A diszkrét kiválasztási modell becslése Cox-regresszióval (Parameter estimation for multinomial discrete choice model using Cox-regression)

Considering the so-called "multinomial discrete choice" model the focus of this paper is on the estimation problem of the parameters. Especially, the basic question arises how to carry out the point and interval estimation of the parameters when the model is mixed i.e. includes both individual and choice-specific explanatory variables while a standard MDC computer program is not available for use. The basic idea behind the solution is the use of the Cox-proportional hazards method of survival analysis which is available in any standard statistical package and provided a data structure satisfying certain special requirements it yields the MDC solutions desired. The paper describes the features of the data set to be analysed.

What, who and when? Incorporating a discrete choice experiment into an economic evaluation

ACKNOWLEDGEMENTS: The Medman study was funded by the Department of Health for England and Wales and managed by a collaboration of the National Pharmaceutical Association, the Royal Pharmaceutical Society of Great Britain, the Company Chemist Association and the Co-operative Pharmacy Technical Panel, led by the Pharmaceutical Services Negotiating Committee. The research in this paper was undertaken while the lead author MT was undertaking a doctoral research fellowship jointly funded by the Economic and Social Research Council (ESRC) and the Medical Research Council (MRC). The Health Economics Research Unit (HERU), University of Aberdeen is funded by the Chief Scientific Office of the Scottish Government Health and Social Care Directorate.

What do UK doctors in training value in a post? : A discrete choice experiment

© 2016 John Wiley & Sons Ltd. Funding: our thanks go to NHS Education for Scotland for funding this programme of work.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Using stated preference methods to assess environmental impacts of forest biomass power plants in Portugal

As a renewable energy source, the use of forest biomass for electricity generation is advantageous in comparison with fossil fuels, however the activity of forest biomass power plants causes adverse impacts, affecting particularly neighbouring communities. The main objective of this study is to estimate the effects of the activity of forest biomass power plants on the welfare of two groups of stakeholders, namely local residents and the general population and we apply two stated preference methods: contingent valuation and discrete choice experiments, respectively. The former method was applied to estimate the minimum compensation residents of neighbouring communities of two forest biomass power plants in Portugal would be willing to accept. The latter method was applied among the general population to estimate their willingness to pay to avoid specific environmental impacts. The results show that the presence of the selected facilities affects individuals’ well-being. On the other hand, in the discrete choice experiments conducted among the general population all impacts considered were significant determinants of respondents’ welfare levels. The results of this study stress the importance of performing an equity analysis of the welfare effects on different groups of stakeholders from the installation of forest biomass power plants, as their effects on welfare are location and impact specific. Policy makers should take into account the views of all stakeholders either directly or indirectly involved when deciding crucial issues regarding the sitting of new forest biomass power plants, in order to achieve an efficient and equitable outcome.

Ethical Consumption and Social Context: Experimental Evidence from Germany and the United States

This research examines the role of social context in ethical consumption, specifically, the extent to which anonymity and social control influence individuals' decisions to purchase organic and Fair Trade coffee. Our research design overcomes biases of prior research by combining framing and discrete choice experiments in a survey. We systematically vary coffee growing method (organic or not), import status (Fair Trade or not), flavor, and price across four social contexts that vary in degree of anonymity and normative social control. The social contexts are buying coffee online, in a large grocery store, in a small neighborhood shop, and for a meeting of a human rights group. Subjects comprise 1,103 German and American undergraduate students. We find that social context indeed influences subjects' ethical consumer decisions, especially in situations with low anonymity and high social control. In addition, gender, coffee buying, and subjective social norms trigger heterogeneity regarding stated ethical consumption and the effects of social context. These results suggest previous research has underestimated the relevance of social context for ethical consumption and overestimated altruistic motives of ethical consumers. Our study demonstrates the great potential of discrete choice experiments for the study of social action and decision making processes in sociology.

GPs' implicit prioritization through clinical choices – evidence from three national health services

Acknowledgments The authors are grateful for valuable comments and inputs from participants at a series of seminars and conferences as well as to our three anonymous referees.

Socioeconomic Differences in Preferences and Willingness-to-Pay for Insulin Delivery Systems in Type 1 and Type 2 Diabetes

Background: An evaluation of patients' preferences is necessary to understand the demand for different insulin delivery systems. The aim of this study was to investigate the association between socioeconomic status (SES) and patients' preferences and willingness to pay (WTP) for various attributes of insulin administration for diabetes management. Methods: We conducted a discrete choice experiment (DCE) to determine patients' preferences and their WTP for hypothetical insulin treatments. Both self-reported annual household income and education completed were used to explore differences in treatment preferences and WTP for different attributes of treatment across different levels of SES. Results: The DCE questionnaire was successfully completed by 274 patients. Overall, glucose control was the most valued attribute by all socioeconomic groups, while route of insulin delivery was not as important. Patients with higher incomes were willing to pay significantly more for better glucose control and to avoid adverse events compared to lower income groups. In addition, they were willing to pay more for an oral short-acting insulin ($Can 71.65 [95% confidence interval, $40.68, $102.62]) compared to the low income group ($Can 9.85 [95% confidence interval, 14.86, 34.56; P < 0.01]). Conversely, there were no differences in preferences when the sample was stratified by level of education. Conclusions: This study revealed that preferences and WTP for insulin therapy are influenced by income but not by level of education. Specifically, the higher the income, the greater desire for an oral insulin delivery system, whereas an inhaled route becomes less important for patients.

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

A Salience Theory of Choice Errors

We study a psychologically based foundation for choice errors. The decision maker applies a preference ranking after forming a 'consideration set' prior to choosing an alternative. Membership of the consideration set is determined both by the alternative specific salience and by the rationality of the agent (his general propensity to consider all alternatives). The model turns out to include a logit formulation as a special case. In general, it has a rich set of implications both for exogenous parameters and for a situation in which alternatives can a¤ect their own salience (salience games). Such implications are relevant to assess the link between 'revealed' preferences and 'true' preferences: for example, less rational agents may paradoxically express their preference through choice more truthfully than more rational agents.

Stochastic Choice and Consideration Sets

We model a boundedly rational agent who suffers from limited attention. The agent considers each feasible alternative with a given (unobservable) probability, the attention parameter, and then chooses the alternative that maximises a preference relation within the set of considered alternatives. We show that this random choice rule is the only one for which the impact of removing an alternative on the choice probability of any other alternative is asymmetric and menu independent. Both the preference relation and the attention parameters are identi fied uniquely by stochastic choice data.

An investigation of the determinants of household demand for bushmeat in the Serengeti using an open-ended choice experiment

Illegal hunting for bushmeat is regarded as an important cause of biodiversity decline in Africa. We use a stated preferences method to obtain information on determinants of demand for bushmeat in villages around the Serengeti National Park, Tanzania. We estimate the effects of changes in the own price of bushmeat and in the prices of two substitute protein sources – fish and chicken. Promoting the availability of protein substitutes at lower prices would be effective at reducing pressures on wildlife. Supply-side measures that raise the price of bushmeat would also be effective.

Demand for pharmaceutical drugs: A choice modelling experiment

Despite the importance of supplier inducement and brand loyalty inthe drug purchasing process, little empirical evidence is to be foundwith regard to the influence that these factors exert on patients decisions. Under the new scenario of easier access to information,patients are becoming more demanding and even go as far asquestioning their physicians prescription. Furthermore, newregulation also encourages patients to adopt an active role in thedecision between brand-name and generic drugs. Using a statedpreference model based on a choice survey, I have found evidenceof how significant physicians prescription and pharmacists recommendation become throughout the drug purchase process and,to what extent, brand loyalty influences the final decision. Asfar as we are aware, this paper is the first to explicitlytake consumers preferences into account rather than focusingon the behavior of health professionals.

A Representation Theorem for Domains with Discrete and Continuous Variables.

This paper proves a new representation theorem for domains with both discrete and continuous variables. The result generalizes Debreu's well-known representation theorem on connected domains. A strengthening of the standard continuity axiom is used in order to guarantee the existence of a representation. A generalization of the main theorem and an application of the more general result are also presented.