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ENDOWING COGNITIVE MAPPING WITH PROPERTIES
Figures and Captions
ENDOWING COGNITIVE MAPPING WITH COMPUTATIONAL PROPERTIES
FOR STRATEGIC ANALYSIS
William Acar
Grad. School of Management
KentStateUniversity
Kent, OH44242, USA
330-672-1156
fax: 330-672-2953
Douglas Druckenmiller
Dep’t of Information Systems
WesternIllinoisUniversity
Macomb, IL61455-1390, USA
563-332-5017
fax: 309-298-2500
FEBRUARY 2005
Based on papers presented at the
2nd International Conference on Organizational Foresight
UNIVERSITY OF STRATHCLYDE
26-28 August 2004
Submitted to FUTURES
Endowing cognitive mapping with computational properties
for strategic analysis
Abstract
A number of cognitive, causal mapping and simulation techniques exist for dealing with the growing importance of environmental uncertainty. After briefly commenting on some of the more salient extant approaches, this paper offers a new one for consideration by the scenario planning community. Comprehensive Situational Mapping (CSM) is a powerful analytical tool combined with a process for framing and debating strategic situations. The CSM approach combines the problem framing features of causal mapping with a dialectical inquiry process patterned after Churchman’s. Like the better approaches to planning through cognitive mapping, it facilitates the “backward analysis” of the underlying strategic assumptions. Its novelty is that it also allows the “forward analysis” of a situation by computing the potential change scenarios. Initially developed for manual application, the principles of CSM were originally tested in appropriate case studies. The contribution of the present paper is to present its theory and point out that its future potential is even greater: in concluding we indicate that, by using recent distributed artificial intelligence (DAI) technology, a fully computerized and interactive prototype is now being set up for commercial applications.
1.Introduction
Foreshadowing the evolution of the dominant paradigm of strategic management toward the theory of dynamic capabilities [1], two parallel content-oriented thrusts have been aiming at facilitating adaptive strategy design. These are the broad categories of Dialectical Inquiry for assumption analysis (DI) and scenario analysis (SA). However, the same content can espouse different methodological forms. Methodologically speaking, a range of approaches has been proposed. This spectrum encompasses quantitative approaches such as System Dynamics (SD) at its top end, and a cluster of informal approaches broadly designated as cognitive mapping (CM) at its informal end. Separate literatures exist within DI, SA, SD and CM. The contribution of this paper pertains to the emerging effort of attempting to bring them all together [2,3].
Bringing together the work of two authors, one involved with the above four thrusts and one involved with evolution of information technology toward artificial intelligence, this paper presents the theory for a simultaneous approach to assumption analysis scenario planning capable of providing its users with dynamic and interactive analytical capabilities for handling strategic uncertainty. It does so by adopting a causal mapping technique capable of bridging the gap between quantitative and qualitative methods.
After presenting the origins of the method, the paper will contrast it with other approaches and discuss how its analytical processes offer modeling support for strategic problem formulation and scenario planning. It will conclude by showing how this branch of the evolution of strategic theory could mesh in with the recent developments in information technology, namely the latest developments in distributed artificial intelligence. The development of a web-based distributed tool for simulating changes within causal maps can help integrate the DI and SA desiderata, generate new types of applications of the sort favored by multinational corporations and international businesses, and thus take strategic theory to a new level.
2. Two desirable features for strategic inquiry
2.1. Dialectical Inquiry and the surfacing of hidden assumptions
Let us first address the content orientations desired from approaches to strategy facilitation. Although modern strategic theory harks back in a sense to the resource orientation of its founders, its current recognition of the import of dealing with uncertainty has affected the way in which strategy design is undertaken [4-6]. While the debate between the proponents of the “design school” (once led by Ansoff) and the incrementalists of the “strategy emergence” school (maybe still led by Mintzberg) have pedagogical value, they are no longer crucial to the field. The reason is that the modern reckoning of the pervasiveness of environmental uncertainty in a sense dissolves the dilemma.
Much in the way of Hegelian dialectic, a synthesis of the design and emergence concepts has been provided by the philosophical insights of Churchman [7] and his followers [8,9]. In their Challenging Strategic Planning Assumptions, Mason and Mitroff [10] brought Churchman’s ideas to the managerial public. Their book redirected strategy design away from attempting to optimize on the basis of shaky assumptions to a dialectical search for “surfacing” the hidden premises and underlying assumptions on which the strategic options rest. This notion is now broadly accepted.
What is not generally well understood is that Churchman’s idea goes even deeper. While the age-old devil’s advocacy approach to debating still finds supporters [11], Churchman’s concept of Dialectical Inquiry (DI) does not limit itself to questioning the validity of basic underlying assumptions – or even coming up with a defensible counterplan. Rather, projecting Hegel’s thought onto the realm of management, Churchman [12,13] informs us that a fruitful dialogue should be a multi-party interchange among the major stakeholders of a plan or an issue, thus raising the notion of assumption analysis from a traditional devil’s advocacy process to the level of a collaborative search process.
The works cited in the above paragraphs provide mild examples of DI. More elaborate and truer-to-form examples are provided in Ackoff’s writing [14] on the way his Interactive Planning form of consultancy leads the major stakeholder groups of an organization to agree on implementing those configurations ideally desired by most of them. Outside the circle of these systems researchers, DI is still awaiting full methodological integration into modern strategic planning approaches. It will be shown that the proposed CSM method contains a “backward analysis” feature into which a DI component is built.
2.2.The second desideratum: scenario analysis
The ascent of scenario analysis (SA) method appears to have had many sources, most of which not unknown to the readers of Futures. Going all the way back to the work of the California “Futurists” (among whom Herman Kahn at RAND) in the 1950s, the scenario method gradually spread. An increasing number of texts describe the scenario method [15]][6,16-21], there is no reason to attempt here to encapsulate all this known history.
However, since we deem SA to be a valuable approach to reach for, we take this opportunity to briefly recall its principal advantages. One of them is the point often made by systems theorists that both quantitative operational research (OR/MS) and qualitative process-consulting approaches have, over time, proved disappointing when used in isolation. As Ackoff [14] and Mintzberg [22] have been quick to point out, quantitative methods appear extremely well suited to mid-level tactical decisions but ill suited to upper-level strategic issues.
Yet, however popular purely qualitative process consulting is with behavioral scientists, there is little evidence that it adds strategic clarity rather than just disseminating good feelings through facile and non-taxing solutions. Moreover, its focus is also on tactical issues, so little is gained by it at the strategic level. Thus one reason for promoting SA is that it holds the promise of blending qualitative and quantitative analytics in a unified approach as witnessed by work at the Battelle Institute.
Another reason is that scenarios can be used to deal with complex, interrelated real-world problems. One of the main challenges of strategic management is how to deal with pervasive uncertainty, and the classic broad environmental typologies do not suffice [15,23,24]. To be effective, one must analytically address the complexity of the entire situation rather than propose solutions to single problems [10,25].
As pointed out by Eden [26] and Ackoff [14], the messiness of reality requires a shift from problem formulation to expressing the messiness of the entire situation. Strategic uncertainty can be the crucible in which the organization’s future might melt away [4] but, opportunistically managed, it could also become the obstacle course in which to pass one’s competitors [6]. Current strategic thinking is that the analysis of change scenarios would allow a firm or institution to design winning strategies, and plan their successful implementation, in spite of situational messiness and uncertainty. The admission of SA into mainstream strategic thinking has become a reality to be acknowledged. It will be shown that our CSM method includes a “forward analysis” feature into which an SA component is built.
3. The quantitative and qualitative approaches to be bridged
3.1. Quantitative power of System Dynamics
The previous section dealt with the content-oriented desiderata. In this section, we turn our attention to the methodological range of potential approaches. The early treatises in OR/MS, dating back to the 1950s and ‘60s already provide for describing complex business situations mathematically as a set of equations. Ackoff [27] offers a clear terminology: diagram and set of equations expressing it constitute the model of the (business or problem) situation, and simulation of the propagation of change among the variables in the model (or “running the model”) is a way of bringing to life what could otherwise remain a set of dry, abstract relationships among the symbolic variables of a model.
Soon enough simulation languages aimed at describing operational and engineering tasks appeared on the scene. GASP, SIMSCRIPT and GPSS simulated well the occurrence of discrete events, and were ideally suited for use in scheduling or tactical management. No such decision aids were aimed at the strategic level until the seminal work of Jay Forrester at the engineering school of the Massachusetts Institute of Technology. Reasoning, like cognitive mappers, that management complications are often due to the presence of time of “feedback” loops and time lags, he devised a theory for simulating interconnected systems called System Dynamics (SD) and developed, with the Pugh-Roberts firm, a simulation language for strategic management situations called DYNAMO[28].
SD is a powerful analysis tool for examining the conditions under which change might occur as well as the extent of it. It was used in the important 1972 and 1992 “world dynamics” studies by the Club of Rome to judge the survivability of our planetary ecosystem. In addition, it has occasionally provided inspiration to authors looking for a new perspective on competitive dynamics [29]. And others [30] have found a way to use SD as a device for group model building. Yet the original DYNAMO software had retained from its roots unappealing and cumbersome graphics and notational system. Still, a number of authors, especially Morecroft [31] and Senge [32], tirelessly worked at promoting SD. However, the “techie” appearance and feel of the method militated against widespread diffusion.
Senior managers find cognitive mapping appealing but are generally indifferent SD and its offshoots, even though fairly simplified and user-friendly PC-oriented systems (DYSMAP2, VenSim, STELLA and “iThink”) have been available for a while now. In recognition of this, Nancy Roberts and a team of SD devotees initiated a qualitative approach to SD. In a very readable book, Roberts et al. [33] had the idea of preceding the complex flow-graphing of SD modeling with the drawing of causal-loop diagrams in which the arrows bear a direction and a sign, in the style of the “influence diagramming” promoted by Maruyama [34]. Other members of Roberts’s team, and Vennix, worked to extend her thrust by using SD to aid and systematize group model building [35]. Finally, Eden and some of his collaborators have devised a cyclical consulting process combining quantitative SD and qualitative CM components [36]. As will be seen in Section 5, this powerful combination of features is methodologically echoed in the design of our proposed CSM.
3.2. Qualitative problem framing and cognitive mapping
In spite of Roberts et al.’s and other valiant efforts to bridge the gap between the problem formulation and scenario simulation steps of SD, by and large the method has not taken hold. We attribute this lack of (otherwise well deserved) success to the fact that these approaches to popularize SD were proceeding from the upper, quantitative end of the problem formulation spectrum down toward its lower, qualitative end. Cognitive mapping (CM), on the other hand, has been far more successful with the general public because it proceeded in the opposite direction, namely from initially easier qualitative approaches to increasingly more complex ones, such as the one we promote in this paper.
As advanced by the theorists of the systems approach [10,12-14,37], the process of strategy making is an interactive, dialectical process that surfaces the emergent strategic assumptions present in an organization and allows them to become an explicit part of a rational strategic plan. A tactical system is then designed that implements the proposed strategies, and tactics are grouped into programmatic actions around which resources are committed. Implementation of programmed actions is evaluated using a structured process of reflection and the cycle repeats itself with a new round of vision, contradictions, etc. This cycle of organizational learning uses explicit rational models as a tool for organizing experiential learning in a continuous improvement process that is both participatory and structured [38-42].
The underlying methodological thrust to which our research subscribes is that traditional problem solving is giving way to problem “framing” or defining [37]. More boldly, some authors following the systems approach claim that single problems cannot be isolated from the surrounding messy realities. This no underhand or disguised incrementalism, but a flexible approach to strategy design whereby an attempt is made at capturing the entire strategic situation in one fell swoop. Such authors no longer talk of problem formulation but of problem framing or, better still, situation formulation[14,26,43].
Cognition is important in confirming and redesigning strategy, for managing complexity and organizational change, for strategic decision making and problem solving [5]. Intuition can be informed and creative processes can be structured. As used by management theorists, cognitive mapping has focused on the challenge of finding illustrative, often visual approaches to the synthetic and creative process of strategy conceptualization. Graphic representations are well known in the management and social science literature as both systems analysis tools and knowledge representation techniques [44].
Cognitive mapping integrates the naturally emergent strategizing of Mintzberg with the deliberately rational learning approach advocated by Ansoff; in recognition of this, it has been gradually moving to the mainstream of strategic thought [Eden & Spender, 1998]. In addition, Forrester [28], Maruyama [34], Weick [45] and Eden et al. [2] all show that complex management situations can be captured by some variety of CM. A number of specific approaches have been developed; they will be reviewed in the next section that will gradually introduce the specific causal mapping method we advocate.
4. Existing varieties of cognitive mapping and structuring
4.1. Flexibility of graphic representations in cognitive mapping
As the domain of strategy analysis shifts from algorithmic problem solving to capturing the essence of strategic situations, the variety of graphic representations of influence networks is thrown center stage. The early qualitative cognitive mapping (CM) derived from cognitive psychology. From Hinkle’s “implication grid”, Armstrong and Eden [46] derive an implications map in which single or double-directional arrows indicate which bipolar constructs imply or are implied by others. While the implication map could be duplicated with some effort by longer statements in prose, the concept map in which a central concept is seen to relate in several directions to many others provides in one glance an even more synoptic view of an entire mental view [47].
Even more flexible is the general-purpose cognitive map found in Eden et al. [2]. In one of their examples, the element or node “number of additional dwellings needed” positively affects the node “Conversion of upper floors of commercial buildings into flats”; also, both of these nodes positively influence the decision box or node “Do/ do not/ separate off upper floors on renewing lease”. At the same time, the “Conversion” node and another stating that “Building is in short supply” influence the result “Get/ do not get/ best out of existing stock of buildings”; and so on to a whole network of inter-connected events or variables.
Basically, cognitive mapping is very flexible because the elements of cognitive maps are freely selected without the strictures of the complex syntax of graph theory or formal logic. Rosenhead [48] and Huff [49] each review a number of CM approaches; between the two, a reasonably complete picture of the flexibility of cognitive mapping emerges. The nodes in CM could be statements, events, actions, impressions, quantifiable variables or even decision rules – and even sometimes restrictions or limitations. Sometimes the term cause mapping is used to denote cases in which the influences are presumed to be causal [49], but Eden, Ackermann and Cropper [50]point to the lack of established usage. In the remainder of this paper, we will use the more grammatically and euphonically acceptable expression “causal mapping”.
4.2. Formal graph and matrix methods
Even though graphic representations are now well known in the management and social science literature as both systems analysis tools and knowledge representation techniques [44], they seem to have originated in sociology and anthropology where there arose a need to model a number of social structures, such as the authority structure and communication structure. Since these are to be done one at a time, these “sociometric structures” were modeled by sociometric graphs. Contrary to the informal flexibility of the varieties of CM, sociometric approaches use formal or mathematical graph theory in which a rigorous and unique interpretation is ascribed to each type of node or line (link) in the graph.
Rigorous approaches to sociometry have been developed by using mathematical graph theory; Harary, Norman & Cartwright [51] provide an early yet impressive compendium of most of these in a single theoretical text devoted to the study of directed graphs or “digraphs”. Their results include the degree of reachability of an element from another, the number and severity of cycles in the structure, and so on. Using the indegrees and outdegrees, or number of lines going into or out of a node, and other layout characteristics, the analysis of the properties resulting from topological characteristics constitutes the structural analysis of a digraph.