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Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?

Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?
Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?

Simple, transparent rules are often frowned upon while complex, black-box models are seen as holding greater promise. Yet in quickly changing situations, simple rules can protect against overfitting and adapt quickly. We show that the surprisingly simple recency heuristic forecasts more accurately than Google Flu Trends (GFT) which used big data analytics and a black-box algorithm. This heuristic predicts that “this week's proportion of flu-related doctor visits equals the proportion from the most recent week.” It is based on psychological theory of how people deal with rapidly changing situations. Other theory-inspired heuristics have outperformed big data models in predicting outcomes, such as U.S. presidential elections, or other uncertain events, such as consumer purchases, patient hospitalizations, and terrorist attacks. Heuristics are transparent, clearly communicating the underlying rationale for their predictions. We advocate taking into account psychological principles that have evolved over millennia and using these as a benchmark when testing big data models.

Big data, Google Flu Trends, Naïve forecasting, Recency, Simple heuristics
0169-2070
Katsikopoulos, Konstantinos
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Şimşek, Özgür
2c25947f-5cce-4f31-9e59-79761cd66ab6
Buckmann, Marcus
a20a5be1-9ed2-470f-8b84-f582ed48e4cf
Gigerenzer, Gerd
95655620-31f2-44ef-bdd6-ddbc5f175b08
Katsikopoulos, Konstantinos
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Şimşek, Özgür
2c25947f-5cce-4f31-9e59-79761cd66ab6
Buckmann, Marcus
a20a5be1-9ed2-470f-8b84-f582ed48e4cf
Gigerenzer, Gerd
95655620-31f2-44ef-bdd6-ddbc5f175b08

Katsikopoulos, Konstantinos, Şimşek, Özgür, Buckmann, Marcus and Gigerenzer, Gerd (2021) Transparent modeling of influenza incidence: Big data or a single data point from psychological theory? International Journal of Forecasting. (doi:10.1016/j.ijforecast.2020.12.006).

Record type: Article

Abstract

Simple, transparent rules are often frowned upon while complex, black-box models are seen as holding greater promise. Yet in quickly changing situations, simple rules can protect against overfitting and adapt quickly. We show that the surprisingly simple recency heuristic forecasts more accurately than Google Flu Trends (GFT) which used big data analytics and a black-box algorithm. This heuristic predicts that “this week's proportion of flu-related doctor visits equals the proportion from the most recent week.” It is based on psychological theory of how people deal with rapidly changing situations. Other theory-inspired heuristics have outperformed big data models in predicting outcomes, such as U.S. presidential elections, or other uncertain events, such as consumer purchases, patient hospitalizations, and terrorist attacks. Heuristics are transparent, clearly communicating the underlying rationale for their predictions. We advocate taking into account psychological principles that have evolved over millennia and using these as a benchmark when testing big data models.

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Accepted/In Press date: 15 December 2020
e-pub ahead of print date: 28 January 2021
Additional Information: Publisher Copyright: © 2020 International Institute of Forecasters Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Big data, Google Flu Trends, Naïve forecasting, Recency, Simple heuristics

Identifiers

Local EPrints ID: 445652
URI: http://eprints.soton.ac.uk/id/eprint/445652
ISSN: 0169-2070
PURE UUID: fc756881-865b-4a61-b3f0-b9baba4c3afb
ORCID for Konstantinos Katsikopoulos: ORCID iD orcid.org/0000-0002-9572-1980

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Date deposited: 05 Jan 2021 17:32
Last modified: 28 Apr 2022 02:18

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Contributors

Author: Özgür Şimşek
Author: Marcus Buckmann
Author: Gerd Gigerenzer

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