With each major crisis, be it war, pandemic, or major new technology, there has been a need to reinvent the relationships between individuals, businesses, and government. In the years leading up to World War I, the rise of mass manufacturing led to such a rebalancing. This period saw the creation of regulation governing working conditions and pay, health protections for mass-produced food, and rules to prevent monopolies. After World War II we saw the end of European colonies, greater access to higher education, and advancement of women’s rights and racial equality. And, at about the same time, the plagues of tuberculosis and polio were defeated by early biotechnology, leading to tough new standards for medicine.
Today we face two simultaneous disruptions: one is the COVID-19 pandemic and resulting economic shock, and the second is the rise of pervasive digital data, crypto systems, and artificial intelligence (AI). These new digital systems are being deployed to fight the current world pandemic, and technologies such as videoconferencing and artificial intelligence applications such as infection modeling have been strong allies. However, digital technologies such as social media have generated disinformation and increased confusion, and the mobile phone applications used to track infection threaten privacy rights. These problems highlight the need to reinvent the ways data and AI are used in all of societies’ civic and government systems, both to guarantee that future pandemics can be handled better and to reinvigorate the economy but also to spread the economic benefits throughout society.
Perhaps more to the point, why isn’t there better medical data sharing infrastructure? Informal networks of doctors sharing data about AIDS and infant care have dramatically improved medicine in those areas. Why don’t we have national coordination between hospitals so that we know what treatments are being tried outside of formal drug trials and how they are working? Hospitals that keep track of what everyone is doing and have daily revisions for best practice treatment do much better at handling the COVID-19 pandemic…in fact, current survival rates in different hospitals differ by a factor of two!
Similarly, a major impediment to drug development is data sharing, even though there are ways to do that without endangering patient confidentiality or companies’ proprietary data rights The same sorts of criticism apply to how we provide social benefits, support small businesses, and levy taxes. Our systems are a siloed patchwork which prevents data sharing, are too often ad-hoc, lacking in transparency, and completely unauditable. No wonder they work so unevenly and respond to emergencies so poorly.
It is time to refresh our ideas about the ways that our society is organized in order to encompass these new digital means of production and rebalance the relationships between all the stakeholders of the economy. Central to this reinvention is building a new economy -- not only restoring vibrancy and spreading wealth, but also incorporating new solutions for more resilient and efficient civic and government systems, for improving digital privacy and cybersecurity, for providing more agile, inclusive, and transparent responses to society’s problems, and for funding the infrastructure required by this new economy.
Within my research group, MIT Connection Science, we and our partner nations are finding that the same technologies that are causing social unrest may also enable the creation of more agile and less fragile types of systems where power and decision-making are distributed among the stakeholders rather than concentrated in just a few hands. The key point is that distributed systems, when done right, are not only more adaptive and agile, but are also more resilient to catastrophes such as pandemic disease or political unrest, and less likely to have unintended consequences such as climate change or social inequalities.
Resilience is the path chosen by most biological systems, and it is achieved by having many diverse system designs, each with continuous monitoring of outcomes that allows for rapid learning and adaptation across the different systems. Evolution itself is a classic example of such a learning system. To create resilient social systems, the key is to not to create a new nation-wide institution or law, or a new international administration, but rather to begin with local institutions, with the goal of building a diverse coalition that learns from each other and constantly adapts.
What are some examples where these sorts of resilient innovation networks have worked?
In the last few decades, informal networks of doctors sharing data about AIDS and infant care have dramatically improved medicine in those areas, reducing death rates by an order of magnitude. It wasn’t the World Health Organization, nor the US National Institutes of Health, that accomplished this miracle, although they provided critical support for experiments and for implementing improvements. The innovations came from local communities of health workers working on new ideas and learning from each other.
Similarly, today we see informal networks of scientists making great strides at designing and testing new treatments for COVID-19. Again, it is not programs of the National Institutes of Health or the World Health Organization that are driving this surge of innovation. Instead what these large organizations have contributed is the scientific infrastructure and some of channels for information sharing that allow individual labs to have the tools and freedom needed to experiment and learn from each other.
The same spirit of resilient, bottom-up innovation is beginning to come to business and investment. A good example is the appearance of crowdsourcing for massive infrastructure projects such as hydroelectric power. The citizens and businesses who will benefit from having local hydropower buy digital tokens that give them future rights to electricity, and the pooled money provides the hundreds of millions of dollars required to build the water dam and hydropower complex. It is not governments who pay for and own this new infrastructure, but rather it is the people that benefit directly.
Workers are also crowdsourcing innovation. Groups of “gig workers” are pooling data about their working conditions in order obtain better pay and safer work conditions, and sharing these innovations with gig workers in other cities and industries. Similarly, creative workers such as musicians and writers are finding new ways to sell to their audience directly and avoid the media platforms that no longer provide them a working wage.
Resiliency is even coming to government, avoiding the polarization and gridlock of national and state governments. Networks of city governments have set up innovation networks, where local governments experiment with different programs and policies. By sharing data about the results of their experiments, the best ideas are able to spread quickly to other cities, all without nation-wide programs or regulations.
So, just as in previous crises, it is sharing ideas and outcomes across networks of local communities that is providing the sort of resilient innovation needed for our society to survive and come out stronger. The time has come to create more resilient systems by shunning optimized central control and embracing learning systems based on diverse innovation and experimentation.
Building a new economy will require addressing contentious social issues such as ownership of data and control of the means of production. In order to be successful at building the new economy we have to present a plausible, positive vision of the future, which explains how data and AI can enable better systems of capital, labor, and property.
To develop this vision of the future this book is organized into three sections: The Human Perspective, Resilient Systems, and finally Data and AI. At the end of these three main sections, there is a concluding chapter on Computational Law, which discusses how to deploy and regulate these new societal systems.
The robot overlords are coming! Everyone is worried about AI will transform work and society. Central to these concerns are the questions of who controls the data, and how is AI using this data? Particularly alarming is the amount of data, and resulting power, held by a small number of actors During the last 200 years, questions about concentration of power have emerged each time the economy has shifted to a new paradigm: industrial employment replacing agricultural employment, consumer banking replacing cash and barter, and now ultra-efficient digital businesses replacing traditional physical businesses and civic systems.
As the economy was transformed by industrialization and then by consumer banking, powerful new players such as Standard Oil, J.P. Morgan, and a handful of others threatened the freedom of citizens. In order to provide a counterweight to these new powers, citizens joined together to form trade unions and cooperative banking institutions, which were federally chartered to represent their members’ interests. These citizen organizations helped balance the economic and social power between large and small players and between employers and workers.
The first chapter in this section of the book, Data Cooperatives, explains that the same sort of collective organization of citizens is emerging to move control of this new means of production to a broader base of stakeholders. The chapter argues that such data cooperatives not only make the economy more responsive to citizen needs, but also promote greater competition and innovation.
The second chapter, Shared Data: Backbone of a New Knowledge Economy, explains how an efficient data economy can develop while also preserving privacy, trade secrets, and general cybersecurity, through the use of the sort of data exchanges we are beginning to see emerging around the world.
The third chapter, Empowering Innovation through Data Cooperatives, provides concrete examples of how digital markets and cooperatives can transform the “gig economy” into a stable ecology that supports artistic production and other individual digital production into safe career choices. The ability for fine-grain, inexpensive auditing of how digital assets are used by others, along with simultaneously enabling payments for use of those assets, can transform the way individuals choose to work and broaden the range of creative work supported by society.
The fourth chapter, From Securitization to Tokenization, describes how large-scale infrastructure investments (e.g., hydropower, train lines, harbors) are already being deployed using broadly distributed tokenized funding mechanisms. Such infrastructure is funded by local alliances of citizen investors that also benefit directly from the way that the new infrastructure improves their city or province. The rise of such citizen-led finance and management systems appears to be the beginning of a new trend to create more widely distributed infrastructure with stronger, more localized buy-in from community stakeholders.
Panics, crashes, and confusion. Can we make our financial systems less fragile, more transparent and less winner-take-all? Can we make our health systems more agile and proactive? Can we spread financial and health benefits more widely? The new distributed, technology-enabled organizations that are emerging seem to offer this promise. Moreover, they are particularly attractive outside of the developed world, in places where existing institutions are either weak or in poorly-served, as well as in the poorer neighborhoods of wealthy nations.
The first chapter in this section of this book, entitled Tradecoin, describes a secure, distributed approach to building currency systems, which can be supported by millions of asset holders. The Tradecoin architecture enables large investment funds that are largely independent of centralized authorities, large nations, large banks and the wealthy western world. Versions of Tradecoin-style architecture have been incorporated into several recent digital currency proposals, ranging from the Facebook Libra to the new Chinese digital currency.
The second chapter in this book is Health IT: Algorithms, Privacy, and Data. Data are crucial for health and the life sciences, and the urgent need for solutions to the limitations of today’s systems is nowhere clearer than in the handling of citizen data in the recent COVID-19 pandemic. This chapter presents a framework for deploying new, highly interoperable Health IT infrastructure that deals with the various aspects of health-related data, based on existing medical, health, and privacy standards in order to permit easy adoption by stakeholders.
The third chapter in this book, Narrow Banks and Fiat-Backed Tokens, describes how banking can be re-invented to become much more stable, and leverage Tradecoin-style digital Token infrastructure to free businesses and consumers from the nightmare system of national currencies and exchange rates.
The fourth paper, Network Dynamics of a Tokenized Financial Ecosystem, shows how a current Token-based ecosystem has become self-stabilizing despite being world-wide and generally unregulated. This analysis suggests new sorts of tools and measurements are critical for avoiding financial catastrophes. Not only can these Tokenized systems have distributed governance and distributed stakeholders, their metadata offer a new level of transparency that enables greater controllability and thus greater safety.
What sort of infrastructure can support a world with billions of data owners, producers, and consumers? If we are to maintain innovation while achieving social goals we will have to avoid uniform, centralized systems and instead support diverse approaches to the problems of citizens, companies and governments. In order to work on a global scale, these diverse approaches must be interoperable, so that knowledge, trade, and interaction can flow seamlessly across company and national boundaries.
The technical problems of supporting such interoperability is addressed by the four chapters of this section of the book.
The first chapter in this section of the book, Stablecoins, Digital Currency, and the Future of Money, describes the evolution of our medium of exchange from the historical idea of money to the more powerful idea of digital currencies. From JP Morgan’s Jamie Dimon to Facebook’s Mark Zuckerberg, stablecoins --- digital currencies with an inherently stable value --- have made their way onto the agenda of today’s top CEOs. We discuss how to go about creating a digital currency that has an inherently stable value, and survey the different use cases for stablecoins and the underlying economic incentives for creating them. Finally, we outline the critical regulatory considerations that constrain them and summarize key factors that are driving their rapid development.
The second chapter in this section is Interoperability of Distributed Systems, where we analyze the notion of interoperability, survivability and manageability for distributed systems, using lessons learned from the three decades of the development of the Internet. We then develop a design framework for an interoperable distributed architecture, and identify particular design principles that promote interoperability.
The third chapter in this section of the book, Exchange Networks for Virtual Assets, builds on the basic function of interoperability, to allow trade and auditing of transactions. Virtual asset service providers (VASP) face a data problem: on order to fulfill the regulatory requirements, they need access to truthful information regarding originators, beneficiaries and other exchanges involved in a virtual asset transfer. However, getting access to data or information – regarding individuals and institutions involved in the asset transfer – means that VASPs must also address the challenges of data privacy and privacy-related regulations. How can we solve these problems? This chapter lays out principles and describes a path forward.
The fourth chapter, Key Management for Virtual Assets, reviews the use of existing standards in the area of public key certificates and certificate management in the context of virtual asset service providers and suggests a set of new policies to ensure more secure systems.
Besides the technical challenges described in these last three chapters, we need to address the problem that some of these new systems are likely to have disruptive and unintended negative effects on society. How can we make sure these complex virtual systems are safe and secure? Ensure that they achieve the sort of social goals that we desire…fairness, inclusiveness, stability… along with high rates of innovation?
Balancing these and other elements of the new economy will be the job of law. However, the current practice of law is already unable to cope with our rapidly changing world, and is increasingly unable to ensure access to justice. To keep up with this rapid pace of change, the practice and application of law is becoming computerized, in ways ranging from filling out forms to tax computation to trial discovery.
However, the migration of our existing set of legal algorithms to computer platforms risks displacing human judgement and sensibility. Consequently, we must think carefully about how computation interacts with the processes of law and regulation. This rethinking of how to manage the computerization of law is the focus of computational law.
The final chapter of this book, Legal Algorithms, presents an introduction to the on-going process of inventing law and computational systems that can manage diverse systems of virtual assets. This chapter describes a path to leveraging computational tools to develop more transparent, accountable, and inclusive legal, civil, and government processes. It is through this digital transformation that people everywhere will reap the benefits of a true stakeholder capitalism, based on a reinvigorated social contract, together with the sort access to justice enjoyed by very few today. This chapter also introduces our new computational law initiative, which may be seen at http://law.mit.edu, and consists of an alliance of law schools and legal scholars hosted by my research group, MIT Connection Science, and which is now producing the world’s first Computational Law Report.
This book is intended both to convey the “big idea” and some supporting details, consequently the last half of many of the chapters will be too technical or too mathematical for many readers’ taste. So, feel free to read the beginning of the chapter and then skip to the end. In fact, even if your goal is to understand the details, it might be a good idea to read the first part of all of the chapters before you to dive into the details. That way you can better understand the context of the system design and analysis…these ideas are intended to be synergistic and to support each other, and so are not completely independent.
Digital Trade Coin chapter:
Network Dynamics of a Financial Ecosystem chapter:
Key management for Virtual Assets chapter:
T. Hardjono, A. Lipton, and A. Pentland, “Towards a Public Key Management Framework for Virtual Assets and Virtual Asset Service Providers,” 2020, Journal of FinTech (to appear June 2020) – Available at https://arxiv.org/pdf/1909.08607.
T. Hardjono, A. Lipton, and A. Pentland, “Towards an Interoperability Architecture Blockchain Autonomous Systems,” 2019, IEEE Transactions on Engineering Management - Special Issue on Blockchain Ecosystems.
Exchange Networks for Virtual Assets chapter:
T. Hardjono, A. Lipton, and A. Pentland, Privacy-Preserving Claims Exchange Networks for Virtual Asset Service Providers" Proceedings of 2nd IEEE International Conference on Blockchain and Cryptocurrency (ICBC2020), May 2020