Search

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.

BackgroundIntegrity of academic publishing is increasingly undermined by fake science publications massively produced by commercial “editing services” (so-called “paper mills”). They use AI-supported, automated production techniques at scale and sell fake publications to students, scientists, and physicians under pressure to advance their careers. Because the scale of fake publications in biomedicine is unknown, we developed a simple method to red-flag them and estimate their number.MethodsTo identify indicators able to red-flagged fake publications (RFPs), we sent questionnaires to authors. Based on author responses, threeindicatorswere identified: “author’s private email”, “international co-author” and “hospital affiliation”. These were used to analyze 15,120 PubMed®-listed publications regarding date, journal, impact factor, and country of author and validated in a sample of 400 known fakes and 400 matched presumed non-fakes using classification (tallying) rules to red-flag potential fakes. For a subsample of 80 papers we used an additional indicator related to the percentage of RFP citations.ResultsThe classification rules using two (three) indicators had sensitivities of 86% (90%) and false alarm rates of 44% (37%). From 2010 to 2020 the RFP rate increased from 16% to 28%. Given the 1.3 million biomedical Scimago-listed publications in 2020, we estimate the scope of >300,000 RFPs annually. Countries with the highest RFP proportion are Russia, Turkey, China, Egypt, and India (39%-48%), with China, in absolute terms, as the largest contributor of all RFPs (55%).ConclusionsPotential fake publications can be red-flagged using simple-to-use, validated classification rules to earmark them for subsequent scrutiny. RFP rates are increasing, suggesting higher actual fake rates than previously reported. The scale and proliferation of fake publications in biomedicine can damage trust in science, endanger public health, and impact economic spending and security. Easy-to-apply fake detection methods, as proposed here, or more complex automated methods can help prevent further damage to the permanent scientific record and enable the retraction of fake publications at scale.

Narratives and heuristics are both tools for dealing with uncertainty, intractability, and incommensurability, that is, for all real-world situations outside the domain of Bayesian decision theory. But how do narratives and heuristics relate? I suggest two links: Heuristics select narratives to explain events, and “big” narratives select the heuristics that people live by, to execute their values and moral principles.

Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics.