Papers Discrete Choice Models
“Discrete Choice Models” 태그가 달린 논문 104편 · 필터 해제
A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used …
Discrete Choice ModelsBayesian Deep Learning for Discrete Choice
Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences. DCMs play a central role in applied econometrics by …
Bayesian InferenceDeep LearningDiscrete Choice ModelsEconometrics+1A Feedback Control Framework for Incentivised Suburban Parking Utilisation and Urban Core Traffic Relief
Urban traffic congestion, exacerbated by inefficient parking management and cruising for parking, significantly hampers mobility and sustainability in smart cities. Drivers often face delays searching for parking spaces,…
Discrete Choice ModelsTwo Level Nested and Sequential Logit
This technical note provides comprehensive derivations of fundamental equations in two-level nested and sequential logit models for analyzing hierarchical choice structures. We present derivations of the Berry (1994) inv…
Discrete Choice ModelsDesigning Graph Convolutional Neural Networks for Discrete Choice with Network Effects
We introduce a novel model architecture that incorporates network effects into discrete choice problems, achieving higher predictive performance than standard discrete choice models while offering greater interpretabilit…
Discrete Choice ModelsModel-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces
Estimation and counterfactual experiments in dynamic discrete choice models with large state spaces pose computational difficulties. This paper develops a novel model-adaptive approach to solve the linear system of fixed…
Computational EfficiencycounterfactualDiscrete Choice ModelsUniversal Inference for Incomplete Discrete Choice Models
A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the unive…
counterfactualDiscrete Choice ModelsRUM-NN: A Neural Network Model Compatible with Random Utility Maximisation for Discrete Choice Setups
This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural…
Discrete Choice ModelsFully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model
Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully aut…
Discrete Choice ModelsA utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam
Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to …
Discrete Choice ModelsThe Relevance of Item-Co-Exposure For Exposure Bias Mitigation
Through exposing items to users, implicit feedback recommender systems influence the logged interactions, and, ultimately, their own recommendations. This effect is called exposure bias and it can lead to issues such as …
Discrete Choice ModelsRecommendation SystemsChoice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning
Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, called an assortment. Well-known use cases include predicting a commuter’s choice of transportation mode or a c…
Discrete Choice ModelsSensitivity Analysis for Dynamic Discrete Choice Models
In dynamic discrete choice models, some parameters, such as the discount factor, are being fixed instead of being estimated. This paper proposes two sensitivity analysis procedures for dynamic discrete choice models with…
Discrete Choice ModelsSensitivityDeep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models
This paper studies the finite sample performance of the flexible estimation approach of Farrell, Liang, and Misra (2021a), who propose to use deep learning for the estimation of heterogeneous parameters in economic model…
Deep LearningDiscrete Choice ModelsSimulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs
Discrete choice models (DCMs) have been widely utilized in various scientific fields, especially economics, for many years. These models consider a stochastic environment influencing each decision maker's choices. Extens…
Decision MakingDiscrete Choice ModelsExperimental DesignEnhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach
Travel choice analysis is crucial for understanding individual travel behavior to develop appropriate transport policies and recommendation systems in Intelligent Transportation Systems (ITS). Despite extensive research,…
Discrete Choice ModelsLanguage ModelingLanguage ModellingLarge Language Model+2Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with 2-period Finite Dependence
This paper extends the work of Arcidiacono and Miller (2011, 2019) by introducing a novel characterization of finite dependence within dynamic discrete choice models, demonstrating that numerous models display 2-period f…
Discrete Choice ModelsThe geometry of consumer preference aggregation
We revisit a classical question of how individual consumer preferences and incomes shape aggregate behavior. We develop a method that applies to populations with homothetic preferences and reduces the hard problem of agg…
Discrete Choice ModelsQuasi-Bayesian Estimation and Inference with Control Functions
This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses k…
Bayesian InferenceComputational EfficiencyDiscrete Choice ModelsvalidThe Mixed Aggregate Preference Logit Model: A Machine Learning Approach to Modeling Unobserved Heterogeneity in Discrete Choice Analysis
This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple'') model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice a…
Decision MakingDiagnosticDiscrete Choice Models