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Gerd Gigerenzer - One of the best experts on this subject based on the ideXlab platform.
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How to Explain Behavior
Topics in cognitive science, 2019Co-Authors: Gerd GigerenzerAbstract:Unlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is called tools-to-theories: Psychologists' new tools for data analysis, such as computers and statistics, are turned into theories of mind. The second proposes as-if theories: Expected utility theory and Bayesian statistics are turned into theories of mind, describing an optimal solution of a problem but not its psychological process. The third studies the Adaptive Toolbox (formal models of heuristics) that describes mental processes in situations of uncertainty where an optimal solution is unknown. Depending on which framework researchers choose, they will model behavior in either situations of risk or of uncertainty, and construct models of cognitive processes or not. The frameworks also determine what questions are asked and what kind of data are generated. What all three frameworks have in common, however, is a clear preference for formal models rather than explanations by general dichotomies or mere verbal concepts. The frameworks have considerable potential to inform each other and to generate points of integration.
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The ecological rationality of situations: Behavior = f(Adaptive Toolbox, environment)
The Oxford Handbook of Psychological Situations, 2019Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:The study of situations involves asking how people behave in particular environmental settings, often in terms of their individual personality differences. The ecological rationality research program explains people’s behavior in terms of the specific decision-making tools they select and use from their mind’s Adaptive Toolbox when faced with specific types of environment structure. These two approaches can be integrated to provide a more precise mapping from features of situation structure to decision heuristics used and behavioral outcomes. This chapter presents three examples illustrating research on ecological rationality and its foundations, along with initial directions for incorporating it into an integrated situation theory.
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Environments That Make Us
2016Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:Traditional views of rationality posit general purpose decision mechanisms based on logic or optimiza tion. The study of ecological rationality focuses on un covering the "Adaptive Toolbox" of domain-specific simple heuristics that real, computationally bounded minds em ploy, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the
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The power of simplicity: A fast-and-frugal heuristics approach to performance science
Frontiers in psychology, 2015Co-Authors: Markus Raab, Gerd GigerenzerAbstract:Performance science is a fairly new multidisciplinary field that integrates performance domains such as sports, medicine, business, and the arts. To give its many branches a structure and its research a direction, it requires a theoretical framework. We demonstrate the applications of this framework with examples from sport and medicine. Because performance science deals mainly with situations of uncertainty rather than known risks, the needed framework can be provided by the fast-and-frugal heuristics approach. According to this approach, experts learn to rely on heuristics in an Adaptive way in order to make accurate decisions. We investigate the Adaptive use of heuristics in three ways: the descriptive study of the heuristics in the cognitive "Adaptive Toolbox;" the prescriptive study of their "ecological rationality," that is, the characterization of the situations in which a given heuristic works; and the engineering study of "intuitive design," that is, the design of transparent aids for making better decisions.
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Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource - Heuristics: Tools for an Uncertain World
Emerging Trends in the Social and Behavioral Sciences, 2015Co-Authors: Hansjörg Neth, Gerd GigerenzerAbstract:We distinguish between situations of risk, where all options, consequences, and probabilities are known, and situations of uncertainty, where they are not. Probability theory and statistics are the best tools for deciding under risk but not under uncertainty, which characterizes most relevant problems that humans have to solve. Uncertainty requires simple heuristics that are robust rather than optimal. We propose to think of the mind as an Adaptive Toolbox and introduce the descriptive study of heuristics, their building blocks, and the core capacities they exploit. The question of which heuristic to select for which class of problems is the topic of the normative study of ecological rationality. We discuss earlier views on the nature of heuristics that maintained that heuristics are always less accurate because they ignore information and demand less effort. Contrary to this accuracy–effort trade-off view, heuristics can lead to more accurate inferences—under uncertainty—than strategies that use more information and computation. The study of heuristics opens up a new perspective on the nature of both cognition and rationality. In a world of uncertainty, Homo sapiens might well be called Homo heuristicus. Keywords: accuracy–effort trade-off; Adaptive Toolbox; ecological rationality; heuristics; satisficing; Herbert A. Simon; uncertainty
Markus Raab - One of the best experts on this subject based on the ideXlab platform.
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The power of simplicity: A fast-and-frugal heuristics approach to performance science
Frontiers in psychology, 2015Co-Authors: Markus Raab, Gerd GigerenzerAbstract:Performance science is a fairly new multidisciplinary field that integrates performance domains such as sports, medicine, business, and the arts. To give its many branches a structure and its research a direction, it requires a theoretical framework. We demonstrate the applications of this framework with examples from sport and medicine. Because performance science deals mainly with situations of uncertainty rather than known risks, the needed framework can be provided by the fast-and-frugal heuristics approach. According to this approach, experts learn to rely on heuristics in an Adaptive way in order to make accurate decisions. We investigate the Adaptive use of heuristics in three ways: the descriptive study of the heuristics in the cognitive "Adaptive Toolbox;" the prescriptive study of their "ecological rationality," that is, the characterization of the situations in which a given heuristic works; and the engineering study of "intuitive design," that is, the design of transparent aids for making better decisions.
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An Adaptive Toolbox approach to the route to expertise in sport.
Frontiers in psychology, 2014Co-Authors: Rita F. De Oliveira, Babett H. Lobinger, Markus RaabAbstract:Expertise is characterized by fast decision-making which is highly Adaptive to new situations. Here we propose that athletes use a Toolbox of heuristics which they develop on their route to expertise. The development of heuristics occurs within the context of the athletes' natural abilities, past experiences, developed skills, and situational context, but does not pertain to any of these factors separately. This is a novel approach because it integrates separate factors into a comprehensive heuristic description. The novelty of this approach lies within the integration of separate factors determining expertise into a comprehensive heuristic description. It is our contention that talent identification methods and talent development models should therefore be geared toward the assessment and development of specific heuristics. Specifically, in addition to identifying and developing separate natural abilities and skills as per usual, heuristics should be identified and developed. The application of heuristics to talent and expertise models can bring the field one step away from dichotomized models of nature and nurture toward a comprehensive approach to the route to expertise.
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Simple heuristics in sports
International Review of Sport and Exercise Psychology, 2012Co-Authors: Markus RaabAbstract:How do people make decisions under conditions of limited knowledge, time, and cognitive capacity in real-life situations such as sports? In this review I will introduce the concept of simple heuristics – rules of thumb that are based on the building blocks of decision making: how to search for information, stop information search, and decide quickly and accurately – and how they can help us understand the decisions made by athletes, coaches, referees, managers, and fans in tasks involving high uncertainty, such as predicting tournament outcomes, allocating balls to teammates, or determining when to buy or sell a talented player. I will present an ‘Adaptive Toolbox’ of such heuristics, that is, a collection of strategies that work effectively in specific environments. Additional building blocks will be added to explain motor behavior itself, which is central to many sport applications. Finally, principles for studying the use of simple heuristics by people involved in sports will be presented to guide futu...
Peter M. Todd - One of the best experts on this subject based on the ideXlab platform.
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The ecological rationality of situations: Behavior = f(Adaptive Toolbox, environment)
The Oxford Handbook of Psychological Situations, 2019Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:The study of situations involves asking how people behave in particular environmental settings, often in terms of their individual personality differences. The ecological rationality research program explains people’s behavior in terms of the specific decision-making tools they select and use from their mind’s Adaptive Toolbox when faced with specific types of environment structure. These two approaches can be integrated to provide a more precise mapping from features of situation structure to decision heuristics used and behavioral outcomes. This chapter presents three examples illustrating research on ecological rationality and its foundations, along with initial directions for incorporating it into an integrated situation theory.
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Environments That Make Us
2016Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:Traditional views of rationality posit general purpose decision mechanisms based on logic or optimiza tion. The study of ecological rationality focuses on un covering the "Adaptive Toolbox" of domain-specific simple heuristics that real, computationally bounded minds em ploy, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the
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Environments That Make Us Smart Rationality
2007Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:Traditional views of rationality posit general- purpose decision mechanisms based on logic or optimiza- tion. The study of ecological rationality focuses on un- covering the ''Adaptive Toolbox'' of domain-specific simple heuristics that real, computationally bounded minds em- ploy,andexplaininghowtheseheuristicsproduceaccurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.
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Environments That Make Us Smart Ecological Rationality
Current Directions in Psychological Science, 2007Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the “Adaptive Toolbox” of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.
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precis of simple heuristics that make us smart
Behavioral and Brain Sciences, 2000Co-Authors: Peter M. Todd, Gerd GigerenzerAbstract:How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattain- able luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics - simple rules in the mind's Adaptive Toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this precis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generaliz- ing to new data - that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.
Ben R. Newell - One of the best experts on this subject based on the ideXlab platform.
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Empirical comparison of the adjustable spanner and the Adaptive Toolbox models of choice.
Journal of experimental psychology. Learning memory and cognition, 2018Co-Authors: Antonia Krefeld-schwalb, Ben R. Newell, Chris Donkin, Benjamin ScheibehenneAbstract:Past research indicates that individuals respond Adaptively to contextual factors in multiattribute choice tasks. Yet it remains unclear how this adaptation is cognitively governed. In this article, empirically testable implementations of two prominent competing theoretical frameworks are developed and compared across two multiattribute choice experiments: the Adaptive Toolbox framework assuming discrete choice strategies and the adjustable spanner framework assuming one comprehensive Adaptive strategy. Results from two experiments indicate that in the environments we tested, in which all cue information was presented openly, the Toolbox makes better predictions than the adjustable spanner both in- and out-of-sample. Follow-up simulation studies indicate that it is difficult to discriminate the models based on choice outcomes alone but allowed the identification of a small subset of cases where the predictions of both models diverged. Our results suggest that people adapt their decision strategies by flexibly switching between using as little information as possible and use of all of the available information. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Challenging some common beliefs: Empirical work within the Adaptive Toolbox metaphor
Judgment and Decision Making, 2008Co-Authors: Arndt Bröder, Ben R. NewellAbstract:The authors review their own empirical work inspired by the Adaptive Toolbox metaphor. The review examines factors influencing strategy selection and execution in multi-attribute inference tasks (e.g., information costs, time pressure, memory retrieval, dynamic environments, stimulus formats, intelligence). An emergent theme is the re-evaluation of contingency model claims about the elevated cognitive costs of compensatory in comparison with non-compensatory strategies. Contrary to common assertions about the impact of cognitive complexity, the empirical data suggest that manipulated variables exert their influence at the meta-level of deciding how to decide (i.e., which strategy to select) rather than at the level of strategy execution. An alternative conceptualisation of strategy selection, namely threshold adjustment in an evidence accumulation model, is also discussed and the difficulty in distinguishing empirically between these metaphors is acknowledged.
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Adjusting the Spanner: Testing an Evidence Accumulation Model of Decision Making
2007Co-Authors: Ben R. Newell, Patrick Collins, Micheal D. LeeAbstract:Adjusting the Spanner: Testing an Evidence Accumulation Model of Decision Making Ben R. Newell (ben.newell@unsw.edu.au) and Patrick Collins (patrickc@unsw.edu.au) School of Psychology, University of New South Wales Sydney, NSW, 2052, Australia Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, University of California, Irvine Irvine, CA, 92697-5100 Abstract An experiment examined two aspects of performance in a multi-attribute inference task: i) the effect of stimulus pre- sentation format (image or text) on the adoption of deci- sion strategies; and ii) the ability of an evidence accumula- tion model, which unifies take-the-best (TTB) and rational (RAT) strategies, to explain participants’ judgments. Presen- tation format had no significant effect on strategy adoption at a group level. Individual level analysis revealed large intra- participant consistency, including some participants who con- sistently changed the amount of evidence considered for a de- cision as a function of format, but wide inter-participant differ- ences. A unified model captured these individual differences and was preferred to the TTB or RAT models on the basis of the minimum description length model selection criterion. Keywords: Decision-making; Multi-cue inference; Heuris- tics; Take the best; Sequential sampling Log−Odds Evidence A B The idea that decision makers have at their disposal a va- riety of ‘tools’ or strategies that can be selected for particu- lar tasks has proved popular in the literature (Gigerenzer & Todd, 1999; Payne, Bettman, & Johnson, 1990; Rieskamp & Otto, 2006). The ‘Adaptive Toolbox’ metaphor proposed by Gigerenzer and colleagues is a good example of such an ap- proach. Proponents suggest that decision makers have access to a “collection of specialized cognitive mechanisms that evo- lution has built into the mind for specific domains of inference and reasoning” (Gigerenzer & Todd, 1999, p. 30). One of the key mechanisms or heuristics in this Toolbox is the ‘take-the- best’ algorithm (TTB), a heuristic for choosing between two alternatives. The defining feature of TTB is that it terminates information search once a single cue that discriminates be- tween alternatives has been discovered. In this sense, TTB differs markedly from ‘rational’ decision models that advo- cate complete information search and optimal weighting of information. Despite the impressive success of TTB in simulation stud- ies, such as its ability to perform well against computation- ally intensive models (Gigerenzer & Goldstein, 1996), em- pirical studies seeking evidence that participants adopt TTB are more equivocal (Br¨oder, 2000; Newell & Shanks, 2003; Newell, Weston, & Shanks, 2003). In many experiments the results suggest that some people make choices consistent with TTB some of the time but a significant proportion of partici- pants adopt strategies that violate all or some of TTB’s rules, especially the ‘single discriminating cue’ stopping rule. Start Cue 1 Cue 2 Cue 3 Cue 4 Cue 5 Cue 6 Cue 7 Cue 8 Cue 9 Cue Sampling Figure 1: The unified sequential sampling model, showing the accumulated evidence as nine cues are sampled in validity order, and TTB-consistent (top) and RAT-consistent (bottom) decision thresholds. In an attempt to account for this wide individual variabil- ity Newell (2005) suggested an alternative metaphor—an ad- justable spanner (or wrench)—in which the width of the jaws represents the amount of evidence a person accumulates be- fore making a decision. The important feature of an evidence accumulation model for two-alternative choice problems is that it can mimic the performance of TTB’s stopping rule, or a strategy that incorporates more evidence, by adjusting the evidence required before a decision is made. Thus, one way of explaining individual variability is to suggest that all par- ticipants use an evidence-accumulation model but that some require greater amounts of evidence than others before mak- ing their decisions. Another appealing aspect of this model is that a ‘single tool’ circumvents the thorny issue of tool selec- tion (cf. Rieskamp & Otto, 2006). Lee and Cummins (2004) presented a formal instantiation of such an evidence accumulation model, which they pro- posed as a unification of TTB and rational models. As shown
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Re-visions of rationality?
Trends in cognitive sciences, 2005Co-Authors: Ben R. NewellAbstract:The appeal of simple algorithms that take account of both the constraints of human cognitive capacity and the structure of environments has been an enduring theme in cognitive science. A novel version of such a boundedly rational perspective views the mind as containing an 'Adaptive Toolbox' of specialized cognitive heuristics suited to different problems. Although intuitively appealing, when this version was proposed, empirical evidence for the use of such heuristics was scant. I argue that in the light of empirical studies carried out since then, it is time this 'vision of rationality' was revised. An alternative view based on integrative models rather than collections of heuristics is proposed.
X. T. Wang - One of the best experts on this subject based on the ideXlab platform.
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From Simon's scissors for rationality to ABC's Adaptive Toolbox
Behavioral and Brain Sciences, 2000Co-Authors: X. T. WangAbstract:The smartness of simple heuristics depends upon their fit to the structure of task environments. Being fast and frugal becomes psychologically demanding when a decision goal is bounded by the risk distribution in a task environment. The lack of clear goals and prioritized cues in a decision problem may lead to the use of simple but irrational heuristics. Future research should focus more on how people use and integrate simple heuristics in the face of goal conflict under risk.