Effectiveness of Q-learning as a tool for calibrating agent-based supply network models

This paper examines effectiveness of Q-learning as a tool for specifying agent attributes and behaviours in agent-based supply network models. Agent-based modelling (ABM) has been increasingly employed to study supply chain and supply network problems. A challenging task in building agent-based supply network models is to properly specify agent attributes and behaviours. Machine learning techniques, such as Q-learning, can be a useful tool for this purpose. Q-learning is a reinforcement learning technique that has been shown to be an effective adaptation and searching mechanism in distributed settings. In this study, Q-learning is employed by supply network agents to search for ‘optimal’ values for a parameter in their operating policies simultaneously and independently. Methods are designed to identify the ‘optimal’ parameter values against which effectiveness of the learning is evaluated. Robustness of the learning's effectiveness is also examined through consideration of different model settings and scenarios. Results show that Q-learning is very effective in finding the ‘optimal’ parameter values in all model settings and scenarios considered.     

Dose response functions and the harvesting effect

Much of the existing literature on air pollution and mortality deals only with the short-term effects of air pollution. Policy on the other hand needs to know when, whether and to what extent pollution-induced increases in mortality counts are reversed. This involves modelling the entire infinite distributed-lag effect of air pollution on mortality counts.Using an ARMAX modelling strategy this paper illustrates how distributed lag effects can be parsimoniously but plausibly estimated in the context of a time-series study into the relationship between ambient levels of air pollution and daily mortality counts for Manchester. The analysis reveals that maximum 1-h ozone levels are strongly associated with daily mortality counts and that a significant harvesting effect is present. The mortality cost of peak 1-h ozone concentrations for Greater Manchester with a population of 2.6 million is estimated to be £572 million annually. This accounts for the fact that some of the deaths associated with maximum 1-h O₃ concentrations have been advanced only by a short period of time.     

CORBA在银行ATM机上的应用

CORBA是Common Object Request Broker Architecture的简称,它是一种OMG开放式的独立于供应商的结构和基础组织,使计算机程序能基于它工作在网络上。CORBA依赖于IIOP（Internet Inte-ORB Protocol）协议服务远程对象,而且支持多程序语言和多平台,在银行ATM机等方面有着广泛的应用。     

To distribute leadership or not? A lesson from the islands

There is a dearth of literature in the area of tourism leadership. This article identifies the theoretical aspects of distributed leadership which features collective responsibility and collective flexibility, and argues how it might be advantageous for tourism firms in general. A longitudinal qualitative case study is used to consider different forms of distributed leadership and their impact upon organisational outcomes. The analysis is presented in terms of the presence or absence of distributed leadership within the case organisation. Evidence is provided of where this style of leadership would support success, but also identifies why it has been so hard to recognise this and then maintain and support it over time. It is argued that it may prove advantageous for tourism firms to actively consider whether distributed leadership would potentially offer increased organisational performance.     

加拿大数字货币的发展实践与启示

分布式账本技术的应用使数字货币进入了新的发展阶段。中央银行数字货币是中央银行的电子化负债,其对计价、交易、支付以至货币政策传导等都存在深刻影响。以加拿大为例,重点分析加拿大银行数字货币的发展实践,着重讨论加拿大贾斯珀项目如何测试分布式账本技术在银行间大额支付系统的适用性,同时分析如何将数字货币支付结算拓展至证券和外汇领域,并与外部合作进行跨境、跨币种支付试验。加拿大央行数字货币实践取得的积极进展表明,数字货币发行能力是维系央行功能的基础保障,但仍需权衡中心化管理体系与去中心化技术系统的匹配问题,分布式账本技术及其在央行数字货币的应用仍需深入研究与评估。     

Online distributed learning in wind power forecasting

Forecasting wind power generation up to a few hours ahead is of the utmost importance for the efficient operation of power systems and for participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement of sharing confidential data with third parties may limit their use in practice. This explains the recent interest in distributed, privacy preserving algorithms for high-dimensional statistical learning, e.g. with auto-regressive models. The few approaches that have been proposed are based on batch learning. However, these approaches are potentially computationally expensive and do not allow for the accommodation of nonstationary characteristics of stochastic processes like wind power generation. This paper closes the gap between online and distributed optimisation by presenting two novel approaches that recursively update model parameters while limiting information exchange between wind farm operators and other potential data providers. A simulation study compared the convergence and tracking ability of both approaches. In addition, a case study using a large dataset from 311 wind farms in Denmark confirmed that online distributed approaches generally outperform existing batch approaches while preserving privacy such that agents do not have to actively share their private data.