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Reasoning the fast and frugal way: models of bounded rationality.
TLDR
The authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making, and found that these fast and frugal algorithms violate fundamental tenets of classical rationality: they neither look up nor integrate all information. Expand
Models of ecological rationality: the recognition heuristic.
TLDR
The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge that leads to the counterintuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. Expand
Do Defaults Save Lives?
The article discusses how should policy-makers choose defaults regarding organ donors. First, consider that every policy must have a no-action default, and defaults impose physical, cognitive, and,Expand
Beyond nudges: Tools of a choice architecture
TLDR
This paper outlines the tools available to choice architects, that is anyone who present people with choices, and divides these tools into two categories: those used in structuring the choice task and Those used in describing the choice options. Expand
The structure of online diffusion networks
TLDR
This work describes the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing, and finds strikingly similar patterns across all domains. Expand
Increasing Saving Behavior Through Age-Progressed Renderings of the Future Self
TLDR
The authors propose that allowing people to interact with age-progressed renderings of themselves will cause them to allocate more resources to the future, and show an increased tendency to accept later monetary rewards over immediate ones. Expand
Manipulating and Measuring Model Interpretability
TLDR
A sequence of pre-registered experiments showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box). Expand
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