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Three Case Studies in Tokenomics

Authors:

Abstract

Blockchain technology has facilitated the creation of artificial economies based on tokens and cryptocurrencies. These artificial economies are different from real economies in many aspects. They are more narrowly focused around the provisioning of only a few (sometimes only one) goods or services. They can be more flexible, as blockchain technology allows the development of smart contracts to control the issuance or burning of tokens under any arbitrary conditions. Also, there are other types of exogenous risks, such as new kinds of regulations, and speculative or hacking attacks on exchanges. This means, that many of the tools, theories, and methods that apply in economics do not apply in the case of token economies. The tokenomist needs to sometimes come up with new models and tools for each individual case. Since tokenomics is a new field, the open discussion around tools and methods is extremely important and can speed up the development of methods that will later turn into standard practices. This article outlines three different case studies around tokenomics and discusses how the challenges of each case were approached.
The JBBA | Volume 1 | Issue 2 | December 2018
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OPEN ACCESS
ISSN Print: 2516-3949
https://doi.org/10.31585/jbba-1-2-(7)2018
Stylianos Kampakis
UCL Centre for Blockchain Technologies, University College London, UK
Correspondence: stelios@thedatascientist.com
Received: 10 October 2018 Accepted: 16 October 2018 Published: 15 November 2018
Case Study
Three Case Studies
in Tokenomics
Blockchain technology has facilitated the creation of articial economies based on tokens and cryptocurrencies.
These articial economies are different from real economies in many aspects. They are more narrowly focused
around the provisioning of only a few (sometimes only one) goods or services. They can be more exible,
as blockchain technology allows the development of smart contracts to control the issuance or burning of
tokens under any arbitrary conditions. Also, there are other types of exogenous risks, such as new kinds of
regulations, and speculative or hacking attacks on exchanges. This means, that many of the tools, theories
and methods that apply in economics do not apply in the case of token economies. The tokenomist needs
to sometimes come up with new models and tools for each individual case. Since tokenomics is a new eld,
the open discussion around tools and methods is extremely important, and can speed up the development of
methods that will later turn into standard practices. This article outlines three different case studies around
tokenomics and discusses how the challenges of each case were approached.
Abstract
Keywords: blockchain, ico, initial coin offering, tokenomics, token economies, cryptoeconomics
Competing Interests:
None declared.
Ethical approval:
Not applicable.
Author’s contribution:
SK designed and coordinated
this research and prepared
the manuscript in entirety.
Funding:
None declared.
Acknowledgements:
SK likes to acknowledge
Dr. Theodosis Mourouzis
for proof reading the
manuscript and providing
feedback on many ideas
around tokenomics. 1. Introduction
Designing a token economy is one of the most
important parts of an ICO. A good tokenomics model
can ensure the long-term viability of a blockchain
startup, whereas a bad tokenomics model can severely
impact the growth and the investment potential of a
business.
Tokenomics can be a quite complicated and challenging
topic for many reasons such as:
There are usually many conicting incentives
within the token economy which interact
with the business model. For example,
investors might want to see appreciation
of the token, but the users might benet
from lower ination or volatility.
There are technological demands which can
impose all sorts of constraints on the
economy. E.g. the speed of transactions
might impose constraints on the velocity of
the token, which can then impact the price.
Pre-ICO companies with no users or clients
often have to base their whole model on
forecasts, which can be quite inaccurate.
This means that the tokenomist might have
to resort to working with boundary scenarios,
rather than use the actual numbers. The lack
of established models in then eld makes
this very difcult, since the tokenomist might
have to devise new techniques and method in
each case.
Since tokenomics is new as a eld, it is important
that the community shares ideas, concepts and stories
(successful or unsuccessful). This article is providing
an overview of three different tokenomics case
studies, and discusses how the problems in each were
approached. It is the hope of the author that some of
the ideas outlined in this article can lead to a degree of
standardization of techniques, models and theories in
the eld of token economics.
2. Qredo
Analysis type: Microtokenomics
Analysis methodology: Agent based modelling
Qredo [1] was examining the creation of a new
blockchain protocol for telecommunications called
“proof-of-speed”. Qredo was faced with the choice
of various mechanisms as to how to synchronize
transactions, reward nodes that conrmed transactions,
and avoid fraud.
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Qredo’s tokenomics problems were around the area
of microtokenomics. Microtokenomics deals with the
incentivisation of the nodes of the network to perform
their duties. Microtokenomics issues revolve around
the following areas:
1) Provide economic incentive to nodes to
conrm transactions. The traditional
mechanism to do that is to reward the nodes
with tokens for validating blocks.
2) Ensure that the nodes do not commit fraud,
through ensuring that the economic
incentives to do so are low or non-existent. In
proof-of-work this is solved through technical
means, by making it computationally
prohibitive to overtake the network. In proof-
of stake this is done through the negative
incentives that the protocol is providing to
the nodes. Nodes are simply punished when
fraud is detected, since they have a stake in
the system.
3) A less discussed problem around
microtokenomics is that the rate of issuance
of tokens can affect the wider economy. In
many cryptocurrencies (e.g. Bitcoin and
Ethereum being the prime examples) the
mechanism of issuance of new tokens is
through the verication of transactions.
Bitcoin’s mechanism, for example, of issuing
new tokens turns into it a commodity that
faces deationary pressure. Ethereum’s
mechanism, on the contrary, is inationary.
An agent based modelling was created for Qredo that
simulated their blockchain network, from users to
nodes. The simulation contained over 10 parameters
including: different mechanisms for verifying
transactions, numbers of evil users, numbers of evil
nodes, frequency of transactions, and more.
With the lack of widely accepted theories around
tokenomics, agent based models can be a very
powerful tool, since they allow the explicit simulation
of any agent within the system (be it user, node, or
otherwise) without the need for theory. The drawback
of this approach is the development cost of setting
the simulation up, as well as the lack of theoretical
guarantees around the outcome. The simulation
is studied by executing many iterations, collecting
statistics over the outcomes and then performing
further analysis using statistical and machine learning
methods.
This is what happened in this case, with the simulation
being executed multiple times in order to examine the
effect of different parameter combinations on events
such the probability of double-spending, or the system
crashing. A random forest model was used in order to
predict the probability of failure based on the input
parameters, and a genetic algorithm was used on top
of that in order to get parameter settings for the best
and worst-case scenarios, that make the network most
or least resilient.
This allowed Qredo to:
1) Prove that their proposed method worked
better for their domain (telecommunications)
over a baseline algorithm which imitated
other existing blockchains.
2) Understand the limits of the system in terms
of fraudulent nodes.
3) Improve the parameters of their current
algorithm.
Whereas agent-based modelling is a very powerful
methodology when we care about modelling
interactions down to the smallest level, quite often
the challenges that we face in tokenomics relate to
structural concerns around the economy. The next two
scenarios fall under this category.
3. Dot
Analysis type: Macrotokenomics
Analysis methodology: Structural and mathematical
modelling
Dot is a nancial cooperative for the digital economy.
The aim of the project is to provide participants with a
exible set of nancial features that are tied into a single
user experience. The core of the app is a bank account
with all of the common nancial features already
bundled together. This includes the set of features
that are common among many challenger banks such
as multi-currency support, free ATM withdrawals, at
cost foreign exchange services and simple remittance
solutions.
Dot is implementing token features that complement
the development of the nancial cooperative. Dot
members will use the staking of tokens to grant
access to lower fees on banking services, earn rewards
through participations in community challenges and
pay with Dot tokens as a single currency for the list of
distributed applications that will be part of Dot.
Dot’s tokenomics problems revolved around
macrotokenomics. Macrotokenomic analysis is
concerned with questions such as:
1) How can we that the volatility of the token is
not prohibitive to the average user, to the
degree that only speculators might want to
invest.
2) How can we ensure enough liquidity, so that
the system can cover rapidly increasing
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demand?
3) How can we ensure the long-term viability of
the venture, through a stable economy?
Dot also faced lots of questions around their ICO,
such as how many tokens to issue, how many stages
their ICO should involve, and what incentives they
should provide to early stage investors.
The structure of an ICO is connected to
macrotokenomics analysis. Some challenges Dot faces
are:
1) Does the total number of tokens that are
issued inuence future valuation?
2) How should the distribution of tokens in the
ecosystem (team, users, bounty, etc.) look
like?
3) How can we ensure token appreciation, while
at the same time no single party has too much
power over the ecosystem and the price stays
stable?
Regarding 1, there is research to indicate that the total
number of tokens issued is not directly affecting the
valuation. It is rather other parameters, such as the
velocity of the token or the total amount transacted in
at that matter.
Regarding 2, experience has demonstrated that it is
more a matter of managing the expectations of the
community . As long as a structure is used that is not too
dissimilar to that of other ICOs, then the community
will not receive this under a positive or negative light.
Point 3, however, is an open question that changes
from ICO to ICO, since it largely relates to the way the
token economy is structured.
The rst thing to do to tackle point 3, was to come up
with a valuation formula. Vitalik Buttarin’s formula was
chosen as a valuation model [2]. This equation, based
on the quantity theory of money, is not necessarily a
golden standard, but in absence of other standards it
was deemed a reasonable choice.
This equation has indicated that reduced volatility is
connected with high prices for the token. Therefore, a
staking mechanism was created which would incentivize
network effects and the growth of the user base, while
at the same time reducing the velocity.
Also, a liquidity pool mechanism was chosen which
combined token burning with token recycling, allows
DOT to operate as a central bank. In addition to that,
staking incentives were provided, which can increase
holding time, and the valuation of the token over time.
This approach ensures token appreciation, but is also
the long term viability of the project, since there is
a guarantee of liquidity, as well as the exibility to
intervene in case the economy faces exogenous or
intrinsic shocks, such as a speculative attack.
5. Kimlic
Analysis type: Macrotokenomics
Analysis methodology: Structural and mathematical
modelling
Kimlic [3] is a KYC(Know-Your-Customer) and "ID
Verication as a Service" marketplace on blockchain. It
enables users to complete identication and KYC once
and onboard any business based on same identication.
Any user data is stored on the mobile device of the
data owner (user) and Kimlic requires no central data
silo to function.
The cryptographic hash of veried user credentials is
stored on Quorum blockchain and later used by relying
parties to validate. Attestation of user credentials is
either done by trusted third parties or the business that
user wants to have account with. Verication cost are
transitionally settled using the KIM token.
Kimlic’s problems revolved around the following
questions:
1) Given forecasts of transactions and user base
expansion, how can we accurately forecast
the price of a token in the future?
2) Is the structure of the token economy
designed in a way that the token will rise in
value over time?
3) The token economy of Kimlic should allow
"verication providers" to have recurring
revenue when those users onboard to
different businesses based on their attestations.
The solution to Kimlic’s relied in using an updated
version [4] of Buterin’s model discussed above. While
this work is still under review, there was a clear need in
the case of Kimlic for a mathematical model that could
account not only for the real value of a token, but also
for market expectations in the post-ICO period.
This model forecasted a sensitive period of around 1
year, during which Kimlic’s real token valuation might
be lower than the ICO price. An additional problem
was detected, which was the low holding time of
tokens. According to Buterin’s formula, this can lead to
the real value of the token dropping.
The solution was to draw some new vesting and staking
mechanisms which ensured the following:
1) The investors are incentivized to hold tokens
during the critical period.
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2) Staking mechanisms ensure that the holding
time increases, which leads to a faster
appreciation of the token’s value.
The basic staking mechanism requires clients to stake
tokens in order to use premium features of the system,
such as more advanced verication or faster service.
KYC providers also need to stake tokens in order to
provide participate in the premium services. Failure to
provide satisfactory service translates into some kind
of punishment such as loss of tokens or lock-out from
the services for a limited period of time.
Conclusion
This article discussed three different tokenomics
case studies. In the rst use case, a fairly technical
quantitative methodology was used. In the second one,
a more qualitative methodology was used, with some
elements of quantitative analysis. The third case study
required the development of a new model, based on
existing theories.
These case studies demonstrate the complexity and
challenges of tokenomics analysis. Tokenomics is
a new eld where many of the models in traditional
economics might not work. However, at the same
time there is the potential to use mechanisms that
would have not been applied in a real economy such as
liquidity pools, or staking.
Furthermore, we have the exibility to try out
approaches, which, while not 100% correct, can be
adapted dynamically on the circumstances. Since the
eld is new, we can’t expect our theories and models to
work correctly all the time, indeed this is not the case for
real-world economics either. However, we can create
the right structures, models and assumptions which
can shield a business against worst case scenarios, and
provide the right incentives for growth.
It is up to the community to come up with the right
models in order to fully utilize the capabilities that
blockchain offers in the creation of articial economies.
References
[1] Qredo, Ltd., "Qredo," 2018. [Online]. Available: https://
www.qredo.com/. [Accessed 2018].
[2] V. Buterin, "Vitalik Buterin's personal blog ," 17
Oct 2017. [Online]. Available: https://vitalik.ca/
general/2017/10/17/moe.html. [Accessed 2018].
[3] Kimlic, LTD, "Kimlic," Kimlic, [Online]. Available:
http://kimlic.com/. [Accessed 2018].
[4] S. Kampakis, "The Data Scientist," 2018. [Online].
Available: http://thedatascientist.com/valuing-ico-crypto-
tokens/. [Accessed 2018].
i This claim is based on an article that is under review by the JBBA. Authors
cannot be disclosed as the paper is still under review.
ii This is more of an empirical evaluation. An academic study remains to be
performed to support this claim.
... The largest number of authors use the agent modeling as a tool for analysis. In [1], approaches for analyzing various crypto-economics are described. The author solves the problem associated with the modeling of microtokenomics using agentbased modeling. ...
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... This contribution enables decision-making to be transferred to the average user and encompasses incentives through monetary rewards for the service provided by the user. This forms the foundation of tokenomics [47], which is powered by blockchain. NFTs would pave the way for assigning rights and privileges in the digital world, equivalent to ownership rights in the physical world. ...
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Decentralized applications developed using blockchain technology provide innovative business models to serve the human race and solve existing challenges. Climate change is one of the biggest problems humanity is facing and there is a dearth of solutions in tackling this grave impediment to the long-term sustainability of our planet. Accountability, greenwashing, traceability, impact assessment and trading of carbon credits are unresolved issues in the ESG sector. In this paper, we present a novel decentralized application software, DCarbonX, that solves the enumerated problems using NFTs on the blockchain platform, through smart contracts. The paper describes the functional architecture of DCarbonX, while elaborating on its salient features and utility in sustainable finance, in particular green sukuk. DCarbonX is a pioneering software providing an exchange for trading of carbon credits. The software facilitates logging of impact and traceable transactions in a carbon market, that would help to prevent duplication of records and greenwashing. The paper discusses the efforts being undertaken to achieve the climate goals as per the Paris Agreement and also highlights the pivotal obstacles to achieving carbon neutrality by 2050, as per COP26. The paper also encompasses a study on the applications of dapps in DeFi, Web 3.0 and ESG, among other areas and gives a comparative analysis of blockchain platforms for dapp development. The paper is also a pioneer in highlighting the challenges that plague dapp development, deployment and usage.
... This contribution enables decision-making to be transferred to the average user and encompasses incentives through monetary rewards for the service provided by the user. This forms the foundation of tokenomics [47], which is powered by blockchain. NFTs would pave the way for assigning rights and privileges in the digital world, equivalent to ownership rights in the physical world. ...
Conference Paper
Decentralized applications developed using blockchain technology provide innovative business models to serve the human race and solve existing challenges. Climate change is one of the biggest problems humanity is facing and there is a dearth of solutions in tackling this grave impediment to the long-term sustainability of our planet. Accountability, greenwashing, traceability, impact assessment and trading of carbon credits are unresolved issues in the ESG sector. In this paper, we present a novel decentralized application software, DCarbonX, that solves the enumerated problems using NFTs on the blockchain platform, through smart contracts. The paper describes the functional architecture of DCarbonX, while elaborating on its salient features and utility in sustainable finance, in particular green sukuk. DCarbonX is a pioneering software providing an exchange for trading of carbon credits. The software facilitates logging of impact and traceable transactions in a carbon market, that would help to prevent duplication of records and greenwashing. The paper discusses the efforts being undertaken to achieve the climate goals as per the Paris Agreement and also highlights the pivotal obstacles to achieving carbon neutrality by 2050, as per COP26. The paper also encompasses a study on the applications of dapps in DeFi, Web 3.0 and ESG, among other areas and gives a comparative analysis of blockchain platforms for dapp development. The paper is also a pioneer in highlighting the challenges that plague dapp development, deployment and usage.
Vitalik Buterin's personal blog
  • V Buterin
V. Buterin, "Vitalik Buterin's personal blog," 17 Oct 2017. [Online]. Available: https://vitalik.ca/ general/2017/10/17/moe.html. [Accessed 2018].