The game theory of sealed-bid auctions

According to Wikipedia, a “frenemy” is someone who is simultaneously a partner and a competitor.

The term is typically used in social relationships, but frenemies can also exist in economic relationships, and I was fortunate enough to encounter such a situation in a project I’m working on, and I decided to explore it. The conclusions are directly applicable to at least one specific situation where real money is at stake (I describe this at the end). More interestingly, this may suggest a selfish rationale for unselfish behavior in a wider class of situations.

First, a little background:

Sealed Bid Auction

Consider a simplified version of eBay where everyone bids once on an item, nobody sees each-other’s bid, and the highest bid wins. This is called a “First-price sealed-bid auction”.

One day you find a trustworthy guy called Bill that promises that they can pay you $200 for a Nexus One phone. You discover that Nexus One phones frequently sell for less than $200 on eBay, and several of these phones are auctioned off every day. Bill hates to use eBay and won’t ever use it no-matter what, and he doesn’t really care what you bid for the phones, so long as he gets them. You realize that there is an opportunity to make some money here.

So what do you bid when you see one of these phones? Its a compromise, since if you bid $200 you’ll definitely win the phone, but you’ll make no money. If you bid less, then you’ll make more money if you win, but you’ll win less frequently. If you bid too little then you’ll never win and you’ll make no money.

This is not a hard question to answer if you are reasonably smart and have a decent amount of information about past winning bids, since you’ll be able to discover, given a bid b1, a function f1(b1) that will tell you the probability of winning the auction, given whatever you bid is. Let’s assume that you are smart enough and you do have enough information. Let’s say your cut of the profit is c1, then, applying some high school math, your expected profit is $200*c1*f1($200*(1-c1)).

From this equation you are able to decide the optimal bid (and therefore how big your cut is) to maximize your own profit. You can either do this through some fancy math, or just experimentally plug in different values for c1 until you find what works best, the latter being my preferred approach (because I’m lazy and computers aren’t).

Bill gets clever

Then Bill comes to you and tells you that he may not always be able to pay you $200 for the phone, sometimes he’ll pay more, sometimes he’ll pay less, but he will tell you what he will pay before you must place your bid. Fair enough you think, you’ve got your function f1(b1), you can determine the optimal bid depending on whatever Bill is willing to pay.

Weeks go by and you are making good money off this relationship. One night you go out for a beer with Bill, and he drops a bombshell. He tells you that actually, he is playing the same game you are. He knows a guy, Jim, who is buying the phones from him. He won’t tell you who Jim is (Bill isn’t an idiot), but it turns out that Jim is paying even more for these Nexus Ones than Bill is! Worse still, it turns out that not only is Bill no idiot, he is at least as smart as you are. He has determined the probability of you winning your bid, and is choosing his cut to optimize his total profit, in the exact same way that you are (although his function, f2(b2), won’t be the same as your’s because you are reducing his bid by your cut).

You go home drunk and don’t think about it much, but the next day you have a real headache, and its not just all the beer you had with Bill last night. What exactly is the relationship between you and Bill? In one sense, you are on the same side. If your cut combined with Bill’s cut is too big, then both of you will make less money. But in another sense, you are on opposite sides, the smaller his cut, the bigger your cut can be, and vice versa.

Given this relationship, and assuming that you can’t deal directly with Jim, Bill can’t deal directly with eBay, and Bill is at least as smart as you are, how do you maximize your profit?

My Simulation

A mathematician or game theorist at this point would probably go into a dark room for a week, month, or year, and come out with the mathematically optimal answer. Since I don’t have the time or patience required for a rigorous mathematical treatment, I decided to do a few experiments instead. You can find the code here.

The Auction

In the results described here, Bill’s buyer pays $1 (ok, we’re somewhat abandoning the Nexus One analogy here) to Bill for a phone. I wanted to create a reasonably realistic auction, which I do in the bidWinProb() method on line 30. Basically I simulate 100,000 auctions, each consisting of 10 bidders whose bids are spread according to a Gaussian distribution with mean of $0.50, and standard distribution of 0.1. I then record all of the winning bids, which allows me to quickly estimate a probability of winning for any given bid.

Yes, I realize that I could do this much more efficiently mathematically, but speed doesn’t really matter here, and this solution is simple enough that bugs are unlikely.

It has the added benefit that you can just provide it with actual data from a real auction, rather than simulated data.

Optimize Cut

The first step is that we want to be able to determine the optimal cut for ourselves, given whatever cut Bill has, or the optimal cut for Bill given whatever our cut is. This is done in the optimizeCut() method on line 78. The approach is basic trial and error. While a more sophisticated, accurate, and efficient approach is certainly possible, the current implementation is more than adequate for our needs (not to mention being easier to be confident that it does what its supposed to).

Experiment #1: Best combined cut

So what happens if Bill and I were entirely transparent with each-other, and agreed that we’d find the best cut for both of us, and then split it down the middle?

In this situation it turns out that Bill and I should collectively take $0.28 of the dollar Bill is paid if the auction is won (which when we consider the possibility of losing the auction, is an expected revenue of $0.24). Split down the middle this is an expected revenue of $0.12 each.

Experiment #2: Iterated selfish cut

But what if Bill and I can’t agree on an even split, and we are basically each left to our own devices to make as much as we each can?

One approach to do this would be for me to optimize my cut based on whatever Bill is paying me (which will depend on his cut), and for Bill to optimize his cut based on his perspective of his probability given various payments to me (which will depend on my cut).

When I tried this the results were interesting. I started from a point where Bill isn’t taking any cut at all, then each of us take turns to optimize our cuts based on the other one:

% Cut          $ Cut
Bill    Me      Bill    Me      Ttl $   1/2
0.000   0.280   0.000   0.244   0.244   0.122
0.100   0.220   0.080   0.159   0.239   0.120
0.140   0.190   0.109   0.128   0.237   0.118
0.160   0.180   0.119   0.112   0.231   0.115
0.160   0.180   0.119   0.112   0.231   0.115

This is interesting because they converge to where Bill takes a 16% cut, and I take an 18% cut. Further, when you look at the expected revenue, it is close to evenly split between us, but not exactly. Note that after this iterative process Bill and I collectively are making only $0.231, whereas we would be making $0.244 if we had worked together and split the difference.

Bill makes 3% more money if we work together and split the profit, and I make 9% more!

Experiment #3: Machiavellianism

My last two experiments I call “Evil Me” and “Evil Bill”. In “Evil Me” I exploit the fact that Bill will optimize his cut based on my cut, so rather than selecting my cut based on Bill’s cut, I select my cut to maximize my profit knowing that Bill will optimize for whatever is in his best interests given my cut. Evil Bill is the same but the other way around.

% Cut          $ Cut
Bill    Me      Bill    Me      Ttl $   1/2
0.100   0.270   0.055   0.134   0.189   0.094  <-- I'm evil
0.240   0.120   0.151   0.057   0.208   0.104  <-- Bill is evil

Wow, so in this situation being Machiavellian allows me to make $0.134 while poor Bill only makes $0.055! Bill reverses this if he is Machiavellian and I am not.

What is fascinating is that, while I increase my profit by 20%, Bill's drops to less than half of what it was before! One of us being Machiavellian helps that person a bit, but it hurts the other a lot.


So what are our conclusions based on this one experiment?

  1. If our sole motivation is to maximize our own profit, we should adopt the Machiavellian approach, even though this will totally screw Bill.
  2. If we at least have some semblance of compassion for Bill, the next best approach is to pre-agree a split with Bill, and then optimize together, as in Experiment #1.
  3. The pre-agreed split should probably be 50:50, because if Bill and I can't agree on a split and were to optimize against each other as in Experiment #2, then we wind up making approximately the same amount anyway, but we both make less than if the split is pre-agreed.

An important caveat is that these conclusions are based on a single experiment that makes a variety of assumptions. These assumptions seem realistic enough that hopefully these results do generalize, but we can't say that for sure.

Unanswered questions

Some may feel dissatisfied by the fact that I've answered these questions experimentally, rather than explore the math behind why we get these results. I would strongly encourage such people to feel free to explore themselves, especially if they enjoy algebra and statistics. I'll gladly link to any serious attempts here.

Further, I've explored a situation where one participant is manipulative, while the other tries to make the best of whatever situation they are in. But what if both participants are manipulative? My suspicion is that in that case it degenerates into a Game of Chicken with an ultimate outcome that is far worse for both participants, but further investigation is definitely warranted.

Why is this problem relevant?

Realtime bidding is a recent innovation in advertising where advertisers bid against each-other for the right to show you an advertisement. The analogy with the situation described above is that the sealed-bid eBay is an ad exchange, I'm an ad network, and Bill is the advertiser. Bill is selling something where he has a (hopefully) known profit margin, and he must decide how much of this profit margin he will spend to sell his product. Similarly, I as the ad network must decide on my cut.

Note 26th July 2010 - This is a republish of an article from 24th July with significant additions and modifications.

Leave a Reply

Your email address will not be published.