arXiv subjects

C. Angelo Guevara

Publications and source records attributed to C. Angelo Guevara.

3 recordsLinked to original sources

arXiv2026

Consistent estimation in logit models using historical choices as practical consideration set

A key challenge in choice modeling lies in specifying the consideration set, the subset of alternatives that individuals actually evaluate when making choices, which is unobserved (latent) to the researcher. The classical homo economicus assumption posits that individuals assess the full universal set of alternatives, a behaviorally implausible premise. Practical options include directly asking individuals, which introduces behavioral biases; treating the consideration set as a latent construct, requiring full enumeration and strong identification assumptions; or relying on ad hoc heuristics that attempt to replicate how individuals form these sets or on non-parametric methods. Recently, some researchers have used historical choices as practical consideration set, an approach made increasingly feasible by the availability of passive data sources such as smartcards, mobile phone records, and scanner data. This article provides a formal demonstration of a sufficient condition, along with Monte Carlo evidence, showing that, under a Logit data-generating process with homogeneous choice probabilities across instances, defining a practical consideration set based on historical choices yields consistent parameter estimates. The demonstration is based on a reinterpretation of the sampling-of-alternatives theorem, viewing historical choices as draws from the true consideration set, and showing that under the stated assumptions, the uniform conditioning property holds. The article concludes by discussing the practical implications of this result and potential extensions to other modeling frameworks and assumptions.

econ.EM↗

arXiv2025

Modelling Real-Life Cycling Decisions in Real Urban Settings Through Psychophysiology and LLM-Derived Contextual Data

Measuring emotional states in transportation contexts is an emerging field. Methods based on self-reported emotions are limited by their low granularity and their susceptibility to memory bias. In contrast, methods based on physiological indicators provide continuous data, enabling researchers to measure changes in emotional states with high detail and accuracy. Not only are emotions important in the analysis, but understanding what triggers emotional changes is equally important. Uncontrolled variables such as traffic conditions, pedestrian interactions, and infrastructure remain a significant challenge, as they can have a great impact on emotional states. Explaining the reasons behind these emotional states requires gathering sufficient and proper contextual data, which can be extremely difficult in real-world environments. This paper addresses these challenges by applying an innovative approach, extracting contextual data (expert annotator level) from recorded multimedia using large language models (LLMs). In this paper, data are collected from an urban cycling case study of the City of Santiago, Chile. The applied models focus on understanding how different environments and traffic situations affect the emotional states and behaviors of the participants using physiological data. Sequences of images, extracted from the recorded videos, are processed by LLMs to obtain semantic descriptions of the environment. These discrete, although dense and detailed, contextual data are integrated into a hybrid model, where fatigue and arousal serve as latent variables influencing observed cycling behaviors (inferred from GPS data) like waiting, accelerating, braking, etc. The study confirms that cycling decisions are influenced by stress-related emotions and highlights the strong impact of urban characteristics and traffic conditions on cyclist behavior.

eess.SP↗

arXiv2022

A Note on "A survey of preference estimation with unobserved choice set heterogeneity" by Gregory S. Crawford, Rachel Griffith, and Alessandro Iaria

Crawford's et al. (2021) article on estimation of discrete choice models with unobserved or latent consideration sets, presents a unified framework to address the problem in practice by using "sufficient sets", defined as a combination of past observed choices. The proposed approach is sustained in a re-interpretation of a consistency result by McFadden (1978) for the problem of sampling of alternatives, but the usage of that result in Crawford et al. (2021) is imprecise in an important matter. It is stated that consistency would be attained if any subset of the true consideration set is used for estimation, but McFadden (1978) shows that, in general, one needs to do a sampling correction that depends on the protocol used to draw the choice set. This note derives the sampling correction that is required when the choice set for estimation is built from past choices. Then, it formalizes the conditions under which such correction would fulfill the uniform condition property and can therefore be ignored when building practical estimators, such as the ones analyzed by Crawford et al. (2021).

econ.EM↗