Discrete Choice and Rational Inattention: a General Equivalence Result

Fosgerau, Mogens; Melo, Emerson; Palma, André de; Shum, Matthew

doi:10.48550/arxiv.1709.09117

Cited by 2 publications

(5 citation statements)

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“…For example, Mehta, Rajiv, and Srinivasan (2003), Honka and Chintagunta (2016), and Abaluck and Compiani (2019) consider search frameworks, where the DM follows a sequential protocol to learn about the payoffs generated by the available alternatives. Csaba (2018) adopts a rational inattention perspective, where the attentional costs sustained by the DM to process information structures are parametrically modelled, along the lines of Caplin and Dean (2015), Matȇjka and McKay (2015), Fosgerau, Melo, de Palma, andShum (2017), andLeahy (2018).…”

Section: Introductionmentioning

confidence: 99%

Identification in discrete choice models with imperfect information

Gualdani¹,

Sinha²

2019

Preprint

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In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest.

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Section: Introductionmentioning

confidence: 99%

Identification in discrete choice models with imperfect information

Gualdani¹,

Sinha²

2019

Preprint

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“…We develop a choice model estimation framework with latent constructs that capture information heterogeneity within the data. The key difference between our work and previous literature is that we show how rational inattention can be framed as a flexible and extendable generative learning model that emulates the cognitive processes in human behaviour [13,14]. We postulate that realistic behavioural patterns can be modelled using a data-driven generative learning process and we estimate a model to represent the underlying heterogeneity of the data.…”

Section: Introductionmentioning

confidence: 87%

“…We propose a generative model framework that extends rational inattentive behaviour in discrete choice, interpreting it as an optimization process rather than a structural model specification. We differentiate our work from the generalized entropy function described in [13] by framing non-normative behaviour as a learning model -allowing for random perturbations to be data-driven. Under this framework, the estimation of a generative model assumes to emulate information processing constraints in rational inattention behaviour and identifies observed and latent variable interactions through a neural network interface.…”

Section: Methodsmentioning

confidence: 99%

“…Prospect Theory and Regret Theory [18,11]. The measure of information heterogeneity is closely related to non-normative representation, involving Shannon entropy [13]. Expected utility representation may not be sufficient in providing the proper specification for these decision problems as individuals may perceive choice probabilities with different levels of uncertainty.…”

Section: Information Theorymentioning

confidence: 99%

“…Eq. ( 13) is a direct interpretation of the generalized entropy formulation for discrete choice [22,13]. The coefficients (βy + d) stand for the unknown parameters of the explanatory variables for each alternative y and for the generative model respectively.…”

Section: Model Specificationmentioning

confidence: 99%

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Information processing constraints in travel behaviour modelling: A generative learning approach

Wong,

Farooq

2019

Preprint

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Travel decisions tend to exhibit sensitivity to uncertainty and information processing constraints. These behavioural conditions can be characterized by a generative learning process. We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations. We outline the methodology of the generative model and the associated learning process as well as provide an intuitive explanation of how this process captures the value of prior information in the choice utility specification. We demonstrate the effects of information heterogeneity on a travel choice, analyze the econometric interpretation, and explore the properties of our generative model. Our findings indicate a strong correlation with rational inattention behaviour theory, which suggest that individuals may ignore certain exogenous variables and rely on prior information for evaluating decisions under uncertainty. Finally, the principles demonstrated in this study can be formulated as a generalized entropy and utility based multinomial logit model.

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Discrete Choice and Rational Inattention: a General Equivalence Result

Cited by 2 publications

References 0 publications

Identification in discrete choice models with imperfect information

Identification in discrete choice models with imperfect information

Information processing constraints in travel behaviour modelling: A generative learning approach

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