Generating Top-k packages via preference elicitation

There are several applications, such as play lists of songs or movies, and shopping carts, where users are interested in finding top-k packages, consisting of sets of items. In response to this need, there has been a recent urry of activity around extending classical recommender systems (RS), which are effective at recommending individual items, to recommend packages, or sets of items. The few recent proposals for package RS suffer from one of the following drawbacks: they either rely on hard constraints which may be difficult to be specified exactly by the user or on returning Paretooptimal packages which are too numerous for the user to sift through. To overcome these limitations, we propose an alternative approach for finding personalized top-k packages for users, by capturing users' preferences over packages using a linear utility function which the system learns. Instead of asking a user to specify this function explicitly, which is unrealistic, we explicitly model the uncertainty in the utility function and propose a preference elicitation-based framework for learning the utility function through feedback provided by the user. We propose several samplingbased methods which, given user feedback, can capture the updated utility function. We develop an efficient algorithm for generating top-k packages using the learned utility function, where the rank ordering respects any of a variety of ranking semantics proposed in the literature. Through extensive experiments on both real and synthetic datasets, we demonstrate the efficiency and effectiveness of the proposed system for finding top-k packages

EEMCS final report for the causal modeling for air transport safety (CATS) project

This document reports on the work realized by the DIAM in relation to the completion of the CATS model as presented in Figure 1.6 and tries to explain some of the steps taken for its completion. The project spans over a period of time of three years. Intermediate reports have been presented throughout the project’s progress. These are presented in Appendix 1. In this report the continuous‐discrete distribution‐free BBNs are briefly discussed. The human reliability models developed for dealing with dependence in the model variables are described and the software application UniNet is presente

Using prior information to identify boundaries in disease risk maps

Disease maps display the spatial pattern in disease risk, so that high-risk clusters can be identified. The spatial structure in the risk map is typically represented by a set of random effects, which are modelled with a conditional autoregressive (CAR) prior. Such priors include a global spatial smoothing parameter, whereas real risk surfaces are likely to include areas of smooth evolution as well as discontinuities, the latter of which are known as risk boundaries. Therefore, this paper proposes an extension to the class of CAR priors, which can identify both areas of localised spatial smoothness and risk boundaries. However, allowing for this localised smoothing requires large numbers of correlation parameters to be estimated, which are unlikely to be well identified from the data. To address this problem we propose eliciting an informative prior about the locations of such boundaries, which can be combined with the information from the data to provide more precise posterior inference. We test our approach by simulation, before applying it to a study of the risk of emergency admission to hospital in Greater Glasgow, Scotland

On the acquisition and representation of procedural knowledge

Historically knowledge acquisition has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some of some types of knowledge, little attention has been devoted to procedural knowledge. NASA personnel frequently perform tasks that are primarily procedural in nature. Previous work is reviewed in the field of knowledge acquisition and then focus on knowledge acquisition for procedural tasks with special attention devoted to the Navy's VISTA tool. The design and development is described of a system for the acquisition and representation of procedural knowledge-TARGET (Task Analysis and Rule Generation Tool). TARGET is intended as a tool that permits experts to visually describe procedural tasks and as a common medium for knowledge refinement by the expert and knowledge engineer. The system is designed to represent the acquired knowledge in the form of production rules. Systems such as TARGET have the potential to profoundly reduce the time, difficulties, and costs of developing knowledge-based systems for the performance of procedural tasks

The Future Prospect of PV and CSP Solar Technologies: An Expert Elicitation Survey

In this paper we present and discuss the results of an expert elicitation survey on solar technologies. Sixteen leading European experts from the academic world, the private sector and international institutions took part in this expert elicitation survey on Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies. The survey collected probabilistic information on (1) how Research, Development and Demonstration (RD&D) investments will impact the future costs of solar technologies and (2) the potential for solar technology deployment both in OECD and non-OECD countries. Understanding the technological progress and the potential of solar PV and CPS technologies is crucial to draft appropriate energy policies. The results presented in this paper are thus relevant for the policy making process and can be used as better input data in integrated assessment and energy models.Expert Elicitation, Research, Development and Demonstration, Solar Technologies

EXPLICIT: a feasibility study of remote expert elicitation in health technology assessment

This is the final version of the article. Available from BioMed Central via the DOI in this recordBACKGROUND: Expert opinion is often sought to complement available information needed to inform model-based economic evaluations in health technology assessments. In this context, we define expert elicitation as the process of encoding expert opinion on a quantity of interest, together with associated uncertainty, as a probability distribution. When availability for face-to-face expert elicitation with a facilitator is limited, elicitation can be conducted remotely, overcoming challenges of finding an appropriate time to meet the expert and allowing access to experts situated too far away for practical face-to-face sessions. However, distance elicitation is associated with reduced response rates and limited assistance for the expert during the elicitation session. The aim of this study was to inform the development of a remote elicitation tool by exploring the influence of mode of elicitation on elicited beliefs. METHODS: An Excel-based tool (EXPLICIT) was developed to assist the elicitation session, including the preparation of the expert and recording of their responses. General practitioners (GPs) were invited to provide expert opinion about population alcohol consumption behaviours. They were randomised to complete the elicitation by either a face-to-face meeting or email. EXPLICIT was used in the elicitation sessions for both arms. RESULTS: Fifteen GPs completed the elicitation session. Those conducted by email were longer than the face-to-face sessions (13 min 30 s vs 10 min 26 s, p = 0.1) and the email-elicited estimates contained less uncertainty. However, the resulting aggregated distributions were comparable. CONCLUSIONS: EXPLICIT was useful in both facilitating the elicitation task and in obtaining expert opinion from experts via email. The findings support the opinion that remote, self-administered elicitation is a viable approach within the constraints of HTA to inform policy making, although poor response rates may be observed and additional time for individual sessions may be required.This paper presents independent research funded by the National Institute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula

Impacts of survey design and model specification on willingness-to-pay estimates from discrete choice models

Discrete choice models infer individuals’ preferences from observed choices. Analysts can thereby contribute to producing more reliable demand forecasts and assess welfare impacts of policy/scenario changes. However, the risk of model misspecification errors may bias parameter estimates and lead to incorrect demand forecasts and policy recommendations. This thesis examines three types of model misspecifications: i) ignoring travel time constraints, ii) measurement error in the income variable, and iii) ignoring the behavioural phenomenon of the zero-price (ZP) effect. We are particularly interested in understanding the policy implications of these misspecifications on the marginal valuation of qualitative variables. Our analyses are relevant to policy makers as these specification errors prevail in some ‘state-of-the-practice’ model representations commonly used in support of cost-benefit analyses. This thesis first examines the issue of ignoring travel time constraints for simple time-cost trade-offs. Analysts may ignore that some alternatives are not available to individuals as the travel times presented could exceed their time allowances for such journey. We find via simulation that the value of travel time (VTT) can be significantly over-estimated when travel time constraints are not accounted for in estimation. More importantly, we identify the confounding issue between travel time constraints and taste heterogeneity. This thesis then turns to the issue of the measurement error in the income variable. We investigate the extent to which the income measure used in the estimation of choice models contributes to the disparity between the cross-sectional and inter-temporal income elasticity of the VTT. We compile various income measures that are varied in terms of the income re-distribution measures and the intra-household budget allocation based on secondary expenditure data. We empirically test the new income measures based on the modelling framework developed for the 2014/15 UK VTT study. Our results indicate that by additionally accounting for social benefits, the cross-sectional income elasticity of VTT approaches unity. This closes the gap between the cross-sectional and inter-temporal income elasticity. We find the behavioural VTTs, which represent the averages of the VTTs estimated from behavioural models across respondents, to be consistent despite the income variations. However, we find that when moving from the stated choice (SC) to the national travel survey to obtain a nationally representative figure for appraisal, appraisal values diverge as per the income variations due to the sampling bias in the income variable in behavioural model. We highlight the requirement for the sampling of the income to be consistent between the estimation and implementation tool. We finally explore the issue of ignoring the ZP effect in choice modelling. ZP effect is a well-established notion in behavioural economics which explains the tendency for individuals to over-react to free alternatives. The lack of attention to the ZP effect in the choice modelling literature is particularly worrying since ‘free’ status quo (SQ) alternatives are at the heart of many SC surveys, especially outside of transport, and form the basis of contrasting the (policy) ‘interventions’. We develop alternative stated survey designs to identify the ZP effect. We find that the observed preference for remaining at the SQ is largely attributed to the ZP effect within our data. We also present experimental design features that allow separation of the ZP effect from the non-linear cost sensitivity. We stress that the prevalence of the ZP effect in observed choice behaviour may introduce bias to the prediction of welfare when the perfect confounding between the ZP and SQ effects is broken. Overall, this thesis highlights the significant bias on WTP estimates that may be caused by ignoring some basic and fundamental misspecification issues. This thesis closes by suggesting some future improvements required to avoid model misspecification issues identified

A Quantitative Decision Support Model to Aid Selection of Combat Aircraft Force Mixes for Contingency Deployment

Selection of combat aircraft during crisis action planning can be of critical importance. In determining the value of force mixes, it is proposed that one can evaluate extrinsic and intrinsic value separately. Intrinsic value is the designed capability of a weapons platform to accomplish a specified aerospace mission. Extrinsic value is the expected appropriateness of such platforms, given the environmental characteristics in which they must operate. This research develops a decision support tool for planners in determining the extrinsic value of force mixes, which then expedites the selection of best overall force mixes. The research included: content analysis of official guidance, Critical Decision Methodology interviews, a Delphi study (to define and quantify the factors, and establish a hierarchy and global weights), generation of the Value Focused Thinking decision tool, and establishment of an appropriate relationship between extrinsic and intrinsic value. This research provides planners-throughout the USAF-with a decision support tool that objectively compares alternative force packages for specific deployments. This represents a first step toward codifying or formalizing the art of force selection. These results will help reduce the crisis action response timeline, and should lead to more accurate modeling of force mix applicability