Gerd Gigerenzer
- Published in print:
- 2002
- Published Online:
- October 2011
- ISBN:
- 9780195153729
- eISBN:
- 9780199849222
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195153729.003.0006
- Subject:
- Philosophy, General
This chapter defines the concepts of natural sampling, natural frequencies, and reports experimental evidence for the impact of various external representations on statistical thinking. The mental ...
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This chapter defines the concepts of natural sampling, natural frequencies, and reports experimental evidence for the impact of various external representations on statistical thinking. The mental strategies or shortcuts people use, not only their numerical estimates of risks, turn out to be a function of the external representation of numbers we choose. This chapter provides a theoretical framework that specifies why frequency formats should improve Bayesian reasoning and presents two studies that test whether they do. Its goal is to lead research on Bayesian inference out of the present conceptual cul-de-sac and to shift the focus from human errors to human engineering: how to help people reason the Bayesian way without even teaching them.Less
This chapter defines the concepts of natural sampling, natural frequencies, and reports experimental evidence for the impact of various external representations on statistical thinking. The mental strategies or shortcuts people use, not only their numerical estimates of risks, turn out to be a function of the external representation of numbers we choose. This chapter provides a theoretical framework that specifies why frequency formats should improve Bayesian reasoning and presents two studies that test whether they do. Its goal is to lead research on Bayesian inference out of the present conceptual cul-de-sac and to shift the focus from human errors to human engineering: how to help people reason the Bayesian way without even teaching them.
Itzhak Gilboa, Larry Samuelson, and David Schmeidler
- Published in print:
- 2015
- Published Online:
- May 2015
- ISBN:
- 9780198738022
- eISBN:
- 9780191801419
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198738022.003.0004
- Subject:
- Economics and Finance, Econometrics
This chapter presents a formal model that captures both case‐based and rule‐based reasoning. The model is general enough to describe Bayesian reasoning, which may be viewed as an extreme example of ...
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This chapter presents a formal model that captures both case‐based and rule‐based reasoning. The model is general enough to describe Bayesian reasoning, which may be viewed as an extreme example of rule‐based reasoning. It suggests conditions under which Bayesian reasoning will give way to other modes of reasoning, and alternative conditions under which the opposite conclusion holds. It discusses how probabilistic reasoning may emerge periodically, with other modes of reasoning used between the regimes of different probabilistic models.Less
This chapter presents a formal model that captures both case‐based and rule‐based reasoning. The model is general enough to describe Bayesian reasoning, which may be viewed as an extreme example of rule‐based reasoning. It suggests conditions under which Bayesian reasoning will give way to other modes of reasoning, and alternative conditions under which the opposite conclusion holds. It discusses how probabilistic reasoning may emerge periodically, with other modes of reasoning used between the regimes of different probabilistic models.
Craig R. M. McKenzie and Valerie M. Chase
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780195315448
- eISBN:
- 9780199932429
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195315448.003.0089
- Subject:
- Psychology, Cognitive Psychology, Human-Technology Interaction
This chapter reviews evidence showing that people are remarkably sensitive to the rarity of events when making inferences about them. Indeed, people are so attuned to event rarity that their implicit ...
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This chapter reviews evidence showing that people are remarkably sensitive to the rarity of events when making inferences about them. Indeed, people are so attuned to event rarity that their implicit assumptions about rarity guide their thinking even in laboratory tasks where experimenters have implicitly assumed that rarity would not matter. Lack of awareness of this problem has led many experimenters to misinterpret people’s adaptive responses as irrational. Indeed, focusing on data that are rare leads people to behave in a qualitatively Bayesian manner. These points are illustrated using tasks that involve assessing the covariation between variables, evaluating hypotheses after passively receiving data, and actively searching for data to test hypotheses. Participants’ sensitivity to, and assumptions about, rarity have important implications for understanding lay inference.Less
This chapter reviews evidence showing that people are remarkably sensitive to the rarity of events when making inferences about them. Indeed, people are so attuned to event rarity that their implicit assumptions about rarity guide their thinking even in laboratory tasks where experimenters have implicitly assumed that rarity would not matter. Lack of awareness of this problem has led many experimenters to misinterpret people’s adaptive responses as irrational. Indeed, focusing on data that are rare leads people to behave in a qualitatively Bayesian manner. These points are illustrated using tasks that involve assessing the covariation between variables, evaluating hypotheses after passively receiving data, and actively searching for data to test hypotheses. Participants’ sensitivity to, and assumptions about, rarity have important implications for understanding lay inference.
Gerd Gigerenzer
- Published in print:
- 2002
- Published Online:
- October 2011
- ISBN:
- 9780195153729
- eISBN:
- 9780199849222
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195153729.003.0012
- Subject:
- Philosophy, General
Cognitive illusions have been linked to perceptual illusions, suggesting that they are “inevitable illusions”. This chapter criticizes the narrow norms that make humans look irrational and shows how ...
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Cognitive illusions have been linked to perceptual illusions, suggesting that they are “inevitable illusions”. This chapter criticizes the narrow norms that make humans look irrational and shows how to make inevitable illusions “evitable”. These so-called cognitive illusions largely disappear when one pays attention to conceptual distinctions. Cognitive illusions largely disappear due to the following: polysemy — not all probabilities are mathematical probabilities; a mathematical probability refers to a reference class, which may differ depending on the task; and natural frequencies facilitate Bayesian reasoning.Less
Cognitive illusions have been linked to perceptual illusions, suggesting that they are “inevitable illusions”. This chapter criticizes the narrow norms that make humans look irrational and shows how to make inevitable illusions “evitable”. These so-called cognitive illusions largely disappear when one pays attention to conceptual distinctions. Cognitive illusions largely disappear due to the following: polysemy — not all probabilities are mathematical probabilities; a mathematical probability refers to a reference class, which may differ depending on the task; and natural frequencies facilitate Bayesian reasoning.
Jan Sprenger and Stephan Hartmann
- Published in print:
- 2019
- Published Online:
- October 2019
- ISBN:
- 9780199672110
- eISBN:
- 9780191881671
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199672110.003.0011
- Subject:
- Philosophy, Philosophy of Science
Subjective Bayesianism is often criticized for a lack of objectivity: (i) it opens the door to the influence of values and biases, (ii) evidence judgments can vary substantially between scientists, ...
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Subjective Bayesianism is often criticized for a lack of objectivity: (i) it opens the door to the influence of values and biases, (ii) evidence judgments can vary substantially between scientists, (iii) it is not suited for informing policy decisions. We rebut these concerns by bridging the debates on scientific objectivity and Bayesian inference in statistics. First, we show that the above concerns arise equally for frequentist statistical inference. Second, we argue that the involved senses of objectivity are epistemically inert. Third, we show that Subjective Bayesianism promotes other, epistemically relevant senses of scientific objectivity—most notably by increasing the transparency of scientific reasoning.Less
Subjective Bayesianism is often criticized for a lack of objectivity: (i) it opens the door to the influence of values and biases, (ii) evidence judgments can vary substantially between scientists, (iii) it is not suited for informing policy decisions. We rebut these concerns by bridging the debates on scientific objectivity and Bayesian inference in statistics. First, we show that the above concerns arise equally for frequentist statistical inference. Second, we argue that the involved senses of objectivity are epistemically inert. Third, we show that Subjective Bayesianism promotes other, epistemically relevant senses of scientific objectivity—most notably by increasing the transparency of scientific reasoning.
Itzhak Gilboa, Larry Samuelson, and David Schmeidler
- Published in print:
- 2015
- Published Online:
- May 2015
- ISBN:
- 9780198738022
- eISBN:
- 9780191801419
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198738022.001.0001
- Subject:
- Economics and Finance, Econometrics
The book describes formal models of reasoning that are aimed at capturing the way that economic agents and decision makers in general think about their environment and make predictions based on their ...
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The book describes formal models of reasoning that are aimed at capturing the way that economic agents and decision makers in general think about their environment and make predictions based on their past experience. The focus is on analogies (case-based reasoning) and general theories (rule-based reasoning), and on the interaction between them, as well as between them and Bayesian reasoning. A unified approach allows us to study the dynamics of inductive reasoning in terms of the mode of reasoning that is used to generate predictions.Less
The book describes formal models of reasoning that are aimed at capturing the way that economic agents and decision makers in general think about their environment and make predictions based on their past experience. The focus is on analogies (case-based reasoning) and general theories (rule-based reasoning), and on the interaction between them, as well as between them and Bayesian reasoning. A unified approach allows us to study the dynamics of inductive reasoning in terms of the mode of reasoning that is used to generate predictions.
Marcel Boumans
- Published in print:
- 2015
- Published Online:
- May 2015
- ISBN:
- 9780199388288
- eISBN:
- 9780199388318
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199388288.003.0005
- Subject:
- Philosophy, Philosophy of Science
Judgment is needed as an additional source of knowledge in measurement. This chapter aims to clarify what kind of judgment is referred to and what it is not. Judgment is defined in the Kantian sense; ...
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Judgment is needed as an additional source of knowledge in measurement. This chapter aims to clarify what kind of judgment is referred to and what it is not. Judgment is defined in the Kantian sense; it presupposes intuition and imagination, and is not a strict deduction of particulars from universals. It requires expertise, that is, real experience with the particulars to which it is applied. But judgments in this sense are subjective. To eliminate this subjectivity, also called “bias,” accounts are developed that ensure that judgments are rational, for example, “unbiased.” A rational judgment, however, is not a Kantian judgment; it is the optimal solution of a model that represents a real-life judgment problem. But a real-life problem can be modeled in various different ways, each with a different rational solution. In the chapter’s appendix it is shown that correct Bayesian deduction can be biased as defined in mathematical statistics.Less
Judgment is needed as an additional source of knowledge in measurement. This chapter aims to clarify what kind of judgment is referred to and what it is not. Judgment is defined in the Kantian sense; it presupposes intuition and imagination, and is not a strict deduction of particulars from universals. It requires expertise, that is, real experience with the particulars to which it is applied. But judgments in this sense are subjective. To eliminate this subjectivity, also called “bias,” accounts are developed that ensure that judgments are rational, for example, “unbiased.” A rational judgment, however, is not a Kantian judgment; it is the optimal solution of a model that represents a real-life judgment problem. But a real-life problem can be modeled in various different ways, each with a different rational solution. In the chapter’s appendix it is shown that correct Bayesian deduction can be biased as defined in mathematical statistics.
Paul Kockelman
- Published in print:
- 2017
- Published Online:
- July 2017
- ISBN:
- 9780190636531
- eISBN:
- 9780190636562
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190636531.003.0007
- Subject:
- Linguistics, Sociolinguistics / Anthropological Linguistics
This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind ...
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This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind of sieve insofar as they readily exhibit key features of sieving devices in general, and algorithmic sieving in particular. More broadly, it describes the relation between ontology (assumptions that drive interpretations) and inference (interpretations that alter assumptions) as it plays out in the classification and transformation of identities, types, or kinds. Focusing on the unstable processes whereby identifying algorithms, identified types, and evasive transformations are dynamically coupled over time, it also theorizes various kinds of ontological inertia and highlights various kinds of algorithmic ineffability. Finally, it shows how similar issues underlie a much wider range of processes, such as the Turing Test, Bayesian reasoning, and machine learning more generally.Less
This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind of sieve insofar as they readily exhibit key features of sieving devices in general, and algorithmic sieving in particular. More broadly, it describes the relation between ontology (assumptions that drive interpretations) and inference (interpretations that alter assumptions) as it plays out in the classification and transformation of identities, types, or kinds. Focusing on the unstable processes whereby identifying algorithms, identified types, and evasive transformations are dynamically coupled over time, it also theorizes various kinds of ontological inertia and highlights various kinds of algorithmic ineffability. Finally, it shows how similar issues underlie a much wider range of processes, such as the Turing Test, Bayesian reasoning, and machine learning more generally.
