“‘Forecasting’ Tag”,2019-12-02 (cognitive bias, Fermi problems; backlinks)⁠:

Bibliography for tag statistics/prediction, most recent first: 4 related tags, 112 annotations, & 45 links (parent).

• See Also
• Gwern
  • “About This Website”, Gwern2010
  • “Embryo Selection For Intelligence”, Gwern2016
  • “LSD Microdosing RCT”, Gwern2012
  • “Complexity No Bar to AI”, Gwern2014
  • “Darknet Market Mortality Risks”, Gwern2013
  • “Technology Forecasting: The Garden of Forking Paths”, Gwern2014
  • “Predicting Google Closures”, Gwern2013
  • “The Ones Who Walk Towards Acre”, Gwern2010
  • “History of Iterated Embryo Selection”, Gwern2019
  • “Nootropics”, Gwern2010
  • “In Defense of Inclusionism”, Gwern2009
  • “Zeo Sleep Self-Experiments”, Gwern2010
  • “Long Bets As Charitable Giving Opportunity”, Gwern2017
  • “Summers of Code, 2006^–₇2013_11ya”, Gwern2009
  • “‘HP: Methods of Rationality’ Review Statistics”, Gwern2012
  • “‘Methods of Rationality’ Predictions”, Gwern2012
  • “Wikipedia & Knol: Why Knol Already Failed”, Gwern2009
  • “NGE Rebuild Predictions”, Gwern2011
  • “Choosing Software”, Gwern2008
• Links
  • “Genetically-Diverse Crowds Are Wiser”, Barneron et al 2024
  • “The Death and Life of Prediction Markets at Google: Over the past Two Decades, Google Has Hosted Two Different Internal Platforms for Predictions. Why Did the First One Fail—And Will the Other Endure?”, Schwarz2024
  • “Can Language Models Use Forecasting Strategies?”, Pratt et al 2024
  • “ChatGPT Can Predict the Future When It Tells Stories Set in the Future About the Past”, Pham & Cunningham2024
  • “Chronos: Learning the Language of Time Series”, Ansari et al 2024
  • “Crowd Prediction Systems: Markets, Polls, and Elite Forecasters”, Atanasov et al 2024
  • “Academics Are More Specific, and Practitioners More Sensitive, in Forecasting Interventions to Strengthen Democratic Attitudes”, Chu et al 2024
  • “Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament”, Schoenegger & Park2023
  • “Cognitive Biases: Mistakes or Missing Stakes?”, Enke et al 2023
  • “Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
  • “Self-Resolving Prediction Markets for Unverifiable Outcomes”, Srinivasan et al 2023
  • “Incentivizing Honest Performative Predictions With Proper Scoring Rules”, Oesterheld et al 2023
  • “Deep Learning Based Forecasting: a Case Study from the Online Fashion Industry”, Kunz et al 2023
  • “On the Accuracy, Media Representation, and Public Perception of Psychological Scientists’ Judgments of Societal Change”, Hutcherson et al 2023
  • “Long-Range Subjective-Probability Forecasts of Slow-Motion Variables in World Politics: Exploring Limits on Expert Judgment”, Tetlock et al 2023
  • “Conditioning Predictive Models: Risks and Strategies”, Hubinger et al 2023
  • “The Unlikelihood Effect: When Knowing More Creates the Perception of Less”, Karmarkar & Kupor2022
  • “Forecasting With Trees”, Januschowski et al 2022
  • “Does Constructing a Belief Distribution Truly Reduce Overconfidence?”, Hu & Simmons2022
  • “Reconciling Individual Probability Forecasts”, Roth et al 2022
  • “Augur: a Decentralized Oracle and Prediction Market Platform (v2.0)”, Peterson et al 2022
  • “An Appropriate Verbal Probability Lexicon for Communicating Surgical Risks Is unlikely to Exist”, Harris et al 2022
  • “A Simple Cognitive Method to Improve the Prediction of Matters of Taste by Exploiting the Within-Person Wisdom-Of-Crowd Effect”, Fujisaki et al 2022
  • “Language Models (Mostly) Know What They Know”, Kadavath et al 2022
  • “Forecasting Future World Events With Neural Networks”, Zou et al 2022
  • “Modeling Transformative AI Risks (MTAIR) Project—Summary Report”, Clarke et al 2022
  • “Taking a Disagreeing Perspective Improves the Accuracy of People’s Quantitative Estimates”, Calseyde & Efendić2022
  • “The Forecast Trap”, Boettiger2022
  • “Teaching Models to Express Their Uncertainty in Words”, Lin et al 2022
  • “Politicizing Mask-Wearing: Predicting the Success of Behavioral Interventions among Republicans and Democrats in the US”, Dimant et al 2022
  • “‘Two Truths and a Lie’ As a Class-Participation Activity”, Gelman2022
  • “DeepMind: The Podcast—Excerpts on AGI”, Kiely2022
  • “Many Heads Are More Utilitarian Than One”, Keshmirian et al 2022
  • “Uncalibrated Models Can Improve Human-AI Collaboration”, Vodrahalli et al 2022
  • “A 680,000-Person Megastudy of Nudges to Encourage Vaccination in Pharmacies”, Milkman et al 2022
  • “TACTiS: Transformer-Attentional Copulas for Time Series”, Drouin et al 2022
  • “Dream Interpretation from a Cognitive and Cultural Evolutionary Perspective: The Case of Oneiromancy in Traditional China”, Hong2022
  • “M5 Accuracy Competition: Results, Findings, and Conclusions”, Makridakis et al 2022
  • “Megastudies Improve the Impact of Applied Behavioral Science”, Milkman et al 2021
  • “Forecasting Skills in Experimental Markets: Illusion or Reality?”, Corgnet et al 2021
  • “Strategically Overconfident (to a Fault): How Self-Promotion Motivates Advisor Confidence”, Zant2021
  • “Market Expectations of a Warming Climate”, Schlenker & Taylor2021
  • “Long-Range Transformers for Dynamic Spatiotemporal Forecasting”, Grigsby et al 2021
  • “Sigmoids Behaving Badly: Why They Usually Cannot Predict the Future as well as They Seem to Promise”, Sandberg et al 2021
  • “Wise Teamwork: Collective Confidence Calibration Predicts the Effectiveness of Group Discussion”, Silver2021
  • “Alignment Problems With Current Forecasting Platforms”, Sempere & Lawsen2021
  • “Behavioral Scientists and Laypeople Misestimate Societal Effects of COVID-19”, Hutcherson et al 2021
  • “How the Wisdom of Crowds, and of the Crowd Within, Are Affected by Expertise”, Fiechter & Kornell2021
  • “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
  • “Kaggle Forecasting Competitions: An Overlooked Learning Opportunity”, Bojer & Meldgaard2020
  • “Danny Hernandez on Forecasting and the Drivers of AI Progress”, Koehler et al 2020
  • “AI and Efficiency: We’re Releasing an Analysis Showing That Since 2012 the Amount of Compute Needed to Train a Neural Net to the Same Performance on ImageNet Classification Has Been Decreasing by a Factor of 2 Every 16 Months”, Hernandez & Brown2020
  • “ForecastQA: A Question Answering Challenge for Event Forecasting With Temporal Text Data”, Jin et al 2020
  • “LightGBM: A Highly Efficient Gradient Boosting Decision Tree”, Ke et al 2019
  • “Predicting History”, Risi et al 2019
  • “N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting”, Oreshkin et al 2019
  • “Evidence on Good Forecasting Practices from the Good Judgment Project”, Impacts 2019
  • “Forecasting Transformative AI: An Expert Survey”, Gruetzemacher et al 2019
  • “The Wisdom of Crowds Approach to Influenza-Rate Forecasting”, Morgan et al 2018
  • “Predicting Replication Outcomes in the Many Labs 2 Study”, Forsell et al 2018
  • “The Wisdom of the Inner Crowd in Three Large Natural Experiments”, Dolder & Assem2017
  • “When Will AI Exceed Human Performance? Evidence from AI Experts”, Grace et al 2017
  • “DeepAR: Probabilistic Forecasting With Autoregressive Recurrent Networks”, Salinas et al 2017
  • “Roosevelt Predicted to Win: Revisiting the 1936 Literary Digest Poll”, Lohr & Brick2017
  • “Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science”, Boudreau et al 2016
  • “Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis”, Kuncel et al 2013
  • “Credit Suisse Global Investment Returns Yearbook2013”, Dimson et al 2013
  • “General Knowledge Norms: Updated and Expanded from the Nelson & Narens1980 Norms”, Tauber et al 2013
  • “Statistical Basis for Predicting Technological Progress”, Nagy et al 2012
  • “Learning Performance of Prediction Markets With Kelly Bettors”, Beygelzimer et al 2012
  • “Can Physicians Accurately Predict Which Patients Will Lose Weight, Improve Nutrition and Increase Physical Activity?”, Pollak et al 2012
  • “A Prediction Market for Macro-Economic Variables”, Teschner et al 2011
  • “Why Do Humans Reason? Arguments for an Argumentative Theory”, Mercier & Sperber2011
  • “Goodbye 2010”, Legg2010
  • “Applying the Fermi Estimation Technique to Business Problems”, Anderson & Sherman2010
  • “Predicting the Next Big Thing: Success As a Signal of Poor Judgment”, Denrell & Fang2010
  • “Conditions for Intuitive Expertise: A Failure to Disagree”, Kahneman & Klein2009
  • “Keep Your Identity Small”, Graham2009
  • “Measuring the Crowd Within: Probabilistic Representations Within Individuals”, Vul & Pashler2008
  • “The Meta-Analysis of Clinical Judgment Project: 56 Years of Accumulated Research on Clinical Versus Statistical Prediction”, Ægisdóttir et al 2006
  • “A Systematic Review on Communicating With Patients about Evidence”, Trevena et al 2005
  • Principles of Forecasting: A Handbook for Researchers and Practitioners, Armstrong2001
  • “Who Is Arguing About the Cat? Moral Action and Enlightenment According to Dōgen”, Mikkelson1997
  • “Eliminating the Hindsight Bias”, Arkes et al 1988
  • “Forecasting Records by Maximum Likelihood”, Smith1988
  • Tools for Thought, Waddington1977
  • “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences”, Werbos1974
  • “The Futurists”, Toffler1972
  • “2022 Expert Survey on Progress in AI”
  • “Prediction Markets in The Corporate Setting”
  • “Tales from Prediction Markets”
  • “George Orwell: In Front of Your Nose”
  • “Why Did Renewables Become so Cheap so Fast?”
  • “Performance Curve Database”
  • “Mining the Silver Lining of the Trump Presidency”
  • “How to Get Good”
  • “A Failed Attempt at Market Manipulation”
  • “Predicting the Future With Data+logistic Regression”
  • “Prediction Markets: Tales from the Election”
  • “Using Learning Curve Theory to Redefine Moore’s Law”
  • “Forecasting S-Curves Is Hard”
  • “Science Fiction As Foresight”
  • “The Track Record of Futurists Seems … Fine”
  • “Why Sigmoids Are so Hard to Predict”
  • “Can AI Outpredict Humans? Results From Metaculus’s Q3 AI Forecasting Benchmark [No]”
  • “Violating the EMH—Prediction Markets”
  • “Getting GPT-3 to Predict Metaculus Questions”
  • “Maths Writer/cowritter Needed: How You Can’t Distinguish Early Exponential from Early Sigmoid”
  • “First Extracorporeal Human Pregnancy”
  • “Demographically Diverse Crowds Are Typically Not Much Wiser Than Homogeneous Crowds”
  • “How Accurate Are Our Predictions?”
  • “Why the State Department’s INR Intelligence Agency May Be the Best in DC”
  • Sort By Magic
    • clinical-decision
    • neural-prediction
    • crowd-wisdom
    • prediction-markets
  • Wikipedia
• Miscellaneous
• Bibliography

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