Abstract
Due to computing and communication facilities, formal procedures, often referred to as “algorithms,” are now extensively used in public, economic, and social areas. These procedures, currently at the forefront of criticisms, share some features with mechanisms as defined by economists, following Hurwicz. My aim is to investigate these relationships and to discuss some of the economic risks generated by the power of algorithms.
This is written for the volume “Social Design: Essays in Memory of Leonid Hurwicz” edited by Walter Trockel. I thank him for his encouragement. This work is partially based on a talk I gave in the stimulating workshop “Social Responsibility of Algorithms” organized by Alexis Tsoukias at Paris-Dauphine University, December 2017.
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Notes
- 1.
Naturally, the relationships do not apply to all algorithms, in particular to those computed in artificial intelligence.
- 2.
Such argument has the same flavor as the one saying that contracts are typically incomplete.
- 3.
This question is very much related to the previous one, as noted by Dwork et al. (2001).
- 4.
As described in McMillan (1994), options were multiple, for example simultaneous versus sequential auctions, open versus sealed bids, or royalties versus reserve prices.
- 5.
The APB procedure did not satisfy this property because it modified the deferred-acceptance algorithm so that universities’ choices depended on the students rankings. Though, students’ misreporting their preferences does not seem to have been a big issue.
- 6.
Another example of reluctance to automatic systems is Centrelink put in place in Australia to recover social security overpayments. Though the system, dubbed “Robodebt” by users, had some flaws initially—people being unable to complain or reach the service—the main objection relied on the automatic nature of the procedure. Information can be found in https://www.humanservices.gov.au/organisations/about-us/publications-and-resources/government-response-community-affairs-references-committee-report.
- 7.
Each student will first select a list of applications without order; each application in the list is sent to the corresponding university. Then there will be a succession of rejection or acceptance by universities and students (on their list). The number of applications in a list is a priori constrained to 10, but each application may regroup up to 20 slots.
- 8.
- 9.
This is not true for new algorithmic methods such as machine learning with evolving data, neuronal networks and all the techniques referred to as “artificial intelligence.”
- 10.
More information can be found at http://europa.eu/rapid/press-release_IP-17-1784_en.htm.
- 11.
Clive Humby, a data scientist, claimed in 2006 “Data is the new oil,” now a popular maxim.
- 12.
- 13.
Balinski and Demange (2018) study whether tax instruments are useful in reducing excessive level of data collection.
- 14.
I am grateful to Carmine Ventre for calling my attention on this point. For more information, see e.g. the book edited by Easley et al. (2014), in particular Chapter 4 by Golub, Dupuis and Olsen and Chapter 10 by Linton, O’Hara and Zigrand.
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Demange, G. (2019). Mechanisms in a Digitalized World. In: Trockel, W. (eds) Social Design. Studies in Economic Design. Springer, Cham. https://doi.org/10.1007/978-3-319-93809-7_19
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