- See Also
-
Gwern
- “About This Website”, Gwern 2010
- “Embryo Selection For Intelligence”, Gwern 2016
- “LSD Microdosing RCT”, Gwern 2012
- “Complexity No Bar to AI”, Gwern 2014
- “Darknet Market Mortality Risks”, Gwern 2013
- “Technology Forecasting: The Garden of Forking Paths”, Gwern 2014
- “Predicting Google Closures”, Gwern 2013
- “The Ones Who Walk Towards Acre”, Gwern 2010
- “History of Iterated Embryo Selection”, Gwern 2019
- “Nootropics”, Gwern 2010
- “In Defense of Inclusionism”, Gwern 2009
- “Zeo Sleep Self-Experiments”, Gwern 2010
- “Long Bets As Charitable Giving Opportunity”, Gwern 2017
- “Summers of Code, 2006–2013”, Gwern 2009
- “‘HP: Methods of Rationality’ Review Statistics”, Gwern 2012
- “‘Methods of Rationality’ Predictions”, Gwern 2012
- “Wikipedia & Knol: Why Knol Already Failed”, Gwern 2009
- “NGE Rebuild Predictions”, Gwern 2011
- “Choosing Software”, Gwern 2008
-
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?”, Schwarz 2024
- “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 & Cunningham 2024
- “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 & Park 2023
- “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 & Kupor 2022
- “Forecasting With Trees”, Januschowski et al 2022
- “Does Constructing a Belief Distribution Truly Reduce Overconfidence?”, Hu & Simmons 2022
- “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 & Efendić 2022
- “The Forecast Trap”, Boettiger 2022
- “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”, Gelman 2022
- “DeepMind: The Podcast—Excerpts on AGI”, Kiely 2022
- “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”, Hong 2022
- “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”, Zant 2021
- “Market Expectations of a Warming Climate”, Schlenker & Taylor 2021
- “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”, Silver 2021
- “Alignment Problems With Current Forecasting Platforms”, Sempere & Lawsen 2021
- “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 & Kornell 2021
- “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
- “Kaggle Forecasting Competitions: An Overlooked Learning Opportunity”, Bojer & Meldgaard 2020
- “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 & Brown 2020
- “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 & Assem 2017
- “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 & Brick 2017
- “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 Yearbook 2013”, Dimson et al 2013
- “General Knowledge Norms: Updated and Expanded from the Nelson & Narens 1980 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 & Sperber 2011
- “Goodbye 2010”, Legg 2010
- “Applying the Fermi Estimation Technique to Business Problems”, Anderson & Sherman 2010
- “Predicting the Next Big Thing: Success As a Signal of Poor Judgment”, Denrell & Fang 2010
- “Conditions for Intuitive Expertise: A Failure to Disagree”, Kahneman & Klein 2009
- “Keep Your Identity Small”, Graham 2009
- “Measuring the Crowd Within: Probabilistic Representations Within Individuals”, Vul & Pashler 2008
- “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, Armstrong 2001
- “Who Is Arguing About the Cat? Moral Action and Enlightenment According to Dōgen”, Mikkelson 1997
- “Eliminating the Hindsight Bias”, Arkes et al 1988
- “Forecasting Records by Maximum Likelihood”, Smith 1988
- Tools for Thought, Waddington 1977
- “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences”, Werbos 1974
- “The Futurists”, Toffler 1972
- “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
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Gwern
“About This Website”, Gwern 2010
“Embryo Selection For Intelligence”, Gwern 2016
“LSD Microdosing RCT”, Gwern 2012
“Complexity No Bar to AI”, Gwern 2014
“Darknet Market Mortality Risks”, Gwern 2013
“Technology Forecasting: The Garden of Forking Paths”, Gwern 2014
“Predicting Google Closures”, Gwern 2013
“The Ones Who Walk Towards Acre”, Gwern 2010
“History of Iterated Embryo Selection”, Gwern 2019
“Nootropics”, Gwern 2010
“In Defense of Inclusionism”, Gwern 2009
“Zeo Sleep Self-Experiments”, Gwern 2010
“Long Bets As Charitable Giving Opportunity”, Gwern 2017
“Summers of Code, 2006–2013”, Gwern 2009
“‘HP: Methods of Rationality’ Review Statistics”, Gwern 2012
“‘Methods of Rationality’ Predictions”, Gwern 2012
“Wikipedia & Knol: Why Knol Already Failed”, Gwern 2009
“NGE Rebuild Predictions”, Gwern 2011
“Choosing Software”, Gwern 2008
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?”, Schwarz 2024
“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 & Cunningham 2024
ChatGPT Can Predict the Future when it Tells Stories Set in the Future About the Past
“Chronos: Learning the Language of Time Series”, Ansari et al 2024
“Crowd Prediction Systems: Markets, Polls, and Elite Forecasters”, Atanasov et al 2024
Crowd prediction systems: Markets, polls, and elite forecasters
“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 & Park 2023
Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament
“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
Incentivizing honest performative predictions with proper scoring rules
“Deep Learning Based Forecasting: a Case Study from the Online Fashion Industry”, Kunz et al 2023
Deep Learning based Forecasting: a case study from the online fashion industry
“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 & Kupor 2022
The unlikelihood effect: When knowing more creates the perception of less
“Forecasting With Trees”, Januschowski et al 2022
“Does Constructing a Belief Distribution Truly Reduce Overconfidence?”, Hu & Simmons 2022
Does constructing a belief distribution truly reduce overconfidence?
“Reconciling Individual Probability Forecasts”, Roth et al 2022
“Augur: a Decentralized Oracle and Prediction Market Platform (v2.0)”, Peterson et al 2022
Augur: a Decentralized Oracle and Prediction Market Platform (v2.0)
“An Appropriate Verbal Probability Lexicon for Communicating Surgical Risks Is unlikely to Exist”, Harris et al 2022
An appropriate verbal probability lexicon for communicating surgical risks is unlikely to exist
“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
Modeling Transformative AI Risks (MTAIR) Project—Summary Report
“Taking a Disagreeing Perspective Improves the Accuracy of People’s Quantitative Estimates”, Calseyde & Efendić 2022
Taking a Disagreeing Perspective Improves the Accuracy of People’s Quantitative Estimates
“The Forecast Trap”, Boettiger 2022
“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”, Gelman 2022
“DeepMind: The Podcast—Excerpts on AGI”, Kiely 2022
“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
A 680,000-person megastudy of nudges to encourage vaccination in pharmacies
“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”, Hong 2022
“M5 Accuracy Competition: Results, Findings, and Conclusions”, Makridakis et al 2022
“Megastudies Improve the Impact of Applied Behavioral Science”, Milkman et al 2021
Megastudies improve the impact of applied behavioral science
“Forecasting Skills in Experimental Markets: Illusion or Reality?”, Corgnet et al 2021
Forecasting Skills in Experimental Markets: Illusion or Reality?
“Strategically Overconfident (to a Fault): How Self-Promotion Motivates Advisor Confidence”, Zant 2021
Strategically overconfident (to a fault): How self-promotion motivates advisor confidence
“Market Expectations of a Warming Climate”, Schlenker & Taylor 2021
“Long-Range Transformers for Dynamic Spatiotemporal Forecasting”, Grigsby et al 2021
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
“Sigmoids Behaving Badly: Why They Usually Cannot Predict the Future as well as They Seem to Promise”, Sandberg et al 2021
Sigmoids behaving badly: why they usually cannot predict the future as well as they seem to promise
“Wise Teamwork: Collective Confidence Calibration Predicts the Effectiveness of Group Discussion”, Silver 2021
Wise teamwork: Collective confidence calibration predicts the effectiveness of group discussion
“Alignment Problems With Current Forecasting Platforms”, Sempere & Lawsen 2021
“Behavioral Scientists and Laypeople Misestimate Societal Effects of COVID-19”, Hutcherson et al 2021
Behavioral scientists and laypeople misestimate societal effects of COVID-19
“How the Wisdom of Crowds, and of the Crowd Within, Are Affected by Expertise”, Fiechter & Kornell 2021
How the wisdom of crowds, and of the crowd within, are affected by expertise
“Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling
“Kaggle Forecasting Competitions: An Overlooked Learning Opportunity”, Bojer & Meldgaard 2020
Kaggle forecasting competitions: An overlooked learning opportunity
“Danny Hernandez on Forecasting and the Drivers of AI Progress”, Koehler et al 2020
Danny Hernandez on forecasting and the drivers of AI progress
“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 & Brown 2020
“ForecastQA: A Question Answering Challenge for Event Forecasting With Temporal Text Data”, Jin et al 2020
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
“LightGBM: A Highly Efficient Gradient Boosting Decision Tree”, Ke et al 2019
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
“Predicting History”, Risi et al 2019
“N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting”, Oreshkin et al 2019
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
“Evidence on Good Forecasting Practices from the Good Judgment Project”, Impacts 2019
Evidence on good forecasting practices from the Good Judgment Project
“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 & Assem 2017
The wisdom of the inner crowd in three large natural experiments
“When Will AI Exceed Human Performance? Evidence from AI Experts”, Grace et al 2017
When Will AI Exceed Human Performance? Evidence from AI Experts
“DeepAR: Probabilistic Forecasting With Autoregressive Recurrent Networks”, Salinas et al 2017
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
“Roosevelt Predicted to Win: Revisiting the 1936 Literary Digest Poll”, Lohr & Brick 2017
Roosevelt Predicted to Win: Revisiting the 1936 Literary Digest Poll
“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
Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis
“Credit Suisse Global Investment Returns Yearbook 2013”, Dimson et al 2013
“General Knowledge Norms: Updated and Expanded from the Nelson & Narens 1980 Norms”, Tauber et al 2013
General knowledge norms: Updated and expanded from the Nelson & Narens 1980 norms
“Statistical Basis for Predicting Technological Progress”, Nagy et al 2012
“Learning Performance of Prediction Markets With Kelly Bettors”, Beygelzimer et al 2012
Learning Performance of Prediction Markets with Kelly Bettors
“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 & Sperber 2011
Why do humans reason? Arguments for an argumentative theory:
“Goodbye 2010”, Legg 2010
“Applying the Fermi Estimation Technique to Business Problems”, Anderson & Sherman 2010
Applying the Fermi Estimation Technique to Business Problems
“Predicting the Next Big Thing: Success As a Signal of Poor Judgment”, Denrell & Fang 2010
Predicting the Next Big Thing: Success as a Signal of Poor Judgment
“Conditions for Intuitive Expertise: A Failure to Disagree”, Kahneman & Klein 2009
“Keep Your Identity Small”, Graham 2009
“Measuring the Crowd Within: Probabilistic Representations Within Individuals”, Vul & Pashler 2008
Measuring the Crowd Within: Probabilistic Representations Within Individuals
“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
A systematic review on communicating with patients about evidence
Principles of Forecasting: A Handbook for Researchers and Practitioners, Armstrong 2001
Principles of Forecasting: A Handbook for Researchers and Practitioners
“Who Is Arguing About the Cat? Moral Action and Enlightenment According to Dōgen”, Mikkelson 1997
Who Is Arguing About the Cat? Moral Action and Enlightenment According to Dōgen
“Eliminating the Hindsight Bias”, Arkes et al 1988
“Forecasting Records by Maximum Likelihood”, Smith 1988
Tools for Thought, Waddington 1977
“Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences”, Werbos 1974
Beyond regression: new tools for prediction and analysis in the behavioral sciences
“The Futurists”, Toffler 1972
“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]”
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”
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”
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”
Why the State Department’s INR intelligence agency may be the best in DC
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
clinical-decision
neural-prediction
crowd-wisdom
prediction-markets
Wikipedia
Miscellaneous
-
/doc/statistics/prediction/2021-hutcherson-figure1-expertandlaymancoronavirusforecastsvsreality.jpg: -
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/doc/statistics/prediction/2001-rowe.pdf:View PDF:
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/doc/statistics/prediction/1996-rowe.pdf:View PDF:
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https://forum.effectivealtruism.org/posts/H7xWzvwvkyywDAEkL/creating-a-database-for-base-rates: -
https://joecarlsmith.com/2023/05/08/predictable-updating-about-ai-risk/ -
https://manifold.markets/IsaacKing/will-this-markets-probability-be-at -
https://marginalrevolution.com/marginalrevolution/2013/12/shiller-on-trills.html -
https://news.manifold.markets/p/isaac-kings-whales-vs-minnows-and -
https://niplav.github.io/notes.html#Subscripts_for_Probabilities: -
https://sumrevija.si/en/eng-peter-watts-the-wisdom-of-crowds-sum11/ -
https://web.archive.org/web/20070714204136/http://www.michaelcrichton.net/speech-whyspeculate.html: -
https://worksinprogress.co/issue/why-prediction-markets-arent-popular/: -
https://www.astralcodexten.com/p/against-learning-from-dramatic-events: -
https://www.astralcodexten.com/p/crowds-are-wise-and-ones-a-crowd: -
https://www.astralcodexten.com/p/the-buying-things-from-a-store-faq -
https://www.lesswrong.com/posts/68TGNutjDcBcq6PCZ/bitcoin-cryonics-fund#5Da2f8n9aXmfJ7FYA: -
https://www.lesswrong.com/posts/t5W87hQF5gKyTofQB/ufo-betting-put-up-or-shut-up#7qyFLsx9WQJdZfpjC: -
https://www.macroscience.org/p/the-frontier-of-scientific-plausibility: -
https://www.maximum-progress.com/p/grading-extropian-predictions
Bibliography
-
https://arxiv.org/abs/2310.13014: “Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament”, -
2023-hutcherson.pdf: “On the Accuracy, Media Representation, and Public Perception of Psychological Scientists’ Judgments of Societal Change”, -
https://www.sciencedirect.com/science/article/pii/S0169207021001679: “Forecasting With Trees”, -
2022-peterson.pdf: “Augur: a Decentralized Oracle and Prediction Market Platform (v2.0)”, -
https://arxiv.org/abs/2207.05221#anthropic: “Language Models (Mostly) Know What They Know”, -
https://arxiv.org/abs/2206.15474: “Forecasting Future World Events With Neural Networks”, -
https://www.nature.com/articles/s41598-022-10524-1: “Politicizing Mask-Wearing: Predicting the Success of Behavioral Interventions among Republicans and Democrats in the US”, -
2022-gelman.pdf: “‘Two Truths and a Lie’ As a Class-Participation Activity”, -
https://www.lesswrong.com/posts/SbAgRYo8tkHwhd9Qx/deepmind-the-podcast-excerpts-on-agi: “DeepMind: The Podcast—Excerpts on AGI”, -
https://www.pnas.org/doi/10.1073/pnas.2115126119: “A 680,000-Person Megastudy of Nudges to Encourage Vaccination in Pharmacies”, -
2022-hong.pdf: “Dream Interpretation from a Cognitive and Cultural Evolutionary Perspective: The Case of Oneiromancy in Traditional China”, -
https://www.sciencedirect.com/science/article/pii/S0169207021001874: “M5 Accuracy Competition: Results, Findings, and Conclusions”, -
2021-milkman.pdf: “Megastudies Improve the Impact of Applied Behavioral Science”, -
https://arxiv.org/abs/2102.01951#scaling&org=deepmind: “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, -
https://aiimpacts.org/evidence-on-good-forecasting-practices-from-the-good-judgment-project-an-accompanying-blog-post/: “Evidence on Good Forecasting Practices from the Good Judgment Project”, -
2017-vandolder.pdf: “The Wisdom of the Inner Crowd in Three Large Natural Experiments”, -
2013-kuncel.pdf: “Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis”, -
https://link.springer.com/article/10.3758/s13428-012-0307-9: “General Knowledge Norms: Updated and Expanded from the Nelson & Narens 1980 Norms”, -
https://www.vetta.org/2010/12/goodbye-2010/: “Goodbye 2010”, -
2009-kahneman.pdf: “Conditions for Intuitive Expertise: A Failure to Disagree”,