Blockchain Papers

Sophie Berghueser, Martin Spann

No abstract is available for this record.

MohammadAmin Fazli, Ali Owfi, MohammadReza Taesiri

No abstract is available for this record.

Reto Hofstetter, Martin P. Fritze, Cait Lamberton

Abstract Over the last half-century, consumer research has often depicted scarcity as a dominant factor increasing price. But should we assume that scarcity’s upward pressure on price remains intact, in a world where novel forms of digital products proliferate? In this article, we propose that blockchain-encrypted digital goods, in particular, non-fungible tokens (NFTs), offer good reason to revisit this assumption. In this context, we argue and find that social value can outweigh intrinsic value as a determinant of willingness-to-pay. As a result, when scarcity threatens access to high levels of social value, its effect on price can be negative rather than positive—an inversion of a pattern typically observed for offline collectibles. Secondary data taken from the NFT platform Opensea and a set of experimental studies support this social value-based lens. Given these findings, we propose a research agenda to ground future work in this area. We also suggest that NFTs offer a laboratory in which past theories related to social value, scarcity, and price can be reconsidered and future theories developed, hopefully allowing consumer researchers to lead knowledge development in these domains over the next 50 years.

Ioannis Filippos Kanellopoulos, Dominik Gutt, Murat Tunç, Ting Li

No abstract is available for this record.

Qun Cao, Yongqiang Ding, Yiran Ye, Riguang Wen

In recent years, with the rapid development of blockchain technology, the emergence of Non-Fungible Tokens (NFTs) has become a disruptive and innovative application that has attracted widespread attention and triggered frenzy. This study examines the momentous but may be easily neglected price factor in the NFT market. Using hand-collected daily data on the number of followers of 150 NFTs on Discord from April 18 to 15 October 2022, empirical results find that the fan economy on social media platforms has a positive impact on NFT pricing. Furthermore, this impact has a certain time-lagged effect. To ensure the robustness of the research, this paper also collects Twitter followers as an alternative indicator to measure the fan economy, and all the empirical results of the Twitter platform are significant. The findings of this paper are of great significance for studying the factors affecting the price of NFTs and provide certain assistance for the decision-making of NFT issuers and investors.

Raphael Auer, Giulio Cornelli, Sebastian Doerr, Jon Frost · 7 authors

No abstract is available for this record.

Philippe Bergault, Louis Bertucci, David Bouba, Olivier Guéant

With the emergence of decentralized finance, new trading mechanisms called Automated Market Makers have appeared. The most popular Automated Market Makers are Constant Function Market Makers. They have been studied both theoretically and empirically. In particular, the concept of impermanent loss has emerged and explains part of the profit and loss of liquidity providers in Constant Function Market Makers. In this paper, we propose another mechanism in which price discovery does not solely rely on liquidity takers but also on an external exchange rate or price oracle. We also propose to compare the different mechanisms from the point of view of liquidity providers by using a mean / variance analysis of their profit and loss compared to that of agents holding assets outside of Automated Market Makers. In particular, inspired by Markowitz' modern portfolio theory, we manage to obtain an efficient frontier for the performance of liquidity providers in the idealized case of a perfect oracle. Beyond that idealized case, we show that even when the oracle is lagged and in the presence of adverse selection by liquidity takers and systematic arbitrageurs, optimized oracle-based mechanisms perform better than popular Constant Function Market Makers.

Akanksha Jalan, Roman Matkovskyy, Andrew Urquhart

In this paper we analyze dynamic demand elasticity for Bitcoin and Ethereum in terms of price, transaction fees, and energy usage. We find that while both BTC and ETH have significantly positive price elasticities, transaction fee elasticity is negative and positive for BTC and ETH respectively, indicating differences in potential uses for these cryptocurrencies.

Qianqian Zhai, Ali Sher, Qian Li, Chao Chen

The blockchain-based traceability in agri-food marketing has brought a disruptive paradigm shift by removing the inherent information asymmetry problem. Likewise, revealing sufficient product quality and attributes information could break agricultural markets' “Lemon Market” dilemma. This study takes the fresh fruit with blockchain traceability QR label as a case and systematically investigates the influence of consumers' food control risk attitude on information-seeking intentions. We utilized online survey data of 1,058 fresh fruit buyers and simultaneously applied ordinary least square (OLS), ordered logit model (Ologit), and propensity score matching (PSM) approaches to overcome the potential self-selection biases and confounding factors. The results show that risk attitude significantly negatively impacts consumers' information seeking fresh fruits. The stronger consumers' risk preference, the lower the probability of information seeking. Furthermore, we used PSM to overcome potential sample selectivity bias; therefore, PSM reinforces the significance of OLS and Ologit results. The sub-sample estimation results show that young individuals with high school and below education have stronger blockchain information-seeking intentions. The study provides new insights into the role of food control risk attitude and agri-food information traceability and offers several measures for policy and practice to realize a border trajectory in agri-food information disclosure.

Nicola Dimitri

Governance in blockchain platforms is an increasingly important topic. A particular concern related to voting procedures is the formation of dominant positions, which may discourage participation of minorities. A main feature of standard majority voting is that individuals can indicate their preferences but cannot express the intensity of their preferences. This could sometimes be a drawback for minorities who may not have the opportunity to obtain their most desirable outcomes, even when such outcomes are particularly important for them. For this reason a voting method, which in recent years gained visibility, is quadratic voting (QV), which allows voters to manifest both their preferences and the associated intensity. In voting rounds, where in each round users express their preference over binary alternatives, what characterizes QV is that the sum of the squares of the votes allocated by individuals to each round has to be equal to the total number, budget, of available votes. That is, the cost associated with a number of votes is given by the square of that number, hence it increases quadratically. In the paper, we discuss QV in proof-of-stake-based blockchain platforms, where a user’s monetary stake also represents the budget of votes available in a voting session. Considering the stake as given, the work focuses mostly on a game theoretic approach to determine the optimal allocation of votes across the rounds. We also investigate the possibility of the so-called Sybil attacks and discuss how simultaneous versus sequential staking can affect the voting outcomes with QV.

Li Zhou, Chunqiao Tan, Huimin Zhao

(1) Background: With the development of blockchain technology and fierce competition between tourism platforms, tourism platforms can adopt blockchain technology to disclose product information to enhance their core competitiveness. As for a tourism O2O, i.e., online to offline, supply chain, the tourism platform sells the product online, and the tour operator provides services offline. (2) Methods: We establish a game theory model and study the optimal strategies of supply chain members in two scenarios (decentralized and centralized) when the online platform does not adopt or adopts blockchain technology. Then, we introduce a two-part tariff contract for coordination. Furthermore, we discuss the impact of the cost of adopting blockchain technology, disclosing information and the proportion of information-sensitive consumers on the optimal strategies. (3) Conclusions: When the costs of adopting blockchain technology and information disclosure are low, if the proportion of information-sensitive consumers is large, adopting blockchain technology is beneficial to supply chain members. Compared with a wholesale price contract, a two-part tariff contract can encourage the platform to improve information disclosure quality, so the tour operator can adjust their cooperation contract to achieve Pareto improvements. Under a two-part tariff contract, the tourism platforms are more likely to disclose information and can effectively regulate the operational performance of the tourism O2O supply chain.

Kentaroh Toyoda

We are often faced with the non-trivial task of designing incentive mechanisms in the era of Web3. As history has shown, many Web3 services failed mostly due to the lack of a rigorous incentive mechanism design based on token economics. However, traditional mechanism design, where there is an assumption that the users of services strategically make decisions so that their expected profits are maximized, often does not capture their real behavior well as it ignores humans' psychological bias in making decisions under uncertainty. In this paper, we propose an incentive mechanism design for crypto-enabled services using behavioral economics. Specifically, we take an example of a crypto lottery game in this work and incorporate a seminal work of cumulative prospect theory into its lottery game mechanism (or rule) design. We designed four mechanisms and compared them in terms of utility, a metric of how appealing a mechanism is to participants, and a game operator's expected profit. Our approach is generic and will be applicable to a wide range of crypto-based services where a decision has to be made under uncertainty.

Paula Tekulova

No abstract is available for this record.

Mauricio Labadie

No abstract is available for this record.

Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, Sururah A. Bello · 5 authors

No abstract is available for this record.

Matheus V. X. Ferreira, Daniel J. Moroz, David C. Parkes, Mitchell Stern

In recent years, prominent blockchain systems such as Bitcoin and Ethereum have experienced explosive growth in transaction volume, leading to frequent surges in demand for limited block space and causing transaction fees to fluctuate by orders of magnitude. Existing systems sell space using first-price auctions; however, users find it difficult to estimate how much they need to bid in order to get their transactions accepted onto the chain. If they bid too low, their transactions can have long confirmation times. If they bid too high, they pay larger fees than necessary. In light of these issues, new transaction fee mechanisms have been proposed, most notably EIP-1559, aiming to provide better usability. EIP-1559 is a history-dependent mechanism that relies on block utilization to adjust a base fee. We propose an alternative design - a dynamic posted-price mechanism - which uses not only block utilization but also observable bids from past blocks to compute a posted price for subsequent blocks. We show its potential to reduce price volatility by providing examples for which the prices of EIP-1559 are unstable while the prices of the proposed mechanism are stable. More generally, whenever the demand for the blockchain stabilizes, we ask if our mechanism is able to converge to a stable state. Our main result provides sufficient conditions in a probabilistic setting for which the proposed mechanism is approximately welfare optimal and the prices are stable. Our main technical contribution towards establishing stability is an iterative algorithm that, given oracle access to a Lipschitz continuous and strictly concave function f, converges to a fixed point of f.

Carol Alexander, Daniel F. Heck, Andreas Kaeck

No abstract is available for this record.

Hong Zhang, Hongchang Wang, Amit Mehra, Eric Zheng

No abstract is available for this record.

Chris Meyns, Fisnik Dalipi

Non-fungible token (NFT) trade has grown drastically over recent years. While scholarship on the technical aspects and potential applications of NFTs has been steadily increasing, less attention has been directed to the human perception of or attitudes toward this new type of digital asset. The aim of this research is to investigate what concerns are expressed in relation to non-fungible tokens by those who engage with NFTs on the social media platform Twitter. In this study, data was gathered through online social media data mining of NFT-related posts on Twitter. Two datasets (with 18,373 and 36,354 individual tweet records, respectively) were obtained. Topic modeling was used as a method of data analysis. Our results reveal 19 overall themes of concerns around NFTs as expressed on Twitter, which broadly fall into two categories: concerns about attacks and threats by third parties; and concerns about trading and the role of marketplaces. Overall, this study offers a better understanding of the expressions of concern, uncertainty, and the perception of possible barriers related to NFT trading. These findings contribute to theoretical insight and can, moreover, function as a basis for developing practical design and policy interventions.

Otabek Sattarov, Jaeyoung Choi

Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.

Nisarg Patel, Raj Parekh, Nihar Thakkar, Rajesh Gupta · 8 authors

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However,Dashcoin is an individual cryptocurrency, but it is derived fromBitcoinandLitecoin. The change inBitcoinandLitecoinprices affects theDashcoin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction ofDashcoin through the past days’ prices ofDash,Litecoin, andBitcoinas they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.

Konstantinos Stylianou, Leonhard Spiegelberg, Maurice Herlihy, Nic Carter

When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not (yet) a defining feature of the cryptocurrency marketas a whole.

Júlia Almeida Valadares, Vinícius Cunha Oliveira, José Eduardo de Azevedo Sousa, Heder S. Bernardino · 7 authors

Ethereum é uma das maiores plataformas de cripto ativos atualmente, e vem se tornando um ambiente de negócios digitais entre usuários. O Ethereum foi concebido para permitir transações descentralizadas entre usuários anônimos. Contudo, o desenvolvimento de métodos para identificar perfis de comportamentos de usuários, mantendo suas identidades anônimas, têm o potencial para alavancar negócios nessa plataforma. Nesse trabalho, investigamos o uso de aprendizado de máquina para classificar um perfil de usuário como profissional ou comum a partir de atributos de suas transações. Essa classificação é desafiadora devido à pequena fração de usuários publicamente rotulados no Ethereum e ainda a fração consideravelmente menor de usuários profissionais. Para conduzir essa investigação, treinamos modelos considerando conjuntos cuidadosamente balanceados de transações com usuários rotulados. Nossos resultados mostram modelos de alto desempenho para a classificação de perfis, alcançando desempenho superior a 90% para acurácia, precisão, revocação e demais medidas relacionadas. Adicionalmente, identificamos as características mais relevantes em transações para essa classificação.

Shashikant Patil, Smita Nirkhi

<strong>The most accepted cryptographic money is the bitcoin, which is highly attracting the traders and investors for making buy or sell decisions. However, the prediction of the bitcoin prices is challenging due to its higher voltality. In this work, a new bitcoin prediction model is introduced with three major phases: Pre-processing, Feature Extraction and Prediction. The collected bit coin data corresponding to minute-by-minute and hour-by-hour data is subjected to pre-processing. From the pre-processed data, the original features are extracted along with the features based on technical indicators. Average True Range (ATR), Exponential Moving Average (EMA) and Relative Strength Index (RSI) are the technical indicators computed. All the extracted features are subjected to prediction phase, where the optimized Neural Network (NN) model is used. To make the prediction more accurate, the training of NN is carried out by the renowned Elephant Herding Optimization (EHO) via tuning the weight. Finally, the algorithmic analysis is carried out by varying the window size.</strong>