Evaluation of the Priority Heuristic as a Descriptive Model of Risky Decision Making: Comment on Brandstätter, Gigerenzer, and Hertwig (2008)

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Abstract

E. Brandstätter, G. Gigerenzer, and R. Hertwig (2006) contended that their priority heuristic, a type of lexicographic semiorder model, is more accurate than cumulative prospect theory (CPT) or transfer of attention exchange (TAX) models in describing risky decisions. However, there are 4 problems with their argument. First, their heuristic is not descriptive of certain data that they did not review. Second, their analysis relied on a global index of fit, percentage of correct predictions of the modal choice. Such analyses can lead to wrong conclusions when parameters are not properly estimated from the data. When parameters are estimated from the data, CPT and TAX fit the D. Kahneman and A. Tversky (1979) data perfectly. Reanalysis shows that TAX and CPT do as well as the priority heuristic for 2 of the data sets reviewed and outperform the priority heuristic for the other 3. Third, when 2 of these sets of data are reexamined, the priority heuristic is seen to make systematic violations. Fourth, new critical implications have been devised for testing the family of lexicographic semiorders including the priority heuristic; new results with these critical tests show systematic evidence against lexicographic semiorder models.

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Evaluation of the Priority Heuristic as a Descriptive Model of Risky Decision Making: Comment on Brandstätter, Gigerenzer, and Hertwig (2008)

See on Scoop.it – Bounded Rationality and Beyond
Abstract

E. Brandstätter, G. Gigerenzer, and R. Hertwig (2006) contended that their priority heuristic, a type of lexicographic semiorder model, is more accurate than cumulative prospect theory (CPT) or transfer of attention exchange (TAX) models in describing risky decisions. However, there are 4 problems with their argument. First, their heuristic is not descriptive of certain data that they did not review. Second, their analysis relied on a global index of fit, percentage of correct predictions of the modal choice. Such analyses can lead to wrong conclusions when parameters are not properly estimated from the data. When parameters are estimated from the data, CPT and TAX fit the D. Kahneman and A. Tversky (1979) data perfectly. Reanalysis shows that TAX and CPT do as well as the priority heuristic for 2 of the data sets reviewed and outperform the priority heuristic for the other 3. Third, when 2 of these sets of data are reexamined, the priority heuristic is seen to make systematic violations. Fourth, new critical implications have been devised for testing the family of lexicographic semiorders including the priority heuristic; new results with these critical tests show systematic evidence against lexicographic semiorder models.

See on citeseerx.ist.psu.edu

Homo Heuristicus: Why Biased Minds Make Better Inferences (2008)

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Abstract

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’ ’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

See on citeseerx.ist.psu.edu

Homo Heuristicus: Why Biased Minds Make Better Inferences (2008)

See on Scoop.it – Bounded Rationality and Beyond
Abstract

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; © an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’ ’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

See on citeseerx.ist.psu.edu

How Human Rights Will Change When Everyone Can Upgrade Their Brains

See on Scoop.it – It Comes Undone-Think About It

In a decade, cognitive enhancement may have gone mainstream. Pills can already help you stay up longer, bring more focus to your work, and who knows what else. But what might sound good on an individual level could create societal disruptions, or so Palo Alto think-tank the Institute for the Future proposes in its latest Ten-Year Forecasts. 

As a result, the Institute has proposed that the world’s citizens need a “Magna Cortica." 

"Magna Cortica is the argument that we need to have a guidebook for both the design spec and ethical rules around the increasing power and diversity of cognitive augmentation,” said IFTF distinguished fellow, Jamais Cascio. “There are a lot of pharmaceutical and digital tools that have been able to boost our ability to think. Adderall, Provigil, and extra-cortical technologies.”

Eli Levine’s insight:

There already is one though.  It’s already been written by the universe.

That which does not abide by the basic laws of nature, that which does not perceive nature accurately and work with nature appropriately and acceptably as it changes, dies.

It’s that simple.

Anything else is just pabulum that we invent.  It’s got nothing to do with us, our chances at survival, nothing.

Enjoy!

Think about it.

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Decision Making: Factors that Influence Decision Making, Heuristics Used, and Decision Outcomes

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Every day, people are inundated with decisions, big and small. Understanding how people arrive at their choices is an area of cognitive psychology that has received attention. Theories have been generated to explain how people make decisions, and what types of factors influence decision making in the present and future. In addition, heuristics have been researched to understand the decision making process.

 

Several factors influence decision making. These factors, including past experience (Juliusson, Karlsson, & Gӓrling, 2005), cognitive biases (Stanovich & West, 2008), age and individual differences (Bruin, Parker, & Fischoff, 2007), belief in personal relevance (Acevedo, & Krueger, 2004), and an escalation of commitment, influence what choices people make. Understanding the factors that influence decision making process is important to understanding what decisions are made. That is, the factors that influence the process may impact the outcomes.

 

Heuristics serve as a framework in which satisfactory decisions are made quickly and with ease (Shah & Oppenheimer, 2008). Many types of heuristics have been developed to explain the decision making process; essentially, individuals work to reduce the effort they need to expend in making decisions and heuristics offer individuals a general guide to follow, thereby reducing the effort they must disburse. Together, heuristics and factors influencing decision making are a significant aspect of critical thinking (West, Toplak, & Stanovich, 2008). There is some indication that this can be taught, which benefits those learning how to make appropriate and the best decisions in various situations (Nokes &Hacker, 2007).

See on www.studentpulse.com

Decision Making: Factors that Influence Decision Making, Heuristics Used, and Decision Outcomes

See on Scoop.it – Bounded Rationality and Beyond

Every day, people are inundated with decisions, big and small. Understanding how people arrive at their choices is an area of cognitive psychology that has received attention. Theories have been generated to explain how people make decisions, and what types of factors influence decision making in the present and future. In addition, heuristics have been researched to understand the decision making process.

Several factors influence decision making. These factors, including past experience (Juliusson, Karlsson, & Gӓrling, 2005), cognitive biases (Stanovich & West, 2008), age and individual differences (Bruin, Parker, & Fischoff, 2007), belief in personal relevance (Acevedo, & Krueger, 2004), and an escalation of commitment, influence what choices people make. Understanding the factors that influence decision making process is important to understanding what decisions are made. That is, the factors that influence the process may impact the outcomes.

Heuristics serve as a framework in which satisfactory decisions are made quickly and with ease (Shah & Oppenheimer, 2008). Many types of heuristics have been developed to explain the decision making process; essentially, individuals work to reduce the effort they need to expend in making decisions and heuristics offer individuals a general guide to follow, thereby reducing the effort they must disburse. Together, heuristics and factors influencing decision making are a significant aspect of critical thinking (West, Toplak, & Stanovich, 2008). There is some indication that this can be taught, which benefits those learning how to make appropriate and the best decisions in various situations (Nokes &Hacker, 2007).

See on studentpulse.com

How to make cognitive illusions disappear: Beyond “heuristics and biases

See on Scoop.it – Bounded Rationality and Beyond
Abstract

Abstract. Most so-called “errors ” in probabilistic reasoning are in fact not violations of probability theory. Examples of such “errors ” include overconfidence bias, conjunction fallacy, and base-rate neglect. Researchers have relied on a very narrow normative view, and have ignored conceptual distinctions—for example, single case versus relative frequency—fundamental to probability theory. By recognizing and using these distinctions, however, we can make apparently stable “errors ” disappear, reappear, or even invert. I suggest what a reformed understanding of judgments under uncertainty might look like. Two Revolutions Social psychology was transformed by the “cognitive revolution. ” Cognitive imperialism has been both praised (e.g., Strack, 1988) and lamented (e.g., Graumann, 1988). But a second revolution has transformed most of the sciences so fundamentally that it is now hard to see that it could have been different before. It has made concepts such as probability, chance, and uncertainty indispensable for understanding nature, society, and the mind. This sweeping conceptual change has been called the “probabilistic revolution ” (Gigerenzer et al., 1989; Krüger, Daston, & Heidelberger, 1987; Krüger, Gigerenzer, & Morgan, 1987). The probabilistic revolution differs from the cognitive revolution in its genuine novelty and its interdisciplinary scope. Statistical mechanics, Mendelian genetics,

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How to make cognitive illusions disappear: Beyond “heuristics and biases

See on Scoop.it – Bounded Rationality and Beyond
Abstract

Abstract. Most so-called “errors ” in probabilistic reasoning are in fact not violations of probability theory. Examples of such “errors ” include overconfidence bias, conjunction fallacy, and base-rate neglect. Researchers have relied on a very narrow normative view, and have ignored conceptual distinctions—for example, single case versus relative frequency—fundamental to probability theory. By recognizing and using these distinctions, however, we can make apparently stable “errors ” disappear, reappear, or even invert. I suggest what a reformed understanding of judgments under uncertainty might look like. Two Revolutions Social psychology was transformed by the “cognitive revolution. ” Cognitive imperialism has been both praised (e.g., Strack, 1988) and lamented (e.g., Graumann, 1988). But a second revolution has transformed most of the sciences so fundamentally that it is now hard to see that it could have been different before. It has made concepts such as probability, chance, and uncertainty indispensable for understanding nature, society, and the mind. This sweeping conceptual change has been called the “probabilistic revolution ” (Gigerenzer et al., 1989; Krüger, Daston, & Heidelberger, 1987; Krüger, Gigerenzer, & Morgan, 1987). The probabilistic revolution differs from the cognitive revolution in its genuine novelty and its interdisciplinary scope. Statistical mechanics, Mendelian genetics,

See on library.mpib-berlin.mpg.de

On Narrow Norms and Vague Heuristics A Reply to Kahneman and Tversky

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This reply clarifies what G. Gigerenzers (e.g., 1991. 1994; Gigerenzer & Murray, 1987) critique of the heuristics-and-biases approach to statistical reasoning is and is not about. At issue is the imposition of unnecessarily narrow norms of sound reasoning that are used to diagnose so-called cognitive illusions and the continuing reliance on vague heuristics that explain everything and nothing. D. Kahneman and A. Tversky (1996) incorrectly asserted that Gigerenzer simply claimed that frequency formats make all cognitive illusions disappear. In contrast, Gigerenzer has proposed and tested models that actually predict when frequency judgments are valid and when they are not. The issue is not whether or not. or how often, cognitive illusions disappear. The focus should be rather the construction of detailed models of cognitive processes that explain when and why they disappear.

A postscript responds to Kahneman and Tversky’s (1996) postscript.

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