Posts Tagged: uncertainty

(vs. risk)

Paper: The echo in flu-vaccination echo chambers


Helge Giese, Hansjörg Neth, Mehdi Moussaïd, Cornelia Betsch, Wolfgang Gaissmaier

The echo in flu-vaccination echo chambers: Selective attention trumps social influence

Immune to influence


Online discussions may impact the willingness to get vaccinated. This experiment tests how groups of individuals with consistent and inconsistent attitudes towards flu vaccination attend to and convey information online, and how they alter their corresponding risk perceptions.

Out of 1859 MTurkers, we pre-selected 208 people with negative and 221 people with positive attitudes towards flu vaccinations into homogeneous or heterogeneous 3-link experimental diffusion chains. We assessed (i) which information about flu vaccinations participants conveyed to the subsequent link, (ii) how flu-vaccination related perceptions were altered by incoming messages, and (iii) how participants perceived incoming information.

Participants (i) selectively conveyed attitude-consistent information, but exhibited no overall anti-vaccination bias, (ii) were reluctant to alter their flu-vaccination related perceptions in response to messages, and (iii) evaluated incoming information consistent with their prior attitudes as more convincing.

Flu-vaccination related perceptions are resilient against contradictions and bias online communication. Contrary to expectations, there was no sign of amplification of anti-vaccine attitudes by online communication.

Keywords: Amplification of risk; Diffusion chain; Opinion dynamics; Vaccine hesitancy; Social media; Polarization

Press release

Reference:  Giese, H., Neth, H., Moussaïd, M., Betsch, C., & Gaissmaier, W. (2020).  The echo in flu-vaccination echo chambers: Selective attention trumps social influence.  Vaccine, 38 (8), 2070–2076.   doi: 10.1016/j.vaccine.2019.11.038

Related:  Social influence and collective opinion formation

Resources:  Download_PDFGoogle Scholar

riskyr: A toolbox for rendering risk literacy more transparent

Solving a problem simply means representing it
so as to make the solution transparent.

Simon, H.A. (1996). The Sciences of the Artificial

Hansjörg Neth, Felix Gaisbauer, Nico Gradwohl, Wolfgang Gaissmaier

riskyr: A toolbox for rendering risk literacy more transparent

Abstract:  Risk-related information — like the prevalence of conditions and the sensitivity and specificity of diagnostic tests or treatment decisions — can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, the R package riskyr computes, translates, and displays risk-related information in a variety of ways. Offering multiple complementary perspectives on the interplay between key parameters renders teaching and training of risk literacy more transparent.

Paper: FFTrees: An R toolbox to create, visualize, and evaluate FFTs

If a decision tree that measures up very well on the performance criterion
is nevertheless totally incomprehensible to a human expert, can it
be described as knowledge? Under the common-sense definition of this term
as material that might be assimilated and used by human beings, it is not…

J. Ross Quinlan (1987), p. 498





Nathaniel Phillips, Hansjörg Neth, Jan Woike, Wolfgang Gaissmaier

FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

An example of an FFT (created by FFTrees) predicting heard disease.

An example of an FFT predicting the risk of having heart disease.

Abstract:  Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees.

Artikel: Warum erfolgreiche Prognosen einfach und unsicher sind

The trouble with free elections is that you never know how they are going to to turn out.
(Vyacheslav Molotov, 1954, see Wikiquote)



Hansjörg Neth, Wolfgang Gaissmaier

Warum erfolgreiche Prognosen einfach und unsicher sind

Von der Wahl des richtigen Werkzeugs für Wähler und die Wahlforschung

Aus der Einleitung:  Für Entscheidungswissenschaftler sind Wahlen – ungeachtet aller Warnungen – ein großartiges und größenwahnsinniges Experiment. Während wir sonst das Entscheidungsverhalten von Individuen und Gruppen in kontrollierten Laborsituationen erforschen, werden bei Wahlen die Meinungen von Millionen Menschen über mehrere Monate systematisch manipuliert und dann ein bundesweites Aggregat ihrer Mehrfachauswahlentscheidungen gebildet, das die Zusammensetzung von Parlament und Regierung und damit die Politik der nächsten Jahre determiniert. Auch wenn das Experiment ethisch nicht unbedenklich und seine Aussagekraft in Ermangelung einer Kontrollgruppe erheblich eingeschränkt ist, finden sich auf der gesamtgesellschaftlichen Ebene viele Elemente wieder, deren Mechanismen wir im mikroskopischen Maßstab aus unseren Studien kennen.