On Monday, the Royal Swedish Academy of Sciences awarded this year’s Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel — popularly, albeit incorrectly, referred to as the Nobel Prize for Economics — to Paul R Milgrom and Robert B Wilson. Both winners are currently with Stanford University, where they teach in different departments.
In its announcement, the Academy said the pair were receiving the award for “for improvements to auction theory and inventions of new auction formats”. They will equally share the 10 million Swedish kronor award money — roughly Rs 8.33 crore.
What is auction theory?
Essentially, it is about how auctions lead to the discovery of the price of a commodity. Auction theory studies how auctions are designed, what rules govern them, how bidders behave and what outcomes are achieved.
When one thinks of auctions, one typically imagines the auction of a bankrupt person’s property to pay off his creditors. Indeed, this is the oldest form of auction. This simple design of such an auction — the highest open bidder getting the property (or the commodity in question) — is intuitively appealing as well.
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But over time, and especially over the last three decades, more and more goods and services have been brought under auction. The nature of these commodities differs sharply. For instance, a bankrupt person’s property is starkly different from the spectrum for radio or telecom use. Similarly, carbon dioxide emission credits are quite different from the spot market for buying electricity, which, in turn, is quite different from choosing which company should get the right to collect the local garbage.
In other words, no one auction design fits all types of commodities or seller.
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This is also true because the purpose of an auction also differs with the commodity and the entity conducting the auction. More often than not, private sellers want to maximise their gains while public authorities may have other goals in mind.
For instance, when selling telecom spectrum, a government could either think in terms of maximising its revenues or aim at making telecom more affordable to everyone. If it wants to maximise revenues, the auction has to be designed one way, but doing so will imply that the company eventually winning the contract will make telecom services costlier and, in the process, deprive the poorest sections of affordable telephony and Internet access. The up-side, however, is that the government will get more money in its kitty and can use it whichever way it likes — possibly even subsidise the telecom costs of the poorest.
On the other hand, if the government’s goal is to enable the broader society to access the benefits of the telecom revolution and allow even the poorest to use the Internet at an affordable rate, it may want to focus more on how best a company can ensure that. In fact, before auctions became the norm for limited resources such as radio waves, governments used to allocate them as one would conduct a beauty contest. This would involve asking how a company might use the spectrum and assessing which company is best suited to receive the license. This approach, however, led to a proliferation of lobbying.
But even when a beauty contest approach was replaced with an auction, it mattered how the auction was designed. For instance, if spectrum is auctioned at the regional level, national players may not get seamless access to optimum quality of spectrum across the country; as a result, they may not bid as aggressively. In the US, such a mistake led to a second-hand market where companies traded among themselves with little revenue accruing to the government.
How an auction is designed, therefore, has a tremendous impact not just on the buyers and the sellers but also on the broader society.
What are the key variables that determine the outcome of an auction?
Three key variables need to be understood while designing an auction.
One is the rules of the auction. Imagine participating in an auction. Your bidding behaviour is likely to differ if the rules stipulate open bids as against closed/sealed bids. The same applies to single bids versus multiple bids, or whether bids are made one after another or everyone bids at the same time.
The second variable is the commodity or service being put up for auction. In essence, the question is how does each bidder value an item. This is not always easy to ascertain. In terms of telecom spectrum, it might be easier to peg the right value for each bidder because most bidders are likely to put the spectrum to the same use. This is called the “common” value of an object. But this may not be the case with some other commodities, say a painting. Person A may derive considerably more “private” or personal value — just by looking at it endlessly — than person B. In most auctions, bidders allocate both “common” as well as “private” values to the object being auctioned and this affects their eventual bids.
The third variable is uncertainty. For instance, which bidder has what information about the object, or even the value another bidder associates with the object.
Milgrom and Wilson have done pioneering work on auction theory and much of our current understanding is due to their research.
As the Academy notes, “Wilson developed the theory for auctions of objects with a common value — a value which is uncertain beforehand but, in the end, is the same for everyone”. Wilson showed what the “winner’s curse” is in an auction and how it affects bidding. As shown in the illustration, it is possible to overbid — $50 when the real value is closer to $25. In doing so, one wins the auction but loses out in reality. The winner’s curse explains “why rational bidders tend to place bids below their own best estimate of the common value: they are worried about the winner’s curse — that is, about paying too much and losing out”.
Milgrom “formulated a more general theory of auctions that not only allows common values but also private values that vary from bidder to bidder”. He “analysed the bidding strategies in a number of well-known auction formats, demonstrating that a format will give the seller higher expected revenue when bidders learn more about each other’s estimated values during bidding”.
With each passing year, as societies have become more complex Milgrom and Wilson responded by inventing new formats “for auctioning off many interrelated objects simultaneously, on behalf of a seller motivated by broad societal benefit rather than maximal revenue”.
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