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Pay for performance can raise output. At Safelite, piece rates lifted productivity about 44%. Half was effort. Half was who stayed. Across studies, financial incentives tracked quantity. Not quality. Small payments can do worse than none. Thresholds invite timing games, discounting, and, at the extreme, changing the number instead of the work. What gets measured crowds out what does not. None of that means incentives never work. It means the design is the argument, not the slogan. The Carrot and The Stick is the case for examining the arrangement before assigning blame. #HumanTheory

Every incentive answers three questions, whether anyone asked them or not: What does it measure? What does it encourage? Is it fair? Most arguments about carrots and sticks skip those and pick a camp instead. One camp says pay for results and you get results. The other says rewards and punishments corrode the work. The research does not pick a winner. It says incentives work on what they touch, in the direction the design points, and sometimes in directions nobody intended. New Human Theory article: #HumanTheory

Nearly 1,000 high school math students practiced with GPT-4. Plain chat raised practice grades 48%. A tutor who gave hints instead of answers raised them 127%. Then the tool was removed for the exam. Chat users scored 17% lower than students who never had it. The tutor version did not show that harm. When the tool makes the attempt for you, the practice score measures the tool. Try first. Ask for a hint before the answer. Find the error while another attempt is still available. #HumanTheory

A good mistake is one you make on purpose, early, cheaply, and while there is still time to try again. If AI can answer every question, what should schools teach? “Ask better questions” sounded right when I said it. It is also a false choice. You cannot ask a good question about a structure you cannot see, and seeing structure depends on what you already know. New Human Theory article: #HumanTheory

Gold Question of the Day: How is the density of drilling fluid calculated according to Hoberock's model? Answer: Hoberock's model for calculating the density of drilling fluid involves considering the effects of pressure and temperature on the fluid's density. The model uses a polynomial equation to account for these effects, which is expressed as: [ \rho(P, T) = (a_1 + b_1 P + c_1 P^2) + (a_2 + b_2 P + c_2 P^2) \times (1.8 T + 32) ] In this equation: ( \rho(P, T) ) is the density of the drilling fluid at a specific pressure ( P ) and temperature ( T ). ( a_1, b_1, c_1, a_2, b_2, ) and ( c_2 ) are correlation coefficients that are determined empirically and can be found in reference tables such as Table 4 in the source document [3]. The term ( (1.8 T + 32) ) converts the temperature from Celsius to Fahrenheit, which is often used in these calculations. This model allows for the estimation of drilling fluid density under varying conditions, which is crucial for maintaining wellbore stability and optimizing drilling operations in high-temperature, high-pressure (HTHP) environments.

I spent more than a year writing about fight-or-flight, parenting, teams, algorithms, and faith. Tens of thousands of words. Then I sat down to explain the whole thing to someone I love, and it fit on a napkin. Here is the napkin. nitter.cf/ScottChambersJr/status… #HumanTheory

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Release the Epstein files or kindly stfu

Sometimes the truth is worse than your darkest imagination