heuristics (not biases), rules of thumb, mental shortcuts, strategies ignoring information
The phenomena of indecision and suspension loom large in both philosophy and psychology. Whereas psychology discusses related phenomena in practical tasks and mostly pathological terms, philosophy strives for conceptual clarification and emphasizes the ubiquity and variety of suspension.
In this chapter, we use fast-and-frugal trees (FFTs) as a drosophila model for developing a positive account of suspension in decision-making. Being designed for handling binary classification tasks, FFTs seem particularly ill-suited for accommodating a third stance. But by replacing one decision outcome by a do not know category or adding it as a third option, we can adapt and extend the FFT framework to explore the causes and consequences of suspension.
Considering the distributions of decision outcomes and contrasting the performance of alternative models in terms of cost-benefit trade-offs illustrates the power of this methodology. Overall, a model-based approach provides surprising insights into the functions and mechanisms of suspension and serves as a productive tool for thinking.
fast-and-frugal trees (FFTs), judgment and decision making (JDM), heuristics, binary classification, cost-benefit trade-offs, indecision, computer modeling, philosophy, machine learning, suspension
Related: FFTrees: An R toolbox to create, visualize, and evaluate FFTs
Resources: 10.4324/9781003474302-20 | Download PDF | Google Scholar
Hansjörg Neth, Felix Gaisbauer, Nico Gradwohl, Wolfgang Gaissmaier
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.
An example of an FFT predicting the risk of having heart disease.
Nathaniel Phillips, Hansjörg Neth, Jan Woike, Wolfgang Gaissmaier
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.
Hansjörg Neth, Wolfgang Gaissmaier
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.