This note is motivated by the collapse of FTX and the publication of
the fascinating book, Proof of Stake by Vtialik Buterin, the major
developer for Ethereum. Buterin, as well as the founders are FTX and
others in the crypto space are brilliant individuals. Yet, the space
is obviously facing growing pains. The message of this note is that
effective regulation of the crypto industry requires integrating
knowledge from experts working in a number of areas. I also point out
that we can use micro-economics, particularly the research discussed
in Chapters 1-4 of Advanced Micro-economics (subsequently cited as
AM), to identify four distinct areas of expertise that are discussed
crypto-technology - the technical advances to the design of block-chain, smart contracts and digital currency.
crypto-markets - the public market for smart contracts, digital currency and other products that build upon the block chain and related technologies.
crypto-regulation - the public regulation of crypto-markets.
crypto-science - Measuring the causal effect of interventions in crypto markets, particularly the evaluation of regulations.
The collapse of FTX provides a reminder that markets are extremely
complex and cannot operate effectively except in the shadow of behind
the scenes regulation. The first welfare theorem of general
equilibrium theory (see appendix of AM) shows that when markets are
complete, then competitive markets are efficient. As discussed in
Chapter 1 of AM, this result very much depends upon having a thick
market for all commodities, combined with the requirement that the
exchange of the commodity can be enforced at no cost.
Neither of these requirements are satisfied in practice. Much of
modern economics can be viewed as understand the limits of markets and
working out appropriate regulation.
Crypto-technology refers to block-chain and related technologies.
Technically, the technology is a brilliant synthesis of game theory
and complexity theory. Standard game theory assumes that parties are
aware of all the strategies available to them. Individuals assumed to
choose what they believe is the optimal strategy from the available
set, given the anticipated behavior of their counter-parties.
Complexity theory is a sub-field of computer science that provides
methods to evaluate the difficulty making decisions that are modeled
using computer algorithms. Hence, when combined with game theory, it
can be viewed as providing a theoretical foundation for a research
agenda started by Nobel prize winner Herbert Simon in the 1950s.
Complexity theory provides a formal way to measure how long it will
take to comprise security systems, that in turn is an important
component of block-chain technologies. The block chain technology
builds on these ideas to ensure that the transfer of property rights
can be safely verified without government intervention (See appendix
of Buterin's book for a nice outline of the bitcoin technology). Smart
contracts build on this technology to provide mechanisms for the
transfer of property rights that can be conditioned upon certain
events that do not require the intervention of a judicial system. For
example, transfer funds to a seller once a product has been verified
as delivered to a buyer.
Conceptually the block-chain technology is not new - it can be thought
of as a super-charged property rights system that is part of the
evolution of property rights systems started thousands of years ago.
For example, early property rights systems relied upon individuals who
follow accepted norms of behavior. We see this in the book of
Deutoeronomy in the Old Testament (chapter 27, verse 17): "Cursed be
he that removeth his neighbour's landmark. And all the people shall
The promise of the block-chain technology is to provide a
decentralized system for the trading of property rights that is
democratic and not controlled by any government, while at the same
time lowering the transactions costs associated with the verification
of property rights. Transactions costs are so low that one is able to
allocate property to low value digital coins - a process that is akin
to assigning a property right for every dollar bill in one's pocket.
In an important paper, Abadi, Joseph, and Markus Brunnermeier.
“Blockchain Economics” https://doi.org/10.3386/w25407 provide a
brilliant economic analysis of what is possible with a block chain
technology. Using ideas from game theory, mechanism design and
asymmetric information (based in part on the economic theory discussed
in chapters 3, 6 and 8 of AM) they show it is impossible to have a
consensus mechanism, such as implemented with proof of work or proof
of stake, that is resistant to computer network failures, resource
efficient and allows any transfer of value that parties have agreed
Thus, the paper proves that there are limits to decentralized finance
systems with self-interested individuals. In particular, any system
that is implemented must compromise between the three goals, a
trade-off that Abadi and Brunnermeier call a trilemma. Resolving the
trilemma is not a theoretical problem, but one that is ultimately
solved with practice in crypto-markets.
A competitive crypto-market is the place where the block-chain
technology can be used and tested. Note that the everyday term for a
competitive market differs from the term as used in economics.
Technically, economists define a "competitive market" as a situation
for which there are many sellers of every commodity, and that parties
can enter into agreements to trade specified quantities of a commodity
at a specific time and place. As discussed in Chapter 1 of AM.
competitive markets, as used in the welfare theorems of economics, are
an abstraction that helps us understand the stringent conditions
required for efficient production and distribution. Give that these
conditions are never satisfied in practice, observing market failures
may be viewed as consistent with economic theory!
In practice, as Hayek emphasized many years ago, markets are chaotic
institutions for innovation and the testing of new products. From this
perspective, crpto-markets provide value by exploring potential
applications of cypto currency. It also allows one to see how the
trilemma, highlighted by Abadi and Brunnermeier, is resolved in
practice. We see this in the fact that bitcoin (that uses proof of
work) and ethereum (that uses proof of stake) rely have very different
resource foot prints. It will be interesting to see over time whether
one system becomes dominant, or whether there will be many different
block-chain protocols co-existing at the same time.
Hence free markets provide a venue for new and exciting innovation,
and over time we learn which solutions are the most useful. However,
the very fact that information is expensive also implies that free
markets can be exploited by harmful agents who sell snake oil, or
other products of dubious quality. As the Noble laureate George
Akerlof showed, when the uncertainty regarding the quality of a
commodity is sufficiently high, then markets may even shut down, and
we may lose the benefit of productive innovation and exchange. In the
case of FTX, the market failed when market participants realized that
the exchange may not have sufficient assets to cover their
liabilities, leading to a run on the exchange.
Such runs are a familiar feature of financial markets. This year's
Nobel prize in economics was awarded to Bernanke, Diamond and Dybvig
for their seminal contributions on the sources of financial
instability. For example, they showed that in theory seemingly small
fluctuations in beliefs may lead to the inefficient failure of banks.
This research ultimately helps provide guidelines for regulation. The
question then is what should be the appropriate policy response in the
case of failures in crypto markets?
Many policy makers worry that excessive regulation may stifle
innovation. It is worth highlighting the fact that most regulation is
not prospective, but reactive, and a consequence of observed market
failures, and not a cause.
A good example of this is the regulation of food and drugs. In the
"Poison Squad", Deborah Blum provides an amazing history of food and
drug regulation in the US. In particular, most regulation did not get
enacted until many individuals were made ill or died from defective
products sold on a free market. Thus, while some may argue that crypto
should have been regulated earlier, the evidence suggests that policy
in United States takes a lassez faire attitude towards markets, and
only regulates when there is clear evidence of a need. Even then, as
Blum documents, the regulation may evolve very slowly over time.
Similarly, the failure of FTX is yet another example of a commodities
(such as investment into interest bearing accounts on the FTX
exchange) sold on a free markets that did not have the advertised
characteristics. The failure of free markets has a long history, as
Michael Cassidy brilliantly documents in /How Markets Fail/. The book
does a wonderful job of introducing the general reader to modern
economics in an accessible fashion. He views economic policy as a
trade-off between "utopia economics" which he characterizes as
unbridled support for free markets, versus behavioral approaches (see
the preface to the 2021 edition of his book).
This is a reasonable representation of the public debate, but not
representative of modern economics where it is recognized that all
markets are imperfect, in large part due in large part of information
failures. The reason that the information failure perspective is not
highlighted in the public debate is because it is /complicated/!
Chapters 6-10 of AM reviews a small subset of the research that
studies the implications of information constraints on the observed
The challenge is that a great deal more work needs to be done in order
to understand how to best trade-off more versus less regulation. In
particular, it is worth highlighting that FTX was not unregulated. The
FDIC sent them a letter on August 18th, 2022 stating that FTX was
making misleading statements regarding their products, and asking for
clarification. Moreover, the fact that the principals of FTX have been
charged with criminal fraud charges, and that two of these executives
have already pleaded guilty is evidence that there were consequences
associated with the miss-information regarding the commodities FTX
supplied to the market.
What Cassidy, and other commentators on economics appear to avoid
discussing is the tremendous progress that has been made in economics
in providing a framework for improving policy.
The 2021 Nobel prize in economics was awarded to David Card, Joshua
Angrist and Guido Imbens for their contributions to economic science.
By the term science I mean research that explores the actual
implications of policy, rather than the hoped for implications. For
example, in his work with Alan Krueger, David Card showed that there
are labor markets in the US where increases in the minimum wage
increases employment. Simple labor market models predict the opposite
and hence there were many criticisms of the work at the time. Yet in
the end, it should be the data and not ideology that determines the
quality of a theory.
A goal of economic science is to measure the causal impact of policy
changes. A contribution of Card, Angrist and Imbens is to clarify that
conditions necessary for a prediction on the effect of policy to be
valid using available data. One necessary condition is the ability to
have multiple observations of the effect of a policy change. This in
turn illustrates the point that science is very different from
innovation. When a new products is brought to market it is a unique
event, and hence in many cases it is impossible to predict how well
they do (for example, consider the many expensive movies brought to
market that are financial failures).
In contrast, a scientific claim depends upon repeated observations of
the same event subject to different treatments. What make economics
science so difficult is the challenge of having repeated observations
of comparable events in a rapidly changing environment. This problem
is particularly acute in the climate change debate. In order to be
able to build credible predictions regarding the impact of different
climate policies one would need to try them out a several similar
planets, something that is clearly impossible.
Rather, practical policy relies upon a combination of science and
models of the world based upon science that attempt to extrapolate
knowledge to new domains. Policy, like new products are tested in the
public domain. This highlights the point both markets and science are
institutions that generate knowledge, but through fundamentally
different processes. In particular, both entrepreneurs and scientists
can be highly skilled, and thus convincing because they are skilled.
In /Proof of Stake/, Buterin presents a number of ideas for crypto
markets that he argues will be useful. It is natural that the
entrepreneur have faith in their new ideas. However, since these
claims are not science, thus one needs to be cautious.
By crypto-science I mean claims regarding the industry that can be
validated through careful research design. Crypto-science needs to be
differentiated from crypto-technology. For example, the results of
Abadi and Brunnermeier are contributions to technology since the
results are based upon stylized models of behavior. In contrast, the
goal of science it to understand actual behavior. The science is not
always sufficiently developed for many important policy questions.
Hence actual policy must rely upon a combination of science and market
experimentation. It is useful to differentiate the two sources of
knowledge in order to be ready to modify policies as new information
Let me conclude by discussing two questions for micro-economic science
that the FTX case raises. The first of these is why did very large
firms invest in FTX? There were news reports of firms who considered
investing in FTX, but upon further investigation decided again
investing. They apparently made the right decision. What explains the
bad decisions? A similar question arise in the 2008 market failure
where some investors were aware of the low value of mortgage backed
securities months before the market price adjusted to reflect this
information. Basic finance models assume that information is
transmitted quickly and efficiently through modern financial markets.
This is clearly not the case and at the moment we still do not have
good theory or evidence to explain the transmission of information in
A second question is why did the principals of FTX commit fraud? Is it
because they did not expect to go to jail? Did they not understand
that the firm would eventually fail? Were the short run returns,
including being part of an interesting and exciting project, out
weight the cost from time they will spend in jail?
Obtaining scientific answers to these questions is central to how one
should regulate markets. The fact that in the case of FTX the
potential jail time did not deter the fraud may explain the demand for
/ex ante/ regulation. This is regulation that places reporting
requirements upon firms before any malfeasance has occurred. Such
regulation places costs on both well manged firms as well as firms
that might commit fraud. If fraud could be deterred with high future
penalties, but as we saw from the FTX case, deterrence is not always
One of the foundations of micro-economics is the hypothesis that
individuals respond in predictable ways to rewards and penalties. We
clearly need much more work on understanding the limits of deterrence,
and in turn the implications of these limits for the effective /ex
ante/ regulation of markets.