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Permissionless blockchains require a protocol to generate consensus. Many prominent permissionless blockchains employ Proof-of-Work (PoW) for that purpose, but PoW possesses significant shortcomings. Various alternatives have been proposed. This paper provides the first formal economic model of the most famous alternative, Proof-of-Stake (PoS), and establishes conditions under which PoS generates consensus. A sufficiently modest reward schedule not only implies the existence of an equilibrium in which consensus obtains as soon as possible but also precludes a persistent forking equilibrium. The latter result arises because PoS, unlike PoW, requires that validators are also stakeholders.

We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don’t provide incremental information for expected returns, and nonlinearities are important. We study our method’s properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods.

The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.

Like numerous other cities, Beijing and Copenhagen are experiencing more frequent urban flooding due to increased impervious cover and climate change. Consequently, huge investments are foreseen to maintain resilience. Analyses of planning documents and interviews with key stakeholders reveal that in their climate resilience strategies both cities do employ alternative approaches based on on-site retention-detention of stormwater runoff. However, when there is an emergency situation with heavy downpours, both cities rely heavily on conventional concepts involving deep tunnels for rapid discharge. The applied alternative solutions tend to be more engineering-based, like underground tanks in Beijing and detention-discharge plazas in Copenhagen. More nature-based solutions lag behind. Both cities are simultaneously targeting specific additional sustainability goals. Nevertheless, other potential goals seem to be neglected, like livability improvements in Beijing and biodiversity support and water footprint reduction in Copenhagen. The main barriers for implementing more nature-based solutions with greater sustainability potentials were a combination of time constraints caused by external political pressures for rapid problem solving, lack of routines for the innovation and documentation of solutions for dense urban areas, and insufficient multi-sectorial collaboration. These factors limit the propagation of alternative solutions and tip the balance of current investments towards a conventional approach.

Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write highly stylized damage functions to speculate about how climate change alters macroeconomic and growth opportunities. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components to a revealing quantitative illustration.

Research streams on competition and cooperation are central to the field of strategic management but have evolved independently. The emerging literature on coopetition has brought attention to the phenomenon of simultaneous competition and cooperation, yet the interplay between the two has remained under-researched. We offer a roadmap for studying this interplay, which identifies some of its antecedents and consequences, highlights debates concerning the nature of competition and cooperation and the association between the two, and directs attention to the tension between competition and cooperation and the alternative approaches for managing this tension. We discuss the broader implications of the interplay, note some intriguing open questions, offer directions for future research, and present an organizing framework for the interplay of competition and cooperation.

This study aims to investigate regional socioeconomic inequality (SEI) using alternative indices. Thefruitful indices of the SEI with a different perspectivefrom those found in previous studies are the maincontribution to the literature. Afterward, a dynamic spatialpanel model (GMM) is used to analyze the effect ofhuman capital, economic growth, and spatial distance forthe regions on the SEI. To simplify the ideas, it uses AcehProvince with 23 regencies, as one of the appropriateregions, for this study. Data are obtained from variousgovernment sources for the period 2010-2018. The resultsshow that the indices demonstrate the economic strengthsand weaknesses, economic capability, environmental inputefficiency, and conditional weighted SEI of the regions’human capital. These indices explain that human capitalhas a critical role on the effect of natural resources,government income, conflict, and natural disasters on theSEI. Besides, the spatial distance of regions also plays animportant role in reducing the SEI of regions. Therefore,the regional development policies should underline theimportant role of those in supporting the quality of theSEI of the regions. For future research, it suggestsinvolving a spatial distance of regions in determining theSEI of a region.

Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a numerical investigation was conducted on 233 mathematical test functions with dimensions of 2–100, and 150,000 function evaluations were performed for optimization purposes. For comparison, a total of ten different classical and new metaheuristic algorithms were utilized as alternative approaches. The statistical measurements include the best, mean, median, and standard deviation of 100 independent optimization runs, while well-known statistical analyses, such as Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, and Post-Hoc analysis, were also conducted. The obtained results prove that the FHO algorithm exhibits better performance than the compared algorithms from literature. In addition, two of the latest Competitions on Evolutionary Computation (CEC), such as CEC 2020 on bound constraint problems and CEC 2020 on real-world optimization problems including the well-known mechanical engineering design problems, were considered for performance evaluation of the FHO algorithm, which further demonstrated the superior capability of the optimizer over other metaheuristic algorithms in literature. The capability of the FHO is also evaluated in dealing with two of the real-size structural frames with 15 and 24 stories in which the new method outperforms the previously developed metaheuristics.

This article examines the labor market payoffs to different types of postsecondary education, including field and institution of study. Instrumental variables (IV) estimation of the payoff to choosing one type of education compared to another is made particularly challenging by individuals choosing between several types of education. Not only does identification require one instrument per alternative, but it is also necessary to deal with the issue that individuals who choose the same education may have different next-best alternatives. We address these difficulties using rich administrative data for Norway’s postsecondary education system. A centralized admission process creates credible instruments from discontinuities that effectively randomize applicants near unpredictable admission cutoffs into different institutions and fields of study. The admission process also provides information on preferred and next-best alternatives from strategy-proof measures of individuals’ ranking of institutions and fields. The results from our IV approach may be summarized with three broad conclusions. First, different fields of study have substantially different labor market payoffs, even after accounting for institution and peer quality. Second, the effect on earnings from attending a more selective institution tends to be relatively small compared to payoffs to field of study. Third, the estimated payoffs to field of study are consistent with individuals choosing fields in which they have a comparative advantage. Comparing our estimates to those obtained from other approaches highlights the importance of using instruments to correct for selection bias and information on individuals’ ranking of institutions and fields to measure their preferred and next-best alternatives.