ترغب بنشر مسار تعليمي؟ اضغط هنا

A Normative Dual-value Theory for Bitcoin and other Cryptocurrencies

309   0   0.0 ( 0 )
 نشر من قبل Zhiyong Tu
 تاريخ النشر 2019
  مجال البحث اقتصاد مالية
والبحث باللغة English
 تأليف Zhiyong Tu




اسأل ChatGPT حول البحث

Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coins intrinsic value. This paper suggests a normative dual-value theory to assess the fundamental value of Bitcoin. We draw on the experience from the art market, where similar replication problems are prevalent. The idea is to decompose the total value of a cryptocurrency into two parts: one is its art value and the other is its use value. The tradeoff between these two values is also analyzed, which enlightens our proposal of an image coin for Bitcoin so as to elevate its use value without sacrificing its art value. To show the general validity of the dual-value theory, we also apply it to evaluate the prospects of four major cryptocurrencies. We find this framework is helpful for both the investors and the exchanges to examine a new coins value when it first appears in the market.



قيم البحث

اقرأ أيضاً

This paper presents a model where intergenerational occupational mobility is the joint outcome of three main determinants: income incentives, equality of opportunity and changes in the composition of occupations. The model rationalizes the use of tra nsition matrices to measure mobility, which allows for the identification of asymmetric mobility patterns and for the formulation of a specific mobility index for each determinant. Italian children born in 1940-1951 had a lower mobility with respect to those born after 1965. The steady mobility for children born after 1965, however, covers a lower structural mobility in favour of upper-middle classes and a higher downward mobility from upper-middle classes. Equality of opportunity was far from the perfection but steady for those born after 1965. Changes in income incentives instead played a major role, leading to a higher downward mobility from upper-middle classes and lower upward mobility from the lower class.
While researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain as obstacles in theory and practice. By using statistical learning theory, this study presents a framework to examine the tradeoff between estimation and approximation errors, and between prediction and interpretation losses. It operationalizes the DNN interpretability in the choice analysis by formulating the metrics of interpretation loss as the difference between true and estimated choice probability functions. This study also uses the statistical learning theory to upper bound the estimation error of both prediction and interpretation losses in DNN, shedding light on why DNN does not have the overfitting issue. Three scenarios are then simulated to compare DNN to binary logit model (BNL). We found that DNN outperforms BNL in terms of both prediction and interpretation for most of the scenarios, and larger sample size unleashes the predictive power of DNN but not BNL. DNN is also used to analyze the choice of trip purposes and travel modes based on the National Household Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation, the flexibility of accommodating various information formats, and the power of automatically learning utility specification. DNN is both more predictive and interpretable than BNL unless the modelers have complete knowledge about the choice task, and the sample size is small. Overall, statistical learning theory can be a foundation for future studies in the non-asymptotic data regime or using high-dimensional statistical models in choice analysis, and the experiments show the feasibility and effectiveness of DNN for its wide applications to policy and behavioral analysis.
Bitcoin and many other similar Cryptocurrencies have been in existence for over a decade, prominently focusing on decentralized, pseudo-anonymous ledger-based transactions. Many protocol improvements and changes have resulted in new variants of Crypt ocurrencies that are known for their peculiar characteristics. For instance, Storjcoin is a Proof-of-Storage-based Cryptocurrency that incentivizes its peers based on the amount of storage owned by them. Cryptocurrencies like Monero strive for user privacy by using privacy-centric cryptographic algorithms. While Cryptocurrencies strive to maintain peer transparency by making the transactions and the entire ledger public, user privacy is compromised at times. Monero and many other privacy-centric Cryptocurrencies have significantly improved from the original Bitcoin protocol after several problems were found in the protocol. Most of these deficiencies were related to the privacy of users. Even though Bitcoin claims to have pseudo-anonymous user identities, many attacks have managed to successfully de-anonymize users. In this paper, we present some well-known attacks and analysis techniques that have compromised the privacy of Bitcoin and many other similar Cryptocurrencies. We also analyze and study different privacy-preserving algorithms and the problems these algorithms manage to solve. Lastly, we touch upon the ethics, impact, legality, and acceptance of imposing these privacy algorithms.
A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these moder n methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.
This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reput ation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا