Efficient transfers to many recipients present a host of issues on Ethereum. First, accounts are identified by long and incompressible constants. Second, these constants have to be stored and communicated for each payment. Third, the standard interface for token transfers does not support lists of recipients, adding repeated communication to the overhead. Since Ethereum charges resource usage, even small optimizations translate to cost savings. Airdrops, a popular marketing tool used to boost coin uptake, present a relevant example for the value of optimizing bulk transfers. Therefore, we review technical solutions for airdrops of Ethereum-based tokens, discuss features and prerequisites, and compare the operational costs by simulating 35 scenarios. We find that cost savings of factor two are possible, but require specific provisions in the smart contract implementing the token system. Pull-based approaches, which use on-chain interaction with the recipients, promise moderate savings for the distributor while imposing a disproportional cost on each recipient. Total costs are broadly linear in the number of recipients independent of the technical approach. We publish the code of the simulation framework for reproducibility, to support future airdrop decisions, and to benchmark innovative bulk payment solutions.
Howell @cite_5 study the success factors of 440 ICOs on based on propriety transaction data, presumably acquired from exchanges and other intermediaries, and manual labeling. Their main interest is in the relationship between issuer characteristics and indicators of success. The regression analyses find highly significant positive effects on liquidity and volume of the token for independent variables measuring the existence of a white paper, the availability of code on Github, the support by venture capitalists, the entrepreneurs' experience, the acceptance of Bitcoin, and the organization of a pre-sale. No significant effect is found for airdrops.
@cite_5
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Initial coin offerings (ICOs) have emerged as a new mechanism for entrepreneurial finance, with parallels to initial public offerings, venture capital, and pre-sale crowdfunding. In a sample of more than 1,500 ICOs that collectively raise $12.9 billion, we examine which issuer and ICO characteristics predict success, measured using real outcomes (employment and issuer failure) and financial outcomes (token liquidity and volume). Success is associated with disclosure, credible commitment to the project, and quality signals. An instrumental variables analysis finds that ICO token exchange listing causes higher future employment, indicating that access to liquidity has important real consequences for the enterprise. Abstract Cryptographic assets such as Bitcoin and Ethereum provide distributed consensus with a Proof-of-Work protocol and incentive-based engineering. The consensus is inherently dependent on the value of the asset due to the incentives. The value of these assets frequently fluctuates, which in turn influences the incentive component of the consensus mechanism. For a proof-of-work consensus to be secure, the participation reward must have a perceived real-world value. The future of this perception is not at all clear. The recent 70 drop in the value of Bitcoin versus the US Dollar may be precipitating a circle of declining security of the platform which we explore in depth in this paper. In this paper, we analyze the impact of fluctuations on the security of Bitcoin now, and in the future. We introduce a novel method to examine the rationale of a miner based on the price fluctuations. We integrate our method with an existing security evaluation framework and simulator. Using our approach, we determine and report on the impact of the value of the cryptographic asset on the security of the blockchain given the miner’s rationale. Our method allows us to evaluate the impact of different methods of incentive manipulation such as reduced block-reward and transaction fees, by simulation. Most popular blockchain solutions, like Bitcoin, rely on proof-of-work, guaranteeing that the output of the consensus is agreed upon with high probability. However, this probability depends on the delivery of messages and that the computational power of the system is sufficiently scattered among pools of nodes in the network so that no pool can mine more blocks faster than the crowd. New approaches, like Ethereum, generalise the proof-of-work approach by letting individuals deploy their own private blockchain with high transaction throughput. As companies are starting to deploy private chains, it has become crucial to better understand the guarantees blockchains offer in such a small and controlled environment. In this paper, we present the , an execution that we experienced when building our private chain at NICTA Data61. Even though this anomaly has never been acknowledged before, it may translate into dramatic consequences for the user of blockchains. Named after the infamous Paxos anomaly, this anomaly makes dependent transactions, like "Bob sends money to Carole after he received money from Alice" impossible. This anomaly relies on the fact that existing blockchains do not ensure consensus safety deterministically: there is no way for Bob to make sure that Alice actually sent him coins without Bob using an external mechanism, like converting these coins into a fiat currency that allows him to withdraw. We also explore smart contracts as a potential alternative to transactions in order to freeze coins, and show implementations of smart contract that can suffer from the Blockchain anomaly and others that may cope with it. Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40 . Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance.