On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization


Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Therefore, we propose a simple generative choice model where agents are assumed to generate the choice probabilities based on latent factor matrices that capture their choice evaluation across multiple attributes. Since the multi-attribute evaluation is typically hidden within the agent's psyche, we consider a signaling mechanism where agents are provided with choice information through private signals, so that the agent's choices provide more insight about his/her latent evaluation across multiple attributes. We estimate the choice model via a novel multi-stage matrix factorization algorithm that minimizes the average deviation of the factor estimates from choice data. Simulation results are presented to validate the estimation performance of our proposed algorithm.

Meeting Name

52nd Annual Conference on Information Sciences and Systems, CISS 2018 (2018: Mar. 21-23, Princeton, NJ)


Computer Science

Keywords and Phrases

Data Acquisition; Factorization; Software Agents, Average Deviation; Estimation Performance; Matrix Factorizations; Multi-Attribute Evaluations; Multiple Attributes; Revealed Preference; Signaling Mechanisms; Utility Functions, Matrix Algebra

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Document Type

Article - Conference proceedings

Document Version


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© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Mar 2018