I agree with much of your reasoning, but come to the opposite conclusion. For _many many_ things, you can’t distinguish between luck, bad modeling (incorrect desires for outcome), or bad behavior (incorrect actions toward the desired outcome). Rewarding effort makes up for luck, but magnifies other failures.
So don’t try. Reward on good outcome, punish on bad outcome. Sure, the agents will “learn” incorrectly on topics where luck dominates. Make up for it with repetition—figure out good reference classes so you can learn from more outcomes. Enough instances will smooth out the luck, leaving the other factors.
Or maybe you’ll actually learn to be luckier. We could surely use a Teela Brown to protect us right about now...
To nitpick on your throwaway Ringworld reference, that’s exactly the opposite of the point. Other humans don’t benefit from the fact that the Ringworld is going to shield Teela Brown from the core explosion. She would be the person who accidentally bought zoom stock in January because it sounded like a cool company name, or the immortal baby from an unreproducible biomedical research accident that is prompted by post COVID-19 research funding, probably extra lucky to be living in a mostly-depopulated high-technology world due to massive death tolls from some other disaster.
I agree that you can’t distinguish between those things. But I wonder if it could be argued that as long as someone is putting in effort and deliberately reflecting and improving after each outcome, then you can’t fault them since they are doing everything in their power; even if they are modeling incorrectly or behaving badly, if they did not have opportunities to learn to do otherwise beforehand then is it still reasonable to fault them if they act that way? The pragmatic part of me says that everyone has “opportunities to learn to do otherwise” with the knowledge on the internet, so we can in fact fault people for modeling poorly. But I’m not sure if this line of reasoning is correct.
I disagree, and that’s my central issue with the post.
“So that is the irony of the situation: An optimal contract punishes you for bad luck, and for nothing else.”
The post gets this exactly backwards—the optimal contract exactly balances punishing lack of effort and bad luck, in a way that the employer is willing to pay as much as the market dictates for that effort under the uncertainty that exists.
Maybe I will have to edit the text to make that clearer, but: the optimal contract in the situation I described (moral hazard with binary states and effort levels) punishes only for bad luck, exactly because it makes the worker choose high effort. In this sense, once revenue is known, you also know that it is not the worker’s fault that revenue is low. From an ex-ante perspective, it offers conditional wages that “would” punish for being lazy, however.
You’re right—but the basic literature on principle agent dynamics corrects this simple model to properly account for non-binary effort and luck, and I think that is the better model for looking at luck and effort.
I agree with much of your reasoning, but come to the opposite conclusion. For _many many_ things, you can’t distinguish between luck, bad modeling (incorrect desires for outcome), or bad behavior (incorrect actions toward the desired outcome). Rewarding effort makes up for luck, but magnifies other failures.
So don’t try. Reward on good outcome, punish on bad outcome. Sure, the agents will “learn” incorrectly on topics where luck dominates. Make up for it with repetition—figure out good reference classes so you can learn from more outcomes. Enough instances will smooth out the luck, leaving the other factors.
Or maybe you’ll actually learn to be luckier. We could surely use a Teela Brown to protect us right about now...
To nitpick on your throwaway Ringworld reference, that’s exactly the opposite of the point. Other humans don’t benefit from the fact that the Ringworld is going to shield Teela Brown from the core explosion. She would be the person who accidentally bought zoom stock in January because it sounded like a cool company name, or the immortal baby from an unreproducible biomedical research accident that is prompted by post COVID-19 research funding, probably extra lucky to be living in a mostly-depopulated high-technology world due to massive death tolls from some other disaster.
I agree that you can’t distinguish between those things. But I wonder if it could be argued that as long as someone is putting in effort and deliberately reflecting and improving after each outcome, then you can’t fault them since they are doing everything in their power; even if they are modeling incorrectly or behaving badly, if they did not have opportunities to learn to do otherwise beforehand then is it still reasonable to fault them if they act that way? The pragmatic part of me says that everyone has “opportunities to learn to do otherwise” with the knowledge on the internet, so we can in fact fault people for modeling poorly. But I’m not sure if this line of reasoning is correct.
I disagree, and that’s my central issue with the post.
The post gets this exactly backwards—the optimal contract exactly balances punishing lack of effort and bad luck, in a way that the employer is willing to pay as much as the market dictates for that effort under the uncertainty that exists.
Maybe I will have to edit the text to make that clearer, but: the optimal contract in the situation I described (moral hazard with binary states and effort levels) punishes only for bad luck, exactly because it makes the worker choose high effort. In this sense, once revenue is known, you also know that it is not the worker’s fault that revenue is low. From an ex-ante perspective, it offers conditional wages that “would” punish for being lazy, however.
You’re right—but the basic literature on principle agent dynamics corrects this simple model to properly account for non-binary effort and luck, and I think that is the better model for looking at luck and effort.