Token Economics 101


Much has been discussed about how modern internet based digital platforms such as Google, Facebook, and EBay to name a few, while they have created market value and consumer choice, are prone to rent seeking due to monopolistic winner-take-all effects (There is No Single Solution To Making Internet More Decentralized). Not just responsible for causing economic distortions, platforms are prime targets for cyber attackers, as recent incidents such as Equifax and Sony security breaches have demonstrated. Further, although such platforms have created consumer surplus, they have also led to consumer deficits of their own, by taking away privacy and control of one’s data. This view of the platforms is a part of a larger story of internet’s balkanization: emergence of independent domains thanks to platforms that do not interoperate, proprietary networks resulting from regulations that target net neutrality and geographic islands due to governments restricting free flow of information. Platform companies, which have built their platforms atop the internet, get blamed the most for this failure. The real failure, however, is that of the market that failed to create effective structures to govern and to incentivize appropriate development of the internet and the applications sitting on top of it.  This malaise is not just limited to Internet based markets, but extends to all existing intermediated centralized market systems of today. Rather serendipitously, the world may have discovered an alternative way to better manage the creation and development of efficient market systems: cryptocurrency or token economies. By aligning the incentives and needs of suppliers, consumers and service providers, token economies create value for all participants in an efficient manner. Indeed, it is not just Internet based solutions, but any market where goods and services are exchanged that token economies can bring efficiencies.

“Token Economy” As In Applied Psychology?

Token markets are those that whose workings are based on a cryptocurrency or token, such as Bitcoin. On one hand, such markets are guided by an explicitly defined crypto-economic system or token economy, but on the other hand, they also evolve dynamically based upon participants’ interactions. Originating in applied psychology, the term “token economy” is a system of incentives that reinforce and build desirable behaviors; essentially the token economy implements the theory of incentives used to explain the origins of motivation. Token economies are a useful tool in behavioral economics, where it has been applied both in lab and in the real world to study human decision making within economic contexts. In crypto markets, a token economy is implemented using digital assets as tokens and a framework of rules as the market protocol implemented in code using cryptography. Token economics in this sense refers to the study, design, and implementation of economic systems based on cryptocurrencies. Supporting the token economies of crypto-currency markets is distributed ledger technology (DLT), such as Blockchain. Tokenization is the process of converting some asset into a token unit that is recorded on the DLT: thus anything of economic value, such as real estate, commodities, currency markets, etc. can be tokenized, giving rise to a variety of different token economies.

What is Different?

By enabling costless verification and reducing the cost of networking, token economies streamline operational functioning of markets and open them up for broad innovation (Some Simple Economics of the Blockchain). Markets are formed when buyers and sellers come to together to exchange goods and services. Effective functioning of the markets depends upon effective verification of market transactions between the sellers and buyers, a function that market intermediaries take on as market complexity increases. The intermediary structure, however, brings its own set of issues: they can misuse information disclosed to them by buyers/sellers, conflicts of interest, agency problems, moral hazard may still persist, they can misuse their market power (as has happened with some tech giants recently) and their presence increases the overall cost structure of the market. Further, hurdles to developing trust between parties limits the extent to which business relationships develop thus limiting innovation: it is costly and time-consuming to develop and enforce contracts to guide the exchange of property rights. Through the use of DLT, token economies provide costless verification (and thus mitigate the need for intermediaries), and reduce cost of networking (and thus provides a way to efficiently exchange property rights). But what is fundamentally different about token economies is that they create a framework in which participants mutualize their interests and have strong incentives to continually improve the functioning of the economy.

Flywheels, Loops, and Knock On Effects

A key part of the token economy design is “mechanism design”, which is the design of system of incentives to encourage fruitful development of the token economy infrastructure as well as the overlying applications and services. Mechanism design addresses how tokens are used for payments to participants to manage the DLT network, service usage, profit sharing, governance and so on. An optimally designed token economy creates the appropriate mix of incentives to ensure a high performing market infrastructure, valuable services, and smooth running of the platform. As overlay protocols, applications and APIs are built on top of the base DLT (The Blockchain Application Stack), mechanism design ensures value is distributed equitably not just within a layer but across the layers. An appropriately designed token system unleashes powerful feedback loops that perpetuate desirable behaviors and actions in the marketplace – indeed, mechanism design can either make the token economy the El Dorado of all crypto economies, or be the death knell of the token even before it has had a fighting chance. Depending upon the specific market it is trying to make, each token economy will have a unique token design and a study of how feedback loops are expected to take hold, but there are some common fundamentals to how incentives work in a token economy.


A new token system often comes to market through an ICO (initial coin offering) which allows the founding team to distribute tokens to raise capital for funding the enterprise. A lot of initial pull for the token is dependent upon the team’s vision and the soundness of the token economy. As the market’s perceived value of the token increases, participants, customers and speculators take note and invest in the token, thus increasing its demand. Typically in token economies, since the token supply has a ceiling, increasing token demand leads to increasing value (assuming token holders hold on to the token at least for some time period), which attracts even more participants, customers and speculators. Increasing number of participants in the network strengthens the network in two ways: it makes it more decentralized and secure, and participants work to improve the network and develop services on the platform. This increases the utility of the system, which attracts more customers, and further strengthens the feedback loop.

These same mechanisms can work in reverse, leading to the token economy’s death spiral if there is a perceived loss of value in the token economy.


A token’s perceived utility can take a hit for a variety of reasons: if it does not create enough utility in the market, or if there is a security weakness that opens it up to a cyberattack. A perceived loss in the utility can lead to selloff in the market which depreciates the value of the token. As the token value depreciates, more speculators start dumping the token. A depreciating token value also disincentivizes participants from developing the token protocol and services, which reduces the token’s utility for consumers leading to reduced demand. An increased supply of tokens in the market further reduces the token’s value, perpetuating the negative feedback loop.

Token Economies Galore

Token markets originated with digital currencies for P2P payments, the first one to the market being Bitcoin, which was then followed by a number of alternative digital currencies (so called “alt-coins”) such as zCashLiteCoin and Monero as a means to address use cases for which Bitcoin was not the best solution. Tokenization moved to computing when Ethereum was launched for decentralized computing through smart contracts, and markets such as StorjFilecoin, and Sia came into being for decentralized storage, which is a challenge in its infancy for centralized cloud providers such as Amazon. Blockstack wants to go further by providing an entire decentralized infrastructure for building decentralized applications of the future, effectively replacing the current Internet based architecture. Not even current day platforms are safe: OpenBazaar offers a decentralized e-commerce marketplace, and Lazooz is a protocol for real time ride sharing. Most of the tokens these economies operate with are “utility tokens” that are used to access a service that such economies provide e.g., using decentralized storage or buying something in a e-commerce marketplace. Attention is now turning to “security/asset-backed tokens” which represent ownership in a company through cash flows, equities, futures, etc. or hard assets such as commodities or real estate. Asset-backed tokenization is in full swing, targeting commodities, precious metals and real estate (Digix and Goldmint for precious metals, D1and Cedex for diamonds, etc.). Security token offerings, like those enabled by Polymath, will provide investors exposure to token economies.

This is Just the Beginning

Just as the Cambrian period enabled the creation of a multitude of new life forms, the emergence of token economies is opening up a wide range of previously unavailable markets as well as new ways to compete against entrenched incumbents. Sure, many of the new token economies coming in to being today will die out, but many of the ones that survive will be epic. Designing the right economic model for tokens is crucial and token economics provides the necessary groundwork and framework for devising such models. More study in token economics is required especially since it involves technically complex mechanisms and market designs that are totally new. As the industry experiments with tokens across various markets, it will learn more about what is just great economic theory versus what really works in the complexity of real-world interactions.


For most business executives, the term “economics” conjures images of either simplistic supply-demand graphs they may have come across in Economics 101, or theoreticians devising arcane macroeconomic models to study the impact of interest rates and money supply on national economies.  Although businesses such as banks and financial institutions have maintained armies of economists on their payrolls, the economist’s stature and standing even in such institutions has been relatively short, limited to providing advice on general market based trends and developments, as opposed to actionable recommendations directly impacting the business bottom-line.  Even the most successful business executive would be stumped when faced with the question of how exactly economics is really applied to improving their day-to-day business.  All this may now be changing, thanks to a more front and center role of economics in new age businesses that now routinely employ economists to sift through all kinds of data to fine tune their product offerings, pricing and other business strategies.  Text book economists of yore are descending down from their ivory towers and taking on a new role, a role that is increasingly being shaped by availability of new analytic tools and raw market data.


Economists, especially the macro kind, are a dispraised bunch, with a large part of criticism stemming from their inability to predict major economic events (economists missed anticipating the 2008 market crash).  For this and other reasons (not least the Lucas Critique), macroeconomic modeling focused on building large scale econometric models has been losing its allure for some time.  Microeconomic modeling enabled by powerful data-driven micro econometric models focused on individual entities has been transforming and expanding over the past few decades.  The ever expanding use of sophisticating micro-models on large data datasets has caused some to perceive this as paving the foundation for “real time econometrics”.  Econometrics, the interdisciplinary study of empirical economics combining economics, statistics and computer science, has continually evolved over the past several decades thanks to advances in computing and statistics, and is yet again ready for disruption – this time due to availability of massive data sets and easy-to-procure computing power to run econometric analyses.  The likes of Google, Yahoo and Facebook are already applying advanced micro econometric models to understand causal statistics surrounding advertising, displays and impressions and their impact on key business variables such as clicks and searches. Applied econometrics is but one feather in the modern economist’s cap: economists are at the forefront in the sharing economy and “market design”.

A celebrated area of economic modeling and research that has found successful application in business is “market design” and “matching theory”, pioneered by Nobel prize-winning economists Al Roth and Lloyd Shapley.  Market design and matching theory is concerned with optimizing the pairing or matching of providers and suppliers in a market place based on “fit” that is driven by dimensions that go beyond just price, for example. Al Roth successfully applied game theory based market design and matching algorithms to improving a number of market places, including placement of New York City’s high school students, the matching of medical students with residency schools, and kidney donation programs.  The fundamentals of matching theory are being widely applied by economists today: many modern day online markets and sharing platforms such as eBay, Lyft etc. are in the business of matching suppliers/providers and consumers, and economists employed by these outfits have successfully applied those fundamentals to improving their businesses, increasingly with the aid of multi-dimensional data that is available in real time. Other market places, including LinkedIn (workers and employers) and Accretive Heath (doctors and patients) have applied similar learnings to improve their matching quality and effectiveness.  Airbnb economists analyzed data to try to figure out why certain hosts were more successful than others in sharing their space with guests, and successfully applied their learnings to help struggling hosts and also to better balance supply and demand in many of Airbnb markets (their analysis pointed out that successful hosts shared high quality pictures of their homes, which led Airbnb to offer a complementary photography service to its hosts).

Beyond market design, economics research is changing in a number of areas thanks to availability of large data sets and analytic tools in various ways as Liran Einav and Jonathan Levin of Stanford University outline in “The Data Revolution and Economic Analysis“.  One such area is measurement of the state of the economy and economic activity and generation of economic statistics to inform policy making. The issue with macroeconomic measurement is that the raw data produced by the official statistical agencies comes with a lag and is subject to revision.  Gross domestic product (GDP), for example, is a quarterly series that is published with a two-month lag and revised over the next four years.  Contrast this with the ability to collect real time economic data, as is being done by the Billion Prices Project, which collects vast amounts of retail transaction data in near real time to develop a retail price inflation index.  What’s more, new data sets may allow economists to shine the light on places of economic activity that have been dark heretofore.  Small businesses’ contribution to the national economic output, for example, is routinely underestimated because of exclusion of certain businesses. Companies such as Intuit, which does business with many small outfits, now have payroll transactional data that can be potentially analyzed to gauge the economic contribution of such small businesses.  Moody’s Analytics has partnered with ADP, the payroll software and services vendor, to enhance official private sector employment statistics based on ADP’s payroll data.

Conservatives and the old guard may downplay the role of data in applied economics, reveling in their grand macroeconomic models and theories.  To be fair, empirical modeling would be lost without theory.  However, data’s “invisible hand” in shaping today’s online markets and business models is perceptible, if not openly visible.  Economists of all stripes will be well advised to pay attention to the increasing role of data in their field.  Next time you see an economist, ask them to go take a course on Machine Learning in the Computer Science department, to pass on Google Chief Economist Hal Varian’s counsel – it will be time worth spent.