Search results for 'Forecasting' (try it on Scholar)

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  1.  14
    Lauren N. Harkrider, Alexandra E. MacDougall, Zhanna Bagdasarov, James F. Johnson, Michael D. Mumford, Shane Connelly & Lynn D. Devenport (2014). Retracted Article: Improving Case-Based Ethics Training: How Modeling Behaviors and Forecasting Influence Effectiveness. Science and Engineering Ethics 20 (1):299-299.
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  2. Augusto Forti (ed.) (1984). Scientific Forecasting and Human Needs: Trends, Methods, and Message: Proceedings of a Symposium Held in Tbilisi, Ussr, 6-11 December 1981. [REVIEW] Pergamon.
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  3. Ota Sulc (1977). Methodology of Forecasting Complex Development Processes of the Scientific and Technological Revolution. Centre for the Study of Science, Technology, and Develop[Ment], Council of Scientific and Industrial Research.
     
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  4.  4
    Lauren N. Harkrider, Chase E. Thiel, Zhanna Bagdasarov, Michael D. Mumford, James F. Johnson, Shane Connelly & Lynn D. Devenport (2012). Improving Case-Based Ethics Training with Codes of Conduct and Forecasting Content. Ethics and Behavior 22 (4):258 - 280.
    Although case-based training is popular for ethics education, little is known about how specific case content influences training effectiveness. Therefore, the effects of (a) codes of ethical conduct and (b) forecasting content were investigated. Results revealed richer cases, including both codes and forecasting content, led to increased knowledge acquisition, greater sensemaking strategy use, and better decision ethicality. With richer cases, a specific pattern emerged. Specifically, content describing codes alone was more effective when combined with short-term forecasts, whereas content (...)
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  5.  27
    Cheryl K. Stenmark, Alison L. Antes, Xiaoqian Wang, Jared J. Caughron, Chase E. Thiel & Michael D. Mumford (2010). Strategies in Forecasting Outcomes in Ethical Decision-Making: Identifying and Analyzing the Causes of the Problem. Ethics and Behavior 20 (2):110 – 127.
    This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of (...)
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  6.  21
    Michael D. Mumford, Chase E. Thiel, Jared J. Caughron, Xiaoqian Wang, Alison L. Antes & Cheryl K. Stenmark (2010). Strategies in Forecasting Outcomes in Ethical Decision-Making: Identifying and Analyzing the Causes of the Problem. Ethics and Behavior 20 (2):110-127.
    This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of (...)
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  7.  5
    Cheryl Stenmark (2013). Forecasting and Ethical Decision Making: What Matters? Ethics and Behavior 23 (6):445-462.
    This study examined how the number and types of consequences considered are related to forecasting and ethical decision making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to forecast potential outcomes and make a decision about each problem. Performance pressure was manipulated by ostensibly making rewards contingent on good problem-solving performance. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical consequences of the (...)
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  8.  11
    Jamie Morgan (2012). Forecasting, Prediction and Precision: A Commentary. Economic Thought 1 (2).
    Forecasting involves an underlying conceptualization of probability. It is this that gives sense to the notion of precision in number that makes us think of economic forecasting as more than simply complicated guesswork. We think of it as well-founded statement, a science and not an art of numbers. However, this understanding is at odds with the nature of social reality and the attributes of the forecaster. We should think differently about how we both anticipate and make the future (...)
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  9.  10
    Robert Evans (2007). Social Networks and Private Spaces in Economic Forecasting. Studies in History and Philosophy of Science Part A 38 (4):686-697.
    The outputs of economic forecasting—predictions for national economic indicators such as GDP, unemployment rates and inflation—are all highly visible. The production of these forecasts is a much more private affair, however, typically being thought of as the work of individual forecasters or forecast teams using their economic model to produce a forecast that is then made public. This conception over-emphasises the individual and the technical whilst silencing the broader social context through which economic forecasters develop the expertise that is (...)
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  10.  22
    Nigel Harvey (2007). Use of Heuristics: Insights From Forecasting Research. Thinking and Reasoning 13 (1):5 – 24.
    Tversky and Kahneman (1974) originally discussed three main heuristics: availability, representativeness, and anchoring-and-adjustment. Research on judgemental forecasting suggests that the type of information on which forecasts are based is the primary factor determining the type of heuristic that people use to make their predictions. Specifically, availability is used when forecasts are based on information held in memory; representativeness is important when the value of one variable is forecast from explicit information about the value of another variable; and anchoring-and-adjustment is (...)
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  11.  4
    Robert S. Goldfarb, H. O. Stekler & Joel David (2005). Methodological Issues in Forecasting: Insights From the Egregious Business Forecast Errors of Late 1930. Journal of Economic Methodology 12 (4):517-542.
    This paper examines some economic forecasts made in late 1930 that were intended to predict economic activity in the United States in order to shed light on several methodological issues. We document that these forecasts were extremely optimistic, predicting that the recession in the US would soon end, and that 1931 would show a recovery. These forecasts displayed egregious errors, because 1931 witnessed the largest negative growth rate for the US economy in any year in the twentieth century. A specific (...)
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  12.  9
    Rosamond Rhodes & James J. Strain (2007). Affective Forecasting and Its Implications for Medical Ethics. Cambridge Quarterly of Healthcare Ethics 17 (1):54-65.
    Through a number of studies recently published in the psychology literature, T.D. Wilson, D.T. Gilbert, and others have demonstrated that our judgments about what our future mental states will be are contaminated by various distortions. Their studies distinguish a variety of different distortions, but they refer to them all with the generic term “affective forecasting.” The findings of their studies on normal volunteers are remarkably robust and, therefore, demonstrate that we are all vulnerable to the distortions of affective (...). a. (shrink)
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  13. Javad Olamaee, Mohsen Mohammadi, Alireza Noruzi & Seyed Mohammad Hassan Hosseini (forthcoming). Day-Ahead Price Forecasting Based on Hybrid Prediction Model. Complexity.
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  14. F. Mobio (2000). Stock-Market Forecasting as Cosmography. Diogenes 48 (190):43-57.
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  15.  11
    Reginald B. Adams & Kestutis Kveraga (2015). Social Vision: Functional Forecasting and the Integration of Compound Social Cues. Review of Philosophy and Psychology 6 (4):591-610.
    For decades the study of social perception was largely compartmentalized by type of social cue: race, gender, emotion, eye gaze, body language, facial expression etc. This was partly due to good scientific practice , and partly due to assumptions that each type of social cue was functionally distinct from others. Herein, we present a functional forecast approach to understanding compound social cue processing that emphasizes the importance of shared social affordances across various cues . We review the traditional theories of (...)
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  16.  4
    Alexandra E. MacDougall, Lauren N. Harkrider, Zhanna Bagdasarov, James F. Johnson, Chase E. Thiel, Juandre Peacock, Michael D. Mumford, Lynn D. Devenport & Shane Connelly (2014). Examining the Effects of Incremental Case Presentation and Forecasting Outcomes on Case-Based Ethics Instruction. Ethics and Behavior 24 (2):126-150.
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  17. Michael Hoerger, Stuart W. Quirk, Benjamin P. Chapman & Paul R. Duberstein (2012). Affective Forecasting and Self-Rated Symptoms of Depression, Anxiety, and Hypomania: Evidence for a Dysphoric Forecasting Bias. Cognition and Emotion 26 (6):1098-1106.
  18.  2
    Reginald B. Adams & Kestutis Kveraga (2015). Social Vision: Functional Forecasting and the Integration of Compound Social Cues. Review of Philosophy and Psychology 6 (4):591-610.
    For decades the study of social perception was largely compartmentalized by type of social cue: race, gender, emotion, eye gaze, body language, facial expression etc. This was partly due to good scientific practice, and partly due to assumptions that each type of social cue was functionally distinct from others. Herein, we present a functional forecast approach to understanding compound social cue processing that emphasizes the importance of shared social affordances across various cues. We review the traditional theories of emotion and (...)
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  19.  2
    Teddy Seidenfeld, Mark J. Schervish & Joseph B. Kadane, Forecasting with Imprecise Probabilities.
    We review de Finetti’s two coherence criteria for determinate probabilities: coherence1defined in terms of previsions for a set of events that are undominated by the status quo – previsions immune to a sure-loss – and coherence2 defined in terms of forecasts for events undominated in Brier score by a rival forecast. We propose a criterion of IP-coherence2 based on a generalization of Brier score for IP-forecasts that uses 1-sided, lower and upper, probability forecasts. However, whereas Brier score is a strictly (...)
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  20.  6
    Philip E. Tetlock, Michael C. Horowitz & Richard Herrmann (2012). Should “Systems Thinkers” Accept the Limits on Political Forecasting or Push the Limits? Critical Review 24 (3):375-391.
    Historical analysis and policy making often require counterfactual thought experiments that isolate hypothesized causes from a vast array of historical possibilities. However, a core precept of Jervis's ?systems thinking? is that causes are so interconnected that the historian can only with great difficulty imagine causation by subtracting all variables but one. Prediction, according to Jervis, is even more problematic: The more sensitive an event is to initial conditions (e.g., butterfly effects), the harder it is to derive accurate forecasts. Nevertheless, if (...)
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  21.  89
    Nada Gligorov (2009). Reconsidering the Impact of Affective Forecasting. Cambridge Quarterly of Healthcare Ethics 18 (2):166.
  22.  15
    Roman Frigg, Seamus Bradley, Reason L. Machete & Leonard A. Smith, Probabilistic Forecasting: Why Model Imperfection is a Poison Pill.
    This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...)
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  23.  28
    David A. Bessler & Zijun Wang (2012). D-Separation, Forecasting, and Economic Science: A Conjecture. [REVIEW] Theory and Decision 73 (2):295-314.
    The paper considers the conjecture that forecasts from preferred economic models or theories d-separate forecasts from less preferred models or theories from the Actual realization of the variable for which a scientific explanation is sought. D-separation provides a succinct notion to represent forecast dominance of one set of forecasts over another; it provides, as well, a criterion for model preference as a fundamental device for progress in economic science. We demonstrate these ideas with examples from three areas of economic modeling.
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  24.  3
    Susanne Scheibe, Rui Mata & Laura L. Carstensen (2011). Age Differences in Affective Forecasting and Experienced Emotion Surrounding the 2008 US Presidential Election. Cognition and Emotion 25 (6):1029-1044.
  25.  9
    George Loewenstein (2007). Affect Regulation and Affective Forecasting. In James J. Gross (ed.), Handbook of Emotion Regulation. Guilford Press 180--203.
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  26.  9
    Seweryn Żurawicki (1975). Seweryn Żurawicki, Problemy Prognozowania Ekonomicznego (Problems of Economic Forecasting). Dialectics and Humanism 2 (4):173-175.
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  27.  17
    Monique Mh Pollmann & Catrin Finkenauer (2009). Empathic Forecasting: How Do We Predict Other People's Feelings? Cognition and Emotion 23 (5):978-1001.
  28.  50
    Peter Ayton, Alice Pott & Najat Elwakili (2007). Affective Forecasting: Why Can't People Predict Their Emotions? Thinking and Reasoning 13 (1):62 – 80.
    Two studies explore the frequently reported finding that affective forecasts are too extreme. In the first study, driving test candidates forecast the emotional consequences of failing. Test failers overestimated the duration of their disappointment. Greater previous experience of this emotional event did not lead to any greater accuracy of the forecasts, suggesting that learning about one's own emotions is difficult. Failers' self-assessed chances of passing were lower a week after the test than immediately prior to the test; this difference correlated (...)
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  29.  12
    Hugh Duncan Grant (1937). Long-Range Weather Forecasting. Thought: A Journal of Philosophy 12 (2):265-282.
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  30.  9
    Vladimir Faifr, Fedor Gal, Martin Potucek & Milos Zeman (1984). Forecasting Modelling by Means of the KPM Method. World Futures 20 (1):105-133.
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  31.  23
    Martin Wachs (1990). Ethics and Advocacy in Forecasting for Public Policy. Business and Professional Ethics Journal 9 (1/2):141-157.
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  32.  28
    Teddy Seidenfeld, Mark Schervish & Jay Kadane, Forecasting with Imprecise/Indeterminate Probabilities [IP] – Some Preliminary Findings.
    Part 1 Background on de Finetti’s twin criteria of coherence: Coherence1: 2-sided previsions free from dominance through a Book. Coherence2: Forecasts free from dominance under Brier (squared error) score. Part 2 IP theory based on a scoring rule.
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  33.  8
    Peter Gärdenfors (1982). Dynamic Models as Tools for Forecasting and Planning: A Presentation and Some Methodological Aspects. Theory and Decision 14 (3):237-273.
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  34.  9
    Rosamond Rhodes & James J. Strain (2009). Further Thoughts About Affective Forecasting Biases in Medicine: A Response to Nada Gligorov. Cambridge Quarterly of Healthcare Ethics 18 (2):174.
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  35.  3
    Nicholas Rescher (1997). Predicting the Future: An Introduction to the Theory of Forecasting. State University of New York Press.
    Develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its promises and problems.
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  36.  4
    Seweryn Żurawicki (1975). Seweryn Żurawicki, Problemy Prognozowania Ekonomicznego (Problems of Economic Forecasting). Dialectics and Humanism 2 (4):173-175.
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  37.  3
    William Thomas & Lambert Williams (2009). The Epistemologies of Non-Forecasting Simulations, Part I: Industrial Dynamics and Management Pedagogy at MIT. Science in Context 22 (2):245.
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  38.  1
    Luke J. Chang, Marianne Reddan, Yoni K. Ashar, Hedwig Eisenbarth & Tor D. Wager (2015). The Challenges of Forecasting Resilience. Behavioral and Brain Sciences 38.
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  39.  3
    Shyi-Ming Chen (2002). Forecasting Enrollments Based on High-Order Fuzzy Time Series. In Robert Trappl (ed.), Cybernetics and Systems. Austrian Society for Cybernetics Studies 33--1.
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  40.  1
    Leon Wansleben (2014). Consistent Forecasting Vs. Anchoring of Market Stories: Two Cultures of Modeling and Model Use in a Bank. Science in Context 27 (4):605-630.
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  41.  1
    Michael Hoerger, Ben Chapman & Paul Duberstein (forthcoming). Realistic Affective Forecasting: The Role of Personality. Cognition and Emotion:1-13.
  42.  3
    Lambert Williams & William Thomas (2009). The Epistemologies of Non-Forecasting Simulations, Part II: Climate, Chaos, Computing Style, and the Contextual Plasticity of Error. Science in Context 22 (2):271.
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  43.  6
    David Nerini, Jean Pierre Durbec, Claude Mante, Fabrice Garcia & Badih Ghattas (2000). Forecasting Physicochemical Variables by a Classification Tree Method. Application to the Berre Lagoon (South France). Acta Biotheoretica 48 (3-4):181-196.
    The dynamics of the "Etang de Berre", a brackish lagoon situated close to the French Mediterranean sea coast, is strongly disturbed by freshwater inputs coming from an hydroelectric power station. The system dynamics has been described as a sequence of daily typical states from a set of physicochemical variables such as temperature, salinity and dissolved oxygen rates collected over three years by an automatic sampling station. Each daily pattern summarizes the evolution, hour by hour of the physicochemical variables. This article (...)
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  44. Newton C. A. da Costa & FranciscoAntonio Doria (1994). Gödel Incompleteness in Analysis, with an Application to the Forecasting Problem in the Social Sciences. Philosophia Naturalis 31 (1):1-24.
     
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  45.  1
    Stanley R. Barrett (1999). Forecasting Theory: Problems and Exemplars in the Twenty-First Century. In E. L. Cerroni-Long (ed.), Anthropological Theory in North America. Bergin & Garvey 255.
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  46.  1
    J. Strain (2009). Reconsidering the Impact of Affective Forecasting. Cambridge Quarterly of Healthcare Ethics 18:166-173.
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  47. J. Barnard Gilmore (1991). On Forecasting Validity and Finessing Reliability. Behavioral and Brain Sciences 14 (1):148-149.
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  48.  2
    Devon Stillwell (2013). Genetic Counseling in Historical Perspective: Understanding Our Hereditary Past and Forecasting Our Genomic Future. Studies in History and Philosophy of Science Part A 44 (4):618-622.
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  49.  2
    Clive Wj Granger (2012). The Philosophy of Economic Forecasting. In Uskali Mäki, Dov M. Gabbay, Paul Thagard & John Woods (eds.), Philosophy of Economics. North Holland
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  50. N. Harvey & P. Ayton (1990). Actor-Observer Differences in Judgmental Probability Forecasting of Control Response Efficacy. Bulletin of the Psychonomic Society 28 (6):523-523.
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