John H. Powell

Identifying strategic action in highly politicized contexts using agent-based qualitative system dynamics

In the early phases of using system dynamics models to support strategic decision-making, the emphasis is on expressing information and physical flows. These aspects appropriately dominate those managed systems that can be thought of as being mechanistic. We suggest, however, that such an emphasis, to the exclusion of equally important system attributes such as power, leverage, influence and control, is inappropriate for a large class of problems involving agents and groups of agents in the system definition. Such politicized systems are ubiquitous, particularly in the strategic context, and in managing them it is necessary to take the political aspects of power into account at an early stage in the analysis. We present an approach to this class of problems, using a qualitative procedure based on influence diagrams. This method has been extensively and successfully used in consultancy to study the motivations and powers of agents and thereby produces naturally an output directed at action planning at the strategic level. While it is complementary to numerical system dynamics approaches, it is more successful in deriving components of strategic action directly from analysis.

Men and measures: capturing knowledge requirements in firms through qualitative system modelling

Knowledge Management (KM) is an issue of great and increasing importance in most if not all areas of managerial endeavour. In this paper, we are concerned with the particular practical difficulty within KM of mapping knowledge in a managed system. This is an important practical issue because without a view of the terrain of explicit and tacit knowledge in the managed system, we have little prospect of planning our managerial interaction. Few if any practical methods exist which reflect the strongly systemic nature of business organizations. We begin by establishing our position with regard to the numerous definitions and perspectives of knowledge in managed systems, and indeed in regard to the disagreements that rack KM over the nature of knowledge itself, where it lies and the role of humans as creators, users and guardians of that knowledge. We relate the nature of system knowledge to well-known taxonomies of knowing what, knowing how, knowing why, knowing who together with the integrated from of knowing in the managed system as a whole. The method presented, Systems Based KM (or SBKM), is based on a non-positivist qualitative method deriving from System Dynamics and it is presented through the medium of a case study of a professional firm.

This is what the fuss is about: a systemic modelling for organisational knowing

Purpose – This paper presents a system‐based approach to action‐directed knowledge management. This approach, known as system‐based knowledge management (SBKM), allows one to respond to the observations made by previous writers that knowledge management should be cognisant of the complexity of knowledge in organisations and of the limitations of codification of that knowledge. Starts with a taxonomic analysis of the nature of organisational knowledge, dividing this critical resource into four: knowing what, knowing how, knowing why, and knowing who. Each of these requires recognition of the system in which it is created and used.Design/methodology/approach – SBKM is an accessible systems analysis tool based on the techniques of qualitative system dynamics. Its fundamental representational technique (the influence diagram) is that of causal mapping and its novel element is the explicit representation of the use of knowledge by human actors in fulfilling their specific system roles.Practical implications – ...

Evolutionary concepts and business economics: Towards a normative approach

Abstract In the field of economics, a distinct ‘new’ subdiscipline called “Business Economics” is steadily evolving where the aim of its contributors is to produce useful insights into the nature of the firm and its competitive environment, which in turn inform the practice of Strategic Management. Inevitably, it has been the practice thus far to base this Business Economics upon neoclassical thinking rather than the emerging evolutionary models of economic systems. Evolutionary economics is essentially concerned with dynamic systems and with behavioural trajectories within such systems. This is in direct contrast with the neoclassical fixation upon stasis, equilibrium and global rationality (i.e. all-seeing, all-knowing decision makers). Such an approach renders difficult the examination of bounded rationality and the development of competitive advantage in a dynamic environment. We argue that the evolutionary perspective on the competitive structures in which strategic management is practised has a pivotal role to play in the practice of strategy. In particular, we identify specific approaches to strategic management (and particularly competitive strategy), which result from an acceptance of the evolutionary nature of the strategic environment.

Soft or approaches in problem formulation stage of a hybrid M&S study

A simulation study consists of several well-defined stages, e.g., problem formulation, model implementation and experimentation. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research, Systems Engineering and Computer Science to one or more stages of a simulation study. We focus on the first stage of a simulation study (and by extension a hybrid M&S study), viz., eliciting the system requirements, and conduct a review of literature in Soft Systems Methodology for healthcare operations management. We discuss the potential for the use of Qualitative System Dynamics as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a Hybrid M&S study.

Scenario networks to align and specify strategic information systems: a case-based study

Contemporary conceptions of the planning component of strategy stress the need for an accommodating approach. This is seen alternatively as contingent planning, where commitments are made stage-by-stage as the surrounding situation develops, or as a robust response, where the emphasis is on retaining freedom of manoeuvre through flexibility. In either concept, the strategic information system (SIS), as a critical asset of the firm, needs to accommodate (and indeed often shape) the emerging strategic plan. This paper presents a method based on discrete state networks that allows close mutual development of the overall strategic plan for a firm and its SIS. The method presents the future in a network of scenarios between which the firm can move. By considering desired trajectories in the network of scenarios the firm can identify the developmental requirements needed to bring about the movement between future states. The high-level SIS specification emerges from this trajectory analysis. An extensive worked example of an insurance firm is used to illustrate the method.

System-focused risk identification and assessment for disaster preparedness: Dynamic threat analysis

Current approaches to risk management stress the need for dynamic (i.e. continuous, ongoing) approaches to risk identification as part of a planned resource application aimed at reducing the expected consequences of undesired outcomes for the object of the assessment. We contend that these approaches place insufficient emphasis on the system knowledge available to the assessor, particularly in respect of three factors, namely the dynamic behavior of the system under threat, the role of human agents and the knowledge availability to those agents.

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.

Generating networks for strategic planning by successive key factor modification

Scenario planning is the most widely used member of a family of strategic planning approaches which use discrete states to explore management issues. Conventional approaches to scenario-based planning emphasise the clarity of using a small number of extrapolations from the present. Recent work has seen the future as a network of states around which movement can take place under the control of various parties. This requires a richer homogeneous set of scenarios and Rhynes Field Anomaly Relaxation (FAR) technique has served as the basis for that state generation process. FAR has some disadvantages. It can be cumbersome and, more importantly, the discriminants of the states are unchanged throughout each cycle of the process. It operates by establishing a large number of possible futures and then clustering these into coherent sets. An alternative approach is presented which grows neighbouring states step by step from existing, plausible self-consistent states. A network of locally related states is thereby established on which basis transition-based planning can be carried out. The relationship of the method to FAR is described, and its use illustrated by an example.

Hybrid simulation studies and hybrid simulation systems: definitions, challenges, and benefits

Hybrid Simulation (HS) is not new. However there is contention in academic discourse as to what qualifies as HS? Is there a distinction between multi-method, multi-paradigm and HS? How do we integrate methods from disciplines like OR and computer science that contribute to the success of a M&S study? How do we validate a hybrid model when the whole (the combined model) is greater than the sum of its parts (the individual models)? Most dynamic simulations have a notion of time, how do we realize a unified representation of simulation time across methodologies, techniques and packages, and how do we prevent causality during inter-model message exchange? These are but some of the questions which we found to be asking ourselves frequently, and this panel paper provided a good opportunity to stimulate a discussion along these lines and to open it up to the M&S community.