John P. Davis

The role of the shaft in the golf swing

Current marketing of golf clubs places great emphasis on the importance of the correct choice of shaft in relation to the golfer. The design of shafts is based on a body of received wisdom for which there appears to be little in the way of hard evidence, either of a theoretical or experimental nature. In this paper the behaviour of the shaft in the golf swing is investigated using a suitable dynamic computer simulation and by making direct strain gauge measurements on the shaft during actual golf swings. The conclusion is, contrary to popular belief, that shaft bending flexibility plays a minor dynamic role in the golf swing and that the conventional tests associated with shaft specification are peculiarly inappropriate to the swing dynamics; other tests are proposed. A concomitant conclusion is that it should be difficult for the golfer to actually identify shaft flexibility. It is found that if golfers are asked to hit golf balls with sets of clubs having different shafts but identical swingweights the success rate in identifying the shaft is surprisingly low.

UNCERTAIN INFERENCE USING INTERVAL PROBABILITY THEORY

Abstract The use of interval probability theory (IPT) for uncertain inference is demonstrated. The general inference rule adopted is the theorem of total probability. This enables information on the relevance of the elements of the power set of evidence to be combined with the measures of the support for and dependence between each item of evidence. The approach recognises the importance of the structure of inference problems and yet is an open world theory in which the domain need not be completely specified in order to obtain meaningful inferences. IPT is used to manipulate conflicting evidence and to merge evidence on the dependability of a process with the data handled by that process. Uncertain inference using IPT is compared with Bayesian inference.

A software-supported process for assembling evidence and handling uncertainty in decision-making

Complex socio-technical decisions, such as infrastructure investment decisions, are based on large quantities of evidence assembled and manipulated by multi-disciplinary teams. Information about decision options and future states of nature will often be ambiguous, incomplete or conflicting. In this article, a software-supported approach to assembling, structuring and representing evidence in a decision, based on hierarchical modelling of the processes leading up to a decision, is presented. Uncertainty in the available evidence is represented and propagated through the evidence hierarchy using Interval Probability Theory (IPT), providing a commentary on sources and implications of uncertainty in the decision. Case studies in the oil and civil engineering industries demonstrate how the approach has helped to develop shared understanding of the implications of uncertainty. It has enabled experts to externalise their knowledge and has facilitated discussion and negotiation.

An argumentation-based method for managing complex issues in design of infrastructural systems

The many interacting and conflicting requirements of a wide range of stakeholders are the main sources of complexity in the infrastructure and utility systems. We propose a systemic methodology based on negotiation and argumentation to help in the resolution of complex issues and to facilitate options appraisal during design of such systems. A process-based approach is used to assemble and propagate the evidence on performance and reliability of the system and its components, providing a success measure for different scenarios or design alternatives. The reliability of information sources and experts opinions are dealt with through an extension of the mathematical theory of evidence. This framework helps not only in capturing the reasoning behind design decisions, but also enables the decision-makers to assess and compare the evidential support for each design option.

A decision-support methodology for performance-based asset management

A software-supported methodology for managing the performance of complex infrastructure systems is described. The infrastructure system is represented hierarchically, so that high level business decisions and more detailed operational decisions can be supported by the same methodology. Performance of each sub-system is captured by a set of Performance Indicators held in a database. Evidence of performance is assembled from all available sources, ranging from monitoring measurements and inspection records, design calculations and model studies to expert judgements, analogous cases and accounts of past failures. These Performance Indicators are projected through value functions reflecting organisational objectives and regulatory standards and are merged to generate a Figure of Merit for the system and each sub-system. Uncertainty in the available evidence is represented and propagated through the evidence hierarchy using Interval Probability Theory, providing a commentary on sources and implications of uncertainty in the decision. A case study of a hydro-electric reservoir system demonstrates how the approach can be used to provide a coherent overview of system performance and support asset management decision-making.

Fuzziness, incompleteness and randomness; classification of uncertainty in reservoir appraisal

The rapid progress in computer technology in recent years has enabled the development of increasingly complex simulators, which can handle large amounts of data. It is often assumed that this automatically leads to more accurate static and dynamic reservoir models. In reality, however, there is still much evidence that the predicted performance of a reservoir often differs vastly from the actual production behaviour. These deviations are an indication of the failure to understand the processes involved and to recognize the uncertainty inherent in the definition of important reservoir characteristics. In this paper, a classification scheme is proposed, in which uncertainty is expressed as fuzziness, incompleteness and randomness. Each of these elements is described in detail and illustrated within the context of reservoir appraisal, although the approach can be applied to the wider aspects of petroleum geoscience. It is believed that adopting this classification scheme will enable the geoscientist to build a more extensive picture of uncertainty in reservoir appraisal. It will also be invaluable as a tool with which to inform management of the existing uncertainty, using a consistent language, thus providing guidance in the decision-making process.

Integrating Problem Solving and Research Methods Teaching for Systems Practice in Engineering

Problem solving and research methods apparently sit within different traditions of development evidenced by disparate sources of literature. However, in the graduate education of engineers taking an Engineering Doctorate (EngD) Program in Systems there is a need for their integration in such a way as to make their relationship clear. We argue from experience of course delivery and project supervision that research methods from business and management need to support a generic problem solving approach – informed from the Problem Structuring Methods (PSM) literature, and specifically Soft Systems Methodology (SSM) – such that they provide the rigorous evidence needed at any stage of a problem solving cycle. There is a clear hierarchy with a problem solving approach providing the guiding methodology for systems practice in engineering, and research methods supplying the means to generate answers to specific questions as they arise. We specifically discuss the special role of action research as both a problem solving and a research strategy and its relevance to engineering education, and suggest a philosophical underpinning for the approach.

COMBINATION METHODS AND CONFLICT HANDLING IN EVIDENTIAL THEORIES

Evidence theory has been acknowledged as an important approach to dealing with uncertain, incomplete and imperfect information. In this framework, different formal techniques have been developed in order to address information aggregation and conflict handling. The variety of proposed models clearly demonstrates the range of possible underlying assumptions in combination rules. In this paper we present a review of some of the most important methods of combination and conflict handling in order to introduce a more generic rule for aggregation of uncertain evidence. We claim that the models based on mass multiplication can address the problem domains where randomness and stochastic independence is the dominant characteristic of information sources, although these assumptions are not always adhered to many practical cases. The proposed combination rule here is not only capable of retrieving other classical models, but also enables us to define new families of aggregation rules with more flexibility on dependency and normalization assumptions.

Early estimation of project performance: The application of a system dynamics rework model

We address the problem of rework in complex project management by the use of System Dynamics modeling and estimated planned performance profiles. We propose that early indications of problems arising with a project can be generated by comparing estimated planed performance profiles with actual performance profiles. Estimating project quality for use in the System Dynamics models thus becomes the key challenge to enable the use of this leading indicator. Estimates of project quality based on experience of project type, size and complexity within the organization can be used to parameterize the System Dynamics models to generate model generated estimated planned performance profiles. Early results indicate that the morphology of these profiles shows good agreement with project data emerging from the metrics initiative within Thales UK.

Dialectical evidence assembly for discovery

We propose and demonstrate a dialectical framework for the assessment and assembly of evidence in discovery processes. This paper addresses the stimulation and capture of dialectical argumentation in the context of complex (wicked) problems. Holonic representation is developed to structure processes hierarchically for the modeling of complex problems. Interval Probability Theory (IPT) is modified to produce an evidential reasoning calculus to represent dialectical argument through the separation of evidence for and evidence against a hypothesis. Support for and against any hypothesis can then be assembled through a weighting of the relevance, importance and degree of evidence. Uncertainty surrounding any hypothesis can be decomposed into randomness, vagueness, conflict, incompleteness and relevance and can be managed within the framework. The framework is illustrated with real examples of discovery from two energy related complex (wicked) problems.