Incentive mechanism design

Markets, cakes, karma, and games


Mecha design, courtesy blueprintbox.

“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. 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.

I like the Benajamin Hoffman bit, the logic of Pol Pot which casts light on this from an odd angle:

Pol Pot’s policies aren’t indicative of his personal badness, they reflect a certain level of skepticism about expertise narratives that benefit extractive elites.

Expertise narratives definitely have an extractive component. (Medical doctors use law and custom to silence others’ claims to be able to heal, but MDs are obviously not responsible for all healing, or only doing healing, and they ARE collecting rents.) If they are 100% extractive, then anyone participating in them is a social parasite and killing or reeducating them is good for the laborers. I think it’s easy to see how this can lead to policies like “kill all the doctors and let teens do surgery.” This naturally escalates to “kill everyone with glasses” if you are enough of a conflict theorist to think that literal impaired vision is mostly a motivated attempt to maintain class privilege as a scholar.

Examples

  • Choron is a case study in designing a mechanism for handling house chores.

  • robovote:

    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.

  • spliddit, a website to use optimal cake cutting algorithms to allocate credit/rent/whatever

  • The free version is the New York Times rent calculator, as mentioned in Albert Sun’s article Sun (2014) about Su’s research into Sperner’s lemma (Su 1999)

Tutorials

Aaron Roth’s Algorithmic Game theory course

In this course, we will take an algorithmic perspective on problems in game theory. We will consider questions such as: how should an auction for scarce goods be structured if the seller wishes to maximize his revenue? How badly will traffic be snarled if drivers each selfishly try to minimize their commute time, compared to if a benevolent dictator directed traffic? How can couples be paired so that no two couples wish to swap partners in hindsight? How can you be as successful at betting on horse races as the best horse racing expert, without knowing anything about horse racing? How can we set prices so that all goods get sold, and everyone gets their favorite good?

A mechanism incentivising coordination.

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Bhattacharya, Jay, and Mikko Packalen. 2020. “Stagnation and Scientific Incentives.” Working Paper 26752. National Bureau of Economic Research. https://doi.org/10.3386/w26752.

Bowles, Samuel, and Herbert Gintis. 1998. “Efficient Redistribution: New Rules for Markets, States and Communities.” Recasting Egalitarianism: New Rules for Communities, States and Markets 3: 1.

Börgers, Tilman. 2015. An Introduction to the Theory of Mechanism Design. New York, NY: Oxford University Press, USA. http://aida.wss.yale.edu/~dirkb/teach/521b-08-09/reading/2008-mechanismdesign.pdf.

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Cai, Yang, Constantinos Daskalakis, and S. Matthew Weinberg. 2013. “Understanding Incentives: Mechanism Design Becomes Algorithm Design.” May 17, 2013. http://arxiv.org/abs/1305.4002.

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———. 2013. “Mechanism Design via Optimal Transport.” In, 269. ACM Press. https://doi.org/10.1145/2492002.2482593.

———. 2012b. “The Complexity of Optimal Mechanism Design.” November 7, 2012. http://arxiv.org/abs/1211.1703.

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Hubbard, Douglas W. 2014. How to Measure Anything: Finding the Value of Intangibles in Business. 3 edition. Hoboken, New Jersey: Wiley.

Hudson, Paul, WJ Wouter Botzen, Jeffrey Czajkowski, and Heidi Kreibich. 2014. “Risk Selection and Moral Hazard in Natural Disaster Insurance Markets: Empirical Evidence from Germany and the United States.”

Jackall, Robert. 2009. Moral Mazes: The World of Corporate Managers. Updated edition. Oxford ; New York: Oxford University Press.

Jackson, Matthew O. 2014. “Mechanism Theory.” SSRN Scholarly Paper ID 2542983. Rochester, NY: Social Science Research Network. https://web.stanford.edu/~jacksonm/mechtheo.pdf.

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