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1. Building the New Economy: what we need and how to get there

Published onApr 30, 2020
1. Building the New Economy: what we need and how to get there

1. Introduction

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 economic and health shock created by the COVID-19 pandemic, 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. Data is now central to the economy, government, and health systems, so why are data and the AI systems that interpret the data in the hands of so few people? Communities without data about themselves and without the tools to use their data are at the mercy of those with data and AI tools.

For instance, 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 the poorest communities. In the recent pandemic survival rates in different hospitals differ by a factor of two, with the worst outcomes in the poorest communities. Why don’t we have efficient data sharing between hospitals so that we know what treatments are being tried outside of formal drug trials and how they are working? 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 “one size fits none”, are lacking in transparency, and completely unauditable. No wonder they work so unevenly and respond to emergencies so poorly.

Data is now a full-fledged means of production, and consequently we need to think about it as a new type of capital, along with human and financial capital. Unfortunately, we do not yet have institutions and rules for ownership, exchange, and use of this new type of capital. Today people are often afraid of the power of this new data capital but, as economist Thomas Piketty said of financial capital, the problem is really that this capital is held in too few hands. This book is about how to make data serve all communities, both by empowering individual communities and by building a stronger, more resilient, and trustworthy fabric of social systems.

2. Toward a more resilient society

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 financial wealth, but also creating 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 of self-governed communities that learn 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.

3. A vision of the new economy

Driving this change from centralized systems to networks of more local systems is not only the realization that current systems are fragile and inadequately agile, there are also concerns about inclusiveness, transparency, cost, and security. Today most countries use distributed communication networks (e.g., the Internet), but most transactions are still carried out using centralized and conventional enterprise management software, and humans are often part of the accounting and audit systems. These centralized elements and requirements that humans to do routine bookkeeping is at the core of many of the problems with today’s systems.

Consequently, just as happened during the development of the internet and the World Wide Web, concerns about the inadequacy of today’s systems are pushing proprietary, private, legal functions to the periphery, leaving the core transaction network more distributed and entirely digital. These new distributed, all-digital systems are typically described as either Distributed Ledgers or Blockchains. Examples of these new systems include Estonia’s long-standing government infrastructure, the Swiss Trust Chain (which we helped develop), China’s “smart city” infrastructure, Singapore’s UBIN trade and logistics infrastructure In addition, there are national digital currencies being deployed by China, Singapore, and now many other countries, as well as notorious systems like Bitcoin. These new technologies offer new opportunities but also new challenges for policy makers and regulators.

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.

This vision must include a renegotiation data rights and uses, in order to create user-centric data ownership and management secure and privacy-preserving machine learning algorithms, transparent and accountable algorithms, and the introduction of machine learning fairness principles and methodologies to overcome biases and discriminatory effects. In our view, individual humans and human communities should be placed at the center of the discussion as humans are ultimately both the actors and the subjects of the decisions made via algorithmic means. If we are able to ensure that these requirements are met, we should be able to realize the positive potential of AI-driven decision-making while minimizing the risks and possible negative unintended consequences on individuals and on the society as a whole.

To develop this vision of the future this book is organized into three sections: The Human perspective, Resilient systems, and finally Data, AI, and the new economy. 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.

4. The human perspective: New types of engagement

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, citizens joined together to form trade unions and cooperative banking institutions in order to provide a counterweight to these new powerful forces. Eventually laws were passed to regulate labor and banking, and it was citizen organizations that were central in helping balance the economic and social power.

This section of the book begins with a chapter on Data Cooperatives, explaining how collective organizations of citizens are emerging to move the control and use of data and AI to a broader base of stakeholders. The chapter argues that such data cooperatives not only make the economy more responsive to citizen needs, but shows how they can increase the resiliency and economic prospects of the community. The last half of the Chapter presents a blueprint of the processes and digital infrastructure needed to achieve this vision, and outlines how these processes address regulatory challenges.

Shared Data: Backbone of a New Knowledge Economy, the next chapter in this section of the book, 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 following 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 Chapter presents a blueprint for how to achieve fine-grain, inexpensive auditing of digital assets are used, along with simultaneously enabling payments for use of those assets, and argues that these new capabilities can transform the way individuals choose to work and broaden the range of creative work supported by society.

The last chapter in this section of the book, 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-centric 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.

5. The human perspective: New types of engagement

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 may offer a path toward a better future. 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.

This section of this book begins with a chapter entitled Tradecoin, describing a secure, distributed approach to building currency systems based on the assets of (potentially) millions of stakeholders. The Tradecoin architecture enables large investment funds that are largely independent of centralized authorities, large nations, large banks and the wealthy western world. It also enables pooling of assets in new ways that can challenge current fiat currencies and multilateral institutions such as the International Monetary Fund. Versions of Tradecoin-style architecture have been incorporated into several recent digital currency proposals, ranging from China and Singapore’s digital currencies to the Libra consortium proposed by Facebook.

The next chapter 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 chapter on 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 that we have today.

The final chapter in this section of the book, Network Dynamics of a Tokenized Financial Ecosystem, shows how an existing 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, and suggest a way to prevent formation of monopolies that is applicable to all financial networks.

6. Data, AI and the new economy engagement

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 is Towards an Ecosystem of Trusted Data and AI, addresses the problems we are encountering as the economy and society move from a world where interactions are physical and based on paper documents, toward a world that is primarily governed by digital data and AI. To manage this transition we must create an ecosystem off trusted data and trusted AI that provides safe, secure and human-centric services for everyone, allowing us to unlock huge societal benefits, including better health, greater financial inclusion, and a population that is more engaged with and better supported by its government.

The next 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 following 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 final 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.

7. Conclusion: Legal Algorithms

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 MIT computational law initiative, which may be seen at, 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.

8. How to read this book

This book is intended to convey both the “big idea” and a blueprint for how to build these systems. The book does not delve into regulatory issues, although it deals with privacy, security, and transparency, because different counties will regulate these systems in different ways.

Because we present both big idea and blueprint, the last half of many of the chapters will be too technical 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.

Additional material can be found at , and The original academic works that support many of the chapters can be found here:

  • Data Cooperatives Chapter

  • Digital Trade Coin Chapter:

  • Network Dynamics of a Financial Ecosystem Chapter:

  • Interoperability Chapter:

    • 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

surbhi [email protected]:

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Tony Camero:


Tony Camero:


Tony Camero:


Tony Camero:

Personal note: mayors

Joby John:

Is there an accepted way to measure effectiveness of such learning systems ?

Patrick Erichsen:

- “in ways ranging from filling out forms to tax computation to trial discovery.”

+ “in ways ranging from filling out forms, to tax computation, to trial discovery.”

Patrick Erichsen:

- “some of channels”

+ “some of the channels”

Patrick Erichsen:

- “data rights The”

+ “data rights. The

Peter Pleijs:

An interesting read. However, I miss an important adaptation in the proposed new systems. The basis of all environmental an climate issues is the faulty price-system in which only direct costs are included. The value of nature and other resources is widely recognized nowadays, but I see no intention of incorporating them in the cost(and benefits) systems. A really new economy should be based on a price system that incorporates all cost and benefits, otherwise adaptation afterwards will prove necessary.

Stephen Coller:


Stephen Coller:


Stephen Coller:


Stephen Coller:


Stephen Coller:

The idea of learning systems that work on OUR behalf is strong.

Stephen Coller:

Do you have examples you can share?

Douglas Kim:

Truly groundbreaking - an industry unchanged in centuries!

Douglas Kim:

Also is the way drug development and approval works. For example there is a movement to fastrack vaccine development by assembling human volunteers to be purposefully infected instead of the typical randomized clinical trial.

Douglas Kim:

The aftermath of WW2 also saw the creation of whole new industries and businesses to accommodate population growth and mobility: Suburbs with mass manufacturing of homes, and McDonalds : two examples.

Bryan Wilson:


Bryan Wilson:

which lead

pras s:

Can you mention data source?

Carlos Mazariegos:

missing text?