My mentor Paul Milgrom

category:Finance
 My mentor Paul Milgrom


Author Luo Hantang

I am an assistant professor of finance at the University of Chicago Booth School of business. I am engaged in market design, including financial derivatives, real estate market, bilateral price collection and allocation of natural resources use rights.

I graduated from Stanford GSB economics in 2019, and Paul Milgrom was one of my two mentors. Paul is an economist and thinker who covers a wide range of fields. As an economist, his 10 most cited papers are distributed in more than five different fields.

In recent years, he has been working with computational scientists to develop an incentive auction method in the signal band, which combines the complex satfc algorithm. Paul is still learning new things, building connections in various fields, and trying to break through the application boundaries of auction. As a student, I am particularly encouraged.

Among auction theorists, Paul is undoubtedly the most important one in terms of contribution theory model. Paul has contributed to almost every field of auction economic theory.

Although his paper has been cited a lot, his achievements in practice may be more important: he has been involved in the design or consultation of almost all band auctions since the first auction by the Federal Communications Commission (FCC) in 1994.

Looking at the written records of the Nobel Committee, it is more his auction design work in practice that makes Paul win the Nobel Prize in economics. Many of the contents of this work have not been well cited by the academic community, because it is specific to the situation and can not be generalized. But this work is far-reaching, and Ill try to describe some of them.

Auction practice vs auction theory

In 1959, the FCC allocated the radio spectrum basically by reviewing and evaluating the companys applications. Its a very casual process, and its very time-consuming and resource intensive.

Economist Ronald Coase has an interesting idea: lets auction bands! Whoever is willing to pay the highest price for the band should get it.

Through auctions, the government will not only save time in processing applications, but also raise a lot of revenue through spectrum sales. Coase attended a hearing before the FCC in 1959 to discuss the feasibility of the auction. At that time, the biggest question was: is the auction a fools Day joke? The FCC believes that it is impossible to auction directly to ensure that the spectrum ends up on the right people!

So the idea was shelved, and it wasnt until 35 years later that it was first tried. The first large-scale band auction is the auction of narrow bandwidth personal communication networks. One of the design teams is McAfee and McMillan, and the other is, of course, Milgrom and Wilson (for which they won the Nobel Prize in economics in 2020).

Interestingly, at some point before the auction, the FCC has no bandwidth (pun) to process all applications, so it can only be allocated by lottery. Coase Theorem says that the market is the most efficient way, but it is not.

Nowadays, the band auction uses a very professional process, which is completely different from the graduate theory class, including simultaneous up auction, combined clock auction, agent clock auction and so on

In practice, the institutional constraints of band allocation are indeed chaotic and easily affected by details, often involving multiple licenses, overlapping of adjacent bands, and other strange permutation and combination problems.

Incentive auctions involve the work of many computer scientists, such as Kevin Leyton brown, who have studied how to combine auctions with basic knapsack problem solving. Its a difficult thing, and theres a theoretical basis behind it - Paul Milgroms work on total substitutes and up auctions, matroids, etc.

Therefore, auction in reality can not be summarized as concisely as one-dimensional mechanism design, and it is even more difficult to teach students. The practical application is usually not as direct and clear as what is taught in school.

In some cases, academic papers will be divided. Some widely concerned theoretical articles are rarely applied in reality. However, many important problems in specific real environment have not been paid attention to by theoretical research.

One example is online advertising auctions. Companies like Google and Facebook have come up with clever tricks like bid leveling.. I worked on Facebooks advertising team in 2013 and found it interesting and practical, but it looked completely different from what was in the classroom.

Maybe its the psychological subjective feeling: the actual work and the theoretical work of the auction have some differences. There are a lot of cool apps that affect billions of dollars in the market, but economists pay little attention to it.

Economists have a lot to do in this field. Market design is chaotic, complex and detail sensitive. But isnt it the same in the real world? If you want to do a job that has a real impact on the world, then you need to face it. And Paul is willing to roll up his sleeves and deal with the real problems of chaos and make a difference. His research inspires peoples minds, and it is also my goal to follow.

Paul still does a lot of work in academia and policy. On the policy front, he recently participated in the design of the 2017 incentive auction, a successful collaboration between economists and computer scientists. This is a very difficult problem for two reasons. First, its a two-way auction: it buys spectrum from the broadcaster at the same time, repackages it, and sells it to new buyers. Secondly, there are many complicated restrictions on the means used in the process of repackaging. These are basically high complexity problems, which need complex computer methods to deal with. A lot of computer experts, like Kevin Layton brown, are involved.

Paul has also written a number of policy documents related to his work, such as redesign spectrum licensing (2017) and 5g fuel: flexible use and valid licensing (2019) with Glen Weyl and I. He is also engaged in basic research. He recently published a game theory paper with Josh mollner: the appropriate equilibrium of expansion. In addition, he and his students wrote a paper on investment incentive mechanism: investment incentive in near optimal mechanism. All in all, Paul is still active in many areas.

Pauls interdisciplinary knowledge really helped his students, and I started to study the JMP topic because Paul invited Darrell Duffie to his study group to speak on financial auctions, which led me to engage in financial related research.

I reported to Paul about ten different versions of my employment thesis, and he showed up every time he gave a speech. He might be really bored later. But it shows how much time he spends on students. Without Pauls advice and support, my senior brothers and sisters, and some luck, it would be difficult for me to write my employment thesis.

Paul was always generous with his students, not only in writing papers, but also in learning how to speak. After each student in the group has spoken, we will circle around the conference room: each student in the audience must comment on the speakers academic content and presentation style. Sometimes, the feedback process takes up half of the whole speech time.

Paul taught his students very successfully. In 2017, Piotr dworczak worked at Northwestern University. Yu Ning worked at Emory University in 2015, and Shengwu Li got a job at Harvard. Muhammad akbarpour was a postdoctoral fellow in Chicago and returned to Stanford Business School in 2016. Shota Ichihashi published his paper on the job market in the top issue of the American economic review (AER) the second year after graduation.

Anthony Zhang Lee is a visiting scholar at rohantang and an assistant professor of finance at the Booth School of business at the University of Chicago

Han Ruobing is a research intern of luohantang University. He is a doctor in Stanford University of economics,

quote

Akbarpour,MohammadandKominers,ScottDukeandLi,ShengwuandMilgrom,PaulR.,InvestmentIncentivesinNear-OptimalMechanisms(February25,2020).Availa bleatSSRN:https / / ssrn.com/abstract=3544100orhttp / / dx.doi.org/10 .2139/ssrn.3544100.

Ichihashi,Shota.2020.OnlinePrivacyandInformationDisclosurebyConsumers.AmericanEconomicReview,110(2):569-95.

ThePrizeinEconomicSciences2020. NobelPrize.org.NobelMediaAB2020 .Thu.15Oct2020. https://www.nobelprize.org/prizes/economic-sciences/2020/summary/.

Li,Shengwu.2017.ObviouslyStrategy-ProofMechanisms.AmericanEconomicReview,107(11):3257-87.

Milgrom, PaulR.andMollner ,Joshua,ExtendedProperEquilibrium(2020).Availa bleatSSRN:https / / ssrn.com/abstract=3035565orhttp / / dx.doi.org/10 .2139/ssrn.3035565.

FUELfor5G:FlexibleUseandEfficientLicensing2019,whitepaper,Auctionomics.

AnthonyZhangLee,CompetitionandManipulationinDerivativeContractMarkets,July2020.

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