Towards weak sustainability in the formal curricula: a case study of research-driven pedagogical reform in Hong Kong

Presentation 3International organizations such as UNESCO have long recognized the important role of education in helping to achieve the goals of sustainability/sustainable development. The launch of the Decade of Education of Sustainable Development (2005-2014) is a strong indication of UNESCO’s commitment to promoting the Education for Sustainable Development. Curiously “Education for Sustainability” has been used interchangeably with “Education for Sustainable Development” by many organizations, even though there are subtle but significant differences between the two phrases. The teaching and learning of both concepts involve capacity-building and life-style changes and, as such, they are different from the conventional type of environmental education that concentrates on problem-solving …published_or_final_versio

Forecasting directional changes in the FX markets

Most of existing studies sample markets' prices as time series when developing models to predict market's trend. Directional Changes (DC) is an approach to summarize market prices other than time series. DC marks the market as downtrend or uptrend based on the magnitude of prices changes. In this paper we address the problem of forecasting trend's direction in the foreign exchange (FX) market under the DC framework. In particularly we aim to answer the question of whether the current trend will continue for a specific percentage before the trend ends. We propose one single independent variable to make the forecast. We assess the accuracy of our approach using three currency pairs in the FX market; namely EUR/CHF, GBP/CHF, and USD/JPY. The experimental results show that the accuracy of the proposed forecasting model is very good; in some cases, forecasting accuracy was over 80%. However, under particular settings the accuracy may not outperform dummy prediction. The results confirm that directional changes are predictable, and the identified independent variable is useful for forecasting under the DC framework

Scheduling internal audit activities:A stochastic combinatorial optimization problem

The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neither approach dominates the other. However, the CP approach is orders of magnitude faster for large audit times, and almost as fast as the MILP approach for small audit times. This work generalises a previous approach by relaxing the assumption of instantaneous audits, and by prohibiting concurrent auditin

Applications of Genetic Programming to Finance and Economics: Past, Present, Future

While the origins of Genetic Programming (GP) stretch back over fifty years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the Genetic Programming Bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics

Empowerment scheduling for a field workforce

Employee empowerment is a flexible management concept. As in traditional scheduling, the employer is still in charge of assigning jobs to staff. However, employees are allowed to express their preferences for the jobs they want to do. The hope is that empowerment will improve morale, which will improve productivity. The challenge is to design such an empowerment scheduling system without undesirable outcomes. In the proposed model, employees submit their preferences as "work plans". The organizational goal and the employees' work plans may not be in conflict. In such situations, win-win schedules can be generated without costing the organization. When there is a conflict, the organization is willing to give up a certain amount of its optimality (which is determined by the organization) in order to consider the employee's work plans. The employer is in charge, and therefore jobs undesirable to any of the employees will still be done. A main consideration in empowerment is to make the employees feel that the system is fair. The proposed model maintains fairness by incorporating an automatic market-like mechanism that controls the violation cost of each employee's request. The model is applied to solve a workforce scheduling problem which involves scheduling a multi-skilled workforce to geographically dispersed tasks. Extensive computational experiments are conducted, which show that this model enables an organization to implement employee empowerment effectively. © Springer Science+Business Media, LLC 2010

An heterogeneous, endogenous and coevolutionary GP-based financial market

Stock markets are very important in modern societies and their behavior has serious implications for a wide spectrum of the world's population. Investors, governing bodies, and society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyzing such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of their results. This motivates alternative methods. In this paper, we report an artificial financial market and its use in studying the behavior of stock markets. This is an endogenous market, with which we model technical, fundamental, and noise traders. Nevertheless, our primary focus is on the technical traders, which are sophisticated genetic programming based agents that coevolve (by learning based on their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identify the conditions under which the statistical properties of price series in the artificial market resemble some of the properties of real financial markets. By performing a careful exploration of the most important aspects of our simulation model, we determine the way in which the factors of such a model affect the endogenously generated price. Additionally, we model the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets, mainly as a suitable model for economic learning on an agentbased simulation. © 2008 IEEE

Understanding residents' environmental knowledge in a metropolitan city of Hong Kong, China

This study aimed to understand the environmental knowledge (EK) of the residents of Hong Kong. A territory-wide survey was administered to investigate the subjective and objective EK of the respondents as well as their means of receiving information about the environment. The results indicated that Hong Kong’s residents have a comparatively low level of EK, with a mean environmental knowledge score of 3.35 out of 8. The youngster (15–24 years old), students, and employed individuals reported more extensive EK than the older and unemployed respondents, thus suggesting that the younger generation and employed individuals have increased opportunities to receive EK through various channels. A weak positive correlation was identified between subjective and objective EK, thus implying that the residents of Hong Kong could not accurately evaluate their own level of EK. The results indicate that traditional media plays a significant role in disseminating EK. Digital media, such as websites and digital social networks, were also determined to be influencing factors in disseminating environmental messages to the younger generation