Soft incentive mechanism design
Markets, cakes, karma, and reverse game theory, applied to humans
September 22, 2014 — June 29, 2022
Mechanism design for the moral wetware of human beings. In practice that means we might relax some of the mathematical axiomatisation and consider more qualitative/fuzzy ways of thinking about it that are more “inspired by” formal algorithmic/game-theoretic models than presuming exactness. What do our rarefied abstract models of mechanisms imply for societies at large?
Sometimes, sometimes I also pretend to be even fuzzier than I claim, because I notice that for many people thinking about how we might design incentive systems to be moral has a flavour of taboo. Observationally, I can get more buy-in talking about “fair systems” that do their best to circumlocute around their own rationale. I find this latter type of fuzziness uncomfortably disingenuous but maybe this is ust a case of sensitive terminology and I should not get stuck on that on the way to making substantive differences.
“Reverse” game theory, bargaining, auctions, pie slicing, swarm sensing for autonomous agents. Reputation mechanisms. Voting systems are mechanisms, and the Arrow impossibility theoryem and Gibber-Satterthwaite theorem are foundational results in mechanism design. The classic, assassination markets, are no longer at the vanguard according to Brian Merchant, but prediction markets are a classic incentive mechanism for distributing forecasts (Merchant 2020). Every blockchain-style cryptowhatsit is a mechanism design problem. More important for this notebook, better governance is a mechanism design problem.
Surprisingly widely applicable; for example, one might frame generative adversarial learning as an ingenious mechanism design.
Also, I feel like this is a situation where the alternative to explicit mechanism design seems to be bad implicit mechanism design. Or, maybe, a misunderstanding of mechanism.
- Measurement and power in organisations (which mentions the Hubbard book on measurement) (Hubbard 2014). For more on that, see analytics
1 Examples
Choron is a case study in designing a mechanism for handling house chores.
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RoboVote is a free service that helps users combine their preferences or opinions into optimal decisions. To do so, RoboVote employs state-of-the-art voting methods developed in artificial intelligence research. […]
For subjective preferences, the approach is known as implicit utilitarian voting. We assume that each participant has a (subjective) utility function that assigns an exact utility to each alternative. Our goal is to choose an outcome that maximizes utilitarian social welfare, which is the total utility assigned to the outcome by all participants. […] we only ask for a ranking of the alternatives. […]
[…] For objective opinions, let us focus first on the case where the desired outcome is a ranking of the alternatives. We assume that there is a true ranking of the alternatives by relative quality, and our goal is to pinpoint a ranking that is as close as possible to the true ranking, given the available information.
2 Iterative conversation games
See iterated conversation games.
3 Quadratic voting
TBD. Is quadratic voting this the same quadratic? Consider public goods provision (Buterin, Hitzig, and Weyl 2019).
4 Incoming
Facebook will change algorithm to demote “borderline content” that almost violates policies
Dàzéxiāng Qǐyì is the name of the perverse incentive uprising during the Qin dynasty
Chen Sheng and Wu Guang were both army officers who were ordered to lead their bands of commoner soldiers north to participate in the defense of Yuyang (simplified Chinese: 渔阳; traditional Chinese: 漁陽). However, they were stopped halfway in present-day Anhui province by flooding from a severe rainstorm. The harsh Qin laws mandated execution for those who showed up late for government jobs, regardless of the nature of the delay. Figuring that they would rather fight than accept execution, Chen and Wu organized a band of 900 villagers to rebel against the government.