Discrete Choice Capabilities to Select Orders

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa.
 Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

A previously reported positive influence of a distractor alternative on decision accuracy was driven by an unintended covariation between key variables, beyond which the distractor has a modest negative influence on accuracy, possibly mirroring asymmetric multiattribute context effects.

We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables.
\nThe solution method we develop for structural estimation extends the endogenous grid-point method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the
\noptimal policy rules, greatly complicating the solution of the model. We show how these problems are ameliorated in the presence of additive choice-specific independent and identically distributed extreme value taste shocks that are typically interpreted as “unobserved state variables” in structural econometric applications, or serve as “random noise” to smooth out kinks in the value functions in
\nnumerical applications. We present Monte Carlo experiments that demonstrate the reliability and efficiency of the DC-EGM algorithm and the associated maximum likelihood estimator for structural estimation of a life-cycle model of consumption with discrete retirement decisions.

Discrete Choice Modeling for the Quantification of Health States: The Case of the EQ-5D

BackgroundHealth states were quantified based on discrete choice (DC) modeling and visual analogue scale (VAS) values using the five-level version of the EQ-5D (EQ-5D-5L). The aim of this study was to determine the extent of the relationship between DC derived values (indirect method) and VAS values (direct method).MethodsData were collected in Canada, the United Kingdom, the Netherlands, and the United States. Respondents were asked to perform paired comparisons between two EQ-5D-5L health states for DC. In total, 400 different EQ-5D-5L states were included. After each DC task, respondents were prompted to score the two states one after another on a VAS. Intraclass correlation coefficients were calculated between DC and VAS values and illuminating graphs were designed.ResultsApproximately 400 respondents participated from each country. High similarity [individual intraclass correlation coefficients (ICC) >0.85] of DC and moderate correspondence of VAS values were observed for the four countries. Cross-country comparison of DC values shows a nonlinear relationship to the VAS values.ConclusionEQ-5D-5L derived DC and VAS values show a close but nonlinear relationship. Given the obvious biases associated with the VAS, DC methods based on ordinal responses may be a better alternative.

Discrete choice in commuter space: Small area analysis of diurnal population change in the Tokyo Metropolitan Region

On substitutability and complementarity in discrete choice models

Discrete choice model is probably one of the most popularly used statistical methods in practice. The common feature of this model is that it considers the behavioral factors of a person and the assumption of independent individuals. However, this widely accepted assumption seems problematic because human beings do not live in isolation. They interact with each other and form complex networks. Then the application of discrete choice model to network data will allow for network dependence in a general framework. In this paper, we focus on a discrete choice model with probit error which is specified as a latent spatial autoregressive model (SAR). This model could be viewed as a natural extension of the classical SAR model. The key difference is that the network dependence is latent and unobservable. Instead, it could be measured by a binary response variable. Parameter estimation then becomes a challenging task due to the complicated objective function. Following the idea of composite likelihood, an approximated paired maximum likelihood estimator (APMLE) is developed. Numerical studies are carried out to assess the finite sample performance of the proposed estimator. Finally a real dataset of Sina Weibo is analyzed for illustration purpose.

Discrete choice models are a group of models that are used to analyze choice data basically because they accommodate the nature of the process that generates the data. The most common types of discrete models include the logit, probit, multinomial logit, nested logit, mixed logit and most recently the generalized multinomial logit. Discrete choice models have been mostly used in the area economics, transportation, energy, psychology, etc. Prediction in these models isn\\'t uncommon, in contexts such as engineering, marketing, and production, discrete choice models are mostly used to forecast demand. Unfortunately, for out-of-sample prediction at the individual level for complex models such as mixed logit, which involves predicting the random effects/parameters, there isn\\'t any work found in literature. Thus, in this is work we propose a method for this scenario in mixed logit discrete models using the nearest neighbour concept. We carry out various simulations and then apply on two types of real-life data. We find that the prediction accuracy of this new method is better than the rudimentary method of using the population parameters especially when the model fitted isn\\'t the very best.

A tractable approach to compare the hedonic and discrete choice frameworks

Models of Count with Endogenous Choices

Prelims

Many research approaches are conceptualized as binary choices, representing endpoints of a spectrum (each of them providing important perspectives within their own discourses). Design and creativity are often conceptualized as being focused on one of these binary choices, thereby overlooking other possibilities. To better stimulate, enhance, and support creativity, our research has explored the middle ground between the endpoints defined by binary choices to identify “sweet spots” based on a careful trade–off analysis of specific goals, objectives, stakeholders, and socio–technical environments. This paper illustrates some of the major trade–offs related to design and creativity that we have explored in our research over the last ten years, including prescriptive and permissive environments, individual and social creativity, communities of practice and communities of interest, and consumer and active contributor cultures. It briefly describes some of the socio–technical environments that we have developed to enhance creativity in specific contexts.

This dissertation presents several novel contributions to the fields of technology management, entrepreneurship, organisational studies, and strategic management. The following sections outline the unique insights and new scientific findings generated through this research: ▪ This study provides a comprehensive examination of blockchain technology through the perspectives of organisation theories, including technology adoption theories and sensemaking theory, as well as two categories of Burrell and Morgan’s typology of sociological paradigms, the functionalist and interpretive paradigms. ▪ A new methodological approach is employed to examine the profiles of high-tech manufacturing enterprises within specific regional contexts, focusing on two Spanish regions. ▪ The application of novel-to-the-topic methodology, specifically two discrete choice models—the multinomial logit and mixed logit models—brings a new perspective to evaluating the likelihood of a firm’s innovation decisions on innovation novelty. ▪ This study explores the implementation of novel blockchain technology within small, early-stage businesses, shedding light on the challenges and opportunities associated with integrating blockchain into organisational processes. ▪ An actor-dependent view of external enablers has been incorporated into the external enabler theory within the context of blockchain-enabled emerging businesses. This theoretical advancement enriches the current framework by highlighting the influence of actors in shaping the adoption and dissemination of blockchain technology within organisations. ▪ The research integrates the theory of dynamic capabilities as a theoretical framework, providing a detailed analysis of the nature and function of dynamic capabilities in the establishment of new ventures. ▪ An in-depth analysis is offered on the advancement of sensing, seizing, and reconfiguring capabilities, supported by their microfoundations, within the context of early-stage companies, with particular attention given to the impact of blockchain technology. ▪ The conceptual model is constructed to demonstrate how the combination of external enablers, founders’ capabilities, and firm capabilities facilitated the formation and growth of new ventures. ▪ Two main propositions and three supporting sub-propositions are formulated that provide directions for future research. ▪ The conceptual framework is developed to illustrate how blockchain-enhanced dynamic capabilities reinforce the operations of emerging companies and sustain their competitiveness. ▪ Through the application of a case study approach, new empirical data on emerging Hungarian firms utilising blockchain was collected, addressing the deficiency in blockchain use cases. In summary, this dissertation makes significant theoretical and empirical contributions by offering novel perspectives, theoretical advancements, and empirical findings that enhance our understanding of the strategic implications of emerging technologies for organisational development and innovation.