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HAZARDOUS WASTE: WE BURY OUR MISTAKESThe dangers of selective memory THE RACE FOR HAPPINESS IS FREQUENTLY RUN ON A TREADMILLThe perils of benchmarking WHAT “GAMING
THE BENCHMARKS WE CHOOSE DETERMINE WHETHER WE WIN OR LOSE…
THE GOAT SOLUTION: Benchmark against process, not outcomes
WHAT PROBABILITIES SOUND LIKE: Illustrated by Math with Bad Drawings
WHAT PROBABILITIES FEEL LIKE: Seeing Theory, for visualizing probability
DON’T HAVE A CRYSTAL BALL? – Base rates are the next best thing.
“WANNA BET?” ON THE STOCK MARKET – An illustration of thinking in bets when you have little knowledge or expertise.
THE PARADOX OF UNCERTAINTY- Counterintuitively, being more uncertain leads to greater accuracy.
CIVIL DISAGREEMENT? ON THE INTERNET? – Reddit’s Change My View forum.
DIFFERENT KINDS OF BAD LUCK – When luck keeps information hidden from view
BLIND TO LUCK – How WE keep luck hidden from view
BEING SMART MAKES IT WORSE, REDUX – And how science curiosity helps
THE STRONG BOND OF DISLIKE – Liking things gets you admiration, but disliking things gets you everywhere
THE MOST IMPORTANT THING TO KNOW? – Jim O’Shaughnessy: “Some things I don’t know”
IDENTIFYING (AND DEVELOPING?) INTUITION – from a Daniel Kahneman speech
THE ELECTRONIC FLU-SHOT NUDGE – from Penn Medicine’s Nudge Unit
HUMANS VS. MACHINES. AGAIN. AND THIS TIME, IT’S NOT SERIOUS – Algorithms outperform humans at what predicting what we think will be funny
PREVAILING OVER UNCERTAINTY? Rick Jones asked, so we came up with something
THE GURU EFFECT – The flip side of processing fluency
TROUBLE DISTINGUISHING FAKE NEWS? “Rating” (rather than “consuming”) gets us to favor accuracy over confirmation
MORAL CONTAGION ON SOCIAL NETWORKS – Moral-emotional language helps messages spread, but only in-group
SPEAKING OF DIFFERENT LANGUAGES … How in-group/out-group language obscures areas of agreement
THE WISDOM OF McRIBS – Nick Maggiulli and our tendency to infer causality from coincidence
CROWDS AS DECISION TOOLS – The power of a large, independent sample
BELIEVE RESPONSIBLY – William Kingdon Clifford and the universal moral responsibility for calibrated beliefs
“THEY RUINED POPCORN!” – Cass Sunstein and the value of information and willingness-to-pay
HOW MUCH, THEN, IS IGNORANCE WORTH? – How and why we’ll PAY to avoid partisan information
CONSTRUCTIVE IDEAS ON PROBABILITIES FROM PRE-ELECTION COVERAGE
Or … as a friend called it, “What happened after Annie lost her shit on Twitter”
SPILLOVER EFFECTS FROM FORECASTING TOURNAMENTS
Can forecasting depolarize our political conversation?
WEIGHT & RATE: GREAT TECHNIQUE FOR CRITICAL THINKING
Vital tool (with a cool name), via Joseph Sweeney
— Why A Good Truth Is Hard to Find (Part I)
Processing fluency: We like and believe things oft repeated
— Why A Good Truth Is Hard to Find (Part II)
The “confidence heuristic”: Make it simple, sound certain, and we’ll believe
— The Hidden Tribes Study
New data on why we’re polarized and where to go from here
The trouble surmounting the intractable minority
— Algorithms To The Rescue?
For fake-news detection, AI finds what we can already spot
— Morality To The Rescue of Algorithms?
If you’re concerned about self-driving cars making moral choices, this should make you feel better: Humans don’t even agree on those choices
1. MY-SIDE BIAS, PART II – The counterintuitive effect of training on mitigating bias
2. WHAT FRESH HELL …? – Midterm-election polls, pre-election expectations, and Nate Silver’s sobering message
3. TRANSPARENCY MATTERS – But sometimes HOW MUCH it matters depends on whether you’re looking OUT or looking IN
4. IS EIGHTY MILLION SUPPOSED TO BE A LOT? – Netflix seeds its narrative with a potentially meaningless statistic
5. TEST YOURSELF – Did these 21 social science experiments replicate?