The second one is a good example for selection incentive: the incentives are there regardless of what we want. I like the counter intuitive actions in the third example: organisms are deliberately trying to achieve high reward by taking counterintuitive actions.
I particularly like the first & third one. John and I talked about incentive mechanisms in biological world (organisms, biological evolution, etc.) and these are really good examples of it.
Credit
An enormous amount of credit goes to johnswentworth who made this new post possible.
This is a framing practicum post. We’ll talk about what incentives are, how to recognize incentives in the wild, and what questions to ask when you find them. Then, we’ll have a challenge to apply the idea.
Today’s challenge: come up with 3 examples of incentives which do not resemble any you’ve seen before. They don’t need to be good, they don’t need to be useful, they just need to be novel (to you).
Expected time: ~15-30 minutes at most, including the Bonus Exercise.
At the beginning of a sowing season, the Government of India announces a list of guaranteed purchase prices for certain crops, e.g., rice, wheat, cotton, etc., to support farmers. In case the market price for a crop falls below the guaranteed purchase price, the government agencies purchase the entire quantity from farmers if the crop quality meets a minimum quality threshold. From an Indian farmer’s perspective, the farmer is encouraged to produce price supported crops with quality just above the threshold level set by the government - and no higher.
This is an economic incentive: There is a reward signal in the system. Farmers are rewarded for producing crops just above the quality threshold. On the other hand, they are not rewarded for producing higher quality crops. Here we see the defining features of incentives: A system (a farmer) “wants” some resource (money), and can get more of that resource in return for some actions (producing crops with quality just above the threshold level) than others (producing crops with quality well above the threshold level).
Another example, with a direct notion of “reward”: cash incentive for taking Covid-19 vaccine shots. Some states in the US are offering rewards for Covid-19 vaccination in the form of direct cash or lottery programs. We can identify a clear reward signal in the system, which is people get rewarded for taking vaccines. Here again we see the defining features of incentives: A system (a human) “wants” some resource (money), and can get more of that resource in return for some actions (taking Covid-19 vaccines) than others (not taking Covid-19 vaccines).
In general, incentives should come to mind whenever there is some kind of reward signal. A system “wants” some resource, and can get more of that resource in return for some actions than others.
In the farmers support price example, the Government of India announces a minimum quality requirement for the crops to be purchased. Crops with lower quality will not qualify for the support program. On the other hand, farmers are not rewarded for having high quality crops. As a result, farmers will not only avoid the work required to produce higher quality crops, they will even make their crops’ quality worse: farmers with high quality crops will mix small rocks, or leftover crops from previous years into their harvested crops to increase the total quantity of “crop”, and thus total revenue. Obviously the Government of India did not intend for farmers to throw gravel into their crops, but they accidentally incentivized it anyway.
In general, whenever we see incentives, we should ask:
What about the cash-reward-for-Covid-19-vaccine example? If there is someone who is urgently in need of money, that person might fake the vaccination status in order to receive rewards more than once.
Come up with 3 examples of incentives which do not resemble any you’ve seen before. They don’t need to be good, they don’t need to be useful, they just need to be novel (to you).
Any answer must include at least 3 to count, and they must be novel to you. That’s the challenge. We’re here to challenge ourselves, not just review examples we already know.
However, they don’t have to be very good answers or even correct answers. Posting wrong things on the internet is scary, but a very fast way to learn, and I will enforce a high bar for kindness in response-comments. I will personally default to upvoting every complete answer, even if parts of it are wrong, and I encourage others to do the same.
Post your answers inside of spoiler tags. (How do I do that?)
Celebrate others’ answers. This is really important, especially for tougher questions. Sharing exercises in public is a scary experience. I don’t want people to leave this having back-chained the experience “If I go outside my comfort zone, people will look down on me”. So be generous with those upvotes. I certainly will be.
If you comment on someone else’s answers, focus on making exciting, novel ideas work — instead of tearing apart worse ideas. Yes, And is encouraged.
I will remove comments which I deem insufficiently kind, even if I believe they are valuable comments. I want people to feel encouraged to try and fail here, and that means enforcing nicer norms than usual.
If you get stuck, look for:
Bonus Exercise: for each of your three examples from the challenge, explain:
This bonus exercise is great blog-post fodder!
Using a framing tool is sort of like using a trigger-action pattern: the hard part is to notice a pattern, a place where a particular tool can apply (the “trigger”). Once we notice the pattern, it suggests certain questions or approximations (the “action”). This challenge is meant to train the trigger-step: we look for novel examples to ingrain the abstract trigger pattern (separate from examples/contexts we already know).
The Bonus Exercise is meant to train the action-step: apply whatever questions/approximations the frame suggests, in order to build the reflex of applying them when we notice incentives.
Hopefully, this will make it easier to notice when an incentive frame can be applied to a new problem you don’t understand in the wild, and to actually use it.