Simulated Route Decision Behaviour: Simple Heuristics and Adaptation

How do mushroom foragers make safe and efficient decisions under uncertainty, or deal with the genuine risks of misiden-tification and poisoning? This article is an inquiry into ecological rationality, heuristics, perception, and decision-makingin mushroom foraging. By surveying 894 Finnish mushroom foragers, this article illustrates how socially learned rules of thumb and heuristics are used in mushroom foraging, and how simple heuristics are often complemented by more complex and intuitive decision-making. The results illustrate how traditional foraging cultures have evolved precautionary heuristics to deal with uncertainties and poisonous species, and how foragers develop selective attention through experience. The study invites us to consider whether other human foraging cultures might use heuristics similarly, how and why such traditions have culturally evolved, and whether early hunter-gatherers might have used simple heuristics to deal with uncertainty.

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Parallel Constraint Satisfaction in Memory-Based Decisions

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000084 ◽

2011 ◽

Vol 58 (3) ◽

pp. 180-195 ◽

Cited By ~ 12

Author(s):

Andreas Glöckner ◽

Sara D. Hodges

Keyword(s):

Decision Making ◽

Constraint Satisfaction ◽

Information Integration ◽

Time Pressure ◽

Human Information Processing ◽

Decision Task ◽

Equal Weight ◽

Decision Strategies ◽

Simple Heuristics ◽

Decision Times

Three studies sought to investigate decision strategies in memory-based decisions and to test the predictions of the parallel constraint satisfaction (PCS) model for decision making (Glöckner & Betsch, 2008). Time pressure was manipulated and the model was compared against simple heuristics (take the best and equal weight) and a weighted additive strategy. From PCS we predicted that fast intuitive decision making is based on compensatory information integration and that decision time increases and confidence decreases with increasing inconsistency in the decision task. In line with these predictions we observed a predominant usage of compensatory strategies under all time-pressure conditions and even with decision times as short as 1.7 s. For a substantial number of participants, choices and decision times were best explained by PCS, but there was also evidence for use of simple heuristics. The time-pressure manipulation did not significantly affect decision strategies. Overall, the results highlight intuitive, automatic processes in decision making and support the idea that human information-processing capabilities are less severely bounded than often assumed.

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How do I know what my theory predicts

10.31234/osf.io/kryqt ◽

2019 ◽

Author(s):

Zoltan Dienes

Keyword(s):

Mediation Analysis ◽

Null Hypothesis ◽

Prior Information ◽

Bayes Factor ◽

Total Effect ◽

Ways Of Thinking ◽

Regression Slopes ◽

Mean Differences ◽

Simple Heuristics

To get evidence for or against a theory relative to the null hypothesis, one needs to know what the theory predicts. The amount of evidence can then be quantified by a Bayes factor. Specifying what one's theory predicts may not come naturally, but I show some ways of thinking about the problem, some simple heuristics that are often useful when one has little relevant prior information. These heuristics include the room-to-move heuristic (for comparing mean differences), the ratio-of-scales heuristic (for regression slopes), the ratio-of-means heuristic (for regression slopes), the basic effect heuristic (for ANOVA effects), and the total effect heuristic (for mediation analysis).

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