Results 1 - 10
of
97
Heuristics made easy: An effort-reduction framework
- Psychological Bulletin
, 2008
"... In this article, the authors propose a new framework for understanding and studying heuristics. The authors posit that heuristics primarily serve the purpose of reducing the effort associated with a task. As such, the authors propose that heuristics can be classified according to a small set of effo ..."
Abstract
-
Cited by 50 (1 self)
- Add to MetaCart
(Show Context)
In this article, the authors propose a new framework for understanding and studying heuristics. The authors posit that heuristics primarily serve the purpose of reducing the effort associated with a task. As such, the authors propose that heuristics can be classified according to a small set of effort-reduction principles. The authors use this framework to build upon current models of heuristics, examine existing heuristics in terms of effort-reduction, and outline how current research methods can be used to extend this effort-reduction framework. This framework reduces the redundancy in the field and helps to explicate the domain-general principles underlying heuristics.
New paradoxes of risky decision making
- Psychological Review
"... During the last 25 years, prospect theory and its successor, cumulative prospect theory, replaced expected utility as the dominant descriptive theories of risky decision making. Although these models account for the original Allais paradoxes, 11 new paradoxes show where prospect theories lead to sel ..."
Abstract
-
Cited by 42 (13 self)
- Add to MetaCart
(Show Context)
During the last 25 years, prospect theory and its successor, cumulative prospect theory, replaced expected utility as the dominant descriptive theories of risky decision making. Although these models account for the original Allais paradoxes, 11 new paradoxes show where prospect theories lead to self-contradiction or systematic false predictions. The new findings are consistent with and, in several cases, were predicted in advance by simple “configural weight ” models in which probability-consequence branches are weighted by a function that depends on branch probability and ranks of consequences on discrete branches. Although they have some similarities to later models called “rank-dependent utility, ” configural weight models do not satisfy coalescing, the assumption that branches leading to the same consequence can be combined by adding their probabilities. Nor do they satisfy cancellation, the “independence ” assumption that branches common to both alternatives can be removed. The transfer of attention exchange model, with parameters estimated from previous data, correctly predicts results with all 11 new paradoxes. Apparently, people do not frame choices as prospects but, instead, as trees with branches.
Do People Make Decisions Under Risk Based on Ignorance? An Empirical Test of the Priority Heuristic against Cumulative Prospect Theory
, 2008
"... Brandstätter, Gigerenzer and Hertwig (2006) put forward the priority heuristic (PH) as a fast and frugal heuristic for decisions under risk. According to the PH, individuals do not make trade-offs between gains and probabilities, as proposed by expected utility models such as cumulative prospect the ..."
Abstract
-
Cited by 37 (14 self)
- Add to MetaCart
Brandstätter, Gigerenzer and Hertwig (2006) put forward the priority heuristic (PH) as a fast and frugal heuristic for decisions under risk. According to the PH, individuals do not make trade-offs between gains and probabilities, as proposed by expected utility models such as cumulative prospect theory (CPT), but use information in a non-compensatory manner and ignore information. We conducted three studies to test the PH empirically by analyzing individual choice patterns, decision times and information search parameters in diagnostic decision tasks. Results on all three dependent variables conflict with the predictions of the PH and can be better explained by the CPT. The predictive accuracy of the PH was high for decision tasks in which the predictions align with the predictions of the CPT but very low for decision tasks in which this was not the case. The findings indicate that earlier results supporting the PH might have been caused by
Decisions from experience and statistical probabilities: why they trigger different choices than a priori probabilities
, 2010
"... The distinction between risk and uncertainty is deeply entrenched in psychologists ’ and economists ’ thinking. Knight (1921), to whom it is frequently attributed, however, went beyond this dichotomy. Within the domain of risk, he set apart a priori and statistical probabilities, a distinction that ..."
Abstract
-
Cited by 17 (0 self)
- Add to MetaCart
(Show Context)
The distinction between risk and uncertainty is deeply entrenched in psychologists ’ and economists ’ thinking. Knight (1921), to whom it is frequently attributed, however, went beyond this dichotomy. Within the domain of risk, he set apart a priori and statistical probabilities, a distinction that maps onto that between decisions from description and experience, respectively. We argue this distinction is important because risky choices based on a priori (described) and statistical (experienced) probabilities can substantially diverge. To understand why, we examine various possible contributing factors to the description–experience gap. We find that payoff variability and memory limitations play only a small role in the emergence of the gap. In contrast, the presence of rare events and their representation as either natural frequencies in decisions from experience or single-event probabilities in decisions from description appear relevant for the gap. Copyright # 2009 John Wiley & Sons, Ltd. key words decisions; experience; information representation; rare events; risk and uncertainty; risky choice; sampling
As-If Behavioral Economics: Neoclassical Economics in Disguise?
"... Abstract: Behavioral economics confronts a problem when it argues for its scientific relevance based on claims of superior empirical realism while defending models that are almost surely wrong as descriptions of true psychological processes (e.g., prospect theory, hyperbolic discounting, and social ..."
Abstract
-
Cited by 17 (5 self)
- Add to MetaCart
Abstract: Behavioral economics confronts a problem when it argues for its scientific relevance based on claims of superior empirical realism while defending models that are almost surely wrong as descriptions of true psychological processes (e.g., prospect theory, hyperbolic discounting, and social preference utility functions). Behavioral economists frequently observe that constrained optimization of neoclassical objective functions rests on unrealistic assumptions, and proceed by adding new terms and parameters to that objective function and constraint set that require even more heroic assumptions about decision processes as arising from solving an even more complex constrained optimization problem. Empirical tests of these more highly parameterized models typically rest on comparisons of fit (something equivalent to R-squared) rather than genuine out-of-sample prediction. Very little empirical investigation seeking to uncover actual decision processes can be found in this allegedly empirically-motivated behavioral literature. For a research program that counts improved empirical realism among its primary goals, it is startling that behavioral economics appears, in many cases, indistinguishable from neoclassical economics in its reliance on as-if arguments to justify “psychological ” models that make no pretense of even attempting to describe the psychological processes that underlie
Hierarchical Bayesian parameter estimation for cumulative prospect theory
, 2011
"... a b s t r a c t Cumulative prospect theory (CPT Tversky & Kahneman, 1992) has provided one of the most influential accounts of how people make decisions under risk. CPT is a formal model with parameters that quantify psychological processes such as loss aversion, subjective values of gains and ..."
Abstract
-
Cited by 14 (2 self)
- Add to MetaCart
(Show Context)
a b s t r a c t Cumulative prospect theory (CPT Tversky & Kahneman, 1992) has provided one of the most influential accounts of how people make decisions under risk. CPT is a formal model with parameters that quantify psychological processes such as loss aversion, subjective values of gains and losses, and subjective probabilities. In practical applications of CPT, the model's parameters are usually estimated using a singleparticipant maximum likelihood approach. The present study shows the advantages of an alternative, hierarchical Bayesian parameter estimation procedure. Performance of the procedure is illustrated with a parameter recovery study and application to a real data set. The work reveals that without particular constraints on the parameter space, CPT can produce loss aversion without the parameter that has traditionally been associated with loss aversion. In general, the results illustrate that inferences about people's decision processes can crucially depend on the method used to estimate model parameters.
Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time
- Judgment and Decision Making
, 2011
"... and response time ..."
(Show Context)
I like what I know: Is recognition a noncompensatory determiner of consumer choice? Judgment and Decision
- Making, 5, 310–325. and Decision Making
, 2010
"... What is the role of recognition in consumer choice? The recognition heuristic (RH) proposes that in situations where recognition is correlated with a decision criterion, recognized objects will be chosen more often than unrecognized ones, regardless of any other relevant information available about ..."
Abstract
-
Cited by 13 (0 self)
- Add to MetaCart
(Show Context)
What is the role of recognition in consumer choice? The recognition heuristic (RH) proposes that in situations where recognition is correlated with a decision criterion, recognized objects will be chosen more often than unrecognized ones, regardless of any other relevant information available about the recognized object. Past research has investigated this non-compensatory decision heuristic in inference. Here we report two experiments on preference using a naturalistic consumer choice task. Results revealed that, although recognition was a powerful driver of preferences, it was used in a compensatory rather than a non-compensatory way. Specifically, additional information learned about recognized brand objects significantly affected choices. It appears that recognition is processed in a compensatory manner and combined with other attributes in preferential choice.
How to study cognitive decision algorithms: The case of the priority heuristic
- Judgment and Decision Making
, 2010
"... Although the priority heuristic (PH) is conceived as a cognitive-process model, some of its critical process assumptions remain to be tested. The PH makes very strong ordinal and quantitative assumptions about the strictly sequential, non-compensatory use of three cues in choices between lotteries: ..."
Abstract
-
Cited by 12 (0 self)
- Add to MetaCart
Although the priority heuristic (PH) is conceived as a cognitive-process model, some of its critical process assumptions remain to be tested. The PH makes very strong ordinal and quantitative assumptions about the strictly sequential, non-compensatory use of three cues in choices between lotteries: (1) the difference between worst outcomes, (2) the difference in worst-case probabilities, and (3) the best outcome that can be obtained. These aspects were manipulated orthogonally in the present experiment. No support was found for the PH. Although the main effect of the primary worst-outcome manipulation was significant, it came along with other effects that the PH excludes. A strong effect of the secondary manipulation of worst-outcome probabilities was not confined to small differences in worst-outcomes; it was actually stronger for large worst-outcome differences. Overall winning probabilities that the PH ignores exerted a systematic influence. The overall rate of choices correctly predicted by the PH was close to chance, although high inter-judge agreement reflected systematic responding. These findings raise fundamental questions about the theoretical status of heuristics as fixed modules.
On the Qualitative Comparison of Decisions Having Positive and Negative Features
"... Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Pro ..."
Abstract
-
Cited by 11 (4 self)
- Add to MetaCart
(Show Context)
Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, human agents often decide on the basis of an ordinal ranking of the pros and the cons, and by focusing on the most salient arguments. In other terms, the decision process is qualitative as well as bipolar. In this article, based on a bipolar extension of possibility theory, we define and axiomatically characterize several decision rules tailored for the joint handling of positive and negative arguments in an ordinal setting. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case, and consequently suffer from poor decisive power. More decisive rules that refine the former are also proposed. These refinements agree both with principles of efficiency and with the spirit of order-of-magnitude reasoning, that prevails in qualitative decision theory. The most refined decision rule uses leximin rankings of the pros and the cons, and the ideas of counting arguments of equal strength and cancelling pros by cons. It is shown to come down to a special case of Cumulative Prospect Theory, and to subsume the “Take the Best ” heuristic studied by cognitive psychologists. 1.
