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Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Our research proposes a new, multi-attribute, parameterisation of Tversky’s Elimination- By-Aspects (EBA) model. The EBA model conceptualises choice as a covert sequential elimination process with choice probabilities formulated over all consideration sets of the choice set. This specification attempts to capture the effect of context on choice behaviour. However, the EBA model has seen limited usage due to the large number of required parameters given the set of items under study. For a set of items T, it has 2|T| - 3 free parameters, which is infeasible for all but the simplest of contexts. To provide a practical operationalisation, we impose a set of a priori constraints on the parameter space. We define a generic multi-attribute structure to the set of aspects. This restricts the cardinality of the set of unknown scale values while retaining the functional (recursive) form of the model. The EBA hypothesis of a population of lexicographic decision-makers can therefore be tested in more market-realistic contexts, and inferences made over a large universal set of items described by the complete factorial. We call this model the Multi-attribute Elimination-By-Aspects (MEBA) model. The MEBA model reduces the set of unknown free parameters to a maximum of |T|-1. We develop a general algebraic expression for the MEBA choice probabilities as a function of the attributes of the options in the choice set. This enables the derivation of a likelihood function, and consequently maximum likelihood estimation. We also consider the form of optimal MEBA paired comparison designs. Using Monte Carlo simulation and a discrete choice experiment with consumers, we conduct an initial empirical test of the model against the special case of the MNL model (that assumes no context effects) and find the MEBA model to be a better approximation of observed choice behaviour. This is achieved on a common set of parameters, and so it is due solely to the difference in functional form of the two models. We conclude with a discussion on future research directions, in particular the introduction of heterogeneity into the model, and the description of optimal choice experiments for larger choice set sizes.

Made available in DSpace on 2015-03-05T19:14:43Z (GMT). No. of bitstreams: 0 Previous issue date: 18 / Nenhuma / Este estudo contribui com a proposição de um modelo flexível de apoio à decisão que sirva como ferramenta para embasar e auxiliar as organizações no momento de optar pela internalização ou externalização de suas atividades logísticas, contemplando aspectos quantitativos e os qualitativos. A pesquisa teve por objetivo geral “desenvolver e testar um modelo adaptável de análise econômico-estratégico de apoio à decisão pela externalização da logística, enquanto atividade secundária”. Para tanto, foi realizada em quatro etapas, iniciando-se com a revisão bibliográfica sobre temas relacionados à externalização, estratégia, logística, teoria de decisão e, dentro dela, metodologia multiatributos. Nas etapas seguintes desenvolve-se o modelo e o estudo de caso. Evidenciou-se o tipo de pesquisa metodológica como um estudo de caso aplicado, com observação participante de forma aberta e abordagem qualitativa/quantitativa, realizada a partir de múltiplas fontes de coleta de dados, tais como: observação, entrevistas, anális / This study proposes a flexible model of decision support that helps and gives information to organizations which wants to opt for internalization or outsourcing of their logistics activities, considering the quantitative and qualitative aspects. This research general aim was “develop and test an economic-strategic model for supporting the decision of outsourcing logistics, as a secondary activity." So, it was performed in four steps, starting with a literature review on topics related to outsourcing, strategy, logistics, theory of decision and, within it, methodology of multiattribute. In the subsequent steps were developed the model and case study. It was demonstrated that the methodology of the research used an implemented case study, with searcher observation in an open, qualitative and quantitative approach, by multiple sources of data collection, such as observation, interviews, analysis of documents and manuals. The attributes used in the case study (regularity, reliability, quality services, infrastruc

ELABORATIONS ON MULTIATTRIBUTE UTILITY THEORY DOMINANCE By David L. Vairo A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2019. Major Director: Dissertation director’s name, Dr. Jason Merrick, Supply Chain Management and Analytics Multiattribute Utility Theory (MAUT) is used to structure decisions with more than one factor (attribute) in play. These decisions become complex when the attributes are dependent on one another. Where linear modeling is concerned with how factors are directly related or correlated with each other, MAUT is concerned with how a decision maker feels about the attributes. This means that direct elicitation of value or utility functions is required. This dissertation focuses on expanding the types of dominance forms used within MAUT. These forms reduce the direct elicitation needed to help structure decisions. Out of this work comes support for current criticisms of gain/loss separability that is assumed as part of Prospect Theory. As such, an alternative to Prospect Theory is presented, derived from within MAUT, by modeling the probability an event occurs as an attribute.

In many areas of Artificial Intelligence (AI), we are interested in helping people make better decisions. This help can result in two advantages. First, computers can process large amounts of data and perform quick calculations, leading to better decisions. Second, if a user does not have to think about some decisions, they have more time to focus on other things they find important. Since users' preferences are private, in order to make intelligent decisions, we need to elicit an accurate model of the users' preferences for different outcomes. We are specifically interested in outcomes involving a degree of risk or uncertainty. A common goal in AI preference elicitation is minimizing regret, or loss of utility. We are often interested in minimax regret, or minimizing the worst-case regret. This thesis examines three important aspects of preference elicitation and minimax regret. First, the standard elicitation process in AI assumes users' preferences follow the axioms of Expected Utility Theory (EUT). However, there is strong evidence from psychology that people may systematically deviate from EUT. Cumulative prospect theory (CPT) is an alternative model to expected utility theory which has been shown empirically to better explain humans' decision-making in risky settings. We show that the standard elicitation process can be incompatible with CPT. We develop a new elicitation process that is compatible with both CPT and minimax regret. Second, since minimax regret focuses on the worst-case regret, minimax regret is often an overly cautious estimate of the actual regret. As a result, using minimax regret can often create an unnecessarily long elicitation process. We create a new measure of regret that can be a more accurate estimate of the actual regret. Our measurement of regret is especially well suited for eliciting preferences from multiple users. Finally, we examine issues of multiattribute preferences. Multiattribute preferences provide a natural way for people to reason about preferences. Unfortunately, in the worst-case, the complexity of a user's preferences grows exponentially with respect to the number of attributes. Several models have been proposed to help create compact representations of multiattribute preferences. We compare both the worst-case and average-case relative compactness.

In many areas of Artificial Intelligence (AI), we are interested in helping people make better decisions. This help can result in two advantages. First, computers can process large amounts of data and perform quick calculations, leading to better decisions. Second, if a user does not have to think about some decisions, they have more time to focus on other things they find important. Since users' preferences are private, in order to make intelligent decisions, we need to elicit an accurate model of the users' preferences for different outcomes. We are specifically interested in outcomes involving a degree of risk or uncertainty. A common goal in AI preference elicitation is minimizing regret, or loss of utility. We are often interested in minimax regret, or minimizing the worst-case regret. This thesis examines three important aspects of preference elicitation and minimax regret. First, the standard elicitation process in AI assumes users' preferences follow the axioms of Expected Utility Theory (EUT). However, there is strong evidence from psychology that people may systematically deviate from EUT. Cumulative prospect theory (CPT) is an alternative model to expected utility theory which has been shown empirically to better explain humans' decision-making in risky settings. We show that the standard elicitation process can be incompatible with CPT. We develop a new elicitation process that is compatible with both CPT and minimax regret. Second, since minimax regret focuses on the worst-case regret, minimax regret is often an overly cautious estimate of the actual regret. As a result, using minimax regret can often create an unnecessarily long elicitation process. We create a new measure of regret that can be a more accurate estimate of the actual regret. Our measurement of regret is especially well suited for eliciting preferences from multiple users. Finally, we examine issues of multiattribute preferences. Multiattribute preferences provide a natural way for people to reason about preferences. Unfortunately, in the worst-case, the complexity of a user's preferences grows exponentially with respect to the number of attributes. Several models have been proposed to help create compact representations of multiattribute preferences. We compare both the worst-case and average-case relative compactness.

Thesis (Ph. D.)--University of Leiden, 1992. / Includes bibliographical references (p. 233-245).