Scaling knowledge: how does knowledge accrue in systems?

This paper addresses the important and somewhat contentious matter of how knowledge accrues in a system. The matter has at its heart the establishment of a scaling function for knowledge (as distinct from the scaling used for information) which is related to the density of the knowledge structure at any point in the system. We commence with a discussion of whether it is possible at all to scale knowledge, dispensing with any concepts of knowledge as a simple finite resource and making a distinction between the establishment of a metric and the act of measurement itself. First, we draw on the Shannon–Weaver (H) measure to establish how knowledge can be seen as contributing to the partitioning of message sets under the H-measure. This establishes how knowledge contributes to the quantity of information held within a system when viewed as a meta-structure for that information. Second, we build on the idea of knowledge as an endemic property of a structure of interconnections between concepts. We observe that knowledge content can be dense both in structures that are highly interconnected deploying a modest number of concepts and in those where the interconnections are more sparse but where the number of concepts deployed is high. A scaling function exhibiting appropriate properties is then proposed. It can be seen that the scaling associated with knowledge as meta-information and the scaling deriving from the interconnectivity point of view are connected. This scaling function is particularly useful in three ways. Firstly, it outlines the properties of knowledge itself which can be used as criteria for future knowledge-based research. Its application in practice creates the ability to identify areas of knowledge concentration within a system. Finally, this identification of knowledge ‘hotspots’ can be used to direct the investment of resources for the management of knowledge and it provides an indication of the appropriate approach for the management of this knowledge. We make some observations on the limitations of the approach, on its potential as a basis for managerial action (particularly in Knowledge Management) and on its relevance and applicability to OR practice (particularly in respect of systems approaches to knowledge mapping). Lastly, we offer a view on the likely line of research which may result from this work.     

A methodology for analyzing decision networks, based on information theory

This paper assumes the organization as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in a decision network, due to interdependence between decision centers.Based on this approach, new quantitative concepts and definitions are proposed in order to measure the information in a decision center, based on Shannon entropy and its complement in possibility theory, U uncertainty. This approach also measures the quantity of interdependence between decision centers and informational complexity of decision networks.The paper presents an agent-based model of organization as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the organization structure efficiency, based on informational communication view. The structure improvement, analysis of information flow in organization and grouping algorithms are investigated in this paper. The results obtained from this model in different systems as distributed decision networks, clarifies the importance of structure and information distribution sources effect’s on network efficiency.     

Broadening the boundaries: an application of critical systems thinking to IS planning in Colombia

Most current information systems (IS) planning methodologies are focused on achieving plans that provide competitive advantage to business and solve the problems of information needs by using the latest technologies available. This paper presents an alternative approach to IS planning based on critical systems thinking—a research perspective that encourages the analysis of stakeholders' understandings of social contexts prior to the selection and/or design of planning methods. The approach is underpinned by a combination of the systems theories of autopoiesis and boundary critique, and it enables participants to reflect on different concerns and values during IS planning. This approach was applied in a project with a Colombian University, and it helped participants to uncover and address important human issues not usually seen as relevant by traditional approaches to IS planning. In addition, it led the authors to undertake further research on ethics after the project had been concluded.     

Dynamic iteration may fail for partitioned network simulation

In many technical applications, complex systems are composed of subsystems that are modelled independently using a network approach. This co‐modelling ansatz is completed very naturally by a co‐simulation approach based on dynamic iteration, where the respective DAE submodels are solved iteratively. For partitioned electric network simulation we show that this approach may fail. As alternative we discuss overlapping techniques based on topological network information.     

Mixing methodologies for information systems development and strategy: A higher education case study

Although informed approaches from an interpretivist perspective have been widely promoted over the last two decades information systems development is largely informed by a functionalist perspective, into which human-centred or interpretivist issues are incorporated as they are seen to be required. By treating such developments as critically informed social systems, this paper offers an alternative viewpoint. Firstly, a theoretical justification for such an approach is offered, drawing on developments in Critical Management Science, themselves based on a strand of Critical Social Theory. Secondly, an action research-based intervention is detailed, demonstrating the practical applicability of such an approach, an applicability that has hitherto been questioned. The outcome is a synthesised approach to operational and strategic information systems development, within a critical framework, which, it is argued, offers a greater potential for success than the currently promoted approaches.     

A systems approach to identifying decisive information for sustainable development

Many governments are striving to implement sustainable development programs. While there are many definitions of `sustainability', most agree that a more comprehensive information infrastructure including economic, social, environmental, and cultural measures is required to assess courses of action and evaluate progress. Also critical is the development of information about the structure and behavior of the systems in which decisions are made. Most of the efforts toward the identification of information to support sustainable development decision making have focused on developing measures of progress toward sustainability. The Pressure-State-Response framework has been suggested as a method for capturing perceptions of causality. This framework fails to capture important information about complex causal relationships and system behavior. A systems approach to identifying decisive information is discussed as an alternative. This approach supports the identification of relationships among the indicators, learning about the behavior of the system, and provides a common language for interdisciplinary communication.     

An interval set model for learning rules from incomplete information table

A novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set.     

User-friendly statistical concepts for process monitoring

Many standard statistical process control techniques involve sophisticated mathematical concepts, which are frequently misunderstood and misused by their users. This means, as this paper argues, that the techniques, and the terminology and concepts underlying them, are inappropriate for their intended uses and users. The paper considers the areas of the statistical background which cause difficulties. It goes on to describe some alternative concepts, techniques and terminology, which are likely to be conceptually simpler and more ‘user-friendly’ (and in some cases more accurate and robust). None of the ideas require users to be familiar with the standard deviation. We also suggest that the approach of reengineering the conceptual background to suit the context of users and uses may be appropriate to other areas of practical statistics and OR. The paper identifies some general principles for achieving this.     

A Fuzzy Logic Approach for Determining Expected Values: A Project Management Application

This paper presents a methodology rooted in the general concepts of fuzzy logic theory with specific emphasis on belief functions and extension principles, and fuzzy probability distributions with fuzzy expectation based on fuzzy probability measures. This approach offers a useful alternative to the traditional approach in the estimation of probabilities in the absence of information about relative frequencies. An algorithm called BIPFET is developed and its application is demonstrated by utilizing data from a real-life research and development project.     

Robust optimization analysis for multiple attribute decision making problems with imprecise information

This paper considers ranking decision alternatives under multiple attributes with imprecise information on both attribute weights and alternative ratings. It is demonstrated that regret results from the decision maker??s inadequate knowledge about the true scenario to occur. Potential optimality analysis is a traditional method to evaluate alternatives with imprecise information. The essence of this approach is to identify any alternative that outperforms the others in its best-case scenario. Our analysis shows that potential optimality analysis is optimistic in nature and may lead to a significant loss if an unfavorable scenario occurs. We suggest a robust optimization analysis approach that ranks alternatives in terms of their worst-case absolute or relative regret. A robust optimal alternative performs reasonably well in all scenarios and is shown to be desirable for a risk-concerned decision maker. Linear programming models are developed to check robust optimality.     

Multivariable state-feedback self-tuning controllers ∗

This paper describes a state-space approach for self-tuning control of a class of multivariable stochastic systems having the same number of inputs as outputs. A multivariable state-feedback self-tuning controller, based on pole-assignment concepts, is derived. The developed multivariable self-tuner can be applied to stable/unstable and minimum/non-minimum phase linear time-invariant multivariable systems. A multivariable reduced-order self-tuner and a state-feedback minimum-variance self-tuner are also derived. The simplicity and flexibility of the proposed state-space approach facilitate the practical applications of self-tuning control concepts to real systems     

A perspective on artificial intelligence: Learning to learn

The classical approach to the acquisition of knowledge in artificial intelligence has been to program the intelligence into the machine in the form of specific rules for the application of the knowledge: expert systems. Unfortunately, the amount of time and resources required to program an expert system with sufficient knowledge for non-trivial problem-solving is prohibitively large. An alternative approach is to allow the machine tolearn the rules based upon trial-and-error interaction with the environment, much as humans do. This will require endowing the machine with a sophisticated set of sensors for the perception of the external world, the ability to generate trial actions based upon this perceived information, and a dynamic evaluation policy to allow it to measure the effectiveness of its trial actions and modify its repertoire accordingly. The principles underlying this paradigm, known ascollective learning systems theory, have already been applied to sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity.The fundamental building block of a collective learning system is thelearning cell, which may be embedded in a massively parallel, hierarchical data communications network. Such a network comprising 100 million learning cells will approach the intelligence capacity of the human cortex. In the not-too-distant future, it may be possible to build a race of robotic slaves to perform a wide variety of tasks in our culture. This goal, while irresistibly attractive, is most certainly fraught with severe social, political, moral, and economic difficulties.This paper was given as an invited talk on the 12th Symposium on Operations Research, University of Passau, September 1987.     

A note on uncertainty and cooperation in a finitely repeated prisoner's dilemma

An intuitive expectation is that in a finitely repeated prisoner's dilemma, the players will achieve mutual cooperation in at least some periods. Existing explanations for equilibrium cooperation (with agents perfectly informed of one another's characteristics) require that the number of repetitions be unknown, which is in many cases an uncomfortably strong uncertainty assertion. This paper demonstrates that if agents have private information concerning the number of repetitions (as opposed to being completely uninformed), equilibrium mutual cooperation can occur in a finitely repeated game. This appears to be a weaker and more palatable assumption then that of complete uncertainty, and hence provides a natural and useful alternative foundation for mutual cooperation.     

基于区间数偏好信息的专家群体共识性研究

针对群决策中基于区间数偏好信息的共识性问题,给出了一种新的分析方法.首先给出了有关区间数和区间数决策矩阵的定义及若干性质;然后.通过定义有关专家群体判断关于方案针对指标的落影函数和专家群体关于方案针对指标的重心值,给出了群决策中基于区间数决策矩阵的共识性的分析方法和非共识的调整方法.最后,通过一个算例说明给出的分析方法.     

Revisiting decimal misconceptions from a new perspective: The significance of whole number bias in the Chinese culture

The aim of this study was to investigate Hong Kong Grade 4 students’ understanding of the decimal notation system including their knowledge of decimal quantities. This is a unique study because most previous studies were conducted in Western cultural settings; therefore we were interested to see whether Chinese students have the same kinds of misconceptions as Western students given the Chinese number naming system is relatively transparent and explicit. Three hundred and forty-one students participated in a written test on decimal numbers. Thirty-two students were interviewed to further explore their mathematical reasoning. In summary, the results indicated that many students had mastered reasonable knowledge of decimal notation and quantities, which may be attributed to the Chinese linguistic clarity of decimal numbers. More importantly, the results showed that some students’ construction of decimal concepts have been adversely affected by persistent misconceptions arising from whole number bias. Two kinds of whole number misconceptions, namely “-ths suffix error” and “reversed place value progression error”, were revealed in this study. This paper suggests that a framework theory approach to conceptual change may be an alternative approach to addressing students’ learning difficulties in decimals.     

Mathematics learning and aesthetic production

Some teaching projects in which the learning of mathematics was combined with mainly theatrical productions are reported on. They are related and opposed to an approach of drama in education by Pesci and the proposals of Sinclair for mathematics teaching and beauty. The analysis is based on the distinction between aesthetics as related to beauty or as related to sensual perception. The usefulness of concepts of model and metaphor for the understanding of aesthetic representations of mathematical subject matter is examined. It is claimed that the Peircean concept of the interpretant contributes to a concise analytical approach. The pedagogical attitude is committed to a balanced relationship of scientific and aesthetic values.     

Using activity focus networks to pressure terrorist organizations

The predominant idea for using network concepts to fight terrorists centers on disabling key parts of their communication networks. Although this counternetwork strategy is clearly a sound approach, it is vulnerable to missing, incomplete, or erroneous information about the network. This paper describes a different and complementary application of network concepts to terrorist organizations. It is based on activity focus networks (AFNs), which represent the complex activity system of an organization. An activity focus is a conceptual or physical entity around which joint activity is organized. Any organization has a number of these, which are in some cases compatible and in some cases incompatible. The set of foci and their relations of compatibility and incompatibility define the AFN. A hypothetical AFN for a terrorist organization is specified and tested in a simulation called AQAS. It shows that certain activity foci, and in particular one combination, have high potential as pressure points for the activity system. The AFN approach complements the counternetwork approach by reducing the downside risk of incomplete information about the communication network, and enhancing the effectiveness of counternetwork approaches over time. Steven R. Corman is Professor in the Hugh Downs School of Human Communication at Arizona State University and Chair of the Organizational Communication Division of the International Communication Association. His research interests include communication networks and activity systems, high-resolution text and discourse analysis, and modeling and simulation of human communication systems.     

Modelling of characteristic delays in multivariable control systems wcbsl

Pure time delays in multivariable control systems place severe restrictions on achievable feedback performance. This paper considers an approach to modelling distributed time-delay systems using discrete convolution. The basis for convolution algebra is briefly outlined and the new concepts of characteristic pattern and vector delays are introduced. A process control example is given that illustrates the concepts and shows typical results obtained using WCBSL (Windows Convolution-Based Simulation Language)     

On Transition to Turbulence and Synthesis of Alternative Approaches

In the study of complex systems, controversial debates often arise among advocates of different schools of thought. In this article, we examine how such controversies should be addressed, with the problem of transition to turbulence as a primary example. It is shown that, in many cases, these controversies may be resolved by first noting that the alternative theories proposed may not be mutually exclusive. Indeed, they may even be mutually complementary, if they were originally developed to address similar issues in different physical contexts. In any case, for the validity of the alternative theories proposed, each should be separately and fully supported from both the theoretical and empirical points of view. Each applies to a specific physical context, and each stands on its own merits and limitations. Synthesis into a broader theory may then be achieved, if commonality is identified among the different alternative theories proposed. To demonstrate this conciliatory approach, we begin with an examination of the move toward resolution of the well‐known controversy over the problem of transition to turbulence from the steady laminar flow in the boundary layer over a flat plate. Several other long‐standing controversies have been successfully addressed on the basis of this approach. In addition to the problem of transition to turbulence, we considered, in some detail, two additional examples: (1) the global structures of spiral galaxies; and (2) the theory of jet noise. In all three cases, it is shown that the model approach is meritorious despite the limitations. Synthesis, with a conciliatory approach to apparent conflicts, will be recommended in general as a new part of an extended paradigm in applied mathematics. It is an approach appropriate to situations where an ideal theory, with universal applicability, is elusive. Parallel development of several alternative theories is natural, and a final synthesis is needed. In contrast, it should be noted that the same perspective is generally not expected useful if the controversies concern the unique solution of well‐defined mathematical issues. The potential success of the application of this conciliatory perception and approach to other areas of science are discussed (see Section 5 ).     

Value and information: A profit maximizing strategy for Maxwell's demon

Applications of Lagrange's method of undetermined multipliers in physics and economics suggest some analogies between the two disciplines. Applying Jaynes' approach to information theory reveals the long standing problem of Maxwell's demon in thermodynamics as a constrained optimization problem where information and energy are dual concepts. Temperature emerges as the energetic price of information. This approach highlights the economic features of the Maxwell demon discussed by Bridgman. It also suggests that an analog to temperature (or the price of information) should be present in any optimization problem constrained by costly information. A trivial economics example is presented where generalized temperature measures informational efficiency in the sense of Grossman and Stiglitz.