Subjective randomness as statistical inference

•Our sense of randomnesscan be understood as statistical inference.•Randomness judgments are inferences about generating processes.•Ideas from computer science can be used to define non-random processes.•We predict randomness judgments for sequences, matrices, and spatial arrays. Some events seem mo...

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Veröffentlicht in:Cognitive psychology Jg. 103; S. 85 - 109
Hauptverfasser: Griffiths, Thomas L., Daniels, Dylan, Austerweil, Joseph L., Tenenbaum, Joshua B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.06.2018
Schlagworte:
Algorithmic complexity
Bayesian inference
Randomness
Randomness
Algorithmic complexity
Bayesian inference
ISSN:0010-0285, 1095-5623, 1095-5623
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Zusammenfassung:•Our sense of randomnesscan be understood as statistical inference.•Randomness judgments are inferences about generating processes.•Ideas from computer science can be used to define non-random processes.•We predict randomness judgments for sequences, matrices, and spatial arrays. Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences – which have been the focus of much of the previous work on subjective randomness – but also to binary matrices and spatial clustering.
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ISSN:0010-0285
1095-5623
1095-5623
DOI:10.1016/j.cogpsych.2018.02.003