineering principles of abstraction, information hiding, modularity, localization, confirmability, uniformity, and completeness

Considerations in development of expert systems for real-time space applications

Over the years, demand on space systems has increased tremendously and this trend will continue for the near future. Enhanced capabilities of space systems, however, can only be met with increased complexity and sophistication of onboard and ground systems. Artificial Intelligence and expert system techniques have great potential in space applications. Expert systems could facilitate autonomous decision making, improve in-orbit fault diagnosis and repair, enhance performance and reduce reliance on ground support. However, real-time expert systems, unlike conventional off-line consultative systems, have to satisfy certain special stringent requirements before they could be used for onboard space applications. Challenging and interesting new environments are faced while developing expert system space applications. This paper discusses the special characteristics, requirements and typical life cycle issues for onboard expert systems. Further, it also describes considerations in design, development, and implementation which are particularly important to real-time expert systems for space applications

Exploring the interface between community-based forest management and systems modelling

Participatory modes of development, in general and community-based forest management in particular, have been widely accepted as a better alternative to traditional management paradigms, which have been discredited for being too expert-driven, centrally planned, and essentially top-down with strong adherence to principles of rationality. Recently however, participatory approaches have also received some criticism for their lack of rigour and highly empiricist orientation. Traditional modelling approaches have often been associated with traditional management paradigms in part because they are highly structured and systematic in their approach. Despite their perceived limitations, participatory approaches and modelling offer a number of desirable properties and strengths that can enhance both the practice and theory of community-based management. This paper explores alternative ways by which systems modelling can be adopted in a participatory framework. Combining these two approaches offers the flexibility, inclusivity and practicality of participatory methods, and the systemic, systematic and analytical capabilities of systems modelling. Examples of models that adhere to this hybrid approach are described in this paper