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

Konstantinos Katsikopoulos, Özgür Şimşek, Marcus Buckmann, Gerd Gigerenzer

Research output: Contribution to journalArticlepeer-review

14 Citations (SciVal)
23 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)613-619
Number of pages7
JournalInternational Journal of Forecasting
Issue number2
Early online date28 Jan 2021
Publication statusPublished - 1 Apr 2022


  • Big data
  • Google Flu Trends
  • Naïve forecasting
  • Recency
  • Simple heuristics

ASJC Scopus subject areas

  • Business and International Management


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