Desire Generalizable Decision Processes Have everyone read Kahneman’s rant on picking the action with the best expected outcome in Thinking, Fast and Slow (chapter 31, Risk Policies, especially the sermon). Encourage people to play enough poker and read enough poker theory to become at least close to neutral-EV in Vegas. Experiencing the concept of pot odds in a real game was my strongest “passing up positive-EV moves is leaving money on the table no matter how loss-averse you are” learning moment. One exercise might be to (several times) present a story in which someone makes a decision and ask participants to a) make up a near-mode explanation of why the decision feels legit, b) give a far-mode explanation of why the reasoning that led to the decision would be disastrous if everyone used it, and c) figure out what the broadest set of people/circumstances is that would allow that reasoning to be generalized and still work well. Concrete example: Holden focuses on charities that are neglected by traditional funding. a) This is great because his marginal actions will actually be at the margin, not pushed back by some giant philanthropist who suddenly funds the whole charity. b) This is awful because if everyone focused on neglected charities, the most valuable charities would receive less than optimal focus. c) As long as very few people are actually applying this heuristic, there’s no danger of high-profile valuable charities suddenly losing all of their focus. So if Holden makes his decisions according to c), he’s doing great, but if just a), then his algorithm is flawed though its output might be correct in this case.
Desire Generalizable Decision Processes Have everyone read Kahneman’s rant on picking the action with the best expected outcome in Thinking, Fast and Slow (chapter 31, Risk Policies, especially the sermon). Encourage people to play enough poker and read enough poker theory to become at least close to neutral-EV in Vegas. Experiencing the concept of pot odds in a real game was my strongest “passing up positive-EV moves is leaving money on the table no matter how loss-averse you are” learning moment. One exercise might be to (several times) present a story in which someone makes a decision and ask participants to a) make up a near-mode explanation of why the decision feels legit, b) give a far-mode explanation of why the reasoning that led to the decision would be disastrous if everyone used it, and c) figure out what the broadest set of people/circumstances is that would allow that reasoning to be generalized and still work well. Concrete example: Holden focuses on charities that are neglected by traditional funding. a) This is great because his marginal actions will actually be at the margin, not pushed back by some giant philanthropist who suddenly funds the whole charity. b) This is awful because if everyone focused on neglected charities, the most valuable charities would receive less than optimal focus. c) As long as very few people are actually applying this heuristic, there’s no danger of high-profile valuable charities suddenly losing all of their focus. So if Holden makes his decisions according to c), he’s doing great, but if just a), then his algorithm is flawed though its output might be correct in this case.