The European Union (“EU”) recently concluded its investigation of the Bayer Monsanto transaction.  As part of the remedy, Bayer has agreed to license to BASF its “entire global digital agriculture product portfolio and pipeline products to ensure continued competition on this emerging market.”  According to the EU’s press release, “[d]igital agriculture uses public data such as satellite pictures and weather data as well as private data collected from farmers’ fields. It applies agronomic knowledge and algorithms to that data to recommend to farmers how to best manage their fields. For example, how many seeds to use, and on how much and when to use pesticide and fertiliser. This makes digital agriculture important, not only to farmers but also to the environment.”

The use of the word “license” is important; it is not, apparently, an assignment. The reason why it’s important is because Bayer can continue to use and market the intellectual property itself.  At first blush, it would appear that the EU has created another “flavor” of the Bayer product that BASF can do with as it pleases.

At the outset, it is important to note that the intellectual property being licensed is not a patent. A patent describes clearly a particular art and grants the owner the ability to exclude others from practicing that art for a period of time.  In exchange for that exclusivity, the world gets to see the art.  A software license, or the code that executes an algorithm, is copyrightable.  But a copyright protects the expression, not the idea.  Moreover, software can be millions of lines long and will almost necessarily change over time.  People’s needs change.  Functionality can be added or deleted.  Bugs are corrected.  Software is a very dynamic product whereas the art in a patent is static.  These are important differences.

Let’s say Microsoft is convicted of monopolization of the spread sheet market, and they are required to “license” Excel to someone who can sell it in competition with Microsoft’s product. At first, the two flavors of Excel sell equally well, perhaps even the divested version sells better because the purchaser can afford to sell the product for less as its sunk costs are less.  Customers use the products and their needs change, adapt and grow.  If I have just the right to sell the software and no knowledge of how it was built, it is very difficult for me to make any meaningful changes to the platform.  Indeed, the more complicated the software is, the more likely I may destabilize it by altering it.  In effect, with just a license, my “flavor” stagnates.  It remains Windows 95 in a world where everyone is using Windows 10.

As the EU mentioned, there is a great deal of data associated with digital farming. It’s not only the weather, but the composition of a particular farmer’s soil, the atmosphere, the fertilizers, the timing of planting.  It is also the type of seeds, the genetic composition of the seeds, how the seeds react in different environments.  There is a great deal of data, and it’s not stagnant.  To be useful, BASF would need access not only to historic data, but ongoing data.  They would need access to the genetic code of the seeds, how that code interacts with the pesticide, how that pesticide is faring with the pests.  And BASF would need “honest and complete” access to that data over time.

The digital farming product therefore is tied to the actual seeds, the traits and the pesticides; needs long term relationships with farmers; and the engineers who created the code in the first place. So long as the divestiture is of all of these things, freeing BASF from any tether to Bayer, the remedy is structural.  If the license is just of the software, however, it would be an almost useless remedy because the government would have to impose a huge number of conditions on the licensing party to support the software in the hands of the competitive.  And it would be inferior.  The employees of Bayer would have little incentive to keep BASF up to date on the new and great functionality and analysis Bayer has discovered.  Such a divestiture would become a behavioral remedy in practice.  Software licenses (including big data) and patents are very different animals.  You should never assume a software license (even an “assignment”) is necessarily a remedy as effective as a patent license.

Ozzy Osbourne has filed an antitrust suit against against AEG, the operator of the O2 Arena in London and operator of the Staples Center in LA, among others.  Ozzy is alleging that AEG is illegally requiring artists perform at Staples if they want to perform at O2, the “Staples Center Commitment.”  It is a fairly straight-forward and plausible claim.  AEG has market power in large-scale venues in London and is leveraging that power to increase sales in Los Angeles to the detriment of competition in Los Angeles, where there is a lot of choice in venues.  The allegation that AEG took the time to give their scheme a name is probably the most shocking, and damming, allegation in the complaint.  And Ozzy has hired highly-regarded Dan Wall of Latham San Francisco.  This case is legitimate.

I suspect that the few of you who read, and the fewer of you who enjoy, antitrust blogs are likely not particularly adamant fans of Mr. Osbourne. He’s purportedly done some interesting things in life, and writes songs some view as a bit “dark.”  Like Mr. Crowley, a song about English occultist Aleister Crowley.  Ozzy has purportedly bit the head off a live bat in concert, a fact that people appear to bring up to him and every one of his relatives on a fairly regular basis.  He also starred in an MTV reality program called the Osbournes, which I actually enjoyed.

There is nothing particularly novel about the case—other than tying cases are increasingly difficult to prove. A positive result for Ozzy will be valuable to any artist forced to play a venue she didn’t want just to get access to a better facility.  But there aren’t many acts that can play O2 in the first place.  It’s just cool that Ozzy Osbourne is the plaintiff.

In what may be the first case ever, a party to an FTC administrative merger challenge has filed suit in U.S. District Court asking the court to halt the administrative proceeding and order the FTC to pursue a preliminary injunction motion in that court. The gravamen of their argument is that the outside date for the transaction is in May, and there is no way that they could conclude an administrative trial in that amount of time.  They claim further that choice to proceed only with an administrative complaint is to delay deliberately the resolution of the suit so that the outside date passes and the deal breaks.  They “deserve their day in court.”  As a matter of substantive law, the case is unsound.  The FTC is free, within the confines of the FTC Act, to choose where it wishes to pursue an action, and this has been so for more than 100 years.  As a means to recapture some negotiating leverage from the FTC and perhaps save a few bucks, it’s probably not a bad strategy.  Perhaps the chance of a loss, however remote, and the potential “embarrassment” of dragging enforcement out “needlessly” will prompt the FTC to settle on more favorable terms.  The cost, however, is Tronox’s, and Tronox’s lawyers’, reputation at the Commission and may only prompt them to dig in their heels.

Tronox Limited and its target, Cristal, both make high purity titanium dioxide (TiO2), an input for products like paint. TiO2 makes paint white.  Tronox announced its intent to acquire Cristal in February 2017.  The FTC investigated the transaction under the HSR Act and purportedly allowed the waiting period to expire on December 1, 2017.  Tronox noted this fact in a press release suggesting it was free to close under the HSR Act.  Four days after that press release, on December 5, 2017, the FTC sued the companies alleging the combination would substantially lessen competition in the market for high purity TiO2.  In their press release announcing the suit, the FTC asserted that the waiting period had in fact expired on October 7, 2017, but the parties had entered into a timing agreement whereby they would provide the Commission 10 days’ notice of their intent to consummate.  The FTC suggested that the parties had not given adequate notice under the timing agreement.  The transaction is still under review in other suspensory jurisdictions that prevent the parties from closing globally. Continue Reading Tronox v. FTC: Ingenious or an Exercise in Futility?

The FCC recently repealed the net neutrality rules.  Now your favorite ISP can charge more for better and faster access, deny you access to sites you really shouldn’t be looking at (we’re looking at you CNN), and degrade all those over-the-top services you should be getting indirectly on their platforms anyway.  One thing repealing net neutrality has done that isn’t particularly helpful to big business is make platform-content provider mergers harder.

How so, you ask.

One argument against platform-content mergers is that the platform provider can favor its own platforms for the delivery of must-have programming.  That could drive consumers away from competing platforms, distorting the market for the platform.  Justice has alleged that AT&T plans to do this very thing once it gets its hands on Time Warner.  And there are a variety of ways short of refusing to deal that a platform provider could favor its own platform.  Providing content on a delay or degrading its quality or delivery to competitors.  Engaging in price squeezes.  The net neutrality rules made doing a lot of these things at least moderately more risky.  Without them, it’s much easier for an integrated platform-content provider to engage in those behaviors.  Net neutrality therefore served as an argument that AT&T would not be able to prefer its platform and thus could not engage in the post-consummation predation Justice argues is possible.

That argument is gone now.

Shortly after the Justice Department’s challenge to AT&T’s acquisition of Time Warner was announced, a rumor floated that AT&T had offered to divest CNN to assuage the Division’s concerns.  The gist of the rumor was that the Division is suing to block an otherwise legitimate transaction as an act of revenge against CNN because CNN is critical of President Trump.  In addition to the President’s remarks, the believers also point to statements by the Assistant Attorney General for Antitrust who, before his nomination, said that he didn’t think there was going to be an antitrust problem with the deal.  The suit, according to the theory, is an affront to the First Amendment.

Randall Stephenson and the Division quickly denied the rumors.  They do keep circulating, however.  Partly because the President has tweeted his support of the suit.  And, more recently, because of Carter Page.  Mr. Page recently filed a pro se request for leave to file an amicus brief in the case arguing, basically, that the combination is illegal because it concentrates too much social power in the hands of a large corporation.  Mr. Page was an advisor to Mr. Trump.  Acknowledging the court has wide latitude to consider amicus, the Division did observe, in its half-page response, that the brief was not helpful suggesting it should be disregarded.

Continue Reading CNN: Is There A First Amendment Defense to an Anticompetitive Merger?

Minimum advertised price (MAP) policies are becoming more and more popular.  Especially with the rise of cutthroat competition in the online marketplace, many producers are hearing from their resellers that they want margin protection, and are getting concerned with the possible erosion of their brand equity from widespread promotion of low pricing.  Naturally, many companies have turned to consultants and the internet for information on MAP policies.

Continue Reading Where to Get Bad Advice on MAP Policies

On July 6, 2017, QVC announced its intent to acquire the remaining 62 percent of Home Shopping Network it doesn’t already own.  More so than DraftKings or Walgreens, this transaction will demonstrate whether Trump’s election has had any effect on antitrust enforcement, and should be watched carefully.  HSN and QVC are very similar, and the ability to do the deal will turn on what product market definition wins the day.  A very broad product market definition, that focuses on means of distributing products to consumers, and that includes the Internet, will suggest the transaction will have very little effect on competition, and should be allowed to close.  A narrow product market definition that focuses, say, on television shopping as a unique form of entertainment to consumers, and therefore a unique channel in which to sell products, may very well result in a challenge.

Continue Reading QVC/HSN :: A Deal You Probably Weren’t Thinking About

The European Commission has fined Google €2.42 billion ($2.7 billion) after concluding the company had abused its dominance in search.  According to a letter from the EC announcing the results of its investigation, Google has a dominant market position in search, and the company leveraged that dominance to give itself an unfair advantage in comparison shopping search.  For example, according to the EC, when a consumer types in the name of a particular product, Google favors its own comparison shopping search results to those of its competitors.  The EC believes this process “denie[s] European consumers a genuine choice of services and the full benefits of innovation.”

The Decision

As of December 31, 2016, Google (Alphabet) had total assets of $167 billion. The fine represents slightly more than 1.6 percent of its total assets.  Perhaps more important to Google and to other companies within the reach of the EU is are the legal and behavioral aspects of the decision.  First, the decision finds that Google search is “dominant,” meaning that it is the predominant means by which consumers search the internet.  In the EU, a dominant company is required to behave in a competitively neutral fashion as between itself or its affiliated companies and its rivals.  In the States, a dominant entity (or “monopolist”) is required only not to behave in a way that unfairly maintains or expands its advantage or disadvantages competition.  What constitutes unfair in the States is more limited than in the EU.  For example, a United States monopolist is under no duty to deal with rivals except in circumstances that suggest its conduct has no legitimate purpose.  By contrast, in the EU, a dominant company is expected to be evenhanded in its dealings whether it is dealing with its rivals or with companies in other markets.

The EC is not the authority of last resort. Google can, and most likely will, appeal.  It may also decline to comply fully with the EC’s decision and force adversarial proceedings.

Significance

The case is significant for Google because the EU is demanding changes in the way Google displays its search results.  As one of the first and still leading search engines, Google’s product enjoys widespread popularity.  The EU is now telling Google that its product should give users a different experience.  And for the foreseeable future, when Google enhances the product, the changes could be second-guessed by a regulator.  While the case purportedly affects conduct only in the EU, it raises the question whether the company would or could offer different search products in the EU and the rest of the world.  For multinational companies operating online, it is difficult to avoid crossing borders.

Issues for Multinational Companies

A big question in the case is where and with whom Google competes. For shopping-related searches, Google argued that it has robust competition with Amazon, Ebay and other comparison shopping tools, and that this competition intensified even in the two years that the EC has been investigating Google.  On Amazon, a customer can locate a product quickly, pay for it using credit cards Amazon knows, and buy it from any number of different vendors.  The same is true for eBay.

Parties with significant market shares need to be vigilant about how they are perceived in the market, particularly in the EU.  While being first and foremost can be a badge of honor and an explicit business goal in the States,  a moniker of dominance can trigger significant interest in the EU.  That interest can lead to investigations and ultimately regulation.

Three main lessons:

One, consider avoiding behavior that the Europeans consider to be exclusionary in the first place. And consider the pros and cons of working with competitors that have incentives to air grievances.  That will blunt their complaints and allow you to control the narrative.

Two, consider your reputation. Discourage internal and external marketing pieces that say you are dominant.  While that might be great for securing initial funding, you can cause yourself significant problems with that puffery later on.

Three, develop solid economic evidence of real competition from platforms and other sources that aren’t “classically” within your wheelhouse. You want to be able to muster evidence that competition is coming from lots of places:  actual evidence that will be compelling to antitrust agencies worldwide.  Charge your antitrust counsel with keeping that data current.  Charge your marketing folks with tracking and memorializing that data.  And keep economists on call.  Periodic assessments of competition will be more compelling to enforcers than something put together at the last minute.

Finally, follow the enforcers and what they are saying, so you can respond to their specific theories and thoughts as they occur and before they become ingrained. We will follow them as well.

Federal Trade Commission Acting Chairman Ohlhausen and Commissioner McSweeny recently gave two very different speeches on algorithmic pricing. Commissioner McSweeny’s speech seemed to express concern that algorithms would lead to price fixing, coordination and higher prices.  The Chairman seemed less concerned.  The Chairman is likely correct.  Pricing bots are not a new, sinister specter haunting society like the trusts of the late 19th Century.  Nor are they boogeymen hiding under your bed waiting to raise price and siphon off what little money you have left after rent, clothing and food.  Pricing bots are digitized, more efficient versions of the same thought processes that any rival engages in to set price.  Because they are digital, they are not constrained by volume. They can take into account significantly more variables than the human mind is incapable of keeping track of—and do it much faster.  Using a bot to set price unilaterally is not an antitrust problem.  It is the essence of the free market.  By contrast, sharing your algorithms, your pricing formulae, with competitors can very well be a problem because it can lead to collusive behavior, like any other exchange of competitively sensitive business information.  Setting up encrypted, digital communications systems to suggest and agree on price increases and market allocations is easier today, but it’s still illegal.  And it’s sufficiently difficult to do now that a trove of evidence to prove its existence and illegality will be available.

Generally, it’s important to remember that if a market is otherwise competitive, a pricing bot cannot make it less competitive. If a firm can raise price after implementing a pricing bot, it means that the firm was not maximizing profits because it lacked sufficient information.  In short, in the pre-bot days, the costs of additional information outweighed the expected benefits of additional information.  The bot reduces the cost of additional information.  The fact that it raised prices (or lowered them) does not tell us anything about the state of competition in the market.

Pricing bots cannot be coded to “agree” to raise price absent the intent of the human coders. Coders must specifically design their systems to seek out competitors, determine collusive prices and police those prices.  Given the highly disparate systems and price setting methodologies that exist across even a commoditized market, the amount of time and effort needed to create such a system is vast.  Extrinsic evidence of those efforts must exist.  That will include all of the normal evidence of collusion.  For a more thorough discussion of what exactly a price-fixing bot might look at, please see our discussion on price fixing bots.

The rest of this brief addresses specific issues raised in each of the speeches.

Commissioner McSweeny Algorithms and Coordinated Effects May 22, 2017

Statement

 Analysis
Algorithms may make price fixing attempts more frequent and potentially more difficult to detect. Price fixing conspiracies are unnecessary in concentrated markets. Parties can simply act in parallel.  Price fixing conspiracies do not work in atomistic markets because identifying and disciplining cheaters is too difficult.  Somewhere in the middle are markets that have just enough participants to make parallel behavior difficult.  For example, there may not be an efficient way for everyone to follow the behavior of others.  What a bot can do in those markets is eliminate the problems with the communications and policing that make parallel behavior difficult.  In this regard, a bot could increase the maximum number of participants that can successfully act in parallel because it makes the process more efficient.

The more harmonized pricing is, the more likely the product is a commodity. The more likely the product is a commodity, the more likely the market will have too many participants to act in parallel or collude.  A price fixing bot will therefore only work in markets with a small number of participants.  How a given firm prices can be highly idiosyncratic.  Most firms in such markets will not share the same inventory and sales software.  Creating a piece of software that will set price uniformly across a group that is sufficiently small enough to  collude is going to require a lot of work by a lot of people.  The more individuals aware of a price fixing conspiracy, the more likely evidence the conspiracy exists.

A price fixing bot comes up with a supracompetitive price the conspirators can charge for their product.   It is basically the digital version of the smoke-filled room.  Knowing how the conspirators arrived at a collusive price is not necessary to prosecute a price fixing claim.  You don’t need to know what the code of the bot is to establish that the parties who coded it were fixing price.  And given the number of people needed to create a price fixing bot, you are going to have a lot more evidence if they are creating and relying on a bot than if they had just sat down and decided on a price in that smoke-filled room.  In this vein, a conspiracy implemented and policed through software is going to leave a much bigger footprint than one accomplished the traditional way.

Here, the Commissioner’s assertion is inaccurate. Coding an industry-wide price fixing bot is time consuming, reads too many people into the scheme, leaves a mammoth paper trail, and ultimately will have little effect on the ability of an end-user to figure out that it’s paying more for the product than it would otherwise.  Bots do not necessarily make price fixing attempts more frequent and potentially more difficult to detect.
Pricing algorithms . . . may facilitate coordinated interaction—sometimes called tacit collusion or parallel accommodating conduct. True, but only in a very narrow number of markets. Coordinated behavior is only possible in concentrated markets.  If a market is atomistic, a price increase by one or several participants will only result in immediate diversion to discounters.  Being able to see a rival’s prices quicker and more broadly will not change that fundamental dynamic.  In a concentrated market, parties may act in parallel because there isn’t a sufficient number of participants to constrain their activity.  A bot won’t change that situation either.  There may be some markets where there are just enough participants to make parallel behavior difficult.  That may be because there is insufficient ability to detect and police behavior.  A bot, combined with a good system of announcing prices to the market, could make it easier for participants to act in parallel in those markets where they couldn’t before.  Here, though, all the bot is doing is making detection and policing more efficient.  The market still must be sufficiently concentrated for the parties to act in parallel.  As such, the bot is no more inherently illegal than any other method of disseminating and reacting to information.

In analyzing whether a price increase is potentially a consequence of illegal collusion facilitated by a bot, one must exclude the possibility that the sellers lacked sufficient information about the price sensitivity of their customers to charge the appropriate price. It is entirely possible that a particular product could have significant value to an as-yet unidentified group that is willing to pay more for that product.  The bot could very well have facilitated the discovery of that smaller discrimination market.  Pricing more to that group does not reduce consumer welfare and is not an antitrust issue.

A pricing bot could be used to signal prices to other competitors by sending them potential price increases before they are presented to the public. This is Airline Tariff Publishing, however.  The parties would be using the bots as a means of proposing and responding to target prices.  This is price fixing; it is not parallel behavior.

It is inaccurate to broadly condemn bots. One must examine the facts of any given market to assess the effects of a bot.  Only in concentrated markets where the bot is intentionally designed to set a common price among participants is it illegal.  And then, due to the complexity of creating such a bot, there will be plenty of evidence.
Multiple competitors might use algorithmic pricing software offered by the same company. This is a hub and spoke conspiracy where the manufacturer of the software has coded it to set prices uniformly for all members of a market. Again, however, such a program will only work in a concentrated market.  If a program raised price in an atomistic market, customers would divert to the discounter.  If everyone used the same bot—an improbable hypothetical—at least one participant would see an opportunity to capture sales and turn off the bot.  Again, the bot may make oligopolistic pricing easier because it makes policing easier, but at some point, there will simply be too many participants for such a scheme to work.
Firms’ nominally independent algorithms may simply gravitate collectively towards higher prices on their own. This only works in concentrated markets where customers cannot readily switch to punish a price increase.   As such, the sellers in this market must not be pricing optimally; there must be an informational asymmetry of which customers were taking advantage.  It is also possible that the algorithms have made seller information more transparent such that the maximum number of sellers capable of behaving in parallel is increased.
Pricing algorithms can be “an effective tool for tacit collusion” with the potential to lead to near-monopolistic pricing. True. The use of the words “collusion” and “near-monopolistic pricing” suggest that this pricing algorithm can be illegal.  For the reasons stated above, that is not accurate.
The model assumes that firms are able to “decode” their competitors’ algorithms. [The author] included a specification in which firms were given an option to mask their algorithms to prevent decoding.  The firms in the model chose not to exercise the option… An algorithm is essentially a mathematical or logical function. Price is the output of that function.  One can display that price as a static number.  One need not display the terms of the function to display the price.  The only way displaying such a formula would be profitable to a company is if the market were sufficiently concentrated that its rivals could use the formula to increase their own prices.  If the algorithms were made available only to rivals, one could argue this was an illegal information exchange—as it would be if the parties shared their actual cost structures with rivals in any other media.  The concern with the information exchange is less if the information is also shared with customers.  But presumably the only condition in which a seller would share with both is if the consumer had insufficient choice to defeat the potential price increases.

It is therefore incorrect to conclude pricing bots will necessarily lead to “near-monopolistic pricing” in all markets. They can facilitate parallel behavior in near-concentrated markets by making the observation of and reaction to rival activity easier.

I do not think you could draw even this conclusion from this paper, however. There is a difference in the output of the algorithm and the terms of the algorithm.  The former is a number; the latter is the method by which a firm arrives at the number.  By making the content of the algorithm discoverable, the author is in effect permitting the parties to engage in price fixing.  The information exchange is so thorough that the parties have no doubt as to the behavior of rivals and can act in concert.  His conclusion is therefore a tautology:  markets which are sufficiently concentrated that price fixing can work will produce higher prices if the parties are allowed to engage in price fixing.
One gas station operator candidly told the Journal that is decision to use the software was promoted by the effects of a years-long price war with its competitors. . . . [W]ithout more information, it’s hard to know whether the reported higher margins are the result of coordinated effects. A merger that results in a market becoming sufficiently concentrated, such that the participants can behave in a coordinated fashion where they could not before, can potentially violate Section 7 of the Clayton Act, which prohibits mergers or acquisitions that substantially lessen competition or create a monopoly. Unilateral conduct that improves understanding of how a market operates is not a violation of Section 7.  Nor is it a violation of Section 2.

It is entirely possible that the algorithms the gas station operator deployed “knew” its customers a lot better than the operator did. The software could know, for example, that consumers that purchase gas at 700a on a Monday are likely on their way to work, need gas to get there and don’t really care how much they have to pay to get that gas.  Consumers who purchase gas at 1100a on a Saturday likely do care what they are paying.  It is perfectly acceptable to charge the Monday morning commuter more for gas because he values that gas more.
The third concern with pricing algorithms is that they may enable price discrimination strategies that lead to higher prices for certain groups of customers. This is the essence of big data.   It enables sellers to discover ever smaller price discrimination markets.  Charging a person more because his utility for the product is higher is neither inefficient nor an antitrust violation.
It works just as well for customers who care very much, but are nonetheless willing to pay a higher price because they lack the practical ability to go elsewhere. If a customer “lacks the practical ability to go elsewhere,” the subject product has a narrow geographic market and that market is concentrated. The bot has nothing to do with that fact.
Pricing algorithms will undoubtedly lead to an increase in price discrimination. Whether that is a good or a bad thing for consumers is likely to depend on facts that are specific to individual markets and individual algorithms. No, it’s economic efficiency.   Consumers that would pay more but do not are free riders:  they are exploiting an informational asymmetry between seller and buyer.  In this case, it benefits a buyer.  But that does not mean eliminating that inefficiency is harmful to society.
If algorithms enable firms to “solve” their unique prisoner’s dilemmas without resorting to overt collusion, that would be great news for them but bad news for consumers. The purpose of the antitrust laws is to create a prisoner’s dilemma: they prohibit communication that would otherwise allow rivals to set supracomeptitive prices to consumers.  Bots do not necessarily create new, undetectable and unassailable communications flows between competitors.  What they can do, in very limited circumstances, is enable sellers to realize that there are categories of customers that will pay more for their products.  In essence, they can eliminate an informational asymmetry that allows those consumers to free ride off the ignorance of a seller.  The antitrust laws were not designed to foster this type of free riding.  Indeed, this reading of the antitrust laws would cause them to lock in seller-side inefficiencies.

This statement assumes all algorithms are the same. It is important to understand what the “algorithm” is doing.  If sellers are sharing the model by which they arrive at a price with their competitors, they are engaging in a potentially illegal information exchange.  If sellers have eliminated inefficient pricing to consumers who are willing to pay more through the use of bots, the sellers are engaging in perfectly legal, and efficient, unilateral behavior.  If a group of sellers have harmonized their pricing software to take input from competitors and respond to it by raising price, those sellers have automated an illegal price fixing scheme and are potentially guilty of a criminal violation of Section 1.  If your market is sufficiently concentrated such that you can set price based on a competitors’ price, and you create a bot that will scan that publicly available price and set your price accordingly, you are automating parallel behavior which is not illegal under the antitrust laws.
[I]t would be helpful to understand whether algorithms are resulting in coordinated effects and, if so, under what conditions.

Pricing bots will not affect concentrated or unconcentrated markets. There may be a small class of markets that could behave in a concentrated fashion that does not because the technology is not sufficient for the actors to detect and police price movements.  In that instance, a bot could make a difference.  But the bot is no more inherently illegal than any other technology that makes it more efficient for people to communicate.  A merger that creates a concentrated market can violate Section 7.  A market where participants can act in parallel because the technology is better is not a violation of Section 7 or Section 1.

Sharing the basis for one’s pricing decisions with ones competitors, or creating competitor information exchanges that allow competitors to agree on a common price, and police those prices, can be price fixing.

 

Chairman Maureen Ohlhausen Should We Fear the Things that Go Beep in the Night? May 23, 2017

Statement Analysis
An algorithm can include a virtually unlimited number of rules, conditions and variables. This means that many extremely complex and nuanced behaviors can be modeled in a set of detailed computer instructions. Correct.
It is axiomatic that we cannot tell firms to ignore the public behavior or their rivals when they set prices without deleting the “free” in free market. Correct.
[W]e try to make sure, primarily through our merger enforcement program, that the conditions that allow this kind of behavior to take place generally do not arise in the first place.   We also prohibit explicit agreements to set prices collusively and exchanges of competitively sensitive, non-public information between competitors. The Salcedo study basically asks whether rivals would prefer to engage in an exchange of competitively sensitive, non-public information or guess what their rivals’ behavior was.   If they are rational, they will always pick the information exchange; that’s why there are laws limiting the exchange of such information and ultimately whey price fixing is illegal.
For example, when the products are highly differentiated, or the market participants have different cost structures, or transactions are relatively infrequent, it is very difficult to maintain stable, interdependent pricing just by watching the behavior of your rivals. This is another way of saying pricing and cost structures are highly idiosyncratic. Creating an algorithm that would be able to interface with several disparate systems would require the heavy and active participation of many, many individuals and firms.  One of the reasons the B2B craze died out so abruptly was that harmonizing all of those back office systems so that they could all communicate with each other—for legitimate commercial purposes—was exceedingly difficult.  Enforcers looking at potential collusion in larger industrial markets may want to see if there are any of these residual ‘e-marketplaces’ functioning and whether there are price harmonization components to them.  In that particular instance, the e-marketplace joint venture could be facilitating coordinated behavior.  If the e-marketplace were independent of the participants, you’d look at it in terms of being a hub of a conspiracy.
What I’d like to suggest to you this evening is that this same analytical framework is sufficiently flexible and robust that it can already accommodate several of the current concerns applicable to the widespread use of algorithms. Correct. You do have to figure out what people mean when they say “algorithm.”  For the most part, it is simply a digitization of the process by which a firm sets price.  All the firm is doing is taking what was their “brain-based” decision making on price and turning it into a computer program that can take into account many, many more variables and calculate responses much more quickly.  After many years selling gas, the man who takes the ladder and changes his price may very well know, instinctively, that he can raise price by 10 cents a gallon at the tail end of a holiday weekend.  A computer can reach that same conclusion much more quickly.  It’s not witchcraft.
[T]he algorithms are programmed to produce some sort of signal to the market, a signal that only the other market participants, similarly armed with algorithms of their own, will be able to detect. Creating a program that is able to understand communications from an alien program, and then set price according to those signals, is a remarkably difficult task. ATP involved using the electronic fare system more as a bulletin board where human beings could see price jumps and drops and react to them than a program that could react to those changes and set price.  A cartel created by people could very well code a communications system that could easily send secret messages to each other regarding price.  This is just the modern equivalent to allocating markets based on the phase of the moon.  These are just communications vehicles.  They do not exist in a vacuum; human beings have communicated with each other about creating and using them.  There will be extrinsic evidence beyond the encrypted communications tools that demonstrate the existence of a conspiracy.  Nor is a case against these malfeasants harder because the communications are better hidden.  A prosecutor does not need to explain how a telephone can convert speech into 1s and 0s and back to establish someone used a phone to commit a crime.

It is important to distinguish between publishing price to the market, where rivals can see and react to those prices, and publishing the algorithms (the formulae) that determine what those prices are. The former is not a problem; the latter can be aclassic illegal information exchange.
[T]he firms themselves don’t directly share their pricing strategies, but that information still ends up in common hands, and that shared information is then used to maximize market-wide prices. Price will go up as a consequence of a bot only if (1) the bot sets a collusive price; or (2) the market is sufficiently concentrated but the ability of participants to observe and react to rivals’ behavior is technologically limited and the bot corrects that. The Agencies must eliminate the possibility of (2) before they can conclude a price increase is a consequence of a collusive bot.  If it’s not (1), then the market is behaving like any other concentrated market exposed to new and better technology.

 

The Economist recently announced that the world’s most valuable resource is now data, displacing oil for the top spot.  The “titans” of the digital era—Alphabet, Amazon, Apple, Facebook and Microsoft—look “unstoppable” as they are now the five “most valuable” firms in the world.  Amazon captures “half of all dollars” spent online, and Google and Facebook accounted for “almost all the revenue growth in digital advertising in America last year.”  The Economist claims that the “abundance” of data “changes the nature of competition,” and in particular that “with data there are extra network effects” (emphasis added).  Ultimately, they declare, “antitrust authorities need to move from the industrial era into the 21st Century.”  These statements are interesting, worth thinking about but basically wrong.  Big data is about your personal preferences, perhaps even your thoughts and future actions, being for sale.  It is about your privacy, not about competition.

Continue Reading And the World’s Most Valuable Resource is . . . Data! Holy Cow, No Way!