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2011.13240 | Blockchain mechanism and distributional characteristics of cryptos | We investigate the relationship between underlying blockchain mechanism of
cryptocurrencies and its distributional characteristics. In addition to price,
we emphasise on using actual block size and block time as the operational
features of cryptos. We use distributional characteristics such as fourier
power spectrum, moments, quantiles, global we optimums, as well as the measures
for long term dependencies, risk and noise to summarise the information from
crypto time series. With the hypothesis that the blockchain structure explains
the distributional characteristics of cryptos, we use characteristic based
spectral clustering to cluster the selected cryptos into five groups. We
scrutinise these clusters and find that indeed, the clusters of cryptos share
similar mechanism such as origin of fork, difficulty adjustment frequency, and
the nature of block size. This paper provides crypto creators and users with a
better understanding toward the connection between the blockchain protocol
design and distributional characteristics of cryptos. | http://arxiv.org/pdf/2011.13240v2 | Min-Bin Lin, Kainat Khowaja, Cathy Yi-Hsuan Chen, Wolfgang Karl Härdle | cs.CR, q-fin.GN | cs.CR | Blockchain mechanism and distributional characteristics of
cryptos∗
Min-Bin Lin†Kainat Khowaja‡Cathy Yi-Hsuan Chen§
Wolfgang Karl H ardle¶
Abstract
We investigate the relationship between underlying blockchain mechanism of cryptocurren-
cies and its distributional characteristics. In addition to price, we emphasise on using actual
block size and block time as the operational features of cryptos. We use distributional charac-
teristics such as fourier power spectrum, moments, quantiles, global we optimums, as well as
the measures for long term dependencies, risk and noise to summarise the information from
crypto time series. With the hypothesis that the blockchain structure explains the distribu-
tional characteristics of cryptos, we use characteristic based spectral clustering to cluster the
selected cryptos into ve groups. We scrutinise these clusters and nd that indeed, the clus-
ters of cryptos share similar mechanism such as origin of fork, diculty adjustment frequency,
and the nature of block size. This paper provides crypto creators and users with a better un-
derstanding toward the connection between the blockchain protocol design and distributional
characteristics of cryptos.
Keywords: Cryptocurrency, price, blockchain mechanism, distributional characteristics,
clustering
JEL Classication: C00
∗Financial support of the European Union's Horizon 2020 research and innovation program \FIN- TECH: A Finan-
cial supervision and Technology compliance training programme" under the grant agreement No 825215 (Topic: ICT-
35-2018, Type of action: CSA), the European Cooperation in Science & Technology COST Action grant CA19130 -
Fintech and Articial Intelligence in Finance - Towards a transparent nancial industry, the Deutsche Forschungs-
gemeinschaft's IRTG 1792 grant, the Yushan Scholar Program of Taiwan and the Czech Science Foundation's grant
no. 19-28231X / CAS: XDA 23020303 are greatly acknowledged.
†International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str. 1, 10178 Berlin,
Germany. Email: [email protected]
‡International Research Training Group 1792, Humboldt-Universit at zu Berlin, Spandauer Str. 1, 10178 Berlin,
Germany. Email: [email protected]
§Adam Smith Business School, University of Glasgow, United Kingdom; IRTG 1792 High Dimensional Non
Stationary Time Series, Humboldt-Universit at zu Berlin. Email: [email protected]
¶BRC Blockchain Research Center, Humboldt-Universit at zu Berlin, Berlin, Germany; Sim Kee Boon Institute,
Singapore Management University, Singapore; WISE Wang Yanan Institute for Studies in Economics, Xiamen Uni-
versity, Xiamen, China; Dept. Information Science and Finance, National Chiao Tung University, Hsinchu, Taiwan,
ROC; Dept. Mathematics and Physics, Charles University, Prague, Czech Republic, Grants{DFG IRTG 1792 grate-
fully acknowledged. Email: [email protected]
1arXiv:2011.13240v2 [cs.CR] 24 Aug 2021
1 Introduction
Cryptocurrency (crypto) is a digital asset designed to be as a medium of exchange wherein individual
coin ownership is recorded in a digital ledger or computerised database. Its creation of monetary
units and verication of fund transactions are secured using encryption techniques and distributed
across several nodes (devices) on a peer-to-peer network. Such technology-enhanced and privacy-
preserving features make it potentially dierent to other existing nancial instruments and has
attracted attention of many investors and researchers (H ardle et al., 2020). Many studies have
investigated the similarity between a pool of cryptocurrencies in order to classify the important
features of digital currencies. For example, Blau et al. (2020) has concluded that the top sixteen
most active cryptocurrencies co-move with bitcoin. Researchers have also focused on describing the
price behaviour of cryptos using economic factors (Ciaian et al., 2016; Sovbetov, 2018). However,
owing to the unique technology of cryptocurrencies, there still exists a gap between the creators of
blockchain mechanism and users operating the nancial market of the crytocurrencies and through
this research, we aim to take a step towards mitigating that gap.
We specialise our research on the following research questions. First, we characterise crypto
behaviour using distributional characteristics of time series data. Also, instead of using the prices
alone, we use actual block time and block size to incorporate the operational features of cryp-
tos. Second, we hypothesise that the blockchain structure that the coin attaches plays a pivotal
role in explaining the behaviour. More explicitly, we investigate the extent to which blockchain
structure leads to explain the distributional characteristics. Using a characteristic based clustering
coupled with spectral clustering technique, we group the selected cryptos into a number of clusters
and stratify the mechanisms that make the coins within the particular cluster showing the same
behaviour in price, actual block time, and actual block size, respectively.
When studying cryptocurrencies, many researchers only focus on crypto price and daily returns
(Trimborn and H ardle, 2018; Hou et al., 2020). While price is important when cryptos are used
as a medium of payment, it is denitely not the only measure for evaluation of cryptocurrencies.
For example, many low price coins are highly traded and many coins that are not used as medium
2
of payment have low prices, e.g., XPR and Dogecoin. Cryptos were introduced to serve various
purposes and the purpose of the coin does matter. This makes it necessary to use other time series
while studying crypto markets. In this research, we propose to use actual block size and actual
block time alongside price.
Actual block size is the average actual size "usage" of a single block in data storage for one
day. Since a block comprises of transaction data, it can represent the status of how a blockchain
mechanism allocates transactions to a block. We consider it a measure of scalability of the system. A
well-functioning blockchain should be able to level the transaction arrivals. Transaction distribution
within a day for any crypto needs such balancing because it aects miners rewards and hence the
demand of the coin. An ideal block size would keep conrmation times from ballooning while
keeping fees and security reasonable. Therefore, actual block size of cryptos can provide insight
into the behaviour of cryptos.
Actual block time, on the other hand, measures the consistency and performance of the system.
It is dened as the mean time required in minutes for each day to create the next block. In other
words, it is the average amount of time for the day a user has to wait, after broadcasting their
transaction, to see this transaction appear on the blockchain. Think of crypto markets as a fast
food franchise and miners as customers who have to wait a certain time to make the purchase. If
the waiting time is shorter on certain days while on other instances, the customers have to wait
much longer, there is a discrepancy in the system. Analogously, the time series of block time, which
is the distribution of waiting time, can be seen as a service level of the whole system and it is
necessary to maintain as the users' expectation or target block time set by the system depend on
it.
The idea of investigating the underlying blockchain mechanism, a cornerstone of crypto technol-
ogy, and its connection to the crypto behaviour is still in its infancy. One of the rst endeavours in
explaining this relationship was made by Guo et al. (2018) who highlight that the the fundamental
characteristics of cryptocurrencies (e.g., algorithm and proof type) have a vital role in dieren-
tiating the performance of cryptocurrencies. They develop a spectral clustering methodology to
3
group cryptos in a dynamic fashion, but their research is limited in the exploitation of blockchain
characteristics. With a similar spirit, Iwamura et al. (2019) start by claiming that high
uctuation
is a re
ection of the lack of
exibility in the Bitcoin supply schedule. They further strengthen their
arguments by considering the predetermined algorithm of cryptos (specically, the proof of work)
to explain the volatility in cryptocurrency market. Zimmerman (2020) argue in their work that the
higher congestion in blockchain technology leads to higher volatility in crypto prices. They claim
that the limited settlement space in blockchain architecture makes users compete with one another,
aecting the demand. In his model, the value of cryptos is governed by its demand, making the
price sensitive to blockchain capacity.
These research results, albeit true, are limited to a particular set of cryptocurrency mechanism
and do not thoroughly explain the dynamics of cryptocurrencies. Also, most of the papers only
use price as a proxy of behaviour. We advance the previous ndings by incorporating a rich set
of underlying mechanisms and connecting them to multiple time series. We take a deep dive into
eighteen cryptos with a variety of mechanisms- concluded in Garriga et al. (2020))- from a technical
perspective to summarise their mechanism and algorithm designs using variables, such as consensus
algorithm, type of hashing algorithm, diculty adjustment frequency and so on.
We investigate a relationship between underlying blockchain mechanism of cryptocurrencies
and the distributional characteristics. Using the a characteristic-based clustering technique, we
cluster the selected coins into a number of clusters and scrutinise the compositions of fundamental
characteristics in each group. We observe that the clusters obtained from these time series indeed
share common underlying mechanism. Through empirical evidence, we show that the cryptos forked
from same origin and same consensus mechanism tend to become part of same clustering group.
Furthermore, the clusters obtained by the time series of block time have same hashing algorithms
and diculty adjustment algorithms. Also, a similar nature (static or dynamic) of block size was
observed within clusters obtained by the time series of actual block size. We conclude with empirical
evidence that the crypto behaviour is actually linked with their blockchain protocol architectures.
The implications of this study are abundant. The creators of cryptocurrencies can manage the
4
impact of blockchain underlying mechanisms on the corresponding distributional characteristics, in
a consideration of adoption rate of invented coins. From the users' perspective, they can make an
optimal decision in which coins should be adopted while concerning the price
uctuation.
This paper proceeds as follows. Section 2 discusses data source and the underlying mechanisms
of the cryptos. Section 3 presents the methodology used for classifying characteristics of time series
and clustering algorithm. Section 4 provides an illustration of analysis results. Section 5 concludes
and provides several avenues for future research.
2 Data Source and Description
According to CoinMarketCap (https://coinmarketcap.com), currently there are over 7,000 cryp-
tocurrencies and their total market capitalisation has surpassed USD $400 billion as of November
09, 2020. Most of studies have focused on the mainstream coins (e.g., Bitcoin, Ethereum), and
little has been investigated on the coins which have been introduced and featured with a diverse
blockchain mechanisms and invented technologies. The work of Guo and Donev (2020) is one of
exceptions. In this study, 18 cryptos with dierent set of blockchain mechanisms have been ex-
amined {Bitcoin, Bitcoin Cash, Bitcoin Gold, Bitcoin SV, Blackcoin, Dash, Dogecoin, Ethereum,
Ethereum Classic, Feathercoin, Litecoin, Monero, Novacoin, Peercoin, Reddcoin, Vertcoin, XRP
(Ripple), and Zcash. We explore an interplay between distributional characteristics of crpytos and
blockchain mechanism. We discuss the key characteristics of blockchain mechanisms and the time
series data in this section.
2.1 Underlying Mechanism
Most of cryptos nowadays apply blockchain-based systems in which transactions are grouped into
blocks and cryptographically interlinked to form a back-linked list of blocks containing transactions.
The transactions are validated using the nodes within the crypto peer-to-peer network through
a majority consensus directed by algorithms instead of a central authority's approval. In such
an operation process, many algorithmic mechanisms are required to govern the performance and
5
outcome of a crypto system. Some key blockchain-based characteristics are discussed below:
Fork: It occurs as user base or developers conduct a fundamental or signicant software change,
see as in Figure 1. There are two types of forks { soft and hard forks. The former is an update
to the protocol architecture and then all the nodes are enforced to follow in order to proceed with
the operations of a crypto. The latter one creates a duplicate copy of the origin blockchain and
modies the copy to meet the desired quality (e.g., safety, scalability). In this case, a new crypto
can be generated accordingly. For example, Peercoin network facilitates an alternative consensus
mechanism {proof-of-stake (PoS) to Bitcoin's proof-of-work (PoW) system for reducing dependency
on energy consumption from mining process (King and Nadal, 2012).
Going beyond a digital currency, Ethereum establishes an open-ended decentralised platform
for diverse applications such as decentralised applications (dapps) and smart contracts (Buterin,
2014).
Consensus mechanism: In order to establish an agreement on a specic subset of the candi-
date transactions, consensus mechanism provides a protocol for a large number of trust-less nodes
in a decentralised blockchain network. For instance, PoW (Proof-of-Work, as adopted by e.g.,
Bitcoin, Litecoin) achieves consensus with a competition among miners on solving computational
puzzles, which consume numerous computational resources; and PoS (Proof-of-Stake, as adopted by
e.g., Peercoin, Blackcoin) randomly assigns a block creator (transaction validator) with probability
proportional to their coins staked.
Hashing algorithm: It is a mathematical algorithm that encrypts a new transaction (or a new
block) into a xed length character string, known as hash value, and later interlinks this string with
a given blockchain to ensure the security and immutability of a crypto. Various hashing algorithms
are implemented in cryptos such as SHA-256, Scrypt and Equihash. These provide dierent degree
of complexity to blockchain operations.
Diculty adjustment algorithm: It is an adaptive mechanism which periodically adjusts the
diculty toward hashrate to target an average time interval between blocks, known as target block
6
Figure 1: Blockchain software forks in cryptocurrency.
time or target conrmation time. It regulates the creation rate of a block and maintains a certain
amount of outputs of a blockchain. Such a mechanism is commonly seen in a PoW framework.
An example from Bitcoin is shown in Figure 2 where its diculty adjustment algorithm, known as
DAA, modies the diculty every 2016 blocks to meet target block time of 10 minutes.
2.2 Time Series Data
The data applied in this paper are collected from Bitinfocharts which is available at https://bitinfocharts.com/.
These time series are composed of data points observed daily from the genesis date of each crypto.
The lengths of these time series are thus varied coin by coin, but as explained in the section 2.2,
we continue to use the whole time series for each coin.
Price: Much previous literature has been triggered by the substantial
uctuations in crypto
7
Figure 2: Bitcoin's diculty adjustment toward actual block time.
Blockchain mechanism -
plotting
prices. In this study we investigate 18 crypto prices in USD on daily time series. Among these
18 cryptos, Bitcoin has been dominant and Reddcoin has the lowest price on balance as seen in
Figure 3. We characterise these price time series in Table 1. Most of these coins (i.e., Bitcoin,
Ethereum, Bitcoin Cash) have high
uctuations in price; while some coins (i.e., XRP, Blackcoin)
tends to be steady.
Figure 3: Time series of prices of the 18 cryptos
Blockchain mechanism plotting
8
Actual block time: It is the mean time required in minutes for each day to create the next
block. In other words, it is the average amount of time for the day a user has to wait, after
broadcasting their transaction, to see this transaction appear on the blockchain. Some literature
also refers it as conrmation time. It can be considered as a service level indicator for cryptos which
should be maintained by underlying mechanisms. Most of the coins discussed in this paper tend to
have lower block time compared with Bitcoin as seen in Figure 4. Also, many coins show outliers
in observations and this can indicate that the extreme events appear in the blockchain system.
The underlying mechanisms can be ineective to accommodate the current system demand. The
distributional characteristics for time series of actual block time are presented in Table 2. The data
for XRP are missing but its designed block time is around 5 second per transaction.
Figure 4: Actual block time in minutes.
Blockchain mechanism plotting
9
Actual block size: It is dened as the average actual size "usage" of a single block in data
storage for one day. Since a block is is comprised of transaction data, it can represent the status
of how a cryptocurrency mechanism allocates transactions to a block. In this study, as introduced
in Section 1, we consider it as an indicator for the stableness of scalability of a crypto. In Figure 5
shows that most of the cryptos under study have smaller block size usage than Bitcoin, except
Bitcoin SV. The plot also depicts that almost all the coins have outliers. These outliers can lead
to the imbalance in transaction fee and reward which can in
uence the ecosystem of a crypto. The
characteristics for block size time series are shown in Table 3. XRP does not have typical blockchain
structure, hence, there is no block size data in the study. The data for Peercoin are missing.
Figure 5: Actual block size in megabytes.
Blockchain mechanism plotting
10
3 Methodology
In order to investigate the relationship between underlying blockchain mechanism of cryptocur-
rencies and the distributional characteristics of cryptos as a proxy of behaviour, we aim to group
them into number of clusters and scrutinise the compositions of features in each group. These
blockchain-based features manifest the underlying mechanism of how the cryptos operate trans-
actions on their chains, and subsequently govern the price, actual block size and block time. As
described in the previous section, we use the time series data of 18 dierent cryptos with a range
of dierent mechanisms.
The time series data available for the cryptos is subject to numerous limitations. The most
important one of them is that dierent coins were introduced at dierent time points, therefore,
the data available for each coin has dierent lengths. For the clustering problems (Aghabozorgi
et al., 2015), dening the distance metric between points in time series with various lengths is not
conventional. For many analytical problems, this issue is easily tackled by truncating the time series
to the shared sample period. We refrain from doing so because, in the analysis of cryptocurrency
prices, the evolution of the data in time is highly crucial for an investigation in the short term
and long term dynamics and therefore, truncating the time series would lead to loss of important
information. Hence, we deal with the time series data of cryptos with dierent lengths and do not
directly impose a distance metric on the input data points.
Furthermore, characterising the behaviour of a time series in terms of a single quantitative
attribute (such as range based volatility) has its own limitations. The chosen attribute usually
captures the dynamics of time series in one particular aspect, which may not be sucient to
encompass an entire behaviour or introduces a biased assessment. This becomes particularly true
in the problems of crypto classication and clustering where these attributes, used as a similarity
measure, are very diverse, resulting in weak robustness in the results.
To cope with these limitations, we resort to the characteristic based clustering method proposed
by Wang et al. (2005). It was recently applied by Pele et al. (2020) for classifying cryptos in order to
11
distinguish them from traditional assets. This methods recommends to incorporate various global
measures describing the structural characteristics of a time series for a clustering problem. These
global measures are obtained by applying statistical operations that best represent the underlying
characteristics. Also, by extracting a set of measures from the original time series we simply bypass
the issue of dening a distance metric. It's understood that the global measures are domain-
specic. Employing a greedy search algorithm, Wang et al. (2005) selects the pivotal features in
the clustering tasks. In our case, we import the experts' discretion on the choice of features as
distributional characteristics which best represent the dynamics of cryptocurrencies.
We choose a variety of measures for our analysis. Starting from the rst four moments and
quantiles that characterises the distribution and symmetry of the data, we include the statistics
for concluding the global structure such as global optimum, as well as the measures for long term
dependencies, risk and noise. The selected features are mean, standard deviation, skewness, kur-
tosis, maximum, minimum, rst quartile, median, third quartile, 1% and 5% extreme quantiles
as a measure of downside risk, linear trend, intercept, autocorrelation for long term dependency,
self-similarity using Hurst exponent and chaos using Lyaponav's exponents.
We further extend the methodology by including the power spectrum of time series as an addi-
tional measure. The power spectrum is obtained in this work using Fast Fourier Transform (FFT).
For computational ease, discrete fourier transform (DFT) has been formalised as a linear operator
that maps the data points in a discrete input signal Xfx1;x2;;xngto the frequency domain
f=ff1;f2;fng.
For a given time series Xofntime points, sine and cosine functions are used to get the
coecients !n=e 2i=and the frequencies are calculated using the matrix multiplication:
12
2
666666666664f1
f2
f3
...
fn3
777777777775=2
6666666666641 1 1 1
1!n!2
n!n 1
n
1!2
n!4
n!2(n 1)
n
...............
1!n 1
n!2(n 1)
n!(n 1)2
n3
7777777777752
666666666664x1
x2
x3
...
xn3
777777777775(1)
This matrix multiplication involves O(n2) and makes DFT computationally expensive. FFT is
a fast algorithm to compute DFT using only O(nlogn) operations (Brunton and Kutz, 2019). A
simple tcommand in python computes the FFT of the given time signal. The power spectrum of
this signal is the normalised squared magnitude of the fand it indicates how much variance of the
initial space each frequency explains (Brunton and Kutz, 2019). Including the power spectrum as
a feature for characteristic based clustering allows capturing the variability in the time signal that
is not explained by any other measure.
Accumulating all the aforementioned features in a vector gives in a reduced dimensional rep-
resentation of time series of each crypto. These vectors are then used to cluster the cryptos into
groups using spectral clustering. Spectral clustering exploits the eigenvalues of similarity matrix
to cluster and results in more balanced clusters than other techniques that were employed during
the process. For details related to spectral clustering, the readers are recommended to follow the
tutorial on spectral clustering by von Luxburg (2006). The results of the above methodology are
discussed in detail in the next section.
4 Empirical Evidence
In this section, we showcase the result from the characteristic based clustering individually on the
crypto price and operational features{which are constructed with price, block size "scalability" and
block time "service level" time series. We explore the clustering results and classify them with the
underlying mechanisms of the investigated 18 cryptos. The 18 cryptos are: Bitcoin, Bitcoin Cash,
Bitcoin Gold, Bitcoin SV, Blackcoin , Dash, Dogecoin, Ethereum, Ethereum Classic, Feathercoin,
13
Litecoin, Monero, Novacoin, Peercoin, Reddcoin, Vertcoin, XRP, and Zcash.
We calculate the characteristics for each of these cryptos for prices, block size and block time
separately. The results of all other attributes except the FFT are summarised in Tables 1, 2, 3
correspondingly in Appendix. Note that the data for XRP are not available for the block size and
block time, and for Peercoin block size is missing as described before in Section 3.
After calculating the attributes and FFT power spectrum described in section 2.2, the feature
space is 216 dimensional (200 dimensional vector of power spectrum and 16 characteristics), vi-
sualisation of which is not possible. We project the feature space into a three dimensional space
using principle component analysis (PCA), and the results of which are exhibited for an intuitive
understanding. We discuss each of the clustering in detail below. Moreover, in order to avoid a
monopoly outcome and sustain a certain level of interpretability, we impose the maximum number
of the clusters to avoid a single coin case in each cluster.
4.1 Clustering with crypto prices
Table 1 shows that as expected, Bitcoin has the highest average price and highest standard deviation,
due to high magnitude of its prices. The VaR99 and VaR95 for Bitcoin are, however, very low,
showing a low downside risk of Bitcoin. On the contrary, Bitcoin Cash, Bitcoin SV, Bitcoin Gold and
Zcash all show high value at risk. This could be due to low persistence of risk shocks (de Souza,
2019; Katsiampa et al., 2019). The high positive coecients of self similarity for all the coins
show high dependency on the previous time values. The high autocorrelation further conrms the
presence of long term dependencies of the time series. The Lyaponov exponent as a measure of
chaos is greater than 0 for all the time series which shows unstable dynamics throughout the prices
of cryptos.
The characteristics of Dogecoin in Table 1 assume very low values, unlike any other coin, because
the prices of Dogecoin are very low, despite it being a popular coin. This can be due to high supply
of the coin with no limit on the total number of coins created. The coin also has no technical
innovations, which is considered as one of the reasons why the coin has such small price. Hence,
14
the uncontrolled underlying mechanism of the coin has signicant impact on the prices, despite the
high trading volumes of the coin. Same can be concluded for XRP and Reddcoin, which also have
a very high maximum supply that is re
ected in their very low prices.
Using characteristic based clustering on price time series, we have the result with 5 clusters as
below:
0. Bitcoin, Dash
1. Bitcoin SV, Zcash
2. Bitcoin Cash, Bitcoin Gold
3. Ethereum, Litecoin, XRP, Monero, Peercoin, Vertcoin, Reddcoin, Feathercoin, Blackcoin
4. Ethereum Classic, Dogecoin, Novacoin
Most of coins are close to each others in a three-dimensional space, as seen in Figure 6. Except
Dash, all the altcoins are in a dierent clusters than Bitcoin. Bitcoin Cash and Bitcoin Gold, which
principally inherit the protocol architecture from Bitcoin, are clustered together, but not centred
around with other coins. However, Bitcoin SV{which is a fork from Bitcoin Cash and mainly
increases the designed block size to lower the transaction fee as a main software change{is not in
the same cluster. This indicates that even as a crypto adopts a similar blockchain mechanism with
the other crypto, it might have dierent price dynamics than its origin.
XRP, Monero, Peercoin, Reddcoin, and Blackcoin which apply signicantly dierent blockchain
protocols in their governance types and consensus mechanisms are in the same cluster. Specically,
XRP, Monero and Peercoin are private based blockchain which possesses a stronger moderator
to control the entrants (users or investors) to their network. Peercoin, Reddcoin, and Blackcoin,
instead of using PoW as their consensus mechanisms, employ PoS which does not depends on miners'
eort to create a block. So that, coin supply and demand can reach an equilibrium without the
interference of miners, which leads to higher transaction costs. Moreover, the forks from Litecoin{
Vertcoin, Reddcoin and Feathercoin are within the same cluster with Litecoin.
15
Figure 6: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on the prices
Blockchain -
mechanism clustering
Ethereum Classic is, in fact, the version of Ethereum that existed before the hard fork of
Ethereum resulting after the DAO attack, but it is not within the cluster with Ethereum.
4.2 Clustering with actual block time
The block time here is measured in minutes. Likewise, we apply the characteristic based clustering
on the data and conclude them into 5 clusters as below.
0. Dogecoin, Feathercoin
16
1. Ethereum, Litecoin, Ethereum Classic, Dash, Zcash, Monero, Blackcoin
2. Bitcoin, Bitcoin Cash, Vertcoin
3. Bitcoin SV, Bitcoin Gold, Novacoin
4. Peercoin, Reddcoin
The result is correspondingly visualised in Figure 4. The gure shows that Peercoin and Redd-
coin lie far away from other coins (marked by cyan cluster). They are clustered in the same group
because they both use PoS and their initial block takes the maximum time to be added, as shown
by the maximum and intercept characteristics in Table 2. This shows that even though the coins
have lower actual block time later (with low mean), their behaviour is still the similar, resulting
them in the same cluster. Also, the cryptos using PoS tend to lower the complexity of their hashing
algorithms since it is not required for miners to spend computational eort on them. The diculty
adjustment algorithms of theirs are purely used as a mechanism for maintaining the certain service
level for users without considering hashrate from miners. Their block time performance is relatively
stable after the initialisation. Here we emphasise that the initial price, block time and block size
that are usually characterised by the underlying mechanism play a pivotal role in determining the
price behaviour of cryptos. This is why we did not truncate the time series, as mentioned in the
Section 4.
Though Bitcoin, Bitcoin Gold, Bitcoin Cash and Bitcoin SV are not completely grouped into
the same cluster, they are close to each others in the three dimensional space as seen in Figure 4.
They apply the same hashing algorithm{SHA-256 and also with the same expected block time for
their diculty adjustment algorithms. Let's call attention to forks again. Dogecoin and Feathercoin
are both forked from Litecoin with the Script-based hashing algorithm and diculty adjustment
frequency after large number of blocks{240 and 504 blocks. Litecoin is in a dierent cluster because
the frequency is much higher as 2016 blocks. Given the cryptos forked from the same origin coins,
their block time can be found in the same group, likewise Ethereum and Ethereum Classic.
17
Figure 7: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on block time
Blockchain -
mechanism clustering
4.3 Clustering with actual block size
As previously done for price and block time, we use the characteristics based clustering and grouped
these cryptos into 5 clusters according to the characteristics of their time series. The block size
here is measured in bytes for a better data representation. As stated before in Section 3, XRP
and Peercoin data are missing due to the mechanism design and incomplete data from the source,
respectively. The clustering result is shown as below and the corresponding visualisation is in
Figure 5.
18
0. Zcash, Bitcoin Gold, Reddcoin, Novacoin
1. Ethereum, Ethereum Classic, Dogecoin
2. Bitcoin Cash, Bitcoin SV
3. Bitcoin, Dash, Monero, Feathercoin
4. Litecoin, Vertcoin, Blackcoin
Figure 8: Visualisation of ve clusters 0, 1, 2, 3, 4 of cryptos based on block size
Blockchain -
mechanism clustering
The actual block size (usage) of these cryptos does rarely meet their designed block size limit
19
(capacity), except for Bitcoin that it nearly outstretches its limit, 1 megabyte, as seen in Table 3.
In this case, it raises an issue: Can increasing crypto's block size limit improves scalability? For
example, Bitcoin SV enlarges dramatically its limit to 128 megabytes but it is out of the necessity for
such a design. Likewise, Bitcoin Cash, which Bitcoin SV forks from, has its limit as 32 megabytes.
These two coins are, therefore, clustered together. Moreover, instead of having a static block size
limit, Ethereum and Ethereum Classic grouped in the same cluster apply block gas limit, which is
the energy consumption limit for a block, to adaptively regulate its block size. Both Monero and
Blackcoin have a dynamic mechanisms to control the block size, however, it does not represent in
the clustering result.
5 Conclusion
In this paper we investigate the relationship between crypto behaviours and their underlying mech-
anisms. We specify the crypto behaviour with their price and operational features dened by actual
block time and block size. We calculate the distributional characteristics to dene the behaviour of
time series. Using a characteristics based spectral clustering technique, we cluster the selected coins
into a number of clusters and scrutinise the blockchain mechanism in each group. We nd that the
underlying mechanism of cryptos are re
ected in the clustering results. We observe that cryptos
forked from same origin and same consensus mechanism tend to become part of same clustering
group. Furthermore, the clusters obtained by the time series of block time have same hashing al-
gorithms and diculty adjustment algorithms. Also, a similar nature (static or dynamic) of block
size was observed within clusters obtained by the time series of actual block size. We conclude
with empirical evidence that the crypto behaviour is indeed linked with their blockchain protocol
architectures. As a result, cryptocurrency users and investors can have a better understanding and
explanation of price and operational features through cryptocurrency mechanism. In the future re-
search, we would elaborate the relation of price and operational features to underlying mechanism
with an economic model and conduct relevant simulations. We would also like to investigate the
impact of versions revisions on the dynamics of cryptos.
20
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22
Appendix
Table 1: Characteristics of prices of dierent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 2659.127 178.966 34.394 537.723 9.381 0.192
standard deviation 3798.466 222.452 48.645 509.244 7.827 0.302
skewness 1.338 1.950 2.389 2.322 1.491 4.193
kurtosis 0.672 4.654 7.272 6.157 2.239 29.471
maximum 19401.000 1356.000 352.799 3526.000 43.765 3.649
minimum 0.050 0.401 0.032 58.626 0.687 0.003
lowerquant 20.193 7.975 3.153 233.404 4.364 0.007
median 455.892 136.557 8.618 324.646 6.571 0.024
upperquant 5128.000 250.965 53.128 620.947 13.813 0.291
VaR99 0.062 0.578 0.040 107.426 0.809 0.004
VaR95 0.393 0.696 0.072 129.491 1.105 0.005
slope 2.781 0.163 0.032 -0.876 -0.002 0.000
intercept 0.050 2.820 0.033 63.765 0.892 0.006
autocorrelation 0.998 0.998 0.997 0.992 0.994 0.991
selfsimilarity 1.574 1.611 1.596 1.609 1.564 1.551
chaos 0.088 0.093 0.091 0.086 0.087 0.085
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 145.401 113.910 135.596 57.588 0.006 43.167
standard deviation 66.784 187.915 125.654 75.569 0.193 70.420
skewness 0.678 3.126 1.756 2.145 49.692 2.879
kurtosis 0.079 11.777 3.208 5.300 2469.511 8.351
maximum 370.647 1436.000 728.159 439.391 9.608 513.293
minimum 52.683 0.516 23.940 0.233 0.000 5.093
lowerquant 87.323 3.950 50.251 1.100 0.000 9.710
median 135.217 66.508 72.251 44.090 0.001 15.869
upperquant 191.739 133.239 199.807 84.834 0.003 29.706
VaR99 53.377 0.711 27.767 0.272 0.000 5.357
VaR95 62.111 1.833 31.842 0.417 0.000 6.604
slope 0.218 0.083 -0.134 0.053 0.000 -0.147
intercept 111.700 1.380 286.297 1.911 0.000 513.293
autocorrelation 0.990 0.997 0.995 0.997 0.002 0.961
selfsimilarity 1.628 1.642 1.573 1.577 1.024 1.431
chaos 0.077 0.090 0.092 0.091 0.086 0.073
CharacteristicPeer
coinVertcoinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean 1.004 0.670 0.001 0.062 0.095 2.185
standard deviation 1.238 1.319 0.003 0.102 0.127 2.989
skewness 2.511 3.637 4.175 3.379 3.397 3.102
kurtosis 7.017 14.792 24.526 17.172 15.251 12.916
maximum 9.118 9.386 0.029 1.203 1.108 24.777
minimum 0.110 0.006 0.000 0.002 0.014 0.078
lowerquant 0.291 0.043 0.000 0.008 0.030 0.507
median 0.445 0.237 0.001 0.019 0.045 0.901
upperquant 1.275 0.626 0.001 0.072 0.088 3.301
VaR99 0.125 0.009 0.000 0.003 0.015 0.156
VaR95 0.168 0.015 0.000 0.004 0.020 0.187
slope 0.000 0.000 0.000 0.000 0.000 -0.001
intercept 0.382 6.315 0.000 0.559 0.035 0.078
autocorrelation 0.993 0.992 0.988 0.983 0.993 0.994
selfsimilarity 1.577 1.603 1.548 1.523 1.537 1.596
chaos 0.088 0.085 0.079 0.078 0.084 0.09123
Table 2: Characteristics of Block time of dierent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 10.453 0.257 2.507 11.167 0.246 NA
standard deviation 8.814 0.045 0.385 11.009 0.032 NA
skewness 21.779 3.098 5.003 11.597 5.144 NA
kurtosis 701.717 11.987 54.589 160.209 61.066 NA
maximum 360.000 0.509 8.521 205.714 0.800 NA
minimum 2.081 0.208 0.149 1.275 0.153 NA
lowerquant 8.623 0.235 2.357 9.664 0.235 NA
median 9.474 0.241 2.474 9.931 0.238 NA
upperquant 10.435 0.268 2.599 10.360 0.242 NA
VaR99 5.923 0.220 1.710 2.331 0.215 NA
VaR95 7.129 0.222 2.111 8.479 0.218 NA
slope -0.001 0.000 0.000 -0.007 0.000 NA
intercept 102.857 0.208 0.149 160.000 0.208 NA
autocorrelation 0.494 0.981 0.705 0.395 0.818 NA
selfsimilarity 1.027 1.522 0.787 0.704 1.249 NA
chaos 0.012 0.070 0.012 0.003 0.068 NA
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 10.195 2.659 2.409 1.686 1.048 9.823
standard deviation 1.639 0.805 0.345 0.541 0.043 0.741
skewness 12.504 19.831 -3.025 3.258 -9.220 -5.375
kurtosis 221.950 409.827 7.261 57.807 222.460 60.686
maximum 40.000 22.500 2.618 10.992 1.288 11.250
minimum 7.310 0.348 1.240 0.829 0.100 0.254
lowerquant 9.600 2.609 2.487 1.025 1.038 9.664
median 10.000 2.623 2.509 1.951 1.044 9.931
upperquant 10.511 2.637 2.531 2.020 1.050 10.141
VaR99 8.361 2.476 1.248 0.947 0.980 7.767
VaR95 9.034 2.571 1.258 0.984 1.031 8.623
slope -0.001 0.000 0.000 0.001 0.000 0.001
intercept 40.000 0.348 2.286 1.627 0.100 0.254
autocorrelation -0.115 0.707 0.982 0.805 0.787 0.378
selfsimilarity 0.367 0.811 1.121 0.922 1.044 0.494
chaos 0.023 0.003 0.010 0.001 0.011 -0.001
CharacteristicPeer-
coinVert-
coinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean 10.085 2.502 4.646 2.005 1.090 6.819
standard deviation 47.070 0.180 68.175 6.443 0.105 2.295
skewness 30.324 -1.782 20.761 11.521 -4.368 24.326
kurtosis 919.356 30.015 434.280 157.793 18.525 891.281
maximum 1440.000 4.079 1440.000 130.909 1.335 96.000
minimum 1.377 0.151 0.646 0.148 0.442 0.451
lowerquant 7.742 2.412 0.986 1.042 1.111 6.154
median 8.372 2.500 1.007 1.048 1.114 6.606
upperquant 9.057 2.590 1.028 1.171 1.117 7.164
VaR99 5.464 2.144 0.935 1.034 0.551 4.364
VaR95 6.545 2.289 0.957 1.036 0.949 5.390
slope -0.003 0.000 -0.010 -0.002 0.000 -0.001
intercept 1440.000 0.151 1440.000 0.291 1.309 1.765
autocorrelation 0.667 0.154 0.821 0.914 0.976 0.373
selfsimilarity 0.717 0.437 1.051 1.210 1.337 0.697
chaos 0.002 0.008 -0.001 0.032 0.006 0.00924
Table 3: Characteristics of Block size of dierent cryptocurrencies
Characteristic Bitcoin Ethereum LitecoinBitcoin
CashEthereum
ClassicXRP
mean 407162.152 14376.916 12909.684 138173.724 1297.638 NA
standard deviation 363245.372 11337.562 15590.195 284058.956 340.581 NA
skewness 0.241 0.285 4.309 9.176 0.679 NA
kurtosis -1.583 -0.819 31.780 109.791 2.106 NA
maximum 998092.000 58953.000 206020.000 4710539.000 3594.000 NA
minimum 134.000 575.164 134.000 4982.000 575.164 NA
lowerquant 21246.000 1627.750 4004.750 60455.500 1054.750 NA
median 310990.000 17024.000 7016.000 94775.000 1310.500 NA
upperquant 777369.500 23068.750 19366.500 122827.500 1492.250 NA
VaR99 134.548 658.423 561.630 15574.520 653.404 NA
VaR95 134.952 788.678 800.306 27169.700 775.052 NA
slope 266.541 17.464 8.806 -89.253 0.189 NA
intercept 204.000 643.886 199.000 385996.000 643.886 NA
autocorrelation 0.985 0.981 0.872 0.626 0.850 NA
selfsimilarity 1.067 1.310 1.148 1.074 1.131 NA
chaos 0.058 0.058 0.065 0.027 0.045 NA
CharacteristicBitcoin
SVDash Zcash Monero DogecoinBitcoin
Gold
mean 1100149.254 12999.389 23802.102 39874.397 10523.242 25312.953
standard deviation 1278250.457 26340.294 38911.209 47310.430 6607.125 67527.275
skewness 6.673 27.654 8.711 1.703 5.917 6.269
kurtosis 84.455 1040.743 117.847 4.063 68.981 45.828
maximum 20460199.000 1059232.000 687685.000 347816.000 116605.000 739259.000
minimum 5005.000 226.545 379.573 375.434 143.000 133.000
lowerquant 257789.500 3038.000 7189.500 3047.250 6775.000 6512.500
median 996071.500 9240.000 11670.000 20980.000 9510.000 9316.000
upperquant 1573243.000 19193.000 28242.000 62002.000 12022.000 14118.000
VaR99 6435.000 1312.960 2605.530 1058.990 3432.400 2727.870
VaR95 14660.750 1736.200 3103.900 1320.350 4491.000 3983.600
slope 2318.003 14.357 -25.267 26.939 1.018 -67.625
intercept 10871172.000 226.545 379.573 375.434 143.000 133.000
autocorrelation 0.377 0.298 0.836 0.958 0.798 0.618
selfsimilarity 1.004 0.947 1.138 1.214 1.070 1.015
chaos 0.009 0.018 0.030 0.041 0.021 -0.012
CharacteristicPeer-
coinVert-
coinRedd-
coinFeather-
coinBlack-
coinNova-
coin
mean NA 2641.881 772.025 806.556 687.622 539.712
standard deviation NA 3611.409 634.442 1621.154 3441.373 1223.175
skewness NA 3.420 3.613 10.605 28.388 38.218
kurtosis NA 16.189 21.857 158.924 894.526 1712.453
maximum NA 36709.000 7808.000 36789.000 120169.000 57527.000
minimum NA 105.000 105.000 109.625 252.514 110.835
lowerquant NA 682.104 388.361 359.746 286.296 360.352
median NA 1149.000 526.043 460.827 386.251 436.181
upperquant NA 3185.000 937.696 598.841 627.727 542.228
VaR99 NA 248.950 317.797 126.333 255.520 262.588
VaR95 NA 310.697 337.320 247.907 261.297 284.524
slope NA -0.586 -0.475 -0.739 -0.025 -0.204
intercept NA 130.000 175.000 109.625 464.500 141.000
autocorrelation NA 0.894 0.609 0.705 0.360 0.069
selfsimilarity NA 1.129 1.007 1.063 0.959 0.951
chaos NA 0.100 0.034 0.034 0.039 0.01125
IRTG 1792 Discussion Paper Series 2020
For a complete list of Discussion Papers published, please visit
http://irtg1792.hu-berlin.de.
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"id": "2011.13240"
} |
2301.02734 | Political, economic, and governance attitudes of blockchain users | We present a survey to evaluate crypto-political, crypto-economic, and
crypto-governance sentiment in people who are part of a blockchain ecosystem.
Based on 3710 survey responses, we describe their beliefs, attitudes, and modes
of participation in crypto and investigate how self-reported political
affiliation and blockchain ecosystem affiliation are associated with these. We
observed polarization in questions on perceptions of the distribution of
economic power, personal attitudes towards crypto, normative beliefs about the
distribution of power in governance, and external regulation of blockchain
technologies. Differences in political self-identification correlated with
opinions on economic fairness, gender equity, decision-making power and how to
obtain favorable regulation, while blockchain affiliation correlated with
opinions on governance and regulation of crypto and respondents' semantic
conception of crypto and personal goals for their involvement. We also find
that a theory-driven constructed political axis is supported by the data and
investigate the possibility of other groupings of respondents or beliefs
arising from the data. | http://arxiv.org/pdf/2301.02734v1 | Lucia M. Korpas, Seth Frey, Joshua Tan | cs.CY | cs.CY | Political, economic, and governance attitudes of blockchain users
Lucia M. Korpas
1
, Seth Fr ey
2,3
, Joshua Tan
1,4*
1
The Metagovernance Project, Brookline, MA, USA
2
Department of Communication, University of California
Davis, Davis, CA, USA
3
The Ostrom Workshop, Indiana University , Bloomington,
IN, USA
4
University of Oxford, Oxford, Oxfordshire, UK
* Corr espondence:
Joshua Tan
[email protected]
Abstract
We
present
a
survey
to
evaluate
crypto-political,
crypto-economic,
and
crypto-governance
sentiment
in
people
who
are
part
of
a
blockchain
ecosystem.
Based
on
3710
survey
responses,
we
describe
their
beliefs,
attitudes,
and
modes
of
participation
in
crypto
and
investigate
how
self-reported
political
affiliation
and
blockchain
ecosystem
affiliation
are
associated
with
these.
We
observed
polarization
in
questions
on
perceptions
of
the
distribution
of
economic
power ,
personal
attitudes
towards
crypto,
normative
beliefs
about
the
distribution
of
power
in
governance,
and
external
regulation
of
blockchain
technologies.
Differences
in
political
self-identification
correlated
with
opinions
on
economic
fairness,
gender
equity ,
decision-making
power
and
how
to
obtain
favorable
regulation,
while
blockchain
affiliation
correlated
with
opinions
on
governance
and
regulation
of
crypto
and
respondents’
semantic
conception
of
crypto
and
personal
goals
for
their
involvement.
We
also
find
that
a
theory-driven
constructed
political
axis
is
supported
by
the
data
and
investigate
the
possibility
of
other
groupings
of
respondents
or
beliefs arising from the data.
1
Introduction
As blockchain technology has evolved over more than a decade, cryptocurrencies and
crypto-economic systems have had a growing impact on the world. Millions of people have involved
themselves in crypto
1
: as of 2021, around 15 percent
of American adults have reported owning
cryptocurrency (Perrin 2021), and many other countries have even higher adoption rates (Buchholz
2021). The past few years have seen the growth of decentralized apps and the crypto startup industry .
Correspondingly , governments are beginning to take regulatory actions. Also, even as blockchain
ecosystems move towards less computationally-intensive consensus mechanisms, the ongoing
environmental impact of blockchain use is huge.
Given the impact of crypto-economic activity on individuals and on shared resources, it is
increasingly important to understand how its users are relating to the technology . While the hard data
of cryptocurrency transactions and account balances is often publicly available by design, users’
1
Throughout the text, we use the term “crypto” to
encompass blockchain technologies such as
cryptocurrencies and the communities and ideologies which drive their development and use.
1
motivations for engaging with crypto are more opaque. There is little existing data on the stated
beliefs or attitudes of the variety of people using blockchain technologies. What do blockchain users
believe about the economic, political, and social relevance of crypto? While there has been attention
to the attitudes of the general population towards cryptocurrencies and blockchain technology (Perrin
2021; “Global State of Crypto, 2022” 2022), there is also a need to understand the beliefs of active
participants of blockchain ecosystems.
What do blockchain ecosystem participants believe about how the technology is being – or should be
– developed, used, and regulated? Are there discrete types of crypto contributors, or is there a
spectrum of beliefs? What specific beliefs are most relevant in distinguishing respondents between
types or along axes? This work is a first step in the development of a framework for thinking about
this spectrum or grouping of beliefs in crypto.
We report the results of a lar ge-scale survey of participants in the blockchain economy . The survey
was designed to shed light on respondents’ socioeconomic and sociopolitical beliefs relating to
crypto, economic modes of engagement with crypto, and attitudes towards governance of blockchain
technology . We describe the distributions of these responses and their relationships to self-reported
political ideology and specific crypto ecosystems such as Bitcoin and Ethereum.
We also evaluate the survey instrument itself: are the questions able to assess distinct and relevant
facets of beliefs? Can we identify underlying factors which describe broader groupings of beliefs?
Using factor analysis methods, we find that a political axis and corresponding typology , informed by
the Pew Research Center ’s Political Typology Quiz, meaningfully describes variation between
respondents.
2
Backgr ound
While there is no existing political theory of crypto per se, there are substantial ethnographic studies
of crypto communities (and related digital communities) that address the political dimensions of
crypto. For example, ethnographic studies have informed the creation of a proposed political
typology of blockchain projects (Husain, Franklin, and Roep 2020), reflecting earlier ideas on the
“intrinsic” political values of technical artifacts (W inner 1980). In this vein, cryptocurrencies have
been characterized as realizations of crypto-anarchist values such as privacy and autonomy (Chohan
2017; Beltramini 2021), following in the footsteps of earlier cypherpunk writings (Hughes 1993;
May 1994) as well as the original Bitcoin whitepaper (Nakamoto 2008). Other ethnographies have
described issues of on- and of f-chain governance (De Filippi and Loveluck 2016) and the political
motivations and cultural context of projects such as Bitcoin (Golumbia 2016) and Ethereum (Brody
and Couture 2021).
A previous industry survey , conducted by CoinDesk in 2018, contained several questions related to
politics and governance (R yan 2018; Bauerle and R yan 2018), though the questions focused more
specifically on individual projects and topical questions such as reactions to SEC rulings on the
securitization status of Ethereum.
Distinct from questions about political values, the topic of blockchain governance—including the
relationship between blockchains and traditional governments—is one of the most salient and
polarizing questions in crypto, one that has led to the creation, forking, and dissolution of many
projects. While we cannot recount all the major positions here (some of which are reflected in the
survey itself; see “Methodology”), there is a broad distinction between approaches that emphasize
on-chain governance and those that emphasize of f-chain governance. A number of academic analyses
2
have studied these dif ferent approaches to blockchain governance (Reijers, O’Brolcháin, and Haynes
2016; Liu et al. 2021; van Pelt et al. 2021), along with a vastly greater number of industry manifestos
and opinion pieces (Zamfir 2019; Szabo 1996).
3
Methodology
3.1
Survey questions
The survey consists of 19 questions related to respondents’ crypto-related beliefs and activities, with
three types of questions interspersed: those eliciting opinions about the political dimensions of crypto
activity (“crypto-political”), those eliciting economics opinions (“crypto-economic”), and those
eliciting attitudes about the governance of crypto projects (“crypto-governance”). All questions were
multiple choice, with 2-4 possible selections, and the respondent could opt not to answer . See Table 1
for the full list of questions.
The survey questions and provided choices included both a formal portion drawing from existing
political survey instruments and a more exploratory portion intended to elicit beliefs relevant to a
general crypto-political typology . In particular , a few of the questions selected (Q1 1-13, Q15, Q19),
were based on questions from Pew’ s Political Typology Quiz (Nadeem 2021) and intended to relate
to political sentiment. Other questions (e.g., Q1, Q17) were developed in collaboration with a number
of community members in crypto, drawing on the culture, memes, and references common in crypto.
Altogether , the content was designed to elicit respondents' primary modes of economic engagement
with crypto, their political sentiment, and opinions as to how crypto communities themselves should
be governed.
3.2
Construction of political “types” and identification of “axes” of belief
Our choice to identify separate “axes” of economic, political, and governance beliefs were based on
discussion with community members and in analogy to existing classifications such as the traditional
“left-right” political axis. For one of these, the political axis, we also leveraged our study design to
group and relate questions more directly by defining a continuous construct intended to assess
respondents' crypto-political leanings. We identified a subset of questions as most relevant to political
orientation, and computed a score for each participant by summing the responses to these questions
(coded with values in the range [-1, 2] as described in Table 1) in analogy to the Pew methodology
(Nadeem 2021). The lowest and highest scores on this political “axis” were designed to highlight
extreme positions of collectivist and anarcho-capitalist approaches to using blockchain technology .
Five discrete types were defined by thresholds in the score according to Table 2:
crypto-anarcho-capitalist, crypto-libertarian, centrist, crypto-communitarian, and crypto-leftist; these
types were developed both with definitions from the Pew typologies and with input from the
community .
3.3
Recruitment
We relied on a convenience (self-selected) sample of participants in the crypto community .
Participants were recruited by distributing the survey through blockchain-focused forums and
listservs, conferences (LisCon and ETHDenver), social media posts, and articles published on
blockchain-focused news sites.
We motivated voluntary participant engagement with two strategies. We presented the survey as a
quiz that assigned respondents one out of an entertaining typology of “types” on the basis of their
3
responses (“crypto-leftist,” “cryptopunk,” etc.) immediately upon completion of the survey . Stylized
as “factions”, the crypto-political types corresponded to the political types we defined based on the
Pew typology , while the crypto-economic and crypto-governance types were constructed by using
thresholds to partition respondents into five ad hoc types (for more detail, see Section 1.1 of the
Supplementary Material). We also incentivized survey completion with the opportunity to receive a
non-fungible token (NFT) corresponding to their assigned “type”, contingent upon their provision of
a valid Ethereum wallet address or ENS name.
3.4
Analysis
To survey the overall landscape of crypto-political beliefs, we observed the distribution of choices
selected by respondents. We aggregated these responses for each question, including the null
response of no choice selected, and computed the mar gins of error for a 95% confidence interval,
assuming a random sample of the population.
To investigate how political self-identification and participation in specific blockchain ecosystems
related to beliefs, we grouped participants by their responses to the corresponding questions. We then
determined which questions displayed a statistically significant dif ference in the distribution of
responses between these groups.
We also wanted to understand which questions were most meaningful in dif ferentiating respondents.
To this end, we first performed a check on the extent to which each question measured a distinct
belief by computing the correlation between responses to dif ferent questions as Cramer ’s V (a
version of Pearson’ s chi-squared statistic scaled to provide a measure of association). Then we used
principal component analysis (PCA) to identify which of the 48 choices provided across 17 questions
explained the most variance between respondents. Specifically , we looked at the component loading
for each feature, i.e., contribution to the first principal component.
For use with PCA, we normalized the feature data (shifted to a mean of 0 with unit variance). Note
that for questions where only two choices were provided, the alternative answer contributed with
equal magnitude (though opposite sign). We omitted questions 2 and 19, relating to specific
blockchain ecosystems. We also omitted answers from respondents that did not answer all questions.
We also wished to evaluate to what extent our crypto-political types, delineated from the political
score we defined, corresponded with patterns in beliefs across respondents. From the PCA results, we
can identify to what extent the assigned types or classes directly correlate with any of the first few
components.
4
Results
4.1
Responses and r espondents
Between September 27, 2021 and March 4, 2022, the survey received 3710 responses. In 3418 (92%)
of these, all questions were answered. For questions presented to all respondents, the percentage of
respondents who chose not to answer each question was between 0.5% and 1.5% across all questions.
The survey took on average 8 minutes and 40 seconds to complete.
4.2
Responses to questions: political, economic, and governance attitudes
Overall, respondents were varied in their perceptions of the distribution of economic power in crypto
and their personal attitudes towards crypto. They were also split between the most common
4
Figure 1.
Responses to
(A)
question 11,
(B)
question 12, and
(C)
question 13 on perceptions of the
distribution of economic power in crypto, with 95% confidence intervals. Though optimistic beliefs
about the current state of crypto-economics were slightly more prevalent, dissatisfaction with the fair
distribution and attainability of crypto-economic wealth was nearly as frequent.
responses to two questions on the distribution of power in governance of crypto. There was
somewhat more agreement on broad beliefs towards external regulation of crypto, though
respondents disagreed on some of the specifics and in matters of degree. The lar gest majorities were
observed in questions relating to the social implications of crypto.
Perceptions of the distribution of economic power in crypto were closely split between the two
choices provided for each question (Figure 1). By a few percentage points, a slightly higher
proportion of respondents believed that most crypto teams make “a fair and reasonable amount of
profit” rather than “too much profit” (Q1 1, Fig. 1(A)) and that the economic system in crypto “is
generally fair to most of its participants” rather than “unfairly favors powerful interests” (Q12, Fig.
1(B)). A majority (58%) believed that “most people who want to get ahead in crypto can make it if
they’re willing to work hard” (Q13, Fig. 1(C)).
5
Figure 2.
Responses to
(A)
question 3 and
(B)
question
4 on personal attitudes towards blockchain,
with 95% confidence intervals. Together, these responses show that both a desire for sociopolitical
change and an interest in personal financial gain were common factors in participants’ interest in
blockchain technologies.
Figure 3.
Responses to
(A)
question 5 and
(B)
question
16 on blockchain governance, with 95%
confidence intervals.
Personal attitudes towards crypto were also diverse (Figure 2). Respondents were divided on whether
they regarded crypto as “mainly a political philosophy and/or lifestyle” or “mainly an economic
technology”, with a slight majority favoring the latter (Q3, Fig. 2(A)). There was no majority in
respondents’ goals for their own involvement in crypto: the most common goal was “to create social
change and/or disrupt the industry” (39%), followed by “to make as much money as possible” (29%)
(Q4, Fig. 2(B)).
Normative beliefs about the distribution of power in governance of crypto appear to be in some
tension (Figure 3). Most respondents favored a hands-of f approach to the governance of crypto, with
45% believing that “most or all cryptogovernance should be on-chain” and 30% believing “crypto
does not need (human) governance” (Q5, Fig. 3(A)). However , a majority of respondents believed
that “a wide variety of on- and of f-chain stakeholders” should have decision-making power over a
blockchain (though the next most common response was “the token holders and/or node operators,
i.e., voters, as determined by the protocol”) (Q16, Fig. 3(B)). Note that while the most common
responses to each of these questions are not incompatible, their coexistence indicates a possible
tension in the community between maximizing on-chain governance and empowering of f-chain
stakeholders.
6
Figure 4.
Responses to
(A)
question 7 and
(B)
question
14 on external regulation of blockchain
technologies, with 95% confidence intervals.
Regarding external regulation of blockchain technologies, respondents were somewhat more
consistent (Figure 4). A majority of respondents believed at least some good will come of
government regulation of crypto, though nearly 40% asserted that “government regulation of crypto
will almost always do more harm than good” (Q7, Fig. 4(A)). In line with the above, when asked
what the most important thing the crypto community can do to get more favorable regulation of
cryptocurrencies from national governments, a plurality of respondents sought a cooperative
relationship with government, choosing to “work hand-in-hand with regulators to identify a solution
that works for both government and industry ,” versus adopting an evasive approach to “adapt our
technology and practices in order to minimize potential conflicts with the law” or even an
antagonistic one to “mount a public pressure campaign on politicians” or to “keep doing what we’re
doing, legal or not” (Q14, Fig. 4(B)). Also, more than three-quarters of respondents believed that
“having a central bank run a cryptocurrency is a bad idea” (Q8). Overall, though a majority of
respondents were willing to accept or even collaborate on regulation, lar ge minorities strongly
disagreed, and distaste for direct government involvement in implementations of crypto technology
was common.
On the social implications of crypto, most respondents were in agreement, believing that blockchain
and DeFi are “beneficial technologies that, on balance, will help most members of society” (Q10).
Even so, more than a quarter of respondents believed that crypto “has a gender problem” (Q15).
Also, around a quarter of respondents indicated privacy is “the most important feature of blockchain
and crypto” (Q6).
We asked two additional questions on political orientation and blockchain ecosystem af filiation
(Figure 5). Only 14 percent of respondents considered themselves “conservative or right-wing” (532
respondents) with the remaining participants split equally (with no statistically significant dif ference)
between “liberal or left-wing” (1550) and “neither” (1599; Q18, Fig. 5(A)). Nearly all participants
(97%) stated an af filiation with at least one blockchain ecosystem or community (Q19, Fig. 5(B)),
supporting our use of this dataset to focus on users of blockchain technology (rather than the general
public). In particular , of the 3591 respondents who indicated af filiation with at least one blockchain,
2175 (61%) selected af filiation with Ethereum and 1 120 (31%) with Bitcoin (Fig. 5b). Note that these
are not mutually exclusive groups (789 indicated af filiation with both); furthermore, though a
majority of respondents only specified one af filiation, less than a quarter believe that “there is one
(layer 1) blockchain that is the best” (Q1). In the following two subsections, we discuss the relation
of these distributions with respondents’ beliefs in more depth.
7
Figure 5.
Responses to
(A)
question 18 on political
orientation and
(B)
question 19 on blockchain
ecosystem affiliations, with 95% confidence intervals.
The distribution of responses for the questions not covered in this section are included in the
Supplementary Material (Supplementary Figures S1-S4).
4.3
Differ ences between r espondents by self-r eported political orientation
To examine the dif ferences in opinion between the left-of-center , right-of-center , and unaligned
groups, we compared the distribution of answers selected by respondents af filiated with each group
(Q18). We found that perceptions of economic fairness and gender equity elicited the clearest
differences between the three political orientation groups, with economic fairness especially
differentiating left-of-center respondents from the other two groups. Beliefs about governance,
regulation, and personal goals in crypto dif ferentiated right-of-center respondents from the other two
groups. Dif ferences between political orientation groups were ubiquitous: all but one question had at
least one statistically significant dif ference between the responses groups.
The economic fairness questions (Q1 1, Q12, and Q13) were among those with the greatest
differentiation between the three groups. Somewhat surprisingly , unlike nonaligned and
right-of-center respondents, a majority of left-of-center respondents believe that “most crypto teams
Figure 6.
Responses, grouped by self-reported political
affiliation, to question 12 on crypto-economic
fairness, with 95% confidence intervals. Taken together with questions 11 and 13, this distribution
shows that left-of-center respondents overall held a different set of beliefs about wealth distribution
and economic opportunity than other respondents.
8
make a fair and reasonable amount of profit” (Q1 1) and “the economic system in crypto is generally
fair to most of its participants” (Q12, Fig. 6). Though a majority of both right-of-center and
nonaligned respondents believed instead that “the economic system in crypto unfairly favors
powerful interests”, right-of-center respondents were more likely than nonaligned respondents to
choose this answer (Q12). However , left-of-center respondents were more likely than right-of-center
or nonaligned respondents to believe that “hard work and determination are no guarantees of
success” in crypto (Q13).
Question 12 was one of three questions for which all three groups had a statistically dif ferent
distribution of responses. Another was on gender equity: right-of-center respondents were least likely
to believe “crypto has a gender problem,” nonaligned respondents somewhat more likely , and
left-of-center respondents most likely , with about half of left-of-center respondents selecting this
answer (Q15). This spread shows that self-reported political alignment relates to not only economic
but also social issues in the use of blockchain technology .
Differences also arose between the groups in the most common answer to questions on
decision-making power and how to obtain favorable regulation. When asked who should hold
decision-making power over a blockchain, right-of-center respondents were more likely to choose
“the token holders and/or node operators” than “a wide variety of on- and of f-chain stakeholders”;
the reverse was true for left-of-center and nonaligned respondents, with left-of-center respondents
more likely than other respondents to choose a variety of stakeholders (Q16, Fig. 7). Concerning how
to obtain favorable regulation, left-of-center and nonaligned respondents were most likely to choose
“work hand-in-hand with regulators” out of the available choices, and more likely to do so than
right-of-center respondents; in contrast, right-of-center respondents were, within confidence
intervals, evenly split between three of the four available choices (Q14).
Other statistically significant dif ferences occurred in the distribution of responses, where one of the
three groups dif fered from the other two. Right-of-center respondents were most likely to choose
“make as much money as possible” as their goal and less likely to select “create social change and/or
disrupt the industry”; the reverse was true for left-of-center and nonaligned respondents (Q4).
However , left-of-center respondents were less likely than others to believe crypto needs to prioritize
“building art and community” to grow (Q9). Also, a smaller proportion of left-of-center respondents
than other respondents believed that privacy is “the most important feature of blockchain” (Q6).
Figure 7.
Responses, grouped by self-reported political
affiliation, to question 16 on decision-making
power, with 95% confidence intervals. Together with question 14, this distribution indicates that
right-of-center respondents were more likely than other respondents to hold beliefs aligned with
minimizing external influence on blockchain governance and development.
9
Left-of-center respondents were less likely to believe that “crypto does not need (human)
governance,” while nonaligned respondents were less likely to believe “however crypto governs
itself, it should also be regulated by the government” (Q5). Left-of-center respondents were also
more polarized on government regulation: they were less likely to believe it “can do some good,” and
more likely to believe it is either “critical to protect the public interest” or “will always do more harm
than good” (Q7).
4.4
Differ ences between r espondents by Bitcoin and Ether eum affiliation
At present, dynamics in the crypto community are lar gely driven by actors in two ecosystems:
Bitcoin and Ethereum. To examine dif ferences in opinion between the 61% of respondents af filiated
with Ethereum and the 31% (non-exclusive) af filiated with Bitcoin, we compared the distribution of
answers selected by respondents af filiated with each of the two blockchains. We found an overall
quite similar distribution of responses regardless of af filiation, with a few statistically significant
differences arising in beliefs about cryptogovernance, the semantics of the term crypto, personal
goals in crypto, and stated political orientation.
Governance and regulation of crypto were a key topic distinguishing Bitcoin af filiates from Ethereum
affiliates (Figure 8). Bitcoin af filiation was associated with a higher likelihood of believing that
“crypto does not need (human) governance” (Q5, Fig. 8(A)) and that “token holders and/or node
operators” should have decision-making power over a blockchain, whereas Ethereum was associated
with “a wide variety of on- and of f-chain stakeholders” (Q16, Fig. 8(B)). Somewhat surprisingly ,
Bitcoin af filiation was also associated with a higher likelihood of believing that government
regulation of crypto “can do some good” (Q7), although there was no statistically significant
difference in opinions on how to obtain favorable regulation (Q14). Thus, it appears that Bitcoin
affiliation is associated with a higher rate of wanting to maximize on-chain governance but also of
tolerance of external regulation, perhaps in particular that which “can help force blockchains to
become more decentralized,” as is included in the wording of question 7.
Respondents’ semantic conception of crypto and their personal goals for their involvement also had
some relation to blockchain af filiation: Bitcoin af filiation was associated with a higher likelihood of
believing “crypto is mainly an economic technology” (Q3) and identifying with the statement “my
goal in crypto is to make as much money as possible” (Q4). Ethereum af filiation was associated with
a higher likelihood of believing that “the economic system in crypto unfairly favors powerful
interests” (Q12) and that “crypto has a gender problem” (Q15).
Figure 8.
Responses, grouped by blockchain affiliation,
to
(A)
question 5 and
(B)
question 16 on
blockchain governance, with 95% confidence intervals. These distributions indicate that Bitcoin
affiliates were more likely to favor a narrow definition of governance and its participants.
10
Figure 9.
Responses, grouped by blockchain affiliation,
to question 18 on self-reported political
affiliation, with 95% confidence intervals. While there was a statistically significant difference
between affiliates of the two blockchains in identifying as right-of-center or nonaligned, Cramer’s V
indicates that the strength of association between blockchain affiliation and political orientation was
low.
For question 18 on political orientation, Bitcoin af filiation correlated with a higher likelihood of
selecting “conservative or right-wing” and lower likelihood of selecting “neither” (Figure 9). There
was no statistically significant dif ference between the proportions of respondents who chose “liberal
or left-wing”. Given that we were interested in analyzing blockchain af filiation separately from stated
political orientation, we additionally checked for the strength of association between Q18 and a
reduced version of Q19 with the options “Bitcoin”, “Ethereum”, and “Neither” (not mutually
exclusive). Cramer ’s V was low (less than 0.15) for all combinations of responses, indicating at most
very weak association between the two questions (Supplementary Figure S5). This gives us
confidence that Bitcoin and Ethereum af filiation were not strongly associated with stated political
orientation.
4.5
Validation of survey instrument
To assess any correlations between responses to dif ferent questions, we computed the correlation
matrix for all pairs of questions (Supplementary Fig. S6). Of the 153 unique pairs, most showed little
if any association (V < 0.1); the strength of association was weak for 52 questions (0.1 <= V < 0.3),
and one question pair related to wealth distribution (Q1 1-Q12) showed a moderate strength of
association (0.33). The prevalence of weak or no association between distinct questions supports our
assertion that each question addresses a distinct facet of a respondent’ s beliefs or actions. This allows
us to assess the relative importance of the specific statements provided in the answer choices to
explain dif ferences between respondents.
4.6
Featur e selection and factor analysis
To identify the beliefs which most contributed to explaining variance between respondents and to test
our hypothesis, we computed the PCA vectors for individual choices (features) and examined the
first principal component. Beliefs above a threshold of magnitude 0.18, corresponding to the loading
each response would have if all questions contributed equally to the component, were labeled as
important. The features with the lar gest contributions to the first principal component were the
following (listed in descending order of importance):
11
-
“The economic system in crypto unfairly favors powerful interests.” (Q12)
-
“Crypto has a gender problem.” (Q15)
-
“Government regulation of crypto will almost always do more harm than good.” (Q7)
-
“[I consider myself] liberal or left-wing.” (Q18)
-
“Crypto teams make too much profit.” (Q1 1)
-
“In crypto, hard work and determination are no guarantee of success for most people.” (Q13)
-
“However crypto governs itself, it should also be regulated by the government.” (Q5)
-
“Blockchain and DeFi are predatory technologies that, on balance, will harm most members
of society .” (Q10)
All three questions relating to wealth distribution and economic fairness (Q1 1-13) contributed more
to explaining variance than most other questions. Polarized opinions on government regulation (Q5
and Q7) and one specific political af filiation (Q18) also featured here. Altogether , 5 of the 8
questions that we had coded as defining an axis of political belief had a lar ge contribution to this
leading component.
The same analysis can be done for the remaining principal components. The features with the lar gest
component loading for the next two principal components are “Privacy is the most important feature
of blockchain and crypto” (Q6) for the second principal component and “Crypto is mainly a political
philosophy and/or lifestyle” (Q3) for the third. These choices, and their corresponding questions, are
therefore among the more salient in explaining variance between respondents.
Altogether , however , the variance explained by only the first few components was relatively low
(21% for the first three components) and less than 10% was explained by the first component alone.
Taken together with weak associations between questions as described in Section 4.5, this implies
that the number of latent variables required to describe respondents' beliefs is lar ge. Indeed, factor
analysis using PCA and feature agglomeration yielded a null result, meaning that features did not
cluster into a few interpretable groupings (see Section 1.3 of the Supplementary Material). Even so,
we find that the first principal component axis corroborates a theory-first constructed axis, as
described in the following section.
4.7
Validation of constructed crypto-political axis
For each respondent, a political score was calculated using the values in Table 1 and a type was
assigned according to the score thresholds described in Table 2. The feature selection and factor
analysis results can be used to evaluate the validity of this constructed crypto-political axis.
The distribution of scores and types assigned to participants who completed the survey is shown in
Figure 10. On the left-of-center side, 20% of respondents were identified as “crypto-communitarian”
or “crypto-leftist”, while 9% of respondents were given the “crypto-centrist” label. The most
commonly assigned type was the “crypto-libertarian” types, with nearly half of respondents receiving
this designation; overall, right-of-center types (“crypto-libertarian” and “crypto-anarcho-capitalist”)
dominated with 71% of respondents. This distribution is unimodal, low skewness, and centered
around the median possible score. However , because the range of possible scores was not centered
around zero, we find that a majority of respondents were labeled as crypto-politically
“right-of-center”. For a summary of how this distribution dif fered with political self-identification
and blockchain af filiation, Section 1.2 of the Supplementary Material.
12
Figure 10.
Distribution of assigned crypto-political
scores and corresponding sentiment types.
Despite a 14% minority of respondents identifying as ideologically conservative or right-wing, our
measure placed 71% in the right-of-center libertarian and anarchocapitalist categories.
Figure 11.
Box plots showing the distribution of values
for each political type along each of the first
three principal components produced by PCA, with the mean scores indicated by a white triangle.
There is essentially no overlap between crypto-leftist and crypto-ancap types for component 0.
Partitioning the respondents by the types we identified for them, and plotting them in the first three
PCA components, we find, again, that the first principal component succeeds at capturing the
political dimension of respondent variation, while the next two components are less informative
(Figure 1 1). The low overlap between the interquartile ranges for adjacent types indicates that the
continuous construct we defined and the defined types which discretize it help to explain dif ferences
between respondents’ beliefs. Thus, the constructed political axis seems to reflect true variation in the
population and may be of use in future work characterizing the ideological structure of the crypto
community .
5
Discussion
Though optimistic beliefs about the current state of cryptoeconomics were slightly more prevalent,
the survey responses indicate nearly as much dissatisfaction with the fair distribution and attainability
of cryptoeconomic wealth. Both a desire for sociopolitical change and an interest in personal
financial gain were common factors in participants’ interest in blockchain technologies. Respondents
13
generally were optimistic about the social potential of blockchain technology , with some having
reservations about its gender equity and some focusing on its privacy implications. Overall, though a
majority of respondents were willing to accept or even collaborate on regulation, lar ge minorities
strongly disagreed, and distaste for direct government involvement in implementations of crypto
technology was common.
Despite low rates of respondents’ self-identification with “conservative or right-wing” politics, we
observed a prevalence of right-of-center crypto-political types. A broadly similar distribution was
observed in a CoinDesk report published in 2018 (R yan 2018). The discrepancy between general
political self-identification and our crypto-specific labeling bears further investigation. It may relate
to an association of the term “conservative” with social conservatism, whereas crypto-libertarianism,
the crypto-political type we found to be most common, emphasizes a form of economic
libertarianism. Furthermore, the connotations of “conservative” and “liberal” vary significantly by
geographic region, so the question may have been interpreted dif ferently across respondents based on
their country of residence.
The correlation of the constructed political axis with the first principal component–a commonly-used,
well-validated axis–suggests a primacy of political variation in explaining patterns of responses.
Furthermore, the existence of dif ferences in the distribution of beliefs between self-identified
political orientations indicates that traditional political ideologies have some bearing on how
participants relate to blockchain technology . For example, the “left-of-center” group articulated
distinct beliefs about economic opportunity , fairness of wealth distribution, privacy , and the growth
of crypto (Q1 1-13, Q6, and Q9), suggesting that left-of-center respondents are more likely to apply
more community-oriented multi-stakeholder values to the blockchain ecosystem. The “nonaligned”
group, on the other hand, articulated distinct beliefs about gender equity and government regulation
(Q15 and Q5), suggesting this group is more clearly defined by lower trust in existing government
institutions
2
. This lower approval of government regulation
suggests that those who identified as
neither left-of-center nor right-of-center are more likely to position themselves as separate from
existing political and governance systems entirely .
We are interested in understanding the extent to which the characteristics of developers and users of
specific blockchains are distinctive of each blockchain. Critics like David Golumbia have ar gued that
Bitcoin, both in its design and ideological constitution, is principally a conservative movement
interested strictly in Bitcoin’ s record of gaining value (Golumbia 2016). Those observations were not
made in opposition to Ethereum or any other blockchain, although Ethereum had been live for a year
at the time of Golumbia’ s writing. Our findings indicate that in fact, there are few dif ferences
between Bitcoin and Ethereum users. However , differences in technical implementation between
Bitcoin and Ethereum may relate to dif ferences in opinion on their governance. Unlike Bitcoin,
which has limited support for transactions other than money transfers, Ethereum as an infrastructure
enables the developing and building of various applications and projects. The broader set of use cases
for Ethereum may lead its users to believe a broader set of stakeholders should be involved in its
governance. Furthermore, Ethereum af filiation was associated with a greater sensitivity to perceived
socioeconomic inequity , which may relate to dif ferences in how the blockchains are used alongside
other technology . In Ethereum ecosystems, users linking their own blockchain activity to other
2
We refer in this work to respondents who chose to
identify as neither left-of-center nor
right-of-center as “nonaligned”. We choose this term in contrast to a term such as “apolitical” in a
nod to ideas of political agnosticism developed by
ethnographers in observing open-source
communities
(Gabriella Coleman 2013)
.
14
personally-identifying information, such as Discord handles or Twitter accounts, is not uncommon;
more research is needed to understand whether lower rates of anonymity relate to greater awareness
of actual or perceived social demographics.
Communities or ganized around crypto are proving to be a laboratory for new ways that humans can
organize collective action, but are not operating in a historical vacuum: it appears that some patterns
observed in early users of other internet technologies have arisen or continue to appear in the
blockchain context as well. To complement this sociological work, further anthropological research
could shed some light on the extent to which the economic and political beliefs held by participants
in crypto echo the ideologies of two earlier movements: the cryptographic hacker and open-source
software communities. The distribution of responses relating to fair rewards for developer teams and
the utility of hard work and in crypto indicates that meritocratic values are prevalent; meritocracy
may play a similar role in blockchain ecosystems, themselves often open source, as it has in prior
open source and hacker communities (Gabriella Coleman 2013; Dunbar -Hester 2019). Privacy has
been at the forefront of concerns in the development of internet technologies since the cypherpunks
(Hughes 1993) and remains prevalent in blockchain (Brunton 2020). Further research is needed to
understand how these values compare to those of open-source software communities and early
adopters of the internet or how they may have changed over time as cryptocurrencies become more
mainstream.
6
Limitations
6.1
Survey methodology
Selection bias may arise given that the random sample assumption is limited by how the survey was
distributed. In particular , since the survey was opt-in, people with stronger and potentially more
extreme opinions may have been more motivated to complete the survey . Also, the survey was made
available only in English, and so is likely not representative of the full geographic distribution of
users of blockchain technologies. Presentation of some preliminary findings prior to finalizing data
collection may also have influenced some respondents. Additionally , we do not have a guarantee of
uniqueness of each respondent; moreover , the two recruitment strategies we used may have
motivated respondents to provide multiple responses.
In choosing the wording of each question and answer choice, we made an ef fort to mitigate response
bias. Still, we have identified some limitations in interpreting questions based on the wording of the
questions. Q5 may have had an insuf ficient distinction between the two most commonly-selected
choices. In answering Q14, respondents who selected “Keep on doing what we’re doing” may have
rejected the premise of the question rather than believed this was a way to achieve the stated goal.
Additionally , while we intended Q15 to refer to perceptions of gender inequity in participation or
compensation within crypto, the wording of the choices may have been too vague.
Further demographic information would be valuable context for interpreting some questions. Future
work could include a question on the geographic location of respondents, where local regulations and
political attitudes could inform a more detailed analysis of questions on national government
regulation and political af filiation. Interpretation of question 18 on political self-identification may be
similarly limited by dif ferences in how terms such as “liberal” and “conservative” are understood
across the world.
15
6.2
Analysis
To be able to use PCA for the discrete data, we one-hot encoded specific choices. While PCA is
generally better suited to continuous data than boolean data, we find that in this context the results
were cleanly interpretable. We also chose not to include null responses as an additional coded choice
for feature selection or factor analysis. While this does result in using only a subset of the responses
and potentially removing relevant information about respondents’ beliefs, it prevents the null
responses from receiving artificially high importance due to their relative rarity and bypasses the
difficulty in interpreting the null response.
7
Conclusion
In this work, we have introduced a new survey of blockchain users’ political, economic, and
governance opinions with respect to crypto. Based on 3710 survey responses, we find that users were
spread across a variety of perceptions of the distribution of economic power , normative beliefs about
the distribution of power in governance, and opinions on the role of external regulation of crypto,
though they were broadly in agreement that crypto has a net-positive impact on the world. Equal
numbers of respondents self-identified as liberal or non-aligned, while only about a third as many
respondents self-identified as conservative; this self-reported political af filiation was associated with
differences in opinions on most questions, but especially on economic fairness, decision-making
power , and how to obtain favorable regulation. In contrast, we observed few dif ferences in opinions
between respondents af filiated with Bitcoin and with Ethereum, on issues of blockchain governance
and regulation and on personal attitudes towards crypto. While the full field of beliefs elides neat
interpretation in terms of underlying factors, we found that the existence of a political dimension was
supported both by a theory-driven construct and by a common, well-validated analytical method
(PCA).
While this dataset is an important step towards understanding the distribution of crypto users’ beliefs
about blockchain technology and its utility , open questions remain as to why users believe what they
do about crypto and how their beliefs match up with reality . For example, considering the question of
who should have decision-making authority over a blockchain: Is the lar ge-minority opinion that
token-holding voters should control a blockchain underlied by a belief that minimizing human input
to governance will make it more ef ficient and less flawed? Is there a disconnect between the common
normative beliefs of what should be happening in cryptogovernance and which types of stakeholders
actually can and do participate in governance of major blockchains?
Although our research found only a few instances where af filiation with a specific blockchain was
associated with dif ferences in beliefs, further research is needed to better understand whether specific
architectures or ecosystems within crypto dif fer in the values or goals embedded in them. Future
interdisciplinary work could shed some light on the extent to which participants have common
understandings of core signifiers such as decentralization and autonomy .
Given that our work takes inspiration from the long-running Pew political survey , we see the need for
a regular survey of cryptopolitical sentiment, with an added demographic panel. This could facilitate
the identification and comparison of ideologies and modes of participation within newer chains such
as Solana, L2s such as Polygon, and even lar ge DAOs.
16
8
Conflict of Inter est
The authors declare that the research was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of interest.
9
Author Contributions
LMK conducted data analysis and wrote manuscript. SF advised on methodology and advised on
manuscript. JT designed and deployed the survey instrument and advised on manuscript.
10
Funding
Lucia Korpas and Joshua Tan were supported by grants from the Filecoin Foundation and from One
Project.
11
Acknowledgments
The authors would like to acknowledge Michael Zar gham for technical discussion, Ann Brody for
qualitative discussion, and Tyler Sullber g and Nathan Schneider for feedback on the manuscript.
12
Data Availability Statement
The dataset generated and analyzed for this study can be found in the Metagovernance Project’ s
Govbase repository on Airtable
(
https://airtable.com/shr gnUrj0dqzZDsOd/tblvwbt4KFm8MOSUQ/viw82nVNrdHFrowoo
).
The
Python code used to conduct the analysis and produce the figures can be found in the GitHub
repository for this work (
https://github.com/metagov/cryptopolitics-paper
).
17
13
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19
14
Tables
Table 1
Question
number
Question text
Choice text
Contribution
to political
score
1
Which statement
comes closest to
your views?
There is one (layer 1) blockchain that is the best.
There is no one best blockchain.
2
Which blockchain
is the best?
Bitcoin
Ethereum
Solana
Cardano
Polkadot
Other
3
Which statement
comes closest to
your views?
Crypto is mainly an economic technology.
Crypto is mainly a political philosophy and/or lifestyle.
4
Which statement
comes closest to
your views?
My goal in crypto is to have fun.
My goal in crypto is to make as much money as possible.
My goal in crypto is to create social change and/or disrupt the
industry.
My goal in crypto is to earn a living and/or build my career.
5
Which statement
comes closest to
your views?
Most or all cryptogovernance should be on-chain.
Most or all cryptogovernance should be off-chain.
Crypto does not need (human) governance; let the algorithms run as
they were designed.
However crypto governs itself, it should also be regulated by the
government.
6
Which statement
comes closest to
your views?
Privacy is the most important feature of blockchain and crypto.
2
Privacy is nice, but it’s not the most important feature of blockchain
and crypto.
0
7
Which statement
comes closest to
your views?
Government regulation of crypto will almost always do more harm
than good.
1
Government regulation of crypto can do some good, e.g. it can help
force blockchains to become more decentralized.
Government regulation of crypto is critical to protect the public
interest in these technologies.
-1
8
Which statement
comes closest to
your views?
Having a central bank run a cryptocurrency is a good idea.
Having a central bank run a cryptocurrency is a bad idea.
9
In order to grow,
the crypto
ecosystem should:
Build art and community.
-1
Help people around the world earn a living.
-1
Build useful tech that solve real problems for a set of users.
1
20
Provide financial instruments for maximum wealth creation.
1
10
Which statement
comes closest to
your views?
Blockchain and DeFi are beneficial technologies that, on balance,
will help most members of society.
Blockchain and DeFi are predatory technologies that, on balance,
will harm most members of society.
11
Which statement
comes closest to
your views?
Most crypto teams make a fair and reasonable amount of profit.
1
Crypto teams make too much profit.
-1
12
Which statement
comes closest to
your views?
The economic system in crypto is generally fair to most of its
participants.
1
The economic system in crypto unfairly favors powerful interests.
-1
13
Which statement
comes closest to
your views?
Most people who want to get ahead in crypto can make it if they're
willing to work hard.
1
In crypto, hard work and determination are no guarantee of success
for most people.
-1
14
To get more
favorable
regulation of
cryptocurrencies
from national
governments, the
most important
thing the crypto
community can do
is:
Adapt our technology and practices in order to minimize potential
conflicts with the law.
Work hand-in-hand with regulators to identify a solution that works
for both government and industry.
Hire lawyers and lobbyists; organize the community to mount a
public pressure campaign on politicians.
Keep on doing what we’re doing, legal or not.
1
15
Which statement
comes closest to
your views?
Crypto has a gender problem.
0
Crypto does not have a gender problem.
1
16
Who should have
decision-making
power over a
blockchain?
The public, elected representatives, and/or national leaders
A wide variety of on- and off-chain stakeholders including token
holders, node operators, application developers, foundations, and
users
The token holders and/or node operators, i.e. voters, as determined
by the protocol
The core developers and technical staff of a blockchain
17
I'm here for...
the memes
the jobs
the tech
the airdrops
18
I identify as:
Liberal or left-wing
-1
Conservative or right-wing
1
Neither
0
21
19
OPTIONAL: Do
you affiliate with
any of the
following
ecosystems or
communities?
Bitcoin
Ethereum
Solana
Cardano
Polkadot
Other
Table 2
Politics score
Assigned type
[-7,9]
Overall possible range of
scores
>= 5
Crypto-anarchocapitalist
0 < x < 5
Crypto-libertarian
x = 0
Crypto-centrist
-3 < x < 0
Crypto-communitarian
<= -3
Crypto-leftist
22
Supplementary Material
1
Supplementary Information
1.1
Recruitment strategy: Ad-hoc typology
Based on their responses to the survey , each respondent was assigned a political faction, an economic
faction, and a governance class. These were computed immediately upon completion of the survey
according to numerical weights assigned to each answer choice and formulas defined for each
faction. The faction definitions and names were developed with input from the community , and
served lar gely as a way to (1) recruit participants and (2) help participants interpret their results.
Depending on the response to Question 3 – whether the respondent considered crypto to be primarily
a political philosophy or an economic technology – each respondent was correspondingly presented
with either their political faction or economic faction as their overall assigned faction. The five
political factions (“crypto-leftist”, “DAOist”, “true neutral”, “crypto-libertarian”, and
“crypto-ancap”) were an alternate naming scheme for our constructed political axis. The five
economic factions (“earner”, “cryptopunk”, “NPC”, “techtrepreneur”, “degen”) were intended to
represent the respondents’ primary mode of economic engagement with crypto. Additionally , we
defined four “classes” (Szabian, Gavinist, Zamfirist, and Walchian) that were intended to capture
respondents’ beliefs about governance and government regulation, inspired by the positions
articulated by Nick Szabo, Gavin Wood, Vlad Zamfir , and Angela Walch.
The mechanism for assigning the factions based on respondents’ answers was a weighted sum of
their responses to each question; every answer choice in every question adds points to one or more
factions, and the faction assigned is the one with the most points, with some thresholding to account
for respondents who did respond to all questions. In cases where not enough responses were given,
the assignment defaulted to “true neutral” or “NPC”. Questions 6, 7, 9, 1 1, 12, 13, 14, 15, and 18
were used in computing the political faction, questions 4, 9, 17 in computing the economic faction,
and questions 5, 7, 8, 14, and 16 in computing the governance class.
The political faction names and definitions were conceptualized and iterated through a
community-based ef fort at crypto conferences and online, especially within the Metagovernance
Project Slack community . The names of the factions reflected both popular slang in crypto (degen,
cryptopunk, crypto-libertarian, crypto-leftist, DAOist, crypto-ancap) as well as a few inventions of
the authors when there was no existing slang or word for that archetype (earner , techtrepreneur , true
neutral, NPC). The visual representations of the factions, which were used as the underlying images
of a series of NFT s, played of f various memes (e.g. shiba inu / doge representing degens) and cultural
icons (e.g. Shrek representing crypto-libertarians) common in crypto and on the internet more
broadly .
Within the main text, the political “factions” were renamed to the described “types”.
1.2
Differ ences in assigned political types by self-r eported political orientation and
blockchain ecosystem affiliation
There was a statistically significant dif ference in the overall distribution of assigned cryptopolitical
factions across the left-of-center , right-of-center , and nonaligned groups. Right-of-center respondents
were the only group more likely to be assigned the crypto-anarcho-capitalist faction than the
crypto-libertarian faction, and less likely than the other two groups to be assigned true neutral,
23
DAOist, or crypto-leftist. Unlike the other two groups, left-of-center respondents were more likely to
be assigned any of crypto-centrist, crypto-communitarian, or crypto-leftist than to be assigned
crypto-anarcho-capitalist. Nonaligned respondents generally split the dif ference between the
proportions of left-of-center and right-of-center respondents assigned each faction except for
crypto-libertarian, which they were more likely to be assigned than either of the other two groups.
Note that while Q18 was used directly in assigning the political faction, it was one of nine such
questions, each with similar weights in the overall cryptopolitical score.
A higher percentage of Bitcoin-af filiated respondents than Ethereum-af filiated respondents received
the “crypto-anarcho-capitalist” label.
1.3
Are ther e clusters of r espondents or featur es?
We were interested in understanding whether the range of responses would be better captured by the
idea of “types” (groupings of respondents, consistent with data-driven clustering methods) or “axes”
(grouping of questions, consistent with factor analysis techniques). We investigated this by extending
our PCA analysis with feature agglomeration, based on a feature set of 48 choices provided across 17
questions.
We used feature agglomeration to hierarchically generate clusters of features, where each feature
corresponded to the selection of a provided choice. Given that these features are boolean in nature,
we used the Dice distance metric to determine distances between features; this can be understood as
the fraction of the sample for which the two features intersect or overlap, as a representation of the
shared information between them. To determine which clusters should be mer ged and when, we used
the complete linkage criterion, which relates to the maximum distances between all features of the
two clusters.
From the feature agglomeration results, we ask whether the assigned political types directly correlate
with any of the identified factors (i.e., to what extent does our categorization of beliefs as political,
economic, or governance-related meaningfully describe joint variations in the choices selected?). At
the threshold where three clusters were present, the clusters contained A=31, B=10, and C=7 choices.
The cluster that shared the most features in common with the important principal component features
listed above was the cluster with 10 features: the specific choices listed above for beliefs on
economic fairness, gender , and political af filiation (Q1 1-Q13, Q15, and Q18) appeared in this feature.
The two specific choices relating to government regulation (Q5 and Q7) were present together in the
cluster with 7 features Although cluster B aligned fairly well with the first (politically-focused)
principal component, our exploration of the other clusters, and the hierarchy that produced them, did
not indicate much qualitative support for this approach. This null assessment is supported by our
mapping of respondents into feature space: both factor analysis methods shown in
Supplementary
Figure S7
shows responses or ganized around a single
centroid, not the multiple clusters that would be
expected in the presence of clear respondent types.
Given that the feature agglomeration method allowed us to traverse the entire hierarchy of feature
clusters, we also looked at the agglomerated clusters to see whether three stable feature clusters arise
from the full feature set. Based on the distances required for clusters to mer ge, it appears instead that
five distinct latent variables may best describe the distribution of respondents’ beliefs, as shown in
Supplementary Figure S8
. That said, the smaller two
out of the three clusters identified above
persisted unchanged at the five-cluster threshold, suggesting that the underlying factors for each of
those two clusters have more explanatory power .
24
2
Supplementary Figur es and Tables
2.1
Supplementary Figur es
Supplementary Figur e S1
. Distribution of responses
to questions 1, 2, and 5. Note that question 2
was presented only to respondents who selected the response “There is one (layer one) blockchain
that is the best” for question 1, and that respondents were given the option to select Ethereum,
Bitcoin, Solana, Polkadot, Cardano, or a fill-in-the-blank “Other”.
25
Supplementary Figur e S2
. Distribution of responses
to questions 6, 8, and 9.
26
Supplementary Figur e S3
. Distribution of responses
to questions 10, 15, and 17.
27
Supplementary Figur e S4
. Distribution of responses
to question 19, and distribution in the number
of those multiple-select choices that respondents who answered the question chose. Note that
respondents were given the option to select Ethereum, Bitcoin, Solana, Polkadot, Cardano, or a
fill-in-the-blank “Other”. For plot legibility , only the ecosystems listed by at least 10 respondents are
shown here.
28
Supplementary Figur e S5.
Correlation (Cramer ’s V)
between choices to questions 18 and 19. The
low correlation between choices belonging to dif ferent questions indicates that political
self-identification and blockchain af filiation are lar gely independent.
29
Supplementary Figur e S6
. Correlation (Cramer ’s V)
between all questions. Gray indicates that the
correlation between questions was not computed; this is used when one question was only presented
to the respondents upon a particular selection for the other question. No clusters are clearly
observable: the questions are not systematically related in macro groupings.
30
(A)
(B)
Supplementary Figur e S7.
(
A
) Projection of the respondents
onto the first two principal
components. (
B
) Projection of the respondents onto
the feature clusters B (10 features) and C (7
features). In both cases, there is a continuous (rather than discrete) distribution of respondents along
the two axes. The unimodal distribution of respondents along the two axes does not suggest the
presence of clusters of respondents by their responses.
31
Supplementary Figur e S8.
Dendrogram describing the
hierarchical agglomeration of feature
clusters. The colors delineate the first five clusters that branch of f from the one-cluster case (i.e., the
one containing all features). The three-cluster case involves grouping some of these together .
32
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2011.13837 | A theory of transaction parallelism in blockchains | "Decentralized blockchain platforms have enabled the secure exchange of\ncrypto-assets without the i(...TRUNCATED) | http://arxiv.org/pdf/2011.13837v4 | Massimo Bartoletti, Letterio Galletta, Maurizio Murgia | cs.CR | cs.CR | "Logical Methods in Computer Science\nVolume 17, Issue 4, 2021, pp. 10:1–10:44\nhttps://lmcs.episc(...TRUNCATED) | {
"id": "2011.13837"
} |
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