Modeling toothpaste brand choice: An empirical comparison of artificial neural networks and multinomial probit model

Copyright @ 2010 Atlantis PressThe purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight

Enhancing Discrete Choice Models with Representation Learning

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.Comment: 35 pages, 12 tables, 6 figures, +11 p. Appendi

Assisted specification of discrete choice models

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models

A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability

Discrete choice models (DCMs) and neural networks (NNs) can complement each other. We propose a neural network embedded choice model - TasteNet-MNL, to improve the flexibility in modeling taste heterogeneity while keeping model interpretability. The hybrid model consists of a TasteNet module: a feed-forward neural network that learns taste parameters as flexible functions of individual characteristics; and a choice module: a multinomial logit model (MNL) with manually specified utility. TasteNet and MNL are fully integrated and jointly estimated. By embedding a neural network into a DCM, we exploit a neural network's function approximation capacity to reduce specification bias. Through special structure and parameter constraints, we incorporate expert knowledge to regularize the neural network and maintain interpretability. On synthetic data, we show that TasteNet-MNL can recover the underlying non-linear utility function, and provide predictions and interpretations as accurate as the true model; while examples of logit or random coefficient logit models with misspecified utility functions result in large parameter bias and low predictability. In the case study of Swissmetro mode choice, TasteNet-MNL outperforms benchmarking MNLs' predictability; and discovers a wider spectrum of taste variations within the population, and higher values of time on average. This study takes an initial step towards developing a framework to combine theory-based and data-driven approaches for discrete choice modeling

Enhancing discrete choice models with representation learning

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science

Avaliação da performance das redes neuronais artificiais e dos modelos de escolha discreta na aquisição de produtos com envolvimento fraco

Neste trabalho comparamos a performance das redes neuronais artificiais com a performance dos modelos de escolha discreta na previsão da aquisição de produtos com envolvimento fraco. Dentro da classe dos modelos de escolha discreta restringimos a comparação ao modelo Logit Multinomial, o modelo base e também o mais simples. Para além desse modelo, considerámos também o Mixed Logit, que é o mais completo e flexível modelo de escolha discreta, conseguindo aproximar as probabilidades de qualquer outro modelo de escolha discreta baseado na maximização da utilidade. A performance dos modelos foi aferida numa base de dados contendo o registo de aquisições de produtos em supermercados. Os resultados obtidos nas execuções das simulações mostraram que não existe uma clara supremacia de um ou outro tipo de modelos. No entanto, os modelos de escolha discreta foram sempre mais robustos e menos exigentes em recursos computacionaisIn this work, we compared the performance of the artificial neuronal networks with the performance of discrete choice models in forecasting acquisition of products with low involvement. Among the class of discrete choice models, we have restricted the comparison to the Multinomial Logit model, which is the base model and the simpler one. We also evaluated the Mixed Logit model, which is the more complete and flexible discrete choice model, and it can approximate the probabilities of any discrete choice model based on the random utility maximization. The performance of the models was evaluated in a database containing records of products purchases in supermarkets. The results obtained from the simulations executions revealed that it does not exist an evident supremacy of one type of models over the others. However, discrete choice models were always more robust and less demanding in computational resources

An analysis of industrial company failure in the UK and Russia for the 1990s

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis provides an examination of the key determinants of industrial company failure in the UK and Russia, for the 1990s. For the UK, some new empirical evidence, presented for the 1990s recession period, is based on binary logit analyses of a cross-section and unbalanced panel of large quoted companies, using accounting-based indicators. Conventional for cross sectional studies empirical design of modelling the failure determinants separately for various risk-horizons, prior to the event of insolvency, is extended here by allowing for unanticipated changes in the nominal interest rate and in the real exchange rate, and also by controlling for the firm's age effect. We find that cross sectional models, conditioned on changes in overall economic conditions, dominate simpler models, utilising financial inputs alone, for comparisons of ex ante, out-of sample classificatory accuracy. Thus, the UK data suggest that for the years before and during the 1990s recession, shifts in the real exchange rate and rises in the nominal interest rate magnified dramatically the risk of failure of highly geared firms. The estimates from the fixed effects models indicate substantial unobserved heterogeneity across members of the panel and reveal that failing UK companies were less liquid, lacked profitability, and had declining net worth. For Russia, the evidence from binary logit is bootstrap-based and controlled by comparison with a similar random sample drawn for the UK over the recession years 1990-91. The Russian data uncover that, unlike in the UK, gearing and liquidity did not appear to explain enterprise liquidation in the mid-1990s, while lower profitability and smaller size were the key determinants of failure risk.Financial support from the ACE Tacis Programme, Contract T95-5127-S in 1996-98, and from the Department of Economics and Finance of Brunel University in 1999-2000 were used in this work

An integrated framework for exploring finite mixture heterogeneity in travel demand and behavior

In recent years we have faced a plethora of social trends and new technologies such as shared mobility, micro-mobility, and information and communication technologies, and we will be facing many more in the future (e.g. self-driving cars, disruptive events). In this context, the perennial mission of transportation behavior analysts and modelers - to model behavior/demand so as to understand behavior, help craft responsive policies, and accurately forecast future demand - has become far more challenging. Specifically, behavioral realism and predictive ability are two key goals of modeling (travel) behavior/demand, and a key strategy for achieving those goals has been to introduce some type of heterogeneity in modeling. Thus, this thesis aims to improve our behavioral modeling by accounting for heterogeneity, with clues from the ideas of data/market segmentation, finite mixture, and mixture modeling. The objectives of the thesis are: (1) to build a framework for modeling finite mixture heterogeneity that connects seemingly less related models and various methodological ideas across domains, (2) to tackle various heterogeneity-related research questions in travel behavior and thus show the empirical usefulness of the models under the framework; and (3) to examine the potential, challenges, and implications of the framework with conceptual considerations and practical applications. Five inter-related studies in this thesis illuminate some part(s) of the framework and delineate how key concepts in the framework are connected to each other. (a) The thesis overviews the topics of heterogeneity and mixture modeling in transportation and provides the landscape and details of how we have used mixture modeling. (b) Extending the idea of a finite segmentation approach, the thesis connects and compares three models for treating finite-valued parameter heterogeneity: deterministic segmentation, endogenous switching, and latent class models. The study discusses their similarities and differences from conceptual and empirical standpoints. (c) The thesis explains the confirmatory latent class approach and its potential usefulness, as opposed to the conventional exploratory approach. Adopting this perspective, the study embraces zero-inflated models under the confirmatory latent class approach and demonstrates their empirical value. (d) The thesis introduces the idea of combining latent class and endogenous switching models. Conceptual and empirical differences between the standard latent class model and the proposed approach are discussed. (e) The dissertation illuminates the linkage between finite mixture modeling (specifically in “indirect application”) and the mixture of experts (MoE) architecture, introduced in machine learning. The study proposes to use MoE as a data-driven exploratory tool to capture nonlinear/interaction effects (which are types of parameter heterogeneity), and exhibits its ability using synthetic and empirical data. The thesis concludes with discussions about challenges, potential technical advances, and outlook for the framework. The dissertation is expected to give conceptual/methodological insights on the framework for modeling finite mixture heterogeneity and how various methodologies are connected under the framework. As well, the studies provide rich discussions about study-specific empirical findings and their implications. Thus, the dissertation can help improve our behavior/demand models by serving as a navigational compass for analysts.Ph.D

Advances in land-use and stated-choice modeling using neural networks and discrete-choice models

Doctor of PhilosophyDepartment of Agricultural EconomicsJason S. BergtoldJessica L. Heier StammApplied research in agricultural economics often involves a discrete process. Most commonly, these applications entail a conceptual framework, such as random utility, that describes a discrete-variable data-generating process. Assumptions in the conceptual framework then imply a particular empirical model. Common approaches include the binary logit and probit models and the multinomial logit when more than two outcomes are possible. Conceptual frameworks based on a discrete choice process have also been used even when the dependent variable of interest is continuous. In any case, the standard models may not be well suited to the problem at hand, as a result of either the assumptions they require or the assumptions they impose. The general theme of this dissertation is to adopt seldom-used empirical models to standard research areas in the field through applied studies. A common motivation in each paper is to lessen the exposure to specification concerns associated with more traditional models. The first paper is an attempt to provide insights into what --- if any --- weather patterns farmers respond to with respect to cropping decisions. The study region is a subset of 11 north-central Kansas counties. Empirically, this study adopts a dynamic multinomial logit with random effects approach, which may be the first use of this model with respect to farmer land-use decisions. Results suggest that field-level land-use decisions are significantly influenced by past weather, at least up to ten years. Results also suggest, however, that that short-term deviations from the longer trend can also influence land-use decisions. The second paper proposes multiple-output artificial neural networks (ANNs) as an alternative to more traditional approaches to estimating a system of acreage-share equations. To assess their viability as an alternative to traditional estimation, ANN results are compared to a linear-in-explanatory variables and parameters heteroskedastic and time-wise autoregressive seemingly unrelated regression model. Specifically, the two approaches are compared with respect to model fit and acre elasticities. Results suggest that the ANN is a viable alternative to a simple traditional model that is misspecified, as it produced plausible acre-response elasticities and outperformed the traditional model in terms of model fit. The third paper proposes ANNs as an alternative to the traditional logit model for contingent valuation analysis. With the correct network specifications, ANNs can be viewed as a traditional logistic regression where the index function has been replaced by a flexible functional form. The paper presents methods for obtaining marginal effect and willingness-to-pay (WTP) measures from ANNs, which has not been provided by the existing literature. To assess the viability of this approach, it is compared with the traditional logit and probit models as well an additional semi-nonparametric estimator with respect to model fit, marginal effects, and WTP estimates. Results suggest ANNs are viable alternative and may be preferable if misspecification of the index function is a concern