Medieval Literature and Young Adult Fiction: A Comparison of Chaucer and Sarah J. Maas

The medieval works of Geoffrey Chaucer and the contemporary works of Sarah J. Maas employ three of the same themes: forbidden love, insta-love, and love triangles. These themes are based in the medieval literary tradition of courtly love as first written by Andreas Cappellanus in the twelfth century. Sarah J. Maas is a contemporary author of the young adult fantasy series A Court of Thorns and Roses that follows a young girl who by magical circumstances becomes romantically involved with two male faeries. This modern series portrays the same themes that Chaucer’s works of Troilus and Criseyde and The Canterbury Tales display, which makes these antiquated ideas of love relevant. In fact, the medieval depiction of women provides a certain agency that the modern teen fiction novel does not. Critics think such themes in young adult fiction do not portray a healthy and realistic view of romance for young women to strive for. While these themes are not modern romantic notions, they do remain entertaining in fiction, despite their critics

ENVY, LEISURE, AND RESTRICTIONS ON WORKING HOURS

We present a simple model of capital accumulation where agents care about their consumption relative to the consumption of other members of society. This concern with "envy" captures the intuition behind the growing body of empirical evidence that places interpersonal comparisons as a key determinant of well-being. In this context we quantify the extent of the distortions and welfare costs associated with envy. Under conservative estimates of envy we find that the implied welfare losses are substantial. Our analysis explores the implications of alternative policy arrangements designed to minimize the effects of the consumption externality. Our results suggest that if the optimal tax policy is not politically feasible restrictions on working hours provide an alternative tool to induce a market outcome that resembles the efficient allocation achieved under a benevolent central planner.

IMPROVING THE EFFICIENCY AND ROBUSTNESS OF THE SMOOTHED MAXIMUM SCORE ESTIMATOR

The binary-response maximum score (MS) estimator is a robust estimator, which can accommodate heteroskedasticity of an unknown form; J. Horowitz (1992) defined a smoothed maximum score estimator SMS) and demonstrated that this improves the convergence rate for sufficiently smooth conditional error densities. In this paper we relax Horowitz’s smoothness assumptions of the model and extend his asymptotic results. We also derive a joint limiting distribution of estimators with different bandwidths and smoothing kernels. We construct an estimator that combines SMS estimators for different bandwidths and kernels to overcome the uncertainty over choice of bandwidth when the degree of smoothnes of error distribution is unknown. A Monte Carlo study demonstrates the gains in efficiency and robustness.

A QUANTITATIVE EXPLORATION OF THE GOLDEN AGE OF EUROPEAN GROWTH: STRUCTURAL CHANGE, PUBLIC INVESTMENT, THE MARSHALL PLAN AND INTRA-EUROPEAN TRADE

Western European income per capita more than tripled in the two and a half decades that followed World War II. The scholarship has identified several potential factors behind this outstanding growth episode, specifically; the large migrations from agriculture to manufacture that took place in post-war Europe, the contribution of the Marshall Plan combined with the public provision of infrastructure and the surge of intra-European trade. This paper can be viewed as an attempt to formalize and quantify the direct contribution of these factors to the outstanding growth of the European Golden Age. Our conclusions highlight their limitations to fully account for that growth experience.

GROWTH OUTSIDE THE STABLE PATH: LESSONS FROM THE EUROPEAN RECONSTRUCTION

This paper exploits a natural experiment, the large destruction of capital in continental Europe during World War II, to characterize the transitional dynamics of an economy that begins with a capital stock below its steady state level. We use these regularities as a benchmark to discriminate among competing growth specifications. A model that combines non-separabilities in preferences with a technology that restricts the degree of substitutability between inputs outperforms the widely used AK and Cobb-Douglas specifications with time-separable preferences. Our results suggest that policy evaluations based in growth models that overlook non-separabilities in preferences or impose strong restrictions on the technological structure might be grossly misleading.

Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads