Associative networks in decision making

Jiangtao Li (), Rui Tang () and Mu Zhang ()
Additional contact information
Jiangtao Li: Singapore Management University
Rui Tang: Department of Economics, Hong Kong University of Science and Technology
Mu Zhang: Department of Economics, University of Michigan

No 9-2024, Economics and Statistics Working Papers from Singapore Management University, School of Economics

Abstract: We present a model of associative networks that captures how a decision maker expands her consideration set through mental associations between alternatives. This model serves as a tool to understand the influence of association on decision making. As a proof of concept, we characterize this model within a random attention framework and demonstrate that all the relevant parameters are uniquely identifiable. Notably, in a novel choice domain where not all observable alternatives are available, the presence of unavailable alternatives can affect the choice frequencies of other alternatives through association.

Keywords: associative network; random attention; consideration set; random choice; availability and observability (search for similar items in EconPapers)
JEL-codes: D01 D91 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2024-09-16
New Economics Papers: this item is included in nep-dcm, nep-net and nep-sea
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ink.library.smu.edu.sg/soe_research/2766/ Full text (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2024_009

Access Statistics for this paper

More papers in Economics and Statistics Working Papers from Singapore Management University, School of Economics 90 Stamford Road, Sigapore 178903. Contact information at EDIRC.
Bibliographic data for series maintained by Cheong Pei Qi ( this e-mail address is bad, please contact ).