Reply to commentaries on “Transparent modelling of influenza incidence”: Recency heuristics and psychological AI

Konstantinos V. Katsikopoulos, Özgür Şimşek, Marcus Buckmann, Gerd Gigerenzer

Research output: Contribution to journalComment/debatepeer-review

Abstract

We structure this response to the commentaries to our article “Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?” around the concept of psychological AI, Herbert Simon's classic idea of using insights from how people make decisions to make computers smart. The recency heuristic in Katsikopoulos, Şimşek, Buckmann, and Gigerenzer (2021) is one example of psychological AI. Here we develop another: the trend-recency heuristic. While the recency heuristic predicts that the next observation will equal the most recent observation, the trend-recency heuristic predicts that the next trend will equal the most recent trend. We compare the performance of these two recency heuristics with forecasting models that use trend damping for predicting flu incidence. Psychological AI prioritizes ecological rationality and transparency, and we provide a roadmap of how to study such issues. We also discuss how this transparency differs from explainable AI and how ecological rationality focuses on the comparative empirical study and theoretical analysis of different types of models.

Original languageEnglish
Pages (from-to)630-634
Number of pages5
JournalInternational Journal of Forecasting
Volume38
Issue number2
Early online date12 Jan 2022
DOIs
Publication statusPublished - 1 Apr 2022

ASJC Scopus subject areas

  • Business and International Management

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