Chain: A Dynamic Double Auction Framework for Matching Patient Agents

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile

Models for Truthful Online Double Auctions

Online double auctions (DAs) model a dynamic two-sided matching problem with private information and self-interest, and are relevant for dynamic resource and task allocation problems. We present a general method to design truthful DAs, such that no agent can benefit from misreporting its arrival time, duration, or value. The family of DAs is parameterized by a pricing rule, and includes a generalization of McAfee’s truthful DA to this dynamic setting. We present an empirical study, in which we study the allocative-surplus and agent surplus for a number of different DAs. Our results illustrate that dynamic pricing rules are important to provide good market efficiency for markets with high volatility or low volume.Engineering and Applied Science

New broad 8Be nuclear resonances

Energies, total and partial widths, and reduced width amplitudes of 8Be resonances up to an excitation energy of 26 MeV are extracted from a coupled channel analysis of experimental data. The presence of an extremely broad J^pi = 2^+ ``intruder'' resonance is confirmed, while a new 1^+ and very broad 4^+ resonance are discovered. A previously known 22 MeV 2^+ resonance is likely resolved into two resonances. The experimental J^pi T = 3^(+)? resonance at 22 MeV is determined to be 3^-0, and the experimental 1^-? (at 19 MeV) and 4^-? resonances to be isospin 0.Comment: 16 pages, LaTe

Balancing cryptoassets and gold: a weighted-risk-contribution index for the alternative asset space

Bitcoin is foremost amongst the emerging asset class knownas cryptoassets. Two noteworthy characteristics of the returns of non-stablecoin cryptoassets are their high volatility, which brings with it ahigh level of risk, and their high intraclass correlation, which limits thebenefits that can be had by diversifying across multiple cryptoassets. Yetcryptoassets exhibit no correlation with gold, a highly-liquid yet scarceasset which has proved to function as a safe haven during crises affectingtraditional financial systems. As exemplified by Shannon’s Demon, a lackof correlation between assets opens the door to principled risk controlthrough so-called volatility harvesting involving periodic rebalancing.In this paper we propose an index which combines a basket of five cryp-toassets with an investment in gold in a way that aims to improve therisk profile of the resulting portfolio while preserving its independencefrom mainstream financial asset classes such as stocks, bonds and fiatcurrencies. We generalise the theory of Equal Risk Contribution to allowfor weighting according to a desired level of contribution to volatility. Wefind a crypto–gold weighting based on Weighted Risk Contribution to behistorically more effective in terms of Sharpe Ratio than several alterna-tive asset allocation strategies including Shannon’s Demon. Within thecrypto-basket, whose constituents are selected and rebalanced monthly,we find an Equal Weighting scheme to be more effective in terms of thesame metric than a market capitalisation weighting

A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry

Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place

Performance and performance persistence of UK closed-end equity funds

Using a comprehensive data set of almost 300 UK closed-end equity funds over the period 1990 to 2013, we use the false discovery rate to assess the alpha-performance of individual funds with both domestic and other mandates, using self-declared benchmarks and additional risk factors. We find evidence to indicate that up to 16% of the funds have truly positive alphas while around 3% have truly negative alphas. Positive post-formation alphas using fund-price returns depend on the factor model used: there is some positive-alpha performance when post-formation returns are evaluated using a one-factor global model but substantial positive-alpha performance when using a four-factor global model

Development and relative validation of a short food frequency questionnaire for assessing dietary intakes of non-alcoholic fatty liver disease patients

Purpose This work aimed to design and validate a novel short food frequency questionnaire (SFFQ) to assess habitual intakes of food items related to non-alcoholic fatty liver disease (NAFLD) in a cohort of European patients. Methods A 48-item SFFQ was created, with questions from existing FFQs and expert knowledge, emphasizing foods and nutrients implicated in NAFLD pathogenesis. Consenting, fibroscan-diagnosed, NAFLD patients completed the SFFQ during a short interview and were asked to complete a 4-day diet diary (4DDD) at home for return by mail. Nutritional intakes were assessed utilizing the myfood24™ food composition dataset and estimated energy requirements (EER) were calculated using sex-, age- and weight-specific equations. Agreement between the dietary instruments was assessed by Spearman correlations and Bland Altman analysis. Results Fifty-five patients completed both the SFFQ and the 4DDD within 30 weeks; 42 (76%) were diagnosed with simple steatosis, whereas 13 (24%) had biopsy-proven steatohepatitis; the majority were overweight or obese, with a median (25th; 75th percentile) BMI of 33.2 kg/m2 (29.3; 36.0). Reported energy intakes were well below EER with a median intake of 73% of requirements, suggesting widespread under-reporting. Significant correlations were observed between sugar (r = 0.408, P = 0.002), fat (r = 0.44, P = 0.001), fruits (r = 0.51, P = 0.0001) and vegetables (r = 0.40, P = 0.0024) measurements by the SFFQ and 4DDD. Bland Altman plots with regression analysis demonstrated broad comparability with the 4DDD for intakes of fat (bias − 13.8 g/day) and sugar (bias  + 12.9 g/day). Conclusions A novel SFFQ designed to be minimally burdensome to participants was effective at assessing dietary intakes in NAFLD patients