I am delighted to publish a 15-video series dedicated to my book, “Blockchain + Antitrust: The Decentralization formula”. You can access all the chapters over here, and all the video transcripts over here.
In this video, I’d like to discuss several concrete proposals for regulating blockchain. First, let me emphasize that I do *not* assume that everything should be regulated because why not, quite the contrary. I see the absence of regulation as the default. But two things, here, especially for the computer scientists watching this video. First, I explained in previous videos that blockchain needs regulation for two main reasons: one, to sanction illegal behaviors, and two, to benefit blockchain participants. Second, guess what… regulation is coming, whether you like it or not. So, let’s dive into the subject, and try to make it as good as possible.
(1) First, I believe that comfort zones are an essential mechanism to be used and developed in the space. But what are they? They are comfort zones are innovation hubs, regulatory sandboxes, and legal safe harbors. They are ways for regulators and policymakers to gather information about a specific technology and test new innovations while ensuring legal comfort. OK, let me explain how this works.
→ Innovation hubs are loose cooperation between firms and regulators that allow these firms to raise questions and seek clarifications and non-binding guidance.
→ Regulatory sandboxes are testing grounds for businesses supervised by regulatory bodies. In a nutshell, firms apply to enter the sandbox, and if they get in, they agree to send regular information to regulators while, in exchange, getting what is typically called a “no-enforcement action” letter, meaning, a letter from the regulator saying it won’t enforce the law against specific aspects of that firm activities.
→ And third, we have safe harbors. They are legal provisions exempting liability on the fulfillment of certain conditions. Section 230 of the Communication Act in the United States is a good example. These safe harbors clarify when regulators won’t enforce the law or, put differently when a practice is legal or not.
Now, regulators tend to like these mechanisms because they enable them to gather information about a technology they do not understand too well or that is moving too fast. But the question is: why should companies join these comfort zones? In fact, when I discuss with regulators, I am often surprised to learn that they struggle to get companies to apply. This is why I believe we ought to work on incentives. As I mentioned already, the incentive can be legal. In the short run, it protects companies from enforcement, and, in the long run, it helps design better regulation, at least, if the firms in it do not capture the sandbox. And we can also imagine economic incentives. In all likelihood, tax breaks would be quite effective.
Once companies are in, they send information and experiment together with regulators. Here, there is a balance between changing blockchain code and governance to enable legal enforcement while preserving the willingness to use these new types of blockchains. If you ask me, this is art as much as science. There are different solutions to be tested, whether we want to rely on enums, modifiers, chameleon hashes, etc. As I said in the previous videos, the end goal is to enable enforcement but only after an illegal practice has been implemented. Let us not use these comfort zones with the idea in mind that we can prevent all illegal practices. If you have seen the movie Minority Report, you know it does not end well.
Now, I do not know which one of these solutions is best, and that’s precisely why we ought to test them using these sandboxes. Please note that we could also use agent-based modeling to simulate their effects or conduct lab experiments.
Before I move on, I want to emphasize the risk of regulatory capture. Market players within the ecosystem will be tempted to promote regulation that benefits them. Market players outside the ecosystem will be tempted to capture regulation to hamper blockchain’s growth, and, why not, prohibit it. And last, public institutions could also be tempted to capture regulation for themselves. Blockchain applications compete with specific public services. It competes with the power to print money. Regulation could protect public power, sometimes for good reasons, sometimes for… less good reasons. Let us monitor that.
(2) OK, on to my second proposal. Antitrust agencies, and more generally all enforcers, should adopt a clear and strategic enforcement strategy. Here is what I mean by that, in three points.
One, conduct investigations and prosecute the practices hurting blockchain participants. Some of the market players from the centralized world will compete fairly, but others will try to implement illegal behaviors to eliminate blockchain applications. Let us go after these practices, for example, when centralized players unfairly prohibit blockchain advertisement or when banks are blockchain developers. There is currently such a case being trialed in Brazil.
Two, agencies should go after illegal practices that recentralizes blockchain because they destroy the point of the ecosystem. That is especially true at key layers such as exposed in the fourth video of this series. For example, cartels between core developers should be prosecuted without a shaking hand. But when they go after practices implemented within blockchain ecosystems or using blockchain to impact the real space, they should ensure not to endanger blockchain survival. As I explored in the third video of this series, maintaining blockchain chances of survival requires adopting a Darwinian perspective. For example, should regulators and enforcers impose a blockchain to name one person in charge, and give that person power to stop or revert transactions, they would force centralization of that basic at a key layer. If that is done, I fail to see why anyone would use such a blockchain instead of a fully centralized service. Alternatively, the remedy could be refused by blockchain users, in which case, it won’t be implemented and efficient at all.
Three, agencies shall avoid prosecuting the central characteristics of blockchain, such as I have explained in the second video of this series. For example, public permissionless blockchains distribute information throughout the marketplace, including the number of transactions implemented by specific users, the fees being paid, etc. This transparency could lead to antitrust concerns, making the market more fluid and mitigating information asymmetry.
And now, my third and final proposal. This one is probably not for today, but the years ahead of us, if it ever passes. I believe that blockchain should be used to decentralize enforcement, and, you know what, the design of regulation. This would resolve a fundamental tension between the decentralization that blockchain participants aim for and the centralization of public institutions.
So, here’s how to proceed toward what can be called “futarchic antitrust”. First, I need to explain what prediction markets are. In simple words, prediction markets form a mechanism to identify sincere beliefs by forcing participants to “put their money where their mouth is”. They ask what people think will happen instead of asking what their preference is. In practice, they are bets. One attributes the probability that a future event will materialize between 0 and 1 and creates a bet in which the entry price equals the perceived probability. After the event has or has not materialized, only those who bet on the correct outcome receive a payoff. The others lose their bet. Let me take an example.
If person X’s likelihood to be elected president of the United States is 70 percent, you would have to spend 0.7 (dollars, euro, or tokens) to bet on person X’s election. Conversely, you would have to put 0.3 to bet on person X’s non-election. Once the result is known, those who bet the correct outcome receive their money back plus the losers’ money.
OK, now I can explain what a futarchy is. Futarchy uses these bets to determine a governance strategy in the private and public sectors. So, in my example, instead of creating a prediction market disconnected from the election, you would create two prediction markets: one for person X election, the other one for person Y election. The one candidate with the most bet in favor would be elected on that basis. And in fact, you could condition the bets. Instead of asking if a candidate will be elected, you could ask whether, if elected, that candidate could reach a specific objective after two years, let’s say, to reduce carbon emission. In the private sector, you could create a prediction market to ask whether the shares of a company would rise over 10% after one year if a specific new product is put on the market. Again, you would — or would not — put the product on the market depending on the results.
The original idea of prediction markets is to open them to everyone. People with bad predictions lose money and stop betting, so prediction markets accuracy improves over time. But you can also restrict access to the prediction market. You can open it only to your employees, for example.
Now, how do blockchains fit within this picture? Well, they can improve prediction markets in different ways. First, blockchain enables to use of tokens, instead of using fiat currencies. two, blockchain enables automatic transactions by way of smart contracts. These smart contracts can automate payment once the result is known. Because they are immutable, it creates trust in the prediction market integrity. And third, blockchain can facilitate a reputation system. The idea is to let users challenge recorded results. If they are right, and the result was not properly recorded, they can get an automatic payoff. If they are wrong, they can lose tokens.
OK, now, let us apply all that to antitrust enforcers. Most antitrust laws are based upon predictions: what would happen if we agreed on this merger? Even the analysis of anticompetitive practices requires a form of prediction. In a sense, agencies are required to imagine a different future going back in time before the practice has been implemented, asking what would have been the state of competition without the practice? Futarchy could therefore prove helpful.
Let me drive the point home with a concrete example. Agencies could create two prediction markets to discover whether, one year after the merger, the average price of the product at stake has greater chances of increasing if the merger is authorized. The first assumes that the merger is accepted and the second that it is prohibited. After the betting period has expired, it presents the antitrust agency with two probabilities, let us say… 80% the price will increase if the merger is accepted, and only 2% if the merger is prohibited. Well, the agency could bind itself to this result and prohibit the merger. It could also just take this result into consideration as one element of its analysis. The same logic could be applied for remedies and commitments to predict if they would be efficient. And it could certainly work for competition policies.
Now, there are three main challenges with futarchic antitrust. First, prediction markets might be manipulated, but remember that such a strategy is very costly as it costs money. Agencies should inquire if the stakes are sufficiently high for a company to manipulate one. Second, non-price-related outcomes are hard to measure, such as whether work conditions have improved, the quality of products, etc. Three, prediction markets are seen as gambling activities in numerous jurisdictions, and they are therefore heavily regulated. This led several Nobel laureates and economists to call for new legal rules to facilitate them in an article they published in 2008.
That is all for today. Thank you very much for listening. Take care of yourself, and, if you can, someone else too. Cheers.