9,115 research outputs found
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
Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach
There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents
Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach
There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents.Choice experiment; latent class; ordering effects; strategic response; willingness-to-pay
Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a probabilistic decision process model
There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Decision processes (or heuristics) such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none has investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting a probabilistic decision process model designed to estimate the proportion of respondents employing defined heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents.Choice experiment, decision process, ordering effects, strategic response, willingness to pay, Research Methods/ Statistical Methods, C25, L94, Q51,
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
It is an enduring question how to combine revealed preference (RP) and stated
preference (SP) data to analyze travel behavior. This study presents a
framework of multitask learning deep neural networks (MTLDNNs) for this
question, and demonstrates that MTLDNNs are more generic than the traditional
nested logit (NL) method, due to its capacity of automatic feature learning and
soft constraints. About 1,500 MTLDNN models are designed and applied to the
survey data that was collected in Singapore and focused on the RP of four
current travel modes and the SP with autonomous vehicles (AV) as the one new
travel mode in addition to those in RP. We found that MTLDNNs consistently
outperform six benchmark models and particularly the classical NL models by
about 5% prediction accuracy in both RP and SP datasets. This performance
improvement can be mainly attributed to the soft constraints specific to
MTLDNNs, including its innovative architectural design and regularization
methods, but not much to the generic capacity of automatic feature learning
endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs
are also interpretable. The empirical results show that AV is mainly the
substitute of driving and AV alternative-specific variables are more important
than the socio-economic variables in determining AV adoption. Overall, this
study introduces a new MTLDNN framework to combine RP and SP, and demonstrates
its theoretical flexibility and empirical power for prediction and
interpretation. Future studies can design new MTLDNN architectures to reflect
the speciality of RP and SP and extend this work to other behavioral analysis
A Trait Specific Model of GM Crop Adoption among U.S. Corn Farmers in the Upper Midwest
This work offers a new approach to the adoption of GM crop varieties by adopting the econometric methodology of the characteristics-based demand literature. A random utility framework was implemented through different specifications of a conditional (CL) and a mixed multinomial logit (MMNL) model of crop-variety choice. Willingness-to-pay and price elasticity estimates for traits were calculated. The MMNL approach demonstrates that individuals' tastes for some traits significantly vary across the population. Results further suggest that labor saving technologies have a much wider potential to be adopted. Overall, the use of a trait-based model to examine the adoption patterns of GM crop varieties among corn farmers in Minnesota and Wisconsin reveals a new set of results and lessons that classic adoption models cannot provide.Research and Development/Tech Change/Emerging Technologies,
- …