More Than Token: Regulation Of Alternative Currencies

By Matthew Leitch
Published by Long Finance (July 2019) sponsored by Cardano Foundation.

Interest in cryptocurrencies has been building for several years but, more recently, so has awareness of some serious problems with cryptocurrencies. Now is a good time to review this experience and draw lessons for regulators and everyone else interested in better management of risk around all types of alternative currency and around cryptocurrencies in particular. The issues discussed concern fund raising scams, lack of attention to control in projects, and foreseeable problems with technical inefficiency and economic control.

Fund raising scams

The issue

The problem, particularly during 2017, was people asking for money to develop the next big thing in cryptocurrencies, then either doing nothing of the kind or doing it incompetently. Exactly why so many people have put so much money into these projects and continue to speculate on cryptocurrencies is not fully understood.

Schemes were promoted energetically, often to people with little understanding of investment or the systems involved, with a thin veneer of respectability provided by websites, endorsements, language that sounded like real finance (e.g. ‘Initial Coin Offering’), and seemingly-responsible disclaimers. This prompted the SEC to produce a website promoting a fake cryptocurrency project; if you clicked to buy in you were taken to an educational site to teach you to be more sceptical.

This kind of purchase is very different to investing in a typical company’s shares because the company does something useful and should add some value somewhere, with the resulting profit being distributed. Even with the most famous and widely distributed cryptocurrency, Bitcoin, the overall situation is that no new value has been created. Miners of the currency have gained (or dropped out) so most likely ‘investors’, on average, have lost or will lose the wealth that has gone to miners. Two groups that have almost certainly gained are electricity companies and computer hardware manufacturers selling to miners.

Worse than that, most cryptocurrencies launched have already failed. The jury is still out on those that survive. A study by Satis Group (Gowlat, 2018) claimed that only 15% of Initial Coin Offerings (ICOs) in 2017 led to coins trading on an exchange. Instead, 78% were scams, about 4% failed, and the remaining 3% had ‘gone dead’. Of those that did lead to coins trading on an exchange a significant proportion quickly became dormant or nearly so.

Similarly bleak numbers are reported by Howson (2018), who found that since March 2016 1,154 projects had been launched but only around 40% had reached the stage of having tradable coins. (Presumably projects in 2016 were more often successful than in 2017.)

Benedetti and Kostovetsky (2018) were positive about the prospects of making quick money by buying tokens in ICOs, based on a dataset from 2016 to April 2018, but also found that 120 days after the end of their ICO, 56% of cryptocurrencies seemed to have died, judging from the lack of activity on their official Twitter accounts. However, for cryptocurrencies that got as far as listing on a currency exchange, only 16% had died after 120 days.

Potential solutions

One approach to this is to promote a voluntary code of conduct for promoters of new alternative currencies, with the idea that they can gain credibility and encourage wise investment if they can show that they are following the code sincerely and effectively.

The London Token Fundraising Manifesto (with many signatories, 2017) is a good example of such a code and could be developed further with more detail and perhaps also an independent review process.

Insufficient Attention To Control In Projects

The issue

In addition to fraud, a contributing factor to failed cryptocurrency development and launch projects will have been insufficient attention to control of the projects and to designing control into the systems to be developed. This relates to all types of risk.

Typically, attention has been paid to the security issues of greatest interest to cryptocurrency developers (e.g. the details of their protection against Sybil attacks and other attempts at double spending), and to solving governance issues using voting mechanisms enforced by the systems. These issues are often addressed in their ‘white papers’.

Unfortunately, this leaves out a long list of more prosaic concerns covering control during the development and launch project, and control built into the system that is to be created. These include software development practices, computer operations, version control, testing and other quality assurance tactics, progress reporting, documentation, financial control, compliance with laws on sales practices and cryptocurrencies, funding, fraud by social engineering and simple methods like stealing private keys, and control of the currency’s supply and value. The extent to which these conventional risk concerns are still relevant to blockchain-based systems (often known as ‘smart ledgers’ or as ‘mutual distributed ledgers’) is explored in Mainelli and Leitch (2017), which examines mutual distributed ledgers from an audit perspective.

Potential solutions

Groups aiming to develop and launch an alternative currency need to have a positive and responsible attitude to managing risk, the skills and experience to do it well, and some kind of framework to help them organize their thinking and activities. Another project under the Long Finance research programme has been to develop control frameworks for this purpose (Leitch and Matanovic, 2018).

Foreseeable technical inefficiency

The issue

The leading group of current cryptocurrencies have two design features that virtually guarantee that they will not be cost effective compared to established payment systems.

  1. They have multiple copies of their blockchain-based database – thousands of them in some cases. This means that the basic work of storing the database is duplicated thousands of times rather than the several times that would be necessary for a secure record. They also require all transactions to be communicated to all nodes, so there is a communications overhead too. As scale increases (in the sense of having more blockchain copies), these systems become less efficient, rather than more efficient as one might expect.
  2. The existence of each node is proved by doing intensive calculations that are duplicated over all the participants and have no other use. This ‘proof of work’ technique compounds the massive duplication problem.

These two problems make these systems inefficient, and this inefficiency was obvious and predictable from the start. They could never have hoped to compete on a sustained basis as electronic payment systems with established services like Visa and Mastercard.

A number of attempts have been made to quantify the resource inefficiency of Bitcoin. One of these comes from Mark Carney and the Bank of England. According to Carney (2018), the electricity consumption of Bitcoin alone was roughly twice that of Scotland, with a population of over 5 million people. In comparison, the global Visa credit card network used less than 0.5% of this while processing 9,000 times more transactions. (This translates into Bitcoin needing at least 1,800,000 times more electricity per transaction than Visa card payments.) Carney further states that the full cost per transaction to retailers of cash is 1.5p, cards is 8p, and online payments is 19p. In comparison, Bitcoin’s charge for faster processing was £2 at the time but had been as high as £40. The processing speed is vastly better with Visa, which also offers further benefits.

There is now a website from the Cambridge Centre for Alternative Finance that shows how Bitcoin electricity consumption compares with countries of the world. At the time of writing, Bitcoin had overtaken Austria, a country of 8.773 million people, and accounted for 0.28% of world electricity consumption.

In summary, Bitcoin is far more costly than Visa and its established competitors, despite providing a service that is inferior in several ways. If Visa provided a ‘no frills’ service as basic as Bitcoin’s then Visa could offer something even cheaper than the service it offers now. So, for an alternative currency to offer a new service that is competitive over a sustained period requires an inherently efficient design.

Potential solutions

What can be done to reduce the risk of such mistakes with future alternative currencies?

The simplest regulatory response to this might be to decide that new or proposed systems based on massive duplication of computing effort and on proof of work cannot be competitive and probably are being proposed as a scam.

Objections might be that the security could be used to solve some problem that overrides efficiency, so a simple ban might not be acceptable. Another approach would be to require some calculations be done and perhaps also published if funds are to be raised.

Since these efficiency issues were obvious from the beginning some straightforward calculations should be enough to compare the future efficiency of new systems with that of conventional designs. The main comparison should be of computer power used, but an expanded comparison might include any human element needed, provided the comparison equates the services provided.

If the efficiency of a system is dependent on scale or on the behaviour of users, for example, the calculations should be repeated to cover a wide range of potential future situations.

Foreseeable economic problems

The issue

The volatile exchange rates seen with most cryptocurrencies over the past few years are another predictable problem that needed to be taken more seriously earlier on. A highly volatile exchange rate means that the currency cannot be used as money. Prices of goods will not stay fixed. Money cannot be used as a store of value – only a speculative gamble.

These problems were predictable because they are the result of well-known economic principles and because, by the beginning of 2015, the price history of Bitcoin already showed huge volatility.

Economic control of alternative currencies is a complicated but vital area. Typically, cryptocurrencies have had a scheme for creating new coins that creates them over time, but not in a way that is fully responsive to the extent to which the currency is being used. If the cryptocoins are used more widely for more transactions then either the supply of the cryptocoins must be increased or the prices of goods when stated in cryptocurrency must fall as the value of the cryptocoins rises.

Beyond this, the technical inefficiency of Bitcoin and similar systems was a strong clue that they would not be successful as payment systems and, if they survived at all, would just be traded speculatively. In this role there would be almost nothing to stabilise their value and reason for holders to welcome large value changes.

Potential solutions

Alternative currencies need economic control mechanisms that keep them viable as money. These may relate to the supply of coins but other mechanisms are possible. A good simulator that reflects the design of the currency, its users, and its economic environment would be useful in doing five important things:

  • designing economic control mechanisms;
  • adapting them in future;
  • staying within the competence of those control mechanisms when making changes or coping with trends;
  • building control mechanisms that involve identifying potential problems and acting before they can materialise; and
  • demonstrating successful control.

As with other financial markets, the price of an alternative currency on an exchange would usually be difficult or even impossible to predict precisely (though if tightly controlled it might be exactly what it is supposed to be). However, this does not prevent a simulator from predicting the overall effect of control mechanisms, provided their operation is relatively predictable.

With this ability, the simulator could be used to test ideas for control mechanisms while the alternative currency is still on the drawing board. Once launched it would still be helpful to test by simulation the potential effects of making changes to the currency or the effects of identified trends continuing or accelerating. This would involve testing new control mechanisms or checking that existing mechanisms will still work even after other aspects of the currency have changed.

The ability to predict the future, even if it is just the distribution of possible outcomes, may itself be part of a vital control. The system might be designed to react to differences between predicted outcomes and fixed, planned, or relative performance standards.

Finally, simulation used for probabilistic prediction can demonstrate or even measure the extent of control achieved. This could be done using one of a number of proper scoring rules that calculate forecasting prowess from probabilities and actual outcomes. Some fascinating work on this has been done by meteorologists (e.g. Roulston and Smith, 2002). Good prediction implies either that you know and understand the system well or you have it under control so that it behaves as you intend.

To investigate these possibilities we carried out a project to scope and design a simulation system capable of testing control mechanisms for alternative currencies. Early observations from a prototype were reported in Mainelli, Leitch, and Demetis (2018) and included some intriguing behaviour generated by different levels of reactivity among speculators, and some clues as to the problems involved in controlling alternative currency values. Illustrative tests of control mechanisms were reported in Mainelli, Leitch, and Demetis (2019), still using the prototype. These showed how particular control mechanisms could be tested by repeated experiments in a simulator. The overall programme of work has also involved:

  • an analysis of control needs for cryptocurrencies (Leitch and Matanovic, 2018), resulting in control frameworks expressed at three levels of detail;
  • a workshop and survey to explore interest in particular features for an interactive simulator, and a second prototype showing the look and feel of the proposed simulator; and
  • detailed design and description of an interactive simulation system to test control mechanisms for alternative currencies (Leitch 2019).

The simulator is described in Leitch (2019) in the form of a detailed user guide with technical details including calculations. This describes an interactive, agent-based simulation system with many options for specifying a proposed alternative currency and its environment, then simulating it in stages with human intervention if desired.

The intention is to then extend the features of the interactive simulator to automate more of the decision-making according to rules and parameters that are pre-specified by users. This ‘experimenter’ version of the simulator would then allow users to specify different conditions (e.g. different settings for control mechanisms) and have multiple trials in each condition run automatically, with results captured and summarised.

All the agents in a simulation make decisions. Modelling those decisions is one of the most complex and important aspects of simulation. The decision rules that agents can use in the specified simulator have been designed with some helpful principles in mind.

  • Agents are diverse and error prone: The way agents ‘think’ is not the same for all agents and they have differing priorities and circumstances. Consequently, even if they appear to be facing the same decision about the alternative currency they are usually not. In most cases this is modelled by having the decision process control the probability of each alternative being chosen in a decision, but the final choice is randomised. In addition, agents sometimes have explicitly different philosophies and sometimes make mistakes randomly. Most alternative currency users are not professional currency traders using mathematical models and automated trading, so the simulation reflects reality.
  • Agent characteristics are controllable: The mix of agents with different characteristics can usually be changed in the simulator as can some important characteristics of those agent types.
  • Collective behaviour is broadly rational despite individual lapses: This is a typical property of human thinking, but especially when people have different sources of evidence. The agents are partly rational and partly consistent, confronted with a theory of the world that is too complex and unquantified for them to deal with.
  • Not blatantly stupid: Although individuals may occasionally make blatantly stupid decisions the collective tendency should be to avoid behaviour that is clearly irrational. For example, opting in as a customer when no goods can be bought with the alternative currency, or when the exchange rate is chaotic, is illogical and few if any agents should do it in a simulation. (But it might still be logical for a speculator.)
  • Limited intelligence: Where a decision analyst should, in theory, go into detailed and sophisticated modelling but this is not what nearly everyone does, the simulator will sometimes avoid the detail and just choose a number randomly from a sensible range. This again reflects real thinking which is bounded and inconsistent.
  • Consistent techniques: Where a decision is similar to another taken in the same or a different role then the mechanism of the decision is also similar.
  • Simplicity: Where there is no strong reason for choosing something more complex, the system uses the simplest mathematical approach available. For example: uniform distributions and simple multiplicative or additive models to combine variables. It has been assumed that causes do not interact unless it is clear that they do.
  • Real world variables: Wherever possible, variables have a real world meaning rather than being arbitrary coefficients. For example, a dimensionless index of publicity is not as good as a variable representing combined publicity in a way that might be measured in the real world e.g. ‘number of positive messages received per day on average per person.’
  • Real world calibration: Where practical, variables have been chosen so that real world data are available to compare with the simulation’s numbers. The main limitation on this is that often real world numbers are not available. For example, the number of people using Bitcoin is unknown.
  • Imaginable calibration situations: For some simulation settings it is necessary for users to choose a value based on experience and judgement. To make this easier there will sometimes be suggested defaults and users will usually be asked for a value of something that can be imagined and judged, rather than a seemingly meaningless parameter within a complex mathematical function. In some cases, what users choose is then converted into a parameter within a complex mathematical function.

From this effort, some key observations are as follows:

  • An agent-based simulation is probably the most suitable. A dynamical model using differential equations is not realistic enough and does not capture the rough and tumble of real alternative currencies.
  • A wide range of features of the currency, its users, and related environment events need to be simulated.
  • The progress of the currency cannot be reliably predicted but the effect of control mechanisms may still be relatively predictable.
  • The aim should be to test control mechanisms, not predict the future evolution of the currency in detail before it is launched.
  • The complexity needed is quite high. Establishing if a currency can be controlled effectively is more difficult than establishing if it is competitively efficient.

Since the effort needed to simulate and test control schemes for an alternative currency is significant, it may be something that developers of alternative currencies with big ambitions need to be required to do by regulators, and it may help to provide a simulator for them to use.

Conclusion

A number of lessons can be learned for regulation and control of alternative currencies from the recent experiences with cryptocurrencies.

First, it is clear that conventional risk control concerns are relevant even when the technology is novel and expert attention has been paid to some aspects of security and governance. The honesty of people raising money is always a concern and attention needs to be paid to all areas of risk and all types of control.

Second, the typical choice of blockchain technology combined with proof of work all but guarantees that a cryptocurrency will not be a competitive payment system. Predictable efficiency problems like this need to be avoided; requiring some simple engineering calculations early on is an obvious precaution.

Third, to make an alternative currency work as a currency requires a much more thoughtful approach to economic control. Testing control mechanisms using agent-based simulation is one way this might be done both during design and during operation of the alternative currency, but the work needed is quite difficult and regulatory pressure and facilitation is probably needed.

References

Benedetti, H., and Kostovetsky, L. (2018). Digital tulips? Returns to investors in initial coin offerings. Available at SSRN: https://ssrn.com/abstract=3182169

Cambridge Centre for Alternative Finance. Cambridge Bitcoin Electricity Consumption Index. At https://www.cbeci.org/

Carney, M. (2018). The future of money. A speech to the inaugural Scottish Economics Conference, March 2018.

Dowlat, S. (2018) Cryptoasset market coverage initiation: network creation. Satis Group.

Leitch, M. (2019). Economic Control of Alternative Currencies. Published by Long Finance and available at https://www.longfinance.net/economic-control-alternate-currencies/

Leitch, M. and Matanovic, A. (2018). Control Frameworks For Cryptocurrencies: An Initial Evaluation. Published by Long Finance.

Mainelli, M.R. and Leitch, M. (2017). Auditing Mutual Distributed Ledgers (aka Blockchains): A Foray Into Distributed Governance & Forensics. Published by Long Finance.

Mainelli, M.R., Leitch, M., and Demetis, R. (2018). Economic simulation of cryptocurrencies. The CAPCO Institute Journal of Financial Transformation, #47.

Mainelli, M., Leitch, M., & Demetis, D. (2019). Economic Simulation of Cryptocurrencies and Their Control Mechanisms. Ledger, 4. doi:https://doi.org/10.5195/ledger.2019.130

Many signatories. (2017). The London Token Fundraising Manifesto. Published by Long Finance.

Roulston, M.W. and Smith, L.A. (2002). Evaluating Probabilistic Forecasts with Information Theory. Monthly Weather Review, 130.