Decision analysis exercises pdf

Exercises and solutions in PD

Classes of decision analysis

The ultimate task of an engineer consists of developing a consistent decision procedure for the planning, design, construction and use and management of a project. Moreover, the utility over the entire lifetime of the project should be maximized, considering requirements with respect to safety of individuals and the environment as specified in regulations. Due to the fact that the information with respect to design parameters is usually incomplete or uncertain, decisions are made under uncertainty. In order to cope with this, Bayesian statistical decision theory can be used to incorporate objective as well as subjective information (e.g. engineering judgement). In this factsheet, the decision tree is presented and answers are given for questions on how new data can be combined with prior probabilities that have been assigned, and whether it is beneficial or not to collect more information before the final decision is made. Decision making based on prior analysis and posterior analysis is briefly explained. Pre-posterior analysis is considered in more detail and the Value of Information (VoI) is defined

Anti-coagulation, anti-platelets or no therapy in haemodialysis patients with atrial fibrillation: a decision analysis

BACKGROUND: Optimal treatment of atrial fibrillation (AF) in the haemodialysis population is uncertain due to the exclusion of this group from randomized trials. The risk-benefit profile for anticoagulation and anti-platelet therapy in haemodialysis differs from the general population due to platelet dysfunction from uraemia, altered pharmacokinetics and increased falls risk. METHODS: This decision analysis used a Markov-state transition model that took a patient perspective over a 5 year timeframe. The Markov model compared life-years gained and quality-adjusted life-years gained (QALY) for three AF treatment strategies: warfarin, aspirin and no treatment. The base case was a 70-year-old man on haemodialysis with non-valvular AF. RESULTS: In the base case, the total health outcomes in life-years and QALY were 2.37 and 1.47 respectively for warfarin, 2.38 and 1.61 respectively for aspirin, and 2.39 and 1.61 respectively for no treatment. Thus, warfarin led to 0.14 fewer QALY or 1.7 fewer months of life lived in full health, compared with either aspirin or no therapy. The finding that warfarin generated the lowest expected QALY was robust to one-way, two-way and probabilistic sensitivity analyses. CONCLUSIONS: Our results suggest that warfarin should not be the default choice for older haemodialysis patients with non-valvular AF as it provides the fewest QALY compared with aspirin or no therapy

Decision analysis with approximate probabilities

This paper concerns decisions under uncertainty in which the probabilities of the states of nature are only approximately known. Decision problems involving three states of nature are studied. This is due to the fact that some key issues do not arise in two-state problems, while probability spaces with more than three states of nature are essentially impossible to graph. The primary focus is on two levels of probabilistic information. In one level, the three probabilities are separately rounded to the nearest tenth. This can lead to sets of rounded probabilities which add up to 0.9, 1.0, or 1.1. In the other level, probabilities are rounded to the nearest tenth in such a way that the rounded probabilities are forced to sum to 1.0. For comparison, six additional levels of probabilistic information, previously analyzed, were also included in the present analysis. A simulation experiment compared four criteria for decisionmaking using linearly constrained probabilities (Maximin, Midpoint, Standard Laplace, and Extended Laplace) under the eight different levels of information about probability. The Extended Laplace criterion, which uses a second order maximum entropy principle, performed best overall

Using decision analysis: connecting "classroom" and "field"

This paper reports on the findings of a small-scale research project investigating the views of social work students on the use of decision analysis. After giving the context of the research, the article reports on what was found when students, who had just completed a Decision Making and Risk module, were asked for their opinions on the component parts of decision analysis, its use as a practice tool and their attitudes to using it on placement. The research found that the respondents in general took a critical and supportive stance towards the use of decision analysis in social work and, with extra teaching and a positive approach from their practice assessor, would be happy to use decision analysis. When the same group of students completed a follow-up questionnaire on a placement recall day, half of them had thought about using decision analysis but only three had gone on to discuss this with their practice assessors. Some issues in relation to connecting 'classroom' and 'field' are identified and the paper concludes that a number of further steps would be necessary to realise the potential of decision analysis to help students be more systematic and analytical in their approach to decision makin

Agricultural Decision Analysis: The Causal Challenge

The paper sets out the agenda for reviewing models of decision making in the context of farmers’ use of seasonal climate forecasting. Such forecasts have been framed in terms of shifts in cumulative distribution functions of yields or gross margins. Typically they have been applied to choices about crop variety, crop type, time of planting or level of fertiliser application. Fundamental questions are: how do farmers conceptualise and make use of the information contained in seasonal climate forecasts? Do our models of decision making represent well the way in which these decisions are made?decision making, farmers, seasonal climate forecasts, conceptualisation, review,

Integrating multiple criteria decision analysis in participatory forest planning

Forest planning in a participatory context often involves multiple stakeholders with conflicting interests. A promising approach for handling these complex situations is to integrate participatory planning and multiple criteria decision analysis (MCDA). The objective of this paper is to analyze strengths and weaknesses of such an integrated approach, focusing on how the use of MCDA has influenced the participatory process. The paper outlines a model for a participatory MCDA process with five steps: stakeholder analysis, structuring of the decision problem, generation of alternatives, elicitation of preferences, and ranking of alternatives. This model was applied in a case study of a planning process for the urban forest in Lycksele, Sweden. In interviews with stakeholders, criteria for four different social groups were identified. Stakeholders also identified specific areas important to them and explained what activities the areas were used for and the forest management they wished for there. Existing forest data were combined with information from interviews to create a map in which the urban forest was divided into zones of different management classes. Three alternative strategic forest plans were produced based on the zonal map. The stakeholders stated their preferences individually by the Analytic Hierarchy Process in inquiry forms and a ranking of alternatives and consistency ratios were determined for each stakeholder. Rankings of alternatives were aggregated; first, for each social group using the arithmetic mean, and then an overall aggregated ranking was calculated from the group rankings using the weighted arithmetic mean. The participatory MCDA process in Lycksele is assessed against five social goals: incorporating public values into decisions, improving the substantive quality of decisions, resolving conflict among competing interests, building trust in institutions, and educating and informing the public. The results and assessment of the case study support the integration of participatory planning and MCDA as a viable option for handling complex forest-management situations. Key issues related to the MCDA methodology that need to be explored further were identified: 1) The handling of place-specific criteria, 2) development of alternatives, 3) the aggregation of individual preferences into a common preference, and 4) application and evaluation of the integrated approach in real case studies

Using Decision Analysis to Improve Malaria Control Policy Making

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets (ITNs) and artemesinin combination therapies (ACTs) for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases

Sequential decision analysis for nonstationary stochastic processes

A formulation of the problem of making decisions concerning the state of nonstationary stochastic processes is given. An optimal decision rule, for the case in which the stochastic process is independent of the decisions made, is derived. It is shown that this rule is a generalization of the Bayesian likelihood ratio test; and an analog to Wald's sequential likelihood ratio test is given, in which the optimal thresholds may vary with time