The tax treatment of employee stock options : generous to a fault

In the 2000 budgets, both the federal and Ontario governments introduced changes to the tax treatment of employee stock options for the explicit purpose of making their tax treatment in Canada similar to or more favourable than that in the United States. The federal budget added a deferral, similar to that currently applicable to options granted by Canadian-controlled private corporations, for up to $100,000 per year of public company stock options. The Ontario budget introduced an exemption from tax for employees involved in research and development on the first $100,000 per year of employee benefits arising on the exercise of qualified stock options or on eligible capital gains arising from the sale of shares acquired by the exercise of eligible stock options. These proposals reflect the apparent acceptance by the two governments that there is a “brain drain” from Canada to the United States of knowledge workers in the “new” economy and that reductions in Canadian taxes should stem this drain. In the author’s view, the tax treatment of employee stock options, even without these changes, is overly generous. Both the federal and provincial proposals ignore the fact that most employee stock options are taxed more favourably in Canada than in the United States in any event. In particular, most employee stock option benefits in Canada are taxed at capital gains tax rates, whereas in the United States most are taxed at full rates. While the US Internal Revenue Code does provide capital gains tax treatment for certain employee stock option benefits, a number of preconditions must be met. Most important, the shares acquired pursuant to the options must be held for a minimum of one year after the option is exercised. In addition, there are monetary limits on the amount of options that qualify for capital gains treatment. In Canada, there are generally no holding period requirements or monetary limits that apply in order for the option holder to benefit from capital gains tax rates. Empirical evidence indicates that the vast majority of employees in the United States exercise their options and immediately sell the shares acquired. These “cashless exercises” do not benefit from capital gains treatment in the United States, whereas similar cashless exercises in Canada generally do. This empirical evidence suggests not only that the 2000 budget proposals are unwarranted, but also that the existing treatment of employee stock options in Canada is already more generous than that in the United States. This article begins with a theoretical “benchmark” for the taxation of employee stock options. The author suggests that employee stock options should be treated in the same manner as other income from employment. In theory, the value of the benefit should be included in income when the option is granted or vests. However, owing to the practical difficulty of valuing employee stock options, the theoretical benchmark proposed is that the value of the benefit (the difference between the fair market value of the shares acquired and the strike price under the option) be taxed when the shares are acquired, and the employer be entitled to a corresponding deduction. The employee stock option rules in Canada and the United States are then compared and contrasted with each other and the benchmark treatment. The article then examines the arguments that have been made for favourable treatment of employee stock options. Included in this critique is a review of the recent empirical work on the Canadian brain drain. Empirical studies suggest that the brain drain—if it exists at all—is small and that, despite what many newspapers and right-wing think-tanks would have us believe, lower taxes in the United States are not the cause. One study, concluding that taxes do have an effect on migration, suggests that even if Canada adopted a tax system identical to that in the United States, the brain drain would be reduced by a mere 10 percent. Indeed, even if Canada eliminated income tax altogether, it would not stop the brain drain. If governments here want to spend money in order to stem the brain drain, they should focus on other areas. For example, Canada produces fewer university graduates in the fields of mathematics, sciences, and engineering than any other G7 country except Italy. The short supply of university graduates in these fields, the apparent loss of top-calibre academics to US
universities, and the consequent lower levels of university research in these areas (an important spawning ground for new ideas in the “new” knowledge-based economy) suggest that Canada may be better served by devoting more resources to its university institutions, particularly in post-graduate programs, rather than continuing the current trend of budget cuts that universities have endured and may further endure if taxes are reduced.
As far as employee stock options are concerned, if Canada does want to look to the United States for guidance on tax reform (which it seems to do with increasing frequency of late), it should adopt the US rules applicable to nonstatutory options, which are close to the proposed benchmark treatment. In the absence of preferential tax treatment, employee stock options would still be included in compensation packages provided that there were sound business reasons for their use. No persuasive evidence has been put forward that the use of stock options, in the absence of tax incentives, is suboptimal. Indeed, the US experience suggests quite the opposite.

Computer-aided design decision support: interfacing knowledge and information

Computer-aided design decision support has proved to be an elusive and intangible project for many researchers as they seek to encapsulate information and knowledge-based systems as useful multifunctional data structures. Definitions of ‘knowledge', ‘information', ‘facts', and ‘data' become semantic footballs in the struggle to identify what designers actually do, and what level of support would suit them best, and how that support might be offered. The Construction Primer is a database-drivable interactive multimedia environment that provides readily updated access to many levels of information aimed to suit students and practitioners alike. This is hardly a novelty in itself. The innovative interface and metadata structures, however, combine with the willingness of national building control legislators, standards authorities, materials producers, building research organisations, and specification services to make the Construction Primer a versatile design decision support vehicle. It is both compatible with most working methodologies while remaining reasonably future-proof. This paper describes the structure of the project and highlights the importance of sound planning and strict adhesion to library-standard metadata protocols as a means to avoid the support becoming too specific or too paradigmatic.

Mixed-initiative decision support for care planning based on clinical practice guidelines

Care Plan On-Line (CPOL) is an intranet based system that supports a “Coordinated Care” model for chronic/complex disease management. CPOL combines provision of solicited and unsolicited advice features based on integration of the electronic medical record (EMR) with its decision support logic. The objective is to support General Practitioners (GPs) in formulating a 12-month care plan of services such that: (a) the plan is proactive and patient-centered; (b) the GP is kept in awareness of project- and diseasespecific clinical practice guidelines; and (c) the support integrates with GP workflow in a natural fashion. A key feature of our approach is to blur the distinction of EMR and decision support by presenting guidelines in layers with the top-most being a problem-oriented presentation of patient status, progressing on through to patient-independent supporting evidence. In conjunction with a degree of automated inclusion of care planning services, the system demonstrates mixed user and software initiative. We describe the CPOL deployment setting, the challenges of guideline-based clinical decision support, our approach to guideline delivery, and the CPOL architecture.

Mature women and the New Zealand qualifications framework : realising the potential of recognising prior learning

Against a background of 'second-wave' lifelong learning in Aotearoa New Zealand a new framework for post-compulsory national qualifications was introduced. The resulting competency-based system was argued to present a number of benefits for mature women including flexibility in curriculum and delivery and portability across educational sectors. Competency-based education was to include provision for recognition of prior skills and knowledge gained in formal learning environments and the workplace as well as informal learning environments such as the home and the community. Such recognition was a significant factor in gaining support from women’s groups given the potential to recognize and value the domestic labour of women and the skills and knowledge that flow from it. This article explores the rhetoric around recognition of prior learning and discusses approaches to realise its potential. It then draws on research undertaken in Aotearoa New Zealand to suggest that the potential of recognition of prior learning is yet to be realised.

On Compositional Modelling

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem solver. This void between problem solvers and the problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.

Case studies for contract-based systems

Of the ways in which agent behaviour can be regulated in a multiagent system, electronic contracting – based on explicit representation of different parties' responsibilities, and the agreement of all parties to them – has significant potential for modern industrial applications. Based on this assumption, the CONTRACT project aims to develop and apply electronic contracting and contract-based monitoring and verification techniques in real world applications. This paper presents results from the initial phase of the project, which focused on requirements solicitation and analysis. Specifically, we survey four use cases from diverse industrial applications, examine how they can benefit from an agent-based electronic contracting infrastructure and outline the technical requirements that would be placed on such an infrastructure. We present the designed CONTRACT architecture and describe how it may fulfil these requirements. In addition to motivating our work on the contract-based infrastructure, the paper aims to provide a much needed community resource in terms of use case themselves and to provide a clear commercial context for the development of work on contract-based system.

Educating for professional capability in the field of information technology: integrating industry-based learning with the academic curriculum

In response to the forces of globalisation, societies and organisations have had to adapt and even proactively transform themselves. Universities, as knowledge-based organisations, have recognised that there are now many other important sites of knowledge construction and use. The apparent monopoly over valued forms of knowledge making and knowledge certification is disappearing. Universities have had to recognise the value of practical working knowledge developed in workplace settings beyond university domains, and promote the value of academic forms of knowledge making to the practical concerns of everyday learning. It is within this broader systems view that professional curriculum development undertaken by universities needs to be examined.

University educational planning responds to these external forces in ways that are drawing together formal academic capability/competence and practice-based capability/competence. Both forms of academic and practice-based knowledge and knowing are being equally valued and related one to the other. University planning in turn gives impetus to the development of new forms of professional education curricula. This paper presents a contemporary case of a designed professional curriculum in the field of information technology that situates workplace learning as a central element in the education of Information Technology (IT)/Information Systems (IS) professionals.

The key dimensions of the learning environment of Deakin University’s BIT (Hons) program are considered with a view to identifying areas of strong integration between the worlds of academic and workplace learning from the perspectives of major stakeholders. The dynamic interplay between forms of theorising and practising is seen as critical in educating students for professional capability in their chosen field. From this analysis, an applied research agenda, relating to desired forms of professional learning in higher education, is outlined, with specific reference to the information and communication technology professions.

Multiple-criteria decision analysis for integrated catchment management

Implementation of integrated catchment management (ICM) is hampered by the lack of a conceptual framework for explaining how landowners select farming systems for their properties. Benefit–cost analysis (a procedure that estimates the costs and benefits of alternative actions or policies) has limitations in this regard, which might be overcome by using multiple-criteria decision analysis (MCDA). MCDA evaluates and ranks alternatives based on a landowner's preferences (weights) for multiple-criteria and the values of those criteria. A MCDA approach to ICM is superior to benefit–cost analysis which focuses only on the monetary benefits and costs, because it: 1) recognizes that human activities within a catchment are motivated by multiple and often competing criteria and/or constraints; 2) does not require monetary valuation of criteria; 3) allows trade-offs between criteria to be measured and evaluated; 4) explicitly considers how the spatial configuration of farming systems in a catchment influences the values of criteria; 5) is comprehensive, knowledge-based, and stakeholder oriented which greatly increases the likelihood of resolving catchment problems; and 6) allows consideration of the fairness and sustainability of land and water resource management decisions. A MCDA based on an additive, multiple-criteria utility function containing five economic and environmental criteria was used to score and rank five farming systems. The rankings were based on the average criteria weights for a sample of 20 farmers in a US catchment. The most profitable farming system was the lowest-ranked farming system. Three possible reasons for this result are evaluated. First, the MCDA method might cause respondents to express socially acceptable attitudes towards environmental criteria even when they are not important from a personal viewpoint. Second, the MCDA method could inflate the ranks of less profitable farming systems for the simple reason that it allows the respondent to assign non-zero weights to non-economic criteria. Third, the MCDA might provide a better framework for evaluating a landowner's selection of farming systems than the profit maximization model.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

Intelligent financial news digest system

We present an agent-based system Intelligent Financial News Digest System (IFNDS) for analyzing online financial news articles and associated material. The system can abstract, synthesize, digest, and classify the contents, and assesses whether the report is favorable to any company discussed in the reports. It integrates artificial intelligence technologies including traditional information retrieval and extraction techniques for the news analysis. It makes use of keyword statistics and backpropagation training data to identify companies named in reportage whether it is, evaluatively speaking, positive, negative or neutral. The system would be of use to media such as clipping services, media management, advertising, public relations, public interest, and e-commerce professionals and government non-governmental bodies interested in monitoring the media profiles of corporations, products, and issues.

A possible journey to an Australian knowledge management framework

World wide there is a transition towards knowledge-based economies In order to foster them, many economies have built frameworks in conjunction with world forums that promote international cooperation. Our research focuses on Australia, an economy that has been named by the OECD as knowledge-based. Specifically, the paper appraises the transition towards knowledge economies in general. We then examine the Australian context through the implemented eEurope framework with the intent of providing suggestions to Australia in its transition towards building its knowledge management
framework.

A scalable and robust framework for agent-based heterogeneous database operation

How to operate database efficiently and unfailingly in agent-based heterogeneous data source environment is becoming a big problem. In this paper, we contribute a framework and develop a couple of agent-oriented matchmakers with logical ring organization structure to match task agents' requests with middleware agents of databases. The middleware agent is a wrapper of a specific database and is run on the same server with the database management system. The matchmaker is of the features of proliferation and self-cancellation according to the sensory input from its environment. The ring-based coordination mechanism of matchmakers is designed. Two kinds of matchmakers, namely, host and duplicate, are designed for improving the scalability and robustness of agent-based system. The middleware agents are improved for satisfying the framework. We demonstrate the potentials of the framework by case study and present theoretical and empirical evidence that our approach is scalable and robust.

Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.