CryptoEconomics : Why Blockchains need Computer Scientists & Economists to come together?

Praphul Chandra
May 24, 2018 · 7 min read
Image Credits: Pexels

For almost a century, the study of economics has been divided into distinct sub-fields. Macroeconomics is concerned with how the overall economy works and studies such things as employment, Gross Domestic Product (GDP), and inflation. On the other hand, Microeconomics is concerned with how supply and demand interact in individual markets for goods and services. This macro/micro split is institutionalized in economics.

Attempts to bridge this gap have attracted not only economists & game theorists but also physicists — not surprising once you realize that the fundamental problem here is equivalent to a Grand Unified Theory in physics. Think of it as the challenge of one unified economics — explaining GDP, unemployment, business cycles and price volatility from first principles of individual behavior — a tough nut to crack. Blockchains may just be the catalyst to reignite these efforts.

Blockchains: An Economic Laboratory

Once you move past the speculative pricing of cryptocurrencies and ICOs, Blockchains are fundamentally a way to create economies. The ability to create new tokens or currencies (monetary policy) and to distribute and allocate these tokens according to economic incentives (mechanism design) will lead to the creation of new forms of commerce & economies.

Every new token is, in fact, like the creation of a new economy with its own monetary policy and regulations. It is important note that not only will the internal rules of these new economies be important but so will “cross-token finance” — the rules governing how these new currencies exchange for other monies both crypto and fiat. It may not be wrong to call blockchains an economic laboratory. The underlying infrastructure on which this economic laboratory is being built is computational. The ledger on which cryptocurrency transactions are recorded is secured using cryptography, trust among parties is ensured using a combination of distributed (consensus) algorithms & cryptography.

What is CryptoEconomics?

One interpretation of CryptoEconomics is at this infrastructure layer : creating incentives for each computing node to participate in the secure maintenance of the ledger which stores all transactions. The various consensus algorithms (Proof-of-Work, Proof-of-Stdake, Proof-of-Authority etc.) being explored all fit in this layer.

An alternate interpretation of CryptoEconomics is at the application layer: creating incentives for each participant to either create or consume value, using a token as a medium of exchange for the transaction.

The ability to use algorithms for the creation, allocation & destruction of new tokens (e.g. ERC20) allows solution (mechanism) designers to offer incentives & reward desirable behavior. The fundamental idea is not new. Alternative currencies like airline miles & loyalty points are used by enterprises to influence consumer behavior even today. What is new is the scale with which it is possible to do this with the advent of Blockchains.

Computer Scientists, of course, understand the challenge of scale very well — designing systems which work with 100 users and ones that work 10 Million users are two very different things. Computational scaling, however is only half the story. The distributed & trustless nature of Blockchains also ensures that economic incentivization solutions designed using such tokens are economically scalable — allowing anyone to participate, trade with & transact in an economy which accepts this token.

Image Credits:

The scale & ease with which we can create new tokens which seed new economies also presents challenges. Solution designers must answer fundamental macroeconomic questions about the token economies they are creating: How many tokens should be created? When? To whom should these tokens be allocated? It is almost as if the solution designer becomes a virtual central banker — strictly speaking, by designing the algorithm which takes these decisions, the solution designer becomes the central bank designer. Note the similarity of these design decisions with macroeconomics.

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A second set of questions solution designers must answer are about the microeconomics of the token: How will these tokens generate value? What economic interactions will the token enable? What will incentivize agents to participate in the token’s economy? How will the economy ensure fairness and promote honest behavior?. In the language of game theory, the incentive mechanisms must be individually rational & incentive compatible. At koinearth, we are creating the building blocks for such solutions.

Micro — Macro CryptoEconomics

The micro-macro split is a challenge in CryptoEconomics too; the challenge is made even more urgent due to the scale and ease with with economies can be created. The challenges of monetary policies, market design & economic regulations which were previously left to policy designers, central bankers & economists are now democratized. This is both a challenge & an opportunity. Token economies which are improperly designed will collapse sooner or later — hurting all participants in the economy. Well designed token systems, on the other hand, will unlock new value by lowering transaction & contract enforcement costs — an implication of the Coase theorem. Over time, we will see the emergence of completely new form of economies & institutions.

Computer Science & Economics

So, what do computer scientists have to do with all this? To understand this, we have to go a little back in time.

In 1978, the American economist Thomas Schelling published Micro Motives and Macro Behavior. A significant part of the book focused on explaining racial segregation in terms of individual preferences. Using pennies, dimes and a graph paper, Schelling showed that even if every individual was happy to live in a mixed community, mild preferences that some of one’s neighbors be of the same color could lead to total segregation. Schelling would go on to win the Nobel prize in Economic Sciences in 2005 for “having enhanced our understanding of conflict and cooperation through game-theory analysis”. He wrote extensively about nuclear conflict, climate change & racial segregation during the course of his life.

Credits : Arnaud Banos

Schelling’s work is often cited in the field of Agent Based Modelling (ABM) — a field of study that seeks to explain macro patterns in terms of micro (individual) behavior. ABM spans multiple social science disciplines. In particular, Agent Based Computational Economics (ACE) seeks to explain the behavior of economies using this bottom-up approach: a simple idea but one which has proven to be elusive.

To connect the dots, think of every entity on a blockchain as an agent. Given the distributed & open nature of blockchains, data about all financial transactions is available to each node in the underlying peer to peer network. This is a rich data set as is but once overlaid with information about token design, & incentive mechanisms used to allocate tokens, it is likely to become a goldmine. ACE will likely both learn from this data and then be used to explain it.

We have been here before, of course. The Internet and the World Wide Web changed both the nature of social relations & their study. Social interconnectedness changed not only quantitatively but also qualitatively. Not only are we more connected to each other now due to technology but we are interacting in completely new ways. Social interactions like those over blogs, Wikipedia contributions and collaboration on open source projects would not have been possible just a couple of decades ago.


Just like the Internet enabled new forms of social interactions, we should expect that Blockchains will enable new forms of economic interactions. As Decentralized Ledger Technologies mature and solution designers experiment with new economic models, we will see the true meaning of Blockchains as an economic laboratory encouraging constant innovation and improvement. Computer Scientists will both build this system but perhaps more interestingly (hopefully) collaborate with economists to design and analyze these bootstrapped economies.

Just like Internet mediated social interactions created an opportunity to study social networks at a scale like never before, CryptoEconomies will enable us to study economies at a scale like never before. In principle, we should be able to study the impact of micro on the macro (e.g. impact of change in economic incentives on token price) at the most granular scale. Similarly, we should be able to study the impact of macro on the micro (e.g. impact of new token creation on individual behavior).

Today however, we have more questions than answers: Will CryptoEconomies scale? Will the study of these economies create insights which can help build a unified theory of micro & macro economics? What will be the role of Agent Based Computational Economics in this world? What would have Schelling said about all this if he were here today?

-Praphul Chandra is the founder & chief scientist at Koinearth, a startup working at the intersection of blockchains, mechanism design & machine learning. A thank you to Prof. Alex Tabarrok for his feedback to improve this post.


At the intersection of blockchains, cryptoEconomics and machine learning.

Praphul Chandra

Written by

Founder, Koinearth | Professor, Insofe.



At the intersection of blockchains, cryptoEconomics and machine learning.

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