scholarly journals Discovering Rational Heuristics for Risky Choice

2022 ◽  
Author(s):  
Paul Krueger ◽  
Frederick Callaway ◽  
Sayan Gul ◽  
Tom Griffiths ◽  
Falk Lieder
Keyword(s):  
Risky Choice ◽  
Behavioral Experiment ◽  
Cognitive Resources ◽  
Machine Learning Method ◽  
Learning Method ◽  
Rational Decision ◽  
Decision Strategies ◽  
Rational Decision Making ◽  
Wide Range ◽  
Order Of Magnitude

For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

scholarly journals Collective decision making by rational individuals

2018 ◽  
Vol 115 (44) ◽  
pp. E10387-E10396 ◽  
Author(s):  
Richard P. Mann
Keyword(s):  
Decision Making ◽  
Full Range ◽  
Social Information ◽  
Information Use ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Rational Decision ◽  
Rational Decision Making ◽  
Wide Range ◽  
Social Information Use

The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules and the extent to which these behavioral rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behavior. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals and between the decision makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behavior, this model makes several testable predictions about how individuals make decisions in groups and offers a valuable perspective on how we view sources of variability in animal, and human, behavior.

scholarly journals Cognitive Aging and Tests of Rationality

2019 ◽  
Vol 22 ◽  
Author(s):  
Sanghyuk Park ◽  
Clintin P. Davis-Stober ◽  
Hope K. Snyder ◽  
William Messner ◽  
Michel Regenwetter
Keyword(s):  
Older Adults ◽  
Cognitive Aging ◽  
Bayesian Model ◽  
Rational Decision ◽  
Younger Adults ◽  
Decision Strategies ◽  
Hierarchical Bayesian Analysis ◽  
Rational Decision Making ◽  
Weak Evidence ◽  
Transitive Preference

Abstract We investigated whether older adults are more likely than younger adults to violate a foundational property of rational decision making, the axiom of transitive preference. Our experiment consisted of two groups, older (ages 60-75; 21 participants) and younger (ages 18-30; 20 participants) adults. We used Bayesian model selection to investigate whether individuals were better described via (transitive) weak order-based decision strategies or (possibly intransitive) lexicographic semiorder decision strategies. We found weak evidence for the hypothesis that older adults violate transitivity at a higher rate than younger adults. At the same time, a hierarchical Bayesian analysis suggests that, in this study, the distribution of decision strategies across individuals is similar for both older and younger adults.

scholarly journals Risky-choice framing and rational decision-making

2021 ◽  
Author(s):  
Sarah A. Fisher ◽  
David R. Mandel
Keyword(s):  
Decision Making ◽  
Framing Effects ◽  
Psychological Research ◽  
Risky Choice ◽  
Decision Makers ◽  
Future Research ◽  
Rational Decision ◽  
Rational Decision Making ◽  
Choice Framing

This article surveys the latest research on risky-choice framing effects, focusing on the implications for rational decision-making. An influential program of psychological research suggests that people’s judgements and decisions depend on the way in which information is presented, or ‘framed’. In a central choice paradigm, decision-makers seem to adopt different preferences, and different attitudes to risk, depending on whether the options specify the number of people who will be saved or the corresponding number who will die. It is standardly assumed that such responses violate a foundational tenet of rational decision-making, known as the principle of description invariance. We discuss recent theoretical and empirical research that challenges the dominant ‘irrationalist’ narrative. These approaches typically pay close attention to how decision-makers represent decision problems (including their interpretation of numerical quantifiers or predicate choice) and they highlight the need for a more robust characterization of the description invariance principle. We conclude by indicating avenues for future research that could bring us closer to a complete – and potentially rationalizing – explanation of framing effects.

scholarly journals Teaching & Learning Guide for: Risky-choice framing and rational decision-making

2021 ◽  
Author(s):  
Sarah A. Fisher ◽  
David R. Mandel
Keyword(s):  
Decision Making ◽  
Psychological Research ◽  
Risky Choice ◽  
Decision Makers ◽  
Rational Decision ◽  
Rational Decision Making ◽  
Alternative Approaches ◽  
Learning Guide ◽  
Choice Framing

An influential program of psychological research suggests that people’s judgements and decisions depend on the way in which information is presented, or ‘framed’. In a central choice paradigm, decision-makers seem to adopt different preferences, and different attitudes to risk, depending on whether the options specify the number of people who will be saved or the corresponding number who will die. It is standardly assumed that such responses violate a foundational tenet of rational decision-making, known as the principle of description invariance. However, recent theoretical and empirical research has begun to challenge the dominant ‘irrationalist’ narrative. The alternative approaches being developed typically pay close attention to how decision- makers represent decision problems (including their interpretation of numerical quantifiers or predicate choice). They also highlight the need for a more robust characterization of the description invariance principle itself.

scholarly journals Collective decision making by rational individuals

2018 ◽  
Author(s):  
Richard P. Mann
Keyword(s):  
Decision Making ◽  
Full Range ◽  
Social Information ◽  
Information Use ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Rational Decision ◽  
Rational Decision Making ◽  
Wide Range ◽  
Social Information Use

The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules, and the extent to which these behavioural rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behaviour. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals, and between the decision-makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behaviour, this model makes several testable predictions about how individuals make decisions in groups, and offers a valuable perspective on how we view sources of variability in animal, and human, behaviour.

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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