Game theory, bargaining, auctions, pie slicing, swarm sensing for smart agents. Surprisingly widely applicable; for example, one might frame generative adversarial learning as an ingenious mechanism design.
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.
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?
- the algorithmic game theory blog’s economics entry
- The downside: Anonymous assassination markets
- spliddit, a website to use optimal cake cutting algorithms to allocate credit/rent/whatever
Ronnie Horesh floats an interesting market, social policy bonds
Social Policy Bonds are non-interest bearing bonds, redeemable for a fixed sum only when a targeted social objective has been achieved. The bonds would be backed by government or private bodies, auctioned on the open market, and freely tradable at all times. A Social Policy Bond regime would:
Inextricably link rewards to outcomes rather than inputs, outputs, activities or institutions; and
Inject the market’s incentives and efficiencies into the achievement of social and environmental goals.
The effect of a Social Policy Bond regime is to contract out the achievement of social and environmental goals to the most efficient operators - whether they be in the private private or public sector. Because Social Policy Bonds do not prejudge how objectives shall be achieved nor who shall achieve them, they would encourage diverse, adaptive solutions.
Every blockchain-style cryptowhatsit is a mechanism design problem.
Better governance is a mechanism design problem.
Akerlof, George A. 1970. “The Market for "Lemons": Quality Uncertainty and the Market Mechanism.” The Quarterly Journal of Economics 84: 488–500. https://doi.org/10.2307/1879431.
Arthur, W Brian. 1994. “Inductive Reasoning and Bounded Rationality: The El Farol Problem.” American Economic Review 84: 406–11.
———. 1995. “Complexity in Economic and Financial Markets.” Complexity 1 (1): 20–25.
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.
Buchanan, James M. 1954. “Social Choice, Democracy, and Free Markets.” Journal of Political Economy 62 (2): 114–23.
Cai, Yang, Constantinos Daskalakis, and S. Matthew Weinberg. 2013. “Understanding Incentives: Mechanism Design Becomes Algorithm Design,” May. http://arxiv.org/abs/1305.4002.
Challet, Damien, Matteo Marsili, and Yi-Chang Zhang. 2000. “Modeling Market Mechanism with Minority Game.” Physica A: Statistical and Theoretical Physics 276 (1-2): 284–315.
Daskalakis, Constantinos, Alan Deckelbaum, and Christos Tzamos. 2012a. “Optimal Pricing Is Hard.” In Internet and Network Economics, edited by Paul W. Goldberg, 7695:298–308. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-35311-6_22.
———. 2012b. “The Complexity of Optimal Mechanism Design,” November. http://arxiv.org/abs/1211.1703.
———. 2013. “Mechanism Design via Optimal Transport.” In, 269. ACM Press. https://doi.org/10.1145/2492002.2482593.
Easley, David, and Jon Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. New York: Cambridge University Press. http://www.cs.cornell.edu/home/kleinber/networks-book/.
Gintis, Herbert. 2010. “The Dynamics of Generalized Market Exchange.”
Gode, Dhananjay K, and Shyam Sunder. 1993. “Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality.” The Journal of Political Economy 101: 119–37. https://doi.org/10.2307/2138676.
———. 1997. “What Makes Markets Allocationally Efficient?” The Quarterly Journal of Economics 112: 603–30.
Goldbaum, David. 2004. “On the Possibility of Informationally Efficient Markets.” Working Papers Rutgers University, Newark 2004-009. Department of Economics, Rutgers University, Newark. http://ideas.repec.org/p/run/wpaper/2004-009.html.
Graham-Tomasi, Theodore, Ford C Runge, and William F Hyde. 1986. “Foresight and Expectations in Models of Natural Resource Markets.” Land Economics 62: 234–49. https://doi.org/10.2307/3146389.
Grossman, Sanford J, and Joseph E Stiglitz. 1980. “On the Impossibility of Informationally Efficient Markets.” The American Economic Review 70 (3): 393–408.
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.
Lo, Andrew W. 2004. “The Adaptive Markets Hypothesis.” The Journal of Portfolio Management 30: 15–29. https://doi.org/10.3905/jpm.2004.442611.
Louzoun, Yoram, Sorin Solomon, Jacob Goldenberg, and David Mazursky. 2003. “World-Size Global Markets Lead to Economic Instability.” Artificial Life 9 (4): 357–70. https://doi.org/10.1162/106454603322694816.
Mcleod, Doug, Garry Emmerson, Robert Kohn, and Geoff Kingston (universit. 2008. “Finding the Invisible Hand: An Objective Model of Financial Markets.”
Merchant, Brian. 2020. “Click Here to Kill.” Harper’s Magazine, January 2020. https://harpers.org/archive/2020/01/click-here-to-kill-dark-web-hitman/.
Milgrom, Paul. 2019. “Auction Market Design: Recent Innovations.” Annual Review of Economics 11 (1): 383–405. https://doi.org/10.1146/annurev-economics-080218-025818.
Nisan, Noam. 2007. Algorithmic Game Theory. Cambridge ; New York: Cambridge University Press. http://www.loc.gov/catdir/toc/ecip0715/2007014231.html.
Nordhaus, William D. 2005. “Schumpeterian Profits and the Alchemist Fallacy.” SSRN Scholarly Paper ID 820309. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=820309.
Offer, Avner. 2002. “Why Has the Public Sector Grown so Large in Market Societies? The Political Economy of Prudence in the UK, c. 1870-2000.” Working Paper 44. Oxford Economic and Social History Working Papers. Oxford University Department of Economics. http://economics.ouls.ox.ac.uk/14826/.
Padgett, John F., and Walter W. Powell. 2012. The Emergence of Organizations and Markets. Princeton University Press.
Paich, Mark, and John D Sterman. 1993. “Boom, Bust, and Failures to Learn in Experimental Markets.” Management Science 39. https://doi.org/10.2307/2633062.
Roughgarden, Tim, and Inbal Talgam-Cohen. 2019. “Approximately Optimal Mechanism Design.” Annual Review of Economics 11 (1): 355–81. https://doi.org/10.1146/annurev-economics-080218-025607.
Sadrieh, Abdolkarim. 1998. The Alternating Double Auction Market: A Game Theoretic and Experimental Investigation (Lecture Notes in Economics and Mathematical Systems). Springer.
Simon, Herbert A. 1991. “Organizations and Markets.” The Journal of Economic Perspectives 5: 25–44. https://doi.org/10.2307/1942684.
Spence, Michael. 2002. “Signaling in Retrospect and the Informational Structure of Markets.” American Economic Review 92: 434–59. https://doi.org/10.1257/00028280260136200.
Su, Francis Edward. 1999. “Rental Harmony: Sperner’s Lemma in Fair Division.” The American Mathematical Monthly 106 (10): 930–42. https://doi.org/10.2307/2589747.
Sun, Albert. 2014. “To Divide the Rent, Start with a Triangle.” The New York Times, April 28, 2014. https://www.nytimes.com/2014/04/29/science/to-divide-the-rent-start-with-a-triangle.html.
Sutton, John. 2001. Technology and Market Structure: Theory and History. The MIT Press.
The Economist. n.d. “Can Technology Plan Economies and Destroy Democracy?” Accessed May 13, 2020. https://www.economist.com/christmas-specials/2019/12/18/can-technology-plan-economies-and-destroy-democracy.
Valentine, Melissa A, Daniela Retelny, Alexandra To, Negar Rahmati, Tulsee Doshi, and Michael S Bernstein. 2017. “Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds as Organizations.” In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 3523–37. ACM. http://hci.stanford.edu/publications/2017/flashorgs/flash-orgs-chi-2017.pdf.
Ye, Yinyu. 2008. “A Path to the Arrow–Debreu Competitive Market Equilibrium.” Mathematical Programming 111 (1-2): 315–48. https://doi.org/10.1007/s10107-006-0065-5.