Uncertainties in Counterfactuals

Uncertainties in Counterfactuals PDF Author: Qiyuan Zhang
Publisher:
ISBN:
Category : Counterfactuals (Logic)
Languages : en
Pages :

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Book Description
Counterfactual representations refer to people's imaginations about the alternative possibilities to the actual world (i.e., what might have been). The present thesis embraces the notion that the psychological impacts of those representations are dictated by the degree of certainty or uncertainty people assign to them, namely, their counterfactual probability judgments (i.e., 'How likely could things have been different?'). The thesis reports six experiments investigating the determinants as well as the emotional consequences of counterfactual probability judgments. Experiments 1, 2 and 3 found that both people's conditional and unconditional counterfactual probability judgments were heightened when a past outcome was physically or numerically proximate to its alternative. Experiments 4 and 5 found that people's counterfactual probability judgments were not only affected by the static proximity cue but also by its dynamic variations. When outcome proximity was equal, the shrinking physical distance towards a counterfactual outcome heightened one's subjective likelihood of that outcome, compared to if the distance stayed constant. Experiment 6 found that the effect of 'shrinking distance' could manifest itself as an antecedent temporal order effect on people's counterfactual probability judgments. That is, a counterfactual outcome was deemed more likely if the factual outcome was preceded by a decisive event that occurred latter in the causal sequence rather than earlier. These results are broadly consistent with the theory of the simulation heuristic which posits that subjective probabilities are estimated by assessing the ease with which a relevant scenario can be mentally constructed. The emotional consequences of counterfactual probability judgments were investigated within the theoretical framework of the Reflective and Evaluative Model of Comparative Thinking (REM). The evidence from Experiments 2, 3, 4 and 5 suggests that the effect of counterfactual probability judgments on emotions are contingent on people's temporal perspective - affective assimilation will be enhanced when future possibility is present (i.e., the outcome is indecisive or changeable) which encourages a reflective simulation while affective contrast will be enhanced when future possibility is absent (i.e., the outcome is decisive or unchangeable) which encourages an evaluative simulation. These findings suggest that the psychological impact of counterfactual thinking should be discussed in terms of a three-way interaction between its direction (upward or downward), probability (low or high), and simulation mode (reflection or evaluation).

Uncertainties in Counterfactuals

Uncertainties in Counterfactuals PDF Author: Qiyuan Zhang
Publisher:
ISBN:
Category : Counterfactuals (Logic)
Languages : en
Pages :

Get Book Here

Book Description
Counterfactual representations refer to people's imaginations about the alternative possibilities to the actual world (i.e., what might have been). The present thesis embraces the notion that the psychological impacts of those representations are dictated by the degree of certainty or uncertainty people assign to them, namely, their counterfactual probability judgments (i.e., 'How likely could things have been different?'). The thesis reports six experiments investigating the determinants as well as the emotional consequences of counterfactual probability judgments. Experiments 1, 2 and 3 found that both people's conditional and unconditional counterfactual probability judgments were heightened when a past outcome was physically or numerically proximate to its alternative. Experiments 4 and 5 found that people's counterfactual probability judgments were not only affected by the static proximity cue but also by its dynamic variations. When outcome proximity was equal, the shrinking physical distance towards a counterfactual outcome heightened one's subjective likelihood of that outcome, compared to if the distance stayed constant. Experiment 6 found that the effect of 'shrinking distance' could manifest itself as an antecedent temporal order effect on people's counterfactual probability judgments. That is, a counterfactual outcome was deemed more likely if the factual outcome was preceded by a decisive event that occurred latter in the causal sequence rather than earlier. These results are broadly consistent with the theory of the simulation heuristic which posits that subjective probabilities are estimated by assessing the ease with which a relevant scenario can be mentally constructed. The emotional consequences of counterfactual probability judgments were investigated within the theoretical framework of the Reflective and Evaluative Model of Comparative Thinking (REM). The evidence from Experiments 2, 3, 4 and 5 suggests that the effect of counterfactual probability judgments on emotions are contingent on people's temporal perspective - affective assimilation will be enhanced when future possibility is present (i.e., the outcome is indecisive or changeable) which encourages a reflective simulation while affective contrast will be enhanced when future possibility is absent (i.e., the outcome is decisive or unchangeable) which encourages an evaluative simulation. These findings suggest that the psychological impact of counterfactual thinking should be discussed in terms of a three-way interaction between its direction (upward or downward), probability (low or high), and simulation mode (reflection or evaluation).

Uncertainty in Econometrics

Uncertainty in Econometrics PDF Author: Julian Reiss
Publisher:
ISBN:
Category : Counterfactuals (Logic)
Languages : en
Pages :

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Book Description


Counterfactual Reasoning

Counterfactual Reasoning PDF Author: Noel Hendrickson
Publisher:
ISBN: 9781452863573
Category :
Languages : en
Pages : 86

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Book Description
Counterfactual reasoning is the process of evaluating conditional claims about alternate possibilities and their consequences (i.e., "What If" statements). These alternatives can be either past possibilities or future possibilities. Counterfactuals are essential to intelligence analysis because they are implicit in all strategic assessments. The process of counterfactual reasoning has three stages. The ?rst two of these are somewhat counterintuitive and are easily ignored by analysts. First, one must establish the particular way in which the alternate possibility comes to be (i.e., develop its "back-story"). Second, one must evaluate the events that occur between the time of the alternate possibility and the time for which one is considering its consequences. And third, one must examine the possible consequences of the alternate possibility's back-story and the events that follow it. In doing so, an analyst must connect their conclusion to the speci?c type of strategic assessment the counterfactual will be used to support: decision making under risk or decision making under uncertainty. Includes notes, glossary and references. Noel Hendrickson is Director of the Institute for National Security Analysis.

Counterfactuals and Probability

Counterfactuals and Probability PDF Author: Moritz Schulz
Publisher: Oxford University Press
ISBN: 0191089060
Category : Philosophy
Languages : en
Pages : 256

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Book Description
Moritz Schulz explores counterfactual thought and language: what would have happened if things had gone a different way. Counterfactual questions may concern large scale derivations (what would have happened if Nixon had launched a nuclear attack) or small scale evaluations of minor derivations (what would have happened if I had decided to join a different profession). A common impression, which receives a thorough defence in the book, is that oftentimes we find it impossible to know what would have happened. However, this does not mean that we are completely at a loss: we are typically capable of evaluating counterfactual questions probabilistically: we can say what would have been likely or unlikely to happen. Schulz describes these probabilistic ways of evaluating counterfactual questions and turns the data into a novel account of the workings of counterfactual thought.

Reasoning about Uncertainty, second edition

Reasoning about Uncertainty, second edition PDF Author: Joseph Y. Halpern
Publisher: MIT Press
ISBN: 0262533804
Category : Computers
Languages : en
Pages : 505

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Book Description
Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

Counterfactual Reasoning

Counterfactual Reasoning PDF Author: Ph D. Noel Hendrickson
Publisher: Lulu.com
ISBN: 1105055639
Category : History
Languages : en
Pages : 87

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Book Description
Counterfactual reasoning evaluates conditional claims about alternate possibilities and their consequences (i.e., ?What If? statements). Counterfactuals are essential to intelligence analysis. The process of counterfactual reasoning has three stages. First, one must establish the particular way in which the alternate possibility comes to be (i.e., develop its ?back-story?). Second, one must evaluate the events that occur between the time of the alternate possibility and the time for which one is considering its consequences. And third, one must examine the possible consequences of the alternate possibility's back-story and the events that follow it. In doing so, an analyst must connect conclusions to speci

The Functional Basis of Counterfactual Thinking [microform]

The Functional Basis of Counterfactual Thinking [microform] PDF Author: Neal J. Roese
Publisher: National Library of Canada = Bibliothèque nationale du Canada
ISBN: 9780315840034
Category : Counterfactuals (Logic)
Languages : en
Pages : 482

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Book Description
These findings provide initial support for a functional theory of counterfactual thinking: people may strategically use downward counterfactuals to make themselves feel better (an affective function), and they may strategically use upward and additive counterfactuals to improve performance in the future (a preparative function). The present studies suggest that the mechanism underlying the preparative function represents a causal link from counterfactuals to intentions to overt behaviours. Implications for current theory and future research are considered.

Causal Inference in Statistics

Causal Inference in Statistics PDF Author: Judea Pearl
Publisher: John Wiley & Sons
ISBN: 1119186862
Category : Mathematics
Languages : en
Pages : 162

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Book Description
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

The Science and Management of Uncertainty

The Science and Management of Uncertainty PDF Author: Bruce G. Marcot
Publisher: CRC Press
ISBN: 1000244512
Category : Business & Economics
Languages : en
Pages : 278

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Book Description
Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling and risk analysis in the face of uncertain conditions and incomplete information. Provided is a clear classification of uncertainty; methods for measuring, modeling, and communicating uncertainty; practical guidelines for capturing and representing expert knowledge and judgment; explanations of the role of uncertainty in decision-making; a guideline to avoiding logical fallacies when dealing with uncertainty; and several example cases of real-world ecological modeling and risk analysis to illustrate the concepts and approaches. Case topics provide examples of structured decision-making, statistical modeling, and related topics. A summary provides practical next steps that the reader can take in analyzing and interpreting uncertainty in real-world situations. Also provided is a glossary and a suite of references.

Reasoning about Uncertainty, second edition

Reasoning about Uncertainty, second edition PDF Author: Joseph Y. Halpern
Publisher: MIT Press
ISBN: 026234050X
Category : Computers
Languages : en
Pages : 505

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Book Description
Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.