Gotowa bibliografia na temat „Multiple discrete-continuous choices”

AbstractThere are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

The COVID-19 pandemic has affected many daily activities, primarily as a result of the perceived contagion risk and government restrictions to mitigate the spread of the virus. To this end, drastic changes in the trip choices for commuting to work have been reported and studied, mostly through descriptive analysis. On the other hand, modeling-based research that can simultaneously understand both changes in mode choice and its frequency at an individual level has not been much used in existing studies. As such, this study aims to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios, in two different countries of the Global South: Colombia and India. A hybrid multiple discrete-continuous nested extreme value model was implemented using the data obtained from online surveys in Colombia and India during the early COVID-19 period of March and April 2020. This study found that, in both countries, utility related to active modes (more used) and public transportation (less used) changed during the pandemic. In addition, this study highlights potential risks in likely unsustainable futures where there may be increased use of private vehicles such as cars and motorcycles, in both countries. It was also identified that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. These results may help decision makers focus on public policies to encourage sustainable transportation by avoiding the detrimental long-term behavioral changes resulting from the COVID-19 pandemic.

Travel-based multitasking and the possibility to perform activities during travel are important factors that can make a transportation mode attractive. However, serious crowding in public transportation systems might adversely affect the passengers’ free choice to participate in activities during travel. This study aims to examine how crowding in public transportation systems is related to discrete-continuous choices in different types of multitasking options using a data set of 500 commuters in the Tokyo Metropolitan Area. Employing a multiple discrete-continuous extreme value model, this study investigates the relationship between crowding levels and multitasking behavior. The results show that high crowding levels, relative to being seated, have a significantly negative association with many multitasking options, which suggests the importance of seat availability. The estimation results also show that information and communication technology (ICT)-dependent leisure activities and non-ICT active activities, such as reading and talking with other passengers, have the lowest satiation and higher baseline preference constants, which indicates that they are preferred by passengers. Meanwhile, crowding levels were observed to have a significant relationship with these multitasking activities. Finally, the key findings, contributions, and policy implications of the findings are discussed.

Telecommuting choices of workers in multiworker households are likely to be interdependent. These telecommuting choices may also affect the activity–time use choices of all people in the household. From the standpoint of travel behavior and travel demand forecasting, it is important to test these hypotheses and quantify the relationship between telecommuting choices and activity–time use patterns. To do this, the present study developed a generalized extreme value–based joint count model for analyzing the monthly frequency of choosing to telecommute of workers in dual-earner households. A panel multiple discrete continuous extreme value model was also developed to study activity–time use decisions while accounting for household-level interaction effects. The study findings confirm the presence of strong intrahousehold interaction effects in both the telecommuting and activity–time use choices of workers. Telecommuting choices were found to have a significant influence on daily activity–time use decisions for both mandatory and nonmandatory activities.

There has been a steady increase in the consumption of Korean beef in recent years, with the per capita consumption of beef reaching a record high in 2022. The domestic beef market provides consumers with more choices, so traditional methods of demand analysis that rely on whole beef products are likely to become less useful. Employing the multiple discrete-continuous extreme value model (MDCEV), we analyze scanner data on consumer panels over a long period. Our findings indicate Hanwoo beef demand is less price sensitive than other beef types. Furthermore, Hanwoo beef has a relatively larger cross-price elasticity than other alternatives. Consumers' income, age, and household size also significantly affect the interrelationship between demand and supply. Higher-income consumers are more likely to choose Hanwoo beef and less likely to choose imported beef. Larger families are more likely to select beef for grilling rather than cooking. These results explain current beef expenditure patterns and help understand how consumers make decisions.

The prevalence of strong brands such as Coca-Cola, McDonald’s, Budweiser, and Marlboro in “vice” categories has important implications for regulators and consumers. While researchers in multiple disciplines have studied the effectiveness of antitobacco countermarketing strategies, little attention has been given to how brand strength may moderate the efficacy of tactics such as excise taxes, usage restrictions, and educational advertising campaigns. In this research, the authors use a multiple discrete-continuous model to study the impact of antismoking techniques on smokers’ choices of brands and quantities. The results suggest that although cigarette excise taxes decrease smoking rates, these taxes also result in a shift in market share toward stronger brands. Market leaders may be less affected by tax policies because their market power allows strong brands such as Marlboro to absorb rather than pass through increased taxes. In contrast, smoke-free restrictions cause a shift away from stronger brands. In terms of antismoking advertising, the authors find minimal effects on brand choice and consumption. The findings highlight the importance of considering brand asymmetries when designing a policy portfolio on cigarette tax hikes, smoke-free restrictions, and antismoking advertising campaigns.

The striatum and hippocampus are widely held to be components of distinct memory systems that can guide competing behavioral strategies. However, some electrophysiological studies have suggested that neurons in both structures encode spatial information and may therefore make similar contributions to behavior. In rats well trained to perform a win-stay radial maze task, we recorded simultaneously from dorsal hippocampus and from multiple striatal subregions, including both lateral areas implicated in motor responses to cues and medial areas that work cooperatively with hippocampus in cognitive operations. In each brain region, movement through the maze was accompanied by the continuous sequential activation of sets of projection neurons. Hippocampal neurons overwhelmingly were active at a single spatial location (place cells). Striatal projection neurons were active at discrete points within the progression of every trial—especially during choices or following reward delivery—regardless of spatial position. Place-cell–type firing was not observed even for medial striatal cells entrained to the hippocampal theta rhythm. We also examined neural coding in earlier training sessions, when rats made use of spatial working memory to guide choices, and again found that striatal cells did not show place-cell–type firing. Prospective or retrospective encoding of trajectory was not observed in either hippocampus or striatum, at either training stage. Our results indicate that, at least in this task, dorsal hippocampus uses a spatial foundation for information processing that is not substantially modulated by spatial working memory demands. By contrast, striatal cells do not use such a spatial foundation, even in medial subregions that cooperate with hippocampus in the selection of spatial strategies. The progressive dominance of a striatum-dependent strategy does not appear to be accompanied by large changes in striatal or hippocampal single-cell representations, suggesting that the conflict between strategies may be resolved elsewhere.

Econometric choice models have been widely used in travel behaviour research to understand human activities, time-use, mobility choices, consumption, and related preferences. Most research in this area had focused on analysing consumer choice of a single discrete alternative from a set of alternatives that are perfect substitutes of each other. In the past two decades, however, a stream of research has emerged to analyse consumer choice of potentially multiple discrete alternatives from a set of alternatives that are imperfect substitutes, along with the continuous choice(s) of “how much to consume?” of the chosen alternative(s). Such choice situations, referred to as multiple discrete-continuous (MDC) choices, are pervasive in travel behaviour research. For example, the most widely analysed MDC choices arise in the context of individuals’ daily time-use, where an individual can potentially participate in multiple activities in a day and allocate the fixed time available in the day to perform those activities. Among the methods used to analyse such MDC choices, the random utility maximization (RUM)-based models have gained traction and resulted in numerous empirical applications in the context of time-use, vehicle ownership and usage, and recreational travel. This dissertation advances the fields of RUM-based MDC choice modelling and travel behaviour research in the following directions: (a) formulation of new models to analyse and forecast MDC choices by introducing greater flexibility in the constraints faced by consumers and flexible stochastic specifications to represent consumers’ utility functions, (b) enhancing current understanding of the properties of state-of-the-art MDC choice models with flexible utility forms, and (c) application of the newly formulated models to understand time-use patterns of non-working adults in Los Angeles region of California, time-use patterns of commuters in major metropolitan cities of India, and tourism travel expenditures of domestic vacation travellers in India. The specific methodological contributions of the dissertation are as follows: (A) A new model formulation to analyse MDC choices at a disaggregate-level, including the number of instances (aka, episodes) different alternatives are chosen and the amount of consumption at each instance of choice, while also accommodating logical constraints across different instances of consumption of an alternative; (B) A new model formulation to accommodate alternative-specific upper (and lower) bounds on consumptions, and the extension of this formulation to the above-mentioned analysis of disaggregate, episode-level consumption; (C) Enhanced understanding of the properties of MDC choice models with alternative utility functional forms, which led to: (a) analytic derivations of the distributions of demand functions arising from a specific class of MDC choice models with linear utility functions, and (b) guidelines on what type of MDC choice formulations to use for modelling different types of consumption patterns; and (D) A new model formulation to accommodate non-IID (not-independent and not-identically distributed) stochastic specifications in MDC choice models with flexible utility functional forms. The substantive contributions of the dissertation are as follows: (A) Application of the newly proposed MDC choice formulations to analyze individuals’ daily activity participation and time allocation decisions at an episode level, while considering episode-level upper and lower bounds on time allocation to different activities – for an empirical analysis of non-working individuals’ time-use in Los Angeles, California; (B) Application of the proposed MDC choice formulations to understand the determinants of expenditure allocations of Indian domestic tourists on their leisure trips – toward identifying strategies for enhancing domestic tourism revenue in India; and (C) Application of MDC choice models to understand the differences in time-use patterns between commuting women and men in major metropolitan cities of India, with a focus on gender differences in the impact of commute duration on their time-use patterns. The above-mentioned empirical applications augmented the extensive simulation experiments conducted in the dissertation to evaluate the efficacy of the proposed MDC choice formulations. Specifically, the analysis of Californian non-workers’ time-use helped demonstrate the benefits of episode-level time allocation models and those that accommodate bounds on time allocation – in terms of improved understanding of time-use, statistical fit, and xii prediction performance – over the traditional MDC choice models. The analysis of Indian tourists’ expenditures (on their leisure trips) and Indian commuters’ daily time-use helped verify the properties of MDC choice models with linear utility functions. The empirical analysis of Indian tourists’ expenditure patterns offered insights that can potentially be used to device strategies toward increasing revenue for the tourism and hospitality industry. The empirical analysis of Indian commuters’ time-use patterns brought to light notable differences in the time-use patterns of working women and men in India. Importantly, this analysis highlighted the need for policies aimed at addressing working women’s time poverty issues exacerbated by their commute.

The overall goal of the dissertation is to contribute to the growing literature on multiple discrete-continuous (MDC) choice models. In MDC choice situations, consumers often encounter two inter-related decisions at a choice instance – which alternative(s) to choose for consumption from a set of available alternatives, and the amount to consume of the chosen alternatives. In the recent literature, there is increasing attention on modeling MDC situations based on a rigorous underlying micro-economic utility maximization framework. Among these models, the multiple-discrete continuous extreme value MDCEV model (Bhat, 2005, 2008) provides a number of advantages over other models. The primary objective of this dissertation is to extend the MDCEV framework to accommodate more realistic decision-making processes from a behavioral standpoint. The dissertation has two secondary objectives. The first is to advance the current operationalization and the econometric modeling of MDC choice situations. The second is to contribute to the transportation literature by estimating MDC models that provide new insights on individuals’ travel decision processes. The proposed extensions of the MDCEV model include: (1) To formulate and estimate a latent choice set generation model within the MDCEV framework, (2) To develop a random utility-based model formulation that extends the MDCEV model to include multiple linear constraints, and (3) To extend the MDCEV model to relax the assumption of an additively separable utility function. The methodologies developed in this dissertation allow the specification and estimation of complex MDC choice models, and may be viewed as a major advance with the potential to lead to significant breakthroughs in the way MDC choices are structured and implemented. These methodologies provide a more realistic representation of the choice process. The proposed extensions are applied to different empirical contexts within the transportation field, including participation in and travel mileage allocated to non-work activities during various time periods of the day for workers, participation in recreational activities and time allocation for workers, and household expenditures in disaggregate transportation categories. The results from these exercises clearly underline the importance of relaxing some of the assumptions made, not only in the MDCEV model, but in MDC models in general.
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碩士
國立臺中科技大學
流通管理系碩士班
107
This research aims to investigate the analysis of activity type choice and time use on weekdays by the multiple discrete continuous extreme value model. Assuming the existence of satiation effects, this study estimated the model with the specification of socio-demographic and activity characteristics. This study compares those proposed models by criteria of likelihood ratio index, Akaike Information Criterion, and Bayes Information Criterion. The result shows that model with heterogeneous saturation effect owns a well explanatory power.The results indicate that the important variables affecting the choice of activity type include male, age, personal monthly income, household monthly income, retirement/ housewife , household population, pick-up demand, number of children in elementary/junior high school; and the important variables affecting time use include women, elderly, occupational status, pick-up demand. In the aspect of activity type choice and time use, women spend more time at shopping than men, and elderly spend more time shopping activities than other age groups. On the whole, the time use of work activity for occupational group of retail, transportation, and restaurant spend more time than other occupations. In terms of the model implication, women and elderly will spend more time on shopping activities, so it is recommended to plan for the shopping area for these two groups.

abstract: Consumers search before making virtually any purchase. The notion that consumers engage in costly search is well-understood to have deep implications for market performance. However to date, no theoretical model allows for the observation that consumers often purchase more than a single product in an individual shopping occasion. Clothing, food, books, and music are but four important examples of goods that are purchased many items at a time. I develop a modeling approach that accounts for multi-purchase occasions in a structural way. My model shows that as preference for variety increases, so does the size of the consideration set. Search models that ignore preference for variety are, therefore, likely to under-predict the number of products searched. It is generally thought that lower search costs increase retail competition which pushes prices and assortments down. However, I show that there is an optimal number of products to offer depending on the intensity of consumer search costs. Consumers with high search costs prefer to shop at a store with a large assortment of goods and purchase multiple products, even if the prices that firm charges is higher than competing firms' prices. On the other hand, consumers with low search costs tend to purchase fewer goods and shop at the stores that have lower prices, as long as the store has a reasonable assortment offering. The implications for market performance are dramatic and pervasive. In particular, the misspecification of demand model in which search is important and/or multiple discreteness is observed will produce biased parameter estimates leading to erroneous managerial conclusions.
Dissertation/Thesis
Ph.D. Business Administration 2013

We report on the development of a fully implantable drug delivery system capable of delivering hundreds of individual doses. This product is intended for the controlled release of potent therapeutic compounds that might otherwise require frequent injections. Our system has the following capabilities: • Stable, hermetic storage of therapeutic drugs in solid, liquid, or gel form; • Individual storage of discrete doses for multiple-drug regimens; • Wireless communication with an external controller for device monitoring and therapy modification; • Choice of preprogrammed release or release on command; • Controlled pulsatile or continuous release. MicroCHIPS’ drug release technology has been successfully demonstrated in vitro and in vivo. We are proceeding with long-term in vivo studies of a fully implantable device containing one hundred individual doses. A future device intended for human clinical trials will contain four hundred doses, enough for a daily release of drug for more than one year.

Automation is becoming an integral part of our daily lives as technology and techniques rapidly develop. Many automation workflows are now routinely being applied within the geoscience domain. The basic structure of automation and its success of modelling fundamentally hinges on the appropriate choice of parameters and speed of processing. The entire process demands that the data being fed into any machine learning model is essentially of good quality. The technological advances in well logging technology over decades have enabled the collection of vast amounts of data across wells and fields. This poses a major issue in automating petrophysical workflows. It necessitates to ensure that, the data being fed is appropriate and fit for purpose. The selection of features (logging curves) and parameters for machine learning algorithms has therefore become a topic at the forefront of related research. Inappropriate feature selections can lead erroneous results, reduced precision and have proved to be computationally expensive. Experienced Eye (EE) is a novel methodology, derived from Domain Transfer Analysis (DTA), which seeks to identify and elicit the optimum input curves for modelling. During the EE solution process, relationships between the input variables and target variables are developed, based on characteristics and attributes of the inputs instead of statistical averages. The relationships so developed between variables can then be ranked appropriately and selected for modelling process. This paper focuses on three distinct petrophysical data scenarios where inputs are ranked prior to modelling: prediction of continuous permeability from discrete core measurements, porosity from multiple logging measurements and finally the prediction of key geomechanical properties. Each input curve is ranked against a target feature. For each case study, the best ranked features were carried forward to the modelling stage, and the results are validated alongside conventional interpretation methods. Ranked features were also compared between different machine learning algorithms: DTA, Neural Networks and Multiple Linear Regression. Results are compared with the available data for various case studies. The use of the new feature selection has been proven to improve accuracy and precision of prediction results from multiple modelling algorithms.

A recent literature within quantitative macroeconomics has advocated the use of continuous-time methods for dynamic programming problems. In this paper we explore the relative merits of continuous-time and discrete-time methods within the context of stationary and nonstationary income fluctuation problems. For stationary problems in two dimensions, the continuous-time approach is both more stable and typically faster than the discrete-time approach for any given level of accuracy. In contrast, for convex lifecycle problems (in which age or time enters explicitly), simply iterating backwards from the terminal date in discrete time is superior to any continuous-time algorithm. However, we also show that the continuous-time framework can easily incorporate nonconvexities and multiple controls—complications that often require either problem-specific ingenuity or nonlinear root-finding in the discrete-time context. In general, neither approach unequivocally dominates the other, making the choice of one over the other an art, rather than an exact science. Code can be found at https://github.com/tphelanECON/The Art of Temporal Approximation WP.