Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty

Multiple criteria decision analysis in the context of health technology assessment: a simulation exercise on metastatic colorectal cancer with multiple stakeholders in the English setting

Background: Multiple criteria decision analysis (MCDA) has appeared as a methodology to address limitations of economic evaluation in health technology assessment (HTA), however there are limited empirical evidence from real world applications. The aim of this study is to test in practice a recently developed MCDA methodological framework known as Advance Value Framework (AVF) through a proof-of-concept case study engaging multiple stakeholders. Methods: A multi-attribute value theory methodological process was adopted involving problem structuring, model building, model assessment and model appraisal phases. A facilitated decision analysis modelling approach was used as part of a decision conference with thirteen participants. An expanded scope of the National Institute for Health and Care Excellence (NICE) remit acted as the study setting with the use of supplementary value concerns. Second-line biological treatments were evaluated for metastatic colorectal cancer (mCRC) patients having received prior chemotherapy, including cetuximab monotherapy, panitumumab monotherapy and aflibercept in combination with FOLFIRI chemotherapy. Initially 18 criteria attributes were considered spanning four value domains relating to therapeutic impact, safety profile, innovation level and socioeconomic impact. Results: Nine criteria attributes were finally included. Cetuximab scored the highest overall weighted preference value score of 45.7 out of 100, followed by panitumumab with 42.3, and aflibercept plus FOLFIRI with 14.4. The relative weights of the two most important criteria (overall survival and Grade 4 adverse events) added up to more than the relative weight of all other criteria together (52.1%). Main methodological limitation was the lack of comparative clinical effects across treatments and challenges included the selection of “lower” and “higher” reference levels on criteria attributes, eliciting preferences across attributes where participants had less experience, and ensuring that all attributes possess the right decision theory properties. Conclusions: This first application of AVF produced transparent rankings for three mCRC treatments based on their value, by assessing an explicit set of evaluation criteria while allowing for the elicitation and construction of participants’ value preferences and their trade-offs. It proved it can aid the evaluation process and value communication of the alternative treatments for the group participants. Further research is needed to optimise its use as part of policy-making

Critical review of methods for risk ranking of food related hazards, based on risks for human health.

This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science and socio-economic sciences. The review used a predefined search protocol, and covered the bibliographic databases Scopus, CAB Abstracts, Web of Sciences, and PubMed over the period 1993-2013. All references deemed relevant, on the basis of of predefined evaluation criteria, were included in the review, and the risk ranking method characterized. The methods were then clustered - based on their characteristics - into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years, multi-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking. The method to be used should be selected on the basis of risk manager/assessor requirements, data availability, and the characteristics of the method. Recommendations for future use and application are provided

Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: the Advance Value Framework

Escalating drug prices have catalysed the generation of numerous “value frameworks” with the aim of informing payers, clinicians and patients on the assessment process of new medicines for the purpose of coverage and treatment selection decisions. Although this is an important step towards a more inclusive Value Based Assessment (VBA) approach, aspects of these frameworks are based on weak methodologies and could potentially result in misleading recommendations or decisions. A Multiple Criteria Decision Analysis (MCDA) methodological process based on Multi Attribute Value Theory (MAVT) is adopted for building a multi-criteria evaluation model. A five-stage model-building process is followed, using a top-down “value-focused thinking” approach, involving literature reviews and expert consultations. A generic value tree is structured capturing decision-makers’ concerns for assessing the value of new medicines in the context of Health Technology Assessment (HTA) and in alignment with decision theory. The resulting value tree (Advance Value Tree) spans three levels of criteria (top level criteria clusters, mid-level criteria, bottom level sub-criteria or attributes) relating to five key domains that can be explicitly measured and assessed: (a) burden of disease, (b) therapeutic impact, (c) safety profile (d) innovation level, and (e) socioeconomic impact. A number of MAVT modelling techniques are introduced for operationalising (i.e. estimating) the model, for scoring the alternative options, assigning relative weights of importance to the criteria, and combining scores and weights. Overall, the combination of these MCDA modelling techniques for the elicitation and construction of value preferences across the generic value tree provides 3 a new value framework (Advance Value Framework) enabling the comprehensive measurement of value in a transparent and structured way. Given the flexibility to meet diverse requirements and become readily adaptable across different settings, it could be tested as a decision-support tool for decision-makers to aid coverage and reimbursement of new medicines

Exploring the utility of Bayesian Networks for modelling cultural ecosystem services: A canoeing case study.

Modelling cultural ecosystem services is challenging as they often involve subjective and intangible concepts. As a consequence they have been neglected in ecosystem service studies, something that needs remedying if environmental decision making is to be truly holistic. We suggest Bayesian Networks (BNs) have a number of qualities that may make them well-suited for dealing with cultural services. For example, they define relationships between variables probabilistically, enabling conceptual and physical variables to be linked, and therefore the numerical representation of stakeholder opinions. We assess whether BNs are a good method for modelling cultural services by building one collaboratively with canoeists to predict how the subjective concepts of fun and danger are impacted on by weir modification. The BN successfully captured the relationships between the variables, with model output being broadly consistent with verbal descriptions by the canoeists. There were however a number of discrepancies indicating imperfect knowledge capture. This is likely due to the structure of the network and the abstract and laborious nature of the probability elicitation stage. New techniques should be developed to increase the intuitiveness and efficiency of probability elicitation. The limitations we identified with BNs are avoided if their structure can be kept simple, and it is in such circumstances that BNs can offer a good method for modelling cultural ecosystem services

Two-envelope system for consultant selection using Weighted Sum Model

© 2018 Authors. Consultant Selection is one of a classical problem in Multi Criteria Decision Making (MCDM). Most of the literature in Operation Research only concentrates on model building rather than developing an inclusive analytic tool that extends to a Decision Support System (DSS). In this paper, we deploy a case study approach to understand the user requirement for DSS development. We observe the process of consultant selection and the decision making at one of the technical department which involve in the infrastructure project in Malaysia. A two-envelope system and a simple Weighted Sum Model are currently in use. We demonstrate the abstraction and application based on two case projects. Sensitivity analysis is also performed and the result shows that the decision changed if it is solely based on fees or with minimal quality criteria. Finally, we gather the findings from the organizational flows, user modelling and decision making process in order to benchmark with our future works. This will helps us to better understand and develop an improved decision support model or tools for consultant selection problem

Design of Automatic User Identification Framework in Crowdsourcing Requirements Engineering : User Mapping and System Architecture

The requirement elicitation is the initial stage of requirement engineering where information collected from users. The process are significantly determined by the quality and quantity of information collected. The crowdsourcing is a method of information gathering from many users. The number and variety of users in the crowdsourcing are both advantages and challenges in the elicitation process. This study purposes a framework for user identification that consists of user mapping and architecture system. The identification process consists of 8 main states, start with defining context, user target and scope determination, data source determination, user data collection, data pre-processing, feature selection, data classification and user identification. The results of this study is an initial state for development of an automated tool for user identification to elicit requirement through crowdsourcing. By the framework can be generated the user classification, which can be used to apply the appropriate method for gathering information in elicitation process

Architectural Street Credibility: Reframing Contemporary Architecture to Sidewalk Level with Images from Google Street View

abstract: The purpose of this research was to assess the condition of the human/building interface at sidewalk level by reframing our view of contemporary architecture using Google Street View images. In particular, the goal was to find a means by which aesthetic engagement in the urban cultural ecology could be measured. Photo-elicitation, semantic differential, and visual assessment methods were adapted and combined to develop a photo-semantic assessment survey instrument for this study aimed at evaluating respondent preference for building images. Architectural adjective usage amongst 14 graduate students was surveyed, and the resulting 175-word list was synthesized down to seven positive and seven negative adjectives. Eleven representative buildings were selected from the Phaidon Atlas of 21st Century World Architecture, and photographic Street Views were created. The photo-semantic assessment survey instrument was administered to 62 graduate students given their demographic is reasonably similar to the urban walker stakeholder in the outcome. Respondent preference for the building images was then ranked ordered and correlations were run against various image factors including facade complexity, transparency, and streetscape quality. Moderate to strong correlations between preference and several image factors were observed indicating that certain building design factors, particularly facade complexity, may play a predictable role. Several avenues for future research are suggested including the comparison of lab versus on-site respondents; the comparison of user types including targeted, passerby and tourist; the effect of skyline on user preference for Street Views; and the effect of participation in the building making process on short and long term respondent preference.Dissertation/ThesisPh.D. Architecture 201