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Autoencoders for strategic decision support

Autoencoders for strategic decision support
Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.

Outlier detection, Strategic decision support, Unsupervised learning
0167-9236
Verboven, Sam
abff119b-84d8-4406-9dfa-5a747cf7fa02
Berrevoets, Jeroen
df47d7ae-d50f-459a-90e9-7e6862bfb0be
Wuytens, Chris
cae14a1d-ed35-4ee6-9ca7-a38d8b8dc306
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Verboven, Sam
abff119b-84d8-4406-9dfa-5a747cf7fa02
Berrevoets, Jeroen
df47d7ae-d50f-459a-90e9-7e6862bfb0be
Wuytens, Chris
cae14a1d-ed35-4ee6-9ca7-a38d8b8dc306
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732

Verboven, Sam, Berrevoets, Jeroen, Wuytens, Chris, Baesens, Bart and Verbeke, Wouter (2020) Autoencoders for strategic decision support. Decision Support Systems, [113422]. (doi:10.1016/j.dss.2020.113422).

Record type: Article

Abstract

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.

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AE_DSS_Revision2 - Accepted Manuscript
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More information

Submitted date: 3 May 2020
Accepted/In Press date: 2 October 2020
e-pub ahead of print date: 14 October 2020
Keywords: Outlier detection, Strategic decision support, Unsupervised learning

Identifiers

Local EPrints ID: 445844
URI: http://eprints.soton.ac.uk/id/eprint/445844
ISSN: 0167-9236
PURE UUID: 94388204-ca53-4a3a-a387-649aefbb4496
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 08 Jan 2021 17:33
Last modified: 28 Apr 2022 05:55

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Contributors

Author: Sam Verboven
Author: Jeroen Berrevoets
Author: Chris Wuytens
Author: Bart Baesens ORCID iD
Author: Wouter Verbeke

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