ASSESSING FARMERS' ATTITUDES TOWARD RISK USING THE "CLOSING-IN" METHOD

The 1996 Farm Bill and low commodity prices have regenerated interest in the impact of risk and farmers' risk attitudes on production agriculture. Previous research has used expected utility theory (EUT) and direct elicitation of utility functions (DEU) for eliciting risk attitudes. To overcome the criticism of EUT and DEU, a recently developed technique called the "closing in" method is adapted for eliciting farmers' risk attitudes. This method is applied to Illinois farmers by using a computerized decision procedure, and is validated by comparing the results to the farmers' self-assessment of their risk attitudes and score to a risk attitudinal scale.Risk and Uncertainty,

Welfare Polls: A Synthesis

"Welfare polls" are survey instruments that seek to quantify the determinants of human well-being. Currently, three "welfare polling" formats are dominant: contingent-valuation surveys, QALY surveys, and happiness surveys. Each format has generated a large, specialized, scholarly literature, but no comprehensive discussion of welfare polling as a general enterprise exists. This Article seeks to fill that gap. Part I describes the trio of existing formats. Part II discusses the actual and potential uses of welfare polls in government decision making. Part III analyzes in detail the obstacles that welfare polls must overcome to provide useful well-being information, and concludes that they can be genuinely informative. Part IV synthesizes the case for welfare polls, arguing against two types of challenges: the revealed-preference tradition in economics, which insists on using behavior rather than surveys to learn about well-being; and the civic-republican tradition in political theory, which accepts surveys but insists that respondents should be asked to take a "citizen", rather than "consumer" perspective. Part V suggests new directions for welfare polls.

Investment risk preferences of decision makers acting on behalf of German charitable trusts

This research programme investigates the subjective utility of monetary outcomes and applies the existing knowledge base regarding the quantification and description of risk preferences to German charitable trusts. Results are discussed on the basis of Expected Utility Theory (EUT) and Prospect Theory (PT) with a focus on the “Fourfold Pattern (4FP)” of PT. The description of risk preferences of trusts enables investors, advisors and portfolio managers to optimise their investment strategies for this specific target group disposing of an estimated asset base of about € 100bn. The subjects of this study, German charitable trusts, are restricted in their investment decisions by a given legal framework and therefore prone to deviate in their preferences from the subjects that have been examined in prior academic studies. The thesis aims at filling this research gap by applying the knowledge base of decision theory to German charitable trusts using an original set of representative data which was generated as part of this study. Firstly, regarding the general investment risk preferences of trusts, the study finds risk aversion predominating in the domain of gains and observes loss aversion, both analogous to prior research on private individuals. The PT pattern of risk-seeking behaviour for losses can only partly be asserted. In contrast to PT, no evidence is found for the subjective overweighting of small probabilities. Secondly, the study identifies and discusses characteristics of trusts which are associated with risk preferences: Equity investments, expected external growth of assets, age of the investment decision makers, type of donor and involvement of the donor in investment decisions.As a contribution to decision theory, the author proposes a utility function representing the preferences of trusts based on decision theoretical backgrounds. As a contribution to practical investment implications, the author proposes to redefine the question of “safe investments” and to focus on distributable yields generated by a higher equity portion in trust portfolios

Best practices for the provision of prior information for Bayesian stock assessment

This manual represents a review of the potential sources and methods to be applied when providing prior information to Bayesian stock assessments and marine risk analysis. The manual is compiled as a product of the EC Framework 7 ECOKNOWS project (www.ecoknows.eu). The manual begins by introducing the basic concepts of Bayesian inference and the role of prior information in the inference. Bayesian analysis is a mathematical formalization of a sequential learning process in a probabilistic rationale. Prior information (also called ”prior knowledge”, ”prior belief”, or simply a ”prior”) refers to any existing relevant knowledge available before the analysis of the newest observations (data) and the information included in them. Prior information is input to a Bayesian statistical analysis in the form of a probability distribution (a prior distribution) that summarizes beliefs about the parameter concerned in terms of relative support for different values. Apart from specifying probable parameter values, prior information also defines how the data are related to the phenomenon being studied, i.e. the model structure. Prior information should reflect the different degrees of knowledge about different parameters and the interrelationships among them. Different sources of prior information are described as well as the particularities important for their successful utilization. The sources of prior information are classified into four main categories: (i) primary data, (ii) literature, (iii) online databases, and (iv) experts. This categorization is somewhat synthetic, but is useful for structuring the process of deriving a prior and for acknowledging different aspects of it. A hierarchy is proposed in which sources of prior information are ranked according to their proximity to the primary observations, so that use of raw data is preferred where possible. This hierarchy is reflected in the types of methods that might be suitable – for example, hierarchical analysis and meta-analysis approaches are powerful, but typically require larger numbers of observations than other methods. In establishing an informative prior distribution for a variable or parameter from ancillary raw data, several steps should be followed. These include the choice of the frequency distribution of observations which also determines the shape of prior distribution, the choice of the way in which a dataset is used to construct a prior, and the consideration related to whether one or several datasets are used. Explicitly modelling correlations between parameters in a hierarchical model can allow more effective use of the available information or more knowledge with the same data. Checking the literature is advised as the next approach. Stock assessment would gain much from the inclusion of prior information derived from the literature and from literature compilers such as FishBase (www.fishbase.org), especially in data-limited situations. The reader is guided through the process of obtaining priors for length–weight, growth, and mortality parameters from FishBase. Expert opinion lends itself to data-limited situations and can be used even in cases where observations are not available. Several expert elicitation tools are introduced for guiding experts through the process of expressing their beliefs and for extracting numerical priors about variables of interest, such as stock–recruitment dynamics, natural mortality, maturation, and the selectivity of fishing gears. Elicitation of parameter values is not the only task where experts play an important role; they also can describe the process to be modelled as a whole. Information sources and methods are not mutually exclusive, so some combination may be used in deriving a prior distribution. Whichever source(s) and method(s) are chosen, it is important to remember that the same data should not be used twice. If the 2 | ICES Cooperative Research Report No. 328 plan is to use the data in the analysis for which the prior distribution is needed, then the same data cannot be used in formulating the prior. The techniques studied and proposed in this manual can be further elaborated and fine-tuned. New developments in technology can potentially be explored to find novel ways of forming prior distributions from different sources of information. Future research efforts should also be targeted at the philosophy and practices of model building based on existing prior information. Stock assessments that explicitly account for model uncertainty are still rare, and improving the methodology in this direction is an important avenue for future research. More research is also needed to make Bayesian analysis of non-parametric models more accessible in practice. Since Bayesian stock assessment models (like all other assessment models) are made from existing knowledge held by human beings, prior distributions for parameters and model structures may play a key role in the processes of collectively building and reviewing those models with stakeholders. Research on the theory and practice of these processes will be needed in the future

Scenario-based portfolio model for building robust and proactive strategies

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to support the selection of such strategic actions when the information about scenario probabilities is possibly incomplete and may depend on the selected actions. This model helps build a strategy that is robust in that it performs relatively well in view of all available probability information, and proactive in that it can help steer the future as reflected by the scenarios toward the desired direction. We also report a case study in which the model helped a group of Nordic, globally operating steel and engineering companies build a platform ecosystem strategy that accounts for uncertainties related to markets, politics, and technological development

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

The Use of Expert Judgement Methods for Deriving Accident Probabilities in Aviation

Improving safety has always been the top interest in the aviation industry. The outcomes of safety and risk analyses have become much more thorough and sophisticated. They have become an industry standard of safety investigations in many airlines nowadays. In the past, airlines were much more limited in answering the questions about hazardous situations, accident probabilities, and accident rates. Airlines try hard to cope with stricter safety standards. The objective of this paper is to find out and quantify the extent of the expert judgment in helping airlines in the evaluation of the Flight Data Monitoring (FDM) events. On top of that, the paper reveals the method for a careful choice of experts, so that their estimations will maximize the potential of an accurate and useful outcome. Also, the paper provides details of implementation of the classical model into this research, then continues with the calculations and visualization of the outcomes. The outcomes are probability distributions per each aircraft type, then per IATA accident type and finally per FDM event

Deliberation, Representation, Equity

"What can we learn about the development of public interaction in e-democracy from a drama delivered by mobile headphones to an audience standing around a shopping center in a Stockholm suburb? In democratic societies there is widespread acknowledgment of the need to incorporate citizens’ input in decision-making processes in more or less structured ways. But participatory decision making is balancing on the borders of inclusion, structure, precision and accuracy. To simply enable more participation will not yield enhanced democracy, and there is a clear need for more elaborated elicitation and decision analytical tools. This rigorous and thought-provoking volume draws on a stimulating variety of international case studies, from flood risk management in the Red River Delta of Vietnam, to the consideration of alternatives to gold mining in Roșia Montană in Transylvania, to the application of multi-criteria decision analysis in evaluating the impact of e-learning opportunities at Uganda's Makerere University. Editors Love Ekenberg (senior research scholar, International Institute for Applied Systems Analysis [IIASA], Laxenburg, professor of Computer and Systems Sciences, Stockholm University), Karin Hansson (artist and research fellow, Department of Computer and Systems Sciences, Stockholm University), Mats Danielson (vice president and professor of Computer and Systems Sciences, Stockholm University, affiliate researcher, IIASA) and Göran Cars (professor of Societal Planning and Environment, Royal Institute of Technology, Stockholm) draw innovative collaborations between mathematics, social science, and the arts. They develop new problem formulations and solutions, with the aim of carrying decisions from agenda setting and problem awareness through to feasible courses of action by setting objectives, alternative generation, consequence assessments, and trade-off clarifications. As a result, this book is important new reading for decision makers in government, public administration and urban planning, as well as students and researchers in the fields of participatory democracy, urban planning, social policy, communication design, participatory art, decision theory, risk analysis and computer and systems sciences.

Deliberation, Representation, Equity: Research Approaches, Tools and Algorithms for Participatory Processes

In democratic societies there is widespread acknowledgment of the need to incorporate citizens’ input in decision-making processes in more or less structured ways. But participatory decision making is balancing on the borders of inclusion, structure, precision and accuracy. To simply enable more participation will not yield enhanced democracy, and there is a clear need for more elaborated elicitation and decision analytical tools. This rigorous and thought-provoking volume draws on a stimulating variety of international case studies, from flood risk management in the Red River Delta of Vietnam, to the consideration of alternatives to gold mining in Roșia Montană in Transylvania, to the application of multi-criteria decision analysis in evaluating the impact of e-learning opportunities at Uganda's Makerere University. This book is important new reading for decision makers in government, public administration and urban planning, as well as students and researchers in the fields of participatory democracy, urban planning, social policy, communication design, participatory art, decision theory, risk analysis and computer and systems sciences