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Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic

Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic
Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic

It is well known that laypersons and practitioners often resist using complex mathematical models such as those proposed by economics or finance, and instead use fast and frugal strategies to make decisions. We study one such strategy: The recognition heuristic. This states that people infer that an object they recognize has a higher value of a criterion of interest than an object they do not recognize. We extend previous studies by including a general model of the recognition heuristic that considers probabilistic recognition, and carry out a mathematical analysis. We derive general closed-form expressions for all the parameters of this general model and show the similarities and differences between our proposal and the original deterministic model. We provide a formula for the expected accuracy rate by making decisions according to this heuristic and analyze whether or not its prediction exceeds the expected accuracy rate of random inference. Finally, we discuss whether having less information could be convenient for making more accurate decisions.

Accuracy rate, Fast and frugal, Judgment and decision making, Less-is-more effect (LIME), Recognition heuristic
1753-9579
83-103
Egozcue, Martín
18d170ae-9a89-414c-974f-ba6e10471ea6
García, Luis Fuentes
5d6e311f-d81a-4095-b83f-b8c184fefb2c
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Smithson, Michael
22c916fb-84f0-4bb7-9ac5-6b37f345c81c
Egozcue, Martín
18d170ae-9a89-414c-974f-ba6e10471ea6
García, Luis Fuentes
5d6e311f-d81a-4095-b83f-b8c184fefb2c
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Smithson, Michael
22c916fb-84f0-4bb7-9ac5-6b37f345c81c

Egozcue, Martín, García, Luis Fuentes, Katsikopoulos, Konstantinos V. and Smithson, Michael (2017) Simple models in finance: a mathematical analysis of the probabilistic recognition heuristic. Journal of Risk Model Validation, 11 (2), 83-103. (doi:10.21314/JRMV.2017.175).

Record type: Article

Abstract

It is well known that laypersons and practitioners often resist using complex mathematical models such as those proposed by economics or finance, and instead use fast and frugal strategies to make decisions. We study one such strategy: The recognition heuristic. This states that people infer that an object they recognize has a higher value of a criterion of interest than an object they do not recognize. We extend previous studies by including a general model of the recognition heuristic that considers probabilistic recognition, and carry out a mathematical analysis. We derive general closed-form expressions for all the parameters of this general model and show the similarities and differences between our proposal and the original deterministic model. We provide a formula for the expected accuracy rate by making decisions according to this heuristic and analyze whether or not its prediction exceeds the expected accuracy rate of random inference. Finally, we discuss whether having less information could be convenient for making more accurate decisions.

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More information

Accepted/In Press date: 31 January 2017
e-pub ahead of print date: 2 June 2017
Published date: June 2017
Keywords: Accuracy rate, Fast and frugal, Judgment and decision making, Less-is-more effect (LIME), Recognition heuristic

Identifiers

Local EPrints ID: 439016
URI: http://eprints.soton.ac.uk/id/eprint/439016
ISSN: 1753-9579
PURE UUID: 2d09bcef-409a-4320-b379-d8ce1b7802cc
ORCID for Konstantinos V. Katsikopoulos: ORCID iD orcid.org/0000-0002-9572-1980

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Date deposited: 01 Apr 2020 16:30
Last modified: 28 Apr 2022 02:18

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Contributors

Author: Martín Egozcue
Author: Luis Fuentes García
Author: Michael Smithson

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