We study the liquid democracy problem, where each voter can either directly vote to a candidate or delegate his voting power to a proxy. We consider the implementation of liquid democracy on the blockchain through Ethereum smart contract and to be compatible with the realtime self-tallying property, where the contract itself can record ballots and update voting status upon receiving each voting massage. A challenge comes due to the gas fee limitation of Ethereum mainnet, that the number of instruction for processing a voting massage can not exceed a certain amount, which restrict the application scenario with respect to algorithms whose time complexity is linear to the number of voters. We propose a fast algorithm to overcome the challenge, such that i) shifts the on-chain initialization to off-chain and ii) the on-chain complexity for processing each voting massage is O(\log n), where n is the number of voters.
Big cities are in a netherland of government. While all types of governments face these challenges, big cities furnish an intensity and complexity that set them apart. Intensity and proximity mark the operations of urban bureaucracies. Big city mayors are often said to be ambassadors, and this role is now duplicated by subordinates. Managers represent their cities before metropolitan planning organizations in order to obtain federal highway funding. Urban competition grows more fierce and is sustained by other complementary factors. Cities not only deal with standard problems, but they must become instruments of innovation, entrepreneurship, and economic development. Underlying pressures continue and there is little big city managers can do to eliminate them. Interlocal competition, corporate mobility, and globalism make up the larger environment and are beyond the bounds of local control. Big city finance managers can also be entrepreneurial by strategic investments. The new paradigm also promotes an incremental and decentralized approach to management.
Ayana T. Aspembitova, Ling Feng, Valentin Melnikov, Lock Yue Chew
Bitcoin is the earliest cryptocurrency and among the most successful ones to date. Recently, its dynamical evolution has attracted the attention of the research community due to its completeness and richness in historical records. In this paper, we focus on the detailed evolution of bitcoin trading with the aim of elucidating the mechanism that drives the formation of the bitcoin transaction network. Our empirical investigation reveals that although the temporal properties of the transaction network possesses scale-free degree distribution like many other networks, its formation mechanism is different from the commonly assumed models of degree preferential attachment or wealth preferential attachment. By defining the fitness value of each node as the ability of the node to attract new connections, we have instead uncovered that the observed scale-free degree distribution results from the intrinsic fitness of each node following a power-law distribution. Our finding thus suggests that the "good-get-richer" rather than the "rich-get-richer" paradigm operates within the bitcoin ecosystem. Based on these findings, we propose a model that captures the temporal generative process by means of a fitness preferential attachment and data-driven birth/death mechanism. Our proposed model is able to produce structural properties in good agreement with those obtained from the empirical bitcoin network.
Blockchain technology provides a groundbreaking computing paradigm that tackles problems in a completely decentralised manner. As the underlying infrastructure and protocol of blockchain, blockchain networks convey communications and coordination across all involving participants. In extensive application scenarios, conducting community detection over blockchain networks has potential effects on both discovering hidden information and enhancing communicating efficiency. However, the decentralised nature poses a restriction on community detection over blockchain networks. In coping with this restriction, we propose a distributed community detection method based on the Propose-Select-Adjust (PSA) framework that runs in an asynchronous way. We extend the PSA framework using the concept of structural entropy and aim to detect a community structure with low entropy. We test our entropy-based distributed community detection algorithm on both benchmark networks and bitcoin trust networks. Experimental results reveal that our algorithm successfully detects communities with low structural entropy.
Cryptocurrencies are a novel and disruptive technology that has prompted a new approach to how currencies work in the modern economy. As such, online discussions related to cryptocurrencies often go beyond posts about the technology and underlying architecture of the various coins, to subjective speculations of price fluctuations and predictions. Furthermore, online discussions, potentially driven by foreign adversaries, criminals or hackers, can have a significant impact on our economy and national security if spread at scale.
Blockchain technology has generated a great deal of interest in recent years, as has the associated area of cryptocurrency trading, not only on the part of individuals but also from traditional financial institutions and hedge funds. However, there is currently limited knowledge as to how to predict future cryptocurrency price movements. This thesis investigates whether online indicators, especially from social media, can be harnessed to predict cryptocurrency price movements – to achieve this, three experiments are conducted. The first experiment analyses time-evolving relationships between chosen online indicators and associated cryptocurrency prices; relationships are considered over short, medium and long-term durations. The work introduces and evaluates several influential factors from the social media platform Reddit, a platform previously unexplored within cryptocurrency prediction literature. It is found that medium and longer-term relationships strengthen in bubble market regimes (compared to non-bubble regimes). The second experiment utilises these promising new factors as inputs to a predictive model. The model used was originally designed to detect influenza epidemic outbreaks, and is repurposed here to model epidemic-like cryptocurrency price bubbles, demonstrating how social media can be used to track the epidemic spread of an investment idea. The predictive power of the model is validated through the generation of a profitable trading strategy. Having considered quantitative count-based metrics in the previous chapters (e.g. posts per day, submissions per day, new authors per day etc.), the next experiment considers the content of social media submissions. More specifically, the third experiment analyses social media submission content to investigate whether certain topics of discussion precede upcoming shorter term (positive or negative) price movements. Information evidencing time-varying interest in various topics is retrieved from social media submissions, upon which hidden interactions with the associated cryptocurrency price are deciphered. It is found that certain topics precede major positive or negative price movements, and also additional analysis shows that certain discussion topics exhibit longer-term relationships with cryptocurrency market prices.
Cryptocurrency is one of the burning issues across the world in the modern era. Literature shows that business analysts always tend to use new technologies and investigate their risks. Some researchers predicted the price fluctuations and investigated the risk of cryptocurrency and blockchain network. However, to the best of our knowledge, there is no study which provides the feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The other cryptocurrencies are not included in the study because of having insufficient relevant data on the social forums. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Apart from that, the public perception and environment feasibility are also determined. Almost 83.7% tweets of Sweden shows positive sentiment for cryptocurrency investment. Which ranks the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the significant terms are also determined using Topic modeling from public opinions. Such as authorization rules, investment, profit, volatility, and security etc. These terms are almost identified as subtopic for each of the inquired keyword. This shows the mutual relationship among different cryptocurrencies and public's concerns about cryptocurrencies.
Public interest in cryptocurrencies has consistently risen over the past decade. Owing to this rapid growth, cryptocurrency-related information is being increasingly shared online. As considerable portions of such information in online communities are noise, extracting meaningful information is important. Therefore, judging whose opinion should be considered more important or who the opinion leaders in online communities are is critical. This study analyzed the topics that contain meaningful information, in particular, user groups, by investigating the correlation between topic weights and their price change. The proposed analysis method involves (1) effective classification of the user groups using a hypertext-induced topic selection algorithm, (2) textual information analysis through topic modeling, and (3) the identification of user groups that have a high interest in the Bitcoin price by measuring the correlation between the price and the topics and by measuring the topic similarities between each user group and all users to determine the user group that can effectively represent the entire community. By analyzing the information shared by users, we observed that most users are interested in the price information, whereas users having social influence are not only interested in the price but also in other information.
In September 2018 the authors released a report funded by the Law Foundation entitled “Regulation of Cryptocurrencies in New Zealand”. This article includes the report’s recommendations as well as background on the implications of cryptocurrencies and blockchain to society and the law.
Cryptocurrencies, in particular bitcoin, have captured the public’s attention. It is hard to find a person who has not heard about bitcoin, albeit blockchain, the technology that the creators of bitcoin devised, is still a mystery to most. Blockchain, however, is just one form of distributed ledger technology (DLT). For the sake of simplicity, the term blockchain is used in this article.
The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms within the broader ecosystem that includes social and communication networks. As an example, we analyse the flash-crash observed on 21st June 2017 in the Ethereum platform and show that a major phenomenon of social coordination led to a catastrophic cascade of events across several interconnected systems. We propose the concept of ``emergent centralisation'' to describe situations where a single system becomes critically important for the functioning of the whole ecosystem, and argue that such situations are likely to become more and more frequent in interconnected socio-technical systems. We anticipate that the systemic approach we propose will have implications for future assessments of trustless systems and call for the attention of policy-makers on the fragility of our interconnected and rapidly changing world.
Gengxin Sun, Sheng Bin, Meng Jiang, Ning Cao · 8 authors
With the emergence and development of blockchain technology, a new type of social networks based on blockchain had emerged. In these social networks high quality content creators, filters and propagators can all be reasonably motivated. Due to the transparency and traceability brought by blockchain technology, the public opinion propagation in such social networks presents new characteristics and laws. Based on the theory of network propagation and blockchain, a new public opinion propagation model for this kind of social network based on blockchain technology is proposed in this paper. The model considers the effect of incentive mechanism produced by reasonably quantifying value contribution on the propagation of information in such social networks, and the income-risk matrix under different propagation behaviors is constructed. Furthermore, the transformation process and transfer probability among different states in the propagation model are defined on the basis of income-risk matrix. The model is helpful to break the bottleneck of network public opinion management by using blockchain technology. The propagation of false network public opinion can be contained, and a good ecological environment of network public opinion propagation would be realized.
This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.
Anton Kolonin, Ben Goertzel, Deborah Duong, Matthew Iklé · 5 authors
Understanding the principles of consensus in communities and finding ways to optimal solutions beneficial for entire community becomes crucial as the speeds and scales of interaction in modern distributed systems increase. Such systems can be both social and information computer networks that unite the masses of people as well as multi-agent computing platforms based on peer-to-peer interactions including those operating on the basis of distributed ledgers. It is now becoming possible for hybrid ecosystems to emerge, having such systems including both humans and computer systems using artificial intelligence. We propose a new form of consensus for all of the listed systems, based on the reputation of the participants, calculated according to the principle of liquid democracy. We believe that such a system will be more resistant to social engineering and reputation manipulation than the existing systems. In this article, we discuss the basic principles and options for implementing such a system, and also present preliminary practical results.
The digital payment system that uses cryptocurrency, such as Bitcoin, is a distributed ledger working on a peer-to-peer network. We present a method to make a scale-free network for such applications. Using some biased physical quantities that are observable in sites, we can make the scale-free network through processes of cooperating distributed sites. Each node only proposes connecting to the more attractive node among randomly known nodes. The candidate node that is found by each node agrees to set two-way links if the requesting node is more attractive than the old node that is already connected. Once they establish the new bidirectional relationship, they, respectively, remove the outgoing link to the less attractive node. We analytically calculate the connectivity distribution and show that the scaling exponent is 2.5. By Monte Carlo simulations, we confirm that a power law distribution of the scaling exponent 2.5 describes the degree distribution of the topology.
The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.
“One of the issues that arise when developing a crowdsourcing monitoring application is the fact that we rely on the measurements taken by the users, but, how to engage users to use this application and provide observations?” 1.
Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.
Aurelio F. Bariviera, Luciano Zunino, Osvaldo A. Rosso
This paper discusses the dynamics of intraday prices of twelve cryptocurrencies during last months' boom and bust. The importance of this study lies on the extended coverage of the cryptoworld, accounting for more than 90\% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.