Central Planning As Overfitting

Vitalik Buterin, E. Glen Weyl

November 26, 2018

There is an intuition shared by many that “central planning” — command-and-control techniques for allocating resources in economies, and fine-grained knob-turning interventionism more generally — is undesirable. There’s quite a lot to this, but it is often misapplied in a way that also leads it to go under-appreciated. In this post we try to clarify the appropriate scope of the intuition.

Some recent examples of the intuition being misapplied are:

While we do not entirely dismiss this last example, for reasons we will return to later, it does seem overplayed. Similarly and conversely, we see many examples where, in the name of defending or promoting “markets” (or at least “economic rationality”) many professional economists advocate schemes that seem much more to us like central planning than the systems they seek to replace:

To understand why we think the concept of “intervention” is being misapplied here, we need to understand two different ways of measuring the extent to which some scheme is “interventionist”. The first approach is to try to measure the absolute magnitude of distortion relative to some imagined state of nature (anarcho-primitivism, or a blockchain with no block size limits, or…). However, this approach clearly fails to capture the intuitions of why central planning is undesirable. For example, property rights in the physical world are a large intervention into almost every person’s behavior, considerably limiting the actions that we can take every day. Many of these restrictions are actually of quite recent historical provenance (beginning with agriculture, and mostly in the West and not the East or Middle East). However, opponents of central planning often tend to be the strongest proponents of property rights!

We can shed some light on this puzzle by looking at another way of measuring the “central-planny-ness” of some social structure: in short, measure the number of knobs. Property rights actually score quite well here: every piece of property is allocated to some person or legal entity, they can use it as they wish, and no one else can touch it without their permission. There are choices to make around the edges (eg. adverse possession rights), but generally there isn’t too much room for changing the scheme around (though note that privatization schemes, ie. transitions from something other than property rights to property rights like the auctions we discussed above, have very many knobs, and so there we can see more risks). Command-and-control regulations with ten thousand clauses (or market designs that specify elaborate probabilistic objects, or optimization protocols, etc.), or attempts to limit use of specific features of the blockchain to drive out specific categories of users, are much less desirable, as such strategies leave many more choices to central planners. A block size limit and a fixed transaction fee (or carbon taxes and a cap-and-trade scheme) have the exact same level of “central-planny-ness” to them: one variable (either quantity or price) is fixed in the protocol, and the other variable is left to the market.

Here are some key underlying reasons why we believe that simple social systems with fewer knobs are so desirable:

These effects are not always achieved (for example, even if a system has very few knobs, it’s often the case that there exists a knob that can be turned to privilege well-connected and wealthy people as a class over everyone else), but the simpler a system is, the more likely the effects are to be achieved.

While avoiding over-complexity and overfit in personal decision-making is also important, avoiding these issues in large-scale social systems is even more important, because of the inevitable possibility of powerful forces attempting to manipulate knobs for the benefit of special interests, and the need to achieve common knowledge that the system has not been greatly corrupted, to the point where the fairness of the system is obvious even to unsophisticated observers.

This is not to condemn all forms or uses of complexity in social systems. Most science and the inner workings of many technical systems are likely to be opaque to the public but this does not mean science or technology is useless in social life; far from it. However, these systems, to gain legitimacy, usually show that they can reliably achieve some goal, which is transparent and verifiable. Planes land safely and on time, computational systems seem to deliver calculations that are correct, etc. It is by this process of verification, rather than by the transparency of the system per se, that such systems gain their legitimacy. However, for many social systems, truly large-scale, repeatable tests are difficult if not impossible. As such, simplicity is usually critical to legitimacy.

Different Notions of Simplicity

However, there is one class of social systems that seem to be desirable, and that intellectual advocates of minimizing central planning tend to agree are desirable, that don’t quite fit the simple “few knobs” characterization that we made above. For example, consider common law. Common law is built up over thousands of precedents, and contains a large number of concepts (eg. see this list under “property law”, itself only a part of common law; have you heard of “quicquid plantatur solo, solo cedit” before?). However, proponents of private property are very frequently proponents of common law. So what gives?

Here, we need to make a distinction between redundant complexity, or many knobs that really all serve a relatively small number of similar goals, and optimizing complexity, in the extreme one knob per problem that the system has encountered. In computational complexity theory, we typically talk about Kolmogorov complexity, but there are other notions of complexity that are also useful here, particularly VC dimension – roughly, the size of the largest set of situations for which we can turn the knobs in a particular way to achieve any particular set of outcomes. Many successful machine learning techniques, such as Support Vector Machines and Boosting, are quite complex, both in the formal Kolmogorov sense and in terms of the outcomes they produce, but can be proven to have low VC dimension.

VC dimension does a nice job capturing some of the arguments for simplicity mentioned above more explicitly, for example:

This is not as clean and convenient as a system with low Kolmogorov complexity, but still much better than a system with high complexity where the complexity is “optimizing” (for an example of this in the blockchain context, see Vitalik’s opposition and alternative to on-chain governance). The primary disadvantage we see in Kolmogorov complex but VC simple designs is for new social institutions, where it may be hard to persuade the public that these are VC simple. VC simplicity is usually easier as a basis for legitimacy when an institutions has clearly been built up without any clear design over a long period of time or by a large committee of people with conflicting interests (as with the United States Constitution). Thus when offering innovations we tend to focus more on Kolmogorov simplicity and hope many redundant each Kolmogorov-simple elements will add up to a VC-simple system. However, we may just not have the imagination to think of how VC simplicity might be effectively explained.

There are forms of the “avoid central planning” intuition that are misunderstandings and ultimately counterproductive. For example, try to automatically seize upon designs that seem at first glance to “look like a market”, because not all markets are created equal. For example, one of us has argued for using fixed prices in certain settings to reduce uncertainty, and the other has (for similar information sharing reasons) argued for auctions that are a synthesis of standard descending price Dutch and ascending price English auctions (Channel auctions). That said, it is also equally a large error to throw the intuition away entirely. Rather, it is a valuable and important insight that can easily is central to the methodology we have been recently trying to develop.

Simplicity to Whom? Or Why Humanities Matter

However, the academic critics of this type of work are not simply confused. There is a reasonable basis for unease with discussions of “simplicity” because they inevitably contain a degree of subjectivity. What is “simple” to describe or appears to have few knobs in one language for describing it is devilishly complex in another, and vice versa. A few examples should help illuminate the point:

Even Kolmogorov complexity (length of the shortest computer program that encodes some given system) is relative to some programming language. Now, to some extent, VC dimension offers a solution: it says that a class of systems is simple if it is not too flexible. But consider what happens when you try to apply this; to do so, let’s return to our example upfront about Harberger taxes v. perpetual licenses for spectrum.

Harberger taxes strike us as quite simple: there is a single tax rate (and the theory even says this is tied down by the rate at which assets turnover, at least if we want to maximally favor allocative efficiency) and the system can be described in a sentence or two. It seems pretty clear that such a system could not be contorted to achieve arbitrary ends. However, an opponent could claim that we chose the Harberger tax from an array of millions of possible mechanisms of a similar class to achieve a specific objective, and it just sounds simple (as with our examples of “deceptive” simplicity above).

To counter this argument, we would respond that the Harberger tax, or very similar ideas, have been repeatedly discovered or used (to some success) throughout human history, beginning with the Greeks, and that we do not propose this system simply for spectrum licenses but in a wide range of contexts. The chances that in all these contexts we are cherry-picking the system to “fit” that setting seems low. We would submit to the critic to judge whether it is really plausible that all these historical circumstances and these wide range of applications just “happen” to coincide.

Focusing on familiarity (ie. conservatism), rather than simplicity in some abstract mathematical sense, also carries many of the benefits of simplicity as we described above; after all, familiarity is simplicity, if the language we are using to describe ideas includes references to our shared historical experience. Familiar mechanisms also have the benefit that we have more knowledge of how similar ideas historically worked in practice. So why not just be conservative, and favor perpetual property licenses strongly over Harberger taxes?

There are three flaws in that logic, it seems to us. First, to the extent it is applied, it should be applied uniformly to all innovation, not merely to new social institutions. Technologies like the internet have contributed greatly to human progress, but have also led to significant social upheavals; this is not a reason to stop trying to advance our technologies and systems for communication, and it is not a reason to stop trying to advance our social technologies for allocating scarce resources.

Second, the benefits of innovation are real, and social institutions stand to benefit from growing human intellectual progress as much as everything else. The theoretical case for Harberger taxes providing efficiency benefits is strong, and there is great social value in doing small and medium-scale experiments to try ideas like them out. Investing in experiments today increases what we know, and so increases the scope of what can be done “conservatively” tomorrow.

Third, and most importantly, the cultural context in which you as a decision maker have grown up today is far from the only culture that has existed on earth. Even at present, Singapore, China, Taiwan and Scandinavia have had significant success with quite different property regimes than the United States. Video game developers and internet protocol designers have had to solve incentive problems of a similar character to what we see today in the blockchain space and have come up with many kinds of solutions, and throughout history, we have seen a wide variety of social systems used for different purposes, with a wide range of resulting outcomes. By learning about the different ways in which societies have lived, understood what is natural and imagined their politics, we can gain the benefits of learning from historical experience and yet at the same time open ourselves to a much broader space of possible ideas to work with.

This is why we believe that balance and collaboration between different modes of learning and understanding, both the mathematical one of economists and computer scientists, and the historical experiences studied by historians, anthropologists, political scientists, etc is critical to avoid the mix of and often veering between extreme conservatism and dangerous utopianism that has become characteristic of much intellectual discourse in e.g. the economics community, the “rationalist” community, and in many cases blockchain protocol design.