Choice set formation for outdoor destinations: the role of motivations and preference discrimination in site selection for the management of public expenditures on protected areas

Effective public expenditure currently dominates the management focus of many protected areas. This calls for explicit modeling of constraints and motivations that, respectively, obstruct and stimulate visits to selected outdoor destinations. Choice set formation is the result of screening and/or inclusion of specific sites (alternatives) to form the set of sites considered in real choices. Evidence shows that the omission of a structural representation of choice set formation is harmful to econometric inference. Yet, the literature has largely ignored the underlying behavioral phenomenon. We show, using a discrete choice experiment involving selection among seven recreational sites in an Italian national park, that choice set formation is behaviorally relevant, even after controlling for preference discrimination. Motivations (why visit?) are important determinants of preliminary site screening for choice set inclusion, as well as site selection, justifying the additional value of such modeling extension

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected

Simulation of carbon fibre composites in an industrial microwave

The ability of microwave radiation to penetrate and interact directly with materials has led to its extensive use in food and drug industries, and more recently in composites manufacturing. Microwave heating of composites allows rapid heat transfer throughout the material thickness with reduced thermal gradients and processing times as well as energy efficiency. Design of microwave systems to process composite parts with various geometries and sizes demands improved understanding of electromagnetic energy distribution and factors influencing it. Finite-element (FE) models can be efficient design tools in such cases, as physical experimentation can be impractical. In this study, a fully-coupled FE model of a carbon fibre composite in an industrial microwave environment is developed using COMSOL Multiphysics®. The effects of the heating process parameters including the number of active magnetrons, specimen thickness and the variation in the frequency of radiation on the electromagnetic field distribution are studied. The FE model showed that a substantial difference in the electromagnetic field distribution exists for the frequencies above 1 GHz compared to the lower frequencies in the microwave regime, resulting in non-uniform heating

Can healthcare choice be predicted using stated preference data?

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance.

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts

Damage detection and location in woven fabric CFRP laminate panels

The need for multifunctional carbon fibre composite laminates has emerged to improve the reliability and safety of carbon fibre composite components and decrease costs. The development of an electrical selfsensing system for woven fabric carbon fibre composite laminate panels which can detect and locate damage due to impact events is presented. The electrical sensing system uses a four probe electrical resistance method. Two different sensing mats are investigated, the main difference between them are the surface area of the electrodes and the distance between the electrodes. To investigate the damage sensitivity of the sensing system for woven fabric carbon fibre composite laminate panels, panels are produced with various thicknesses from 0.84 to 3.5 mm and are impacted at energies from 1 to 10 J to generate barely visible impact damage. Damage is detected using global electrical resistance changes, the changes in electrical resistance vary depending on carbon fibre volume fraction, spacing distance between the sensing electrodes in the sensing mats, the surface area of the electrodes, damage size, and damage type; it is found that the thicker the panel, the less sensitive the electrical resistance system is. The effect of the surface area of the sensing electrodes is high on the electrical resistance baseline, where the baseline increases by up to 55% when the surface area of the sensing electrodes increases from 100 mm2 to 400 mm2; while spacing distance between electrodes has a greater effect on damage sensitivity of the electrical resistance sensing system than the surface area of the sensing electrodes

Eliciting preferences of persons with dementia and informal caregivers to support ageing in place in the Netherlands:a protocol for a discrete choice experiment

Introduction:Ageing in place (AIP) for persons with dementia is encouraged by European governments and societies. Healthcare packages may need reassessment to account for the preferences of care funders, patients and informal caregivers. By providing insight into people's preferences, discrete choice experiments (DCEs) can help develop consensus between stakeholders. This protocol paper outlines the development of a Dutch national study to cocreate a healthcare package design methodology built on DCEs that is person-centred and helps support informal caregivers and persons with dementia to AIP. A subpopulation analysis of persons with dementia with a migration background is planned due to their high risk for dementia and under-representation in research and care. Methods and analysis:The DCE is designed to understand how persons with dementia and informal caregivers choose between different healthcare packages. Qualitative methods are used to identify and prioritise important care components for persons with dementia to AIP. This will provide a list of care components that will be included in the DCE, to quantify the care needs and preferences of persons with dementia and informal caregivers. The DCE will identify individual and joint preferences to AIP. The relative importance of each attribute will be calculated. The DCE data will be analysed with the use of a random parameters logit model. Ethics and dissemination:Ethics approval was waived by the Amsterdam University Medical Center (W23_112 #23.137). A study summary will be available on the websites of Alzheimer Nederland, Pharos and Amsterdam Public Health institute. Results are expected to be presented at (inter)national conferences, peer-reviewed papers will be submitted, and a dissemination meeting will be held to bring stakeholders together. The study results will help improve healthcare package design for all stakeholders.</p

On ordinal utility, cardinal utility, and random utility

Though the Random Utility Model (RUM) was conceived  entirely in terms of ordinal utility, the apparatus throughwhich it is widely practised exhibits properties of  cardinal utility.  The adoption of cardinal utility as a  working operation of ordinal is perfectly valid, provided  interpretations drawn from that operation remain faithful  to ordinal utility.  The paper considers whether the latterrequirement holds true for several measurements commonly  derived from RUM.  In particular it is found that  measurements of consumer surplus change may depart from  ordinal utility, and exploit the cardinality inherent in  the practical apparatus.