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Graphical Representation

It is often more comprehensible to display results of an analysis method as an image, a graph, or other form of illustration, than as purely written text.

We argue that there is a reason for this phenomenon. Besides containing descriptions of the facts surrounding an incident, say written in a language L, a written text must also indicate the causal interrelations of those facts, which necessitates some use of specific technical language, say T, along with a method to distinguish this language from the language in which the facts of the incident are expressed. This method would usually be the syntax: T would use keywords, and a certain sentence structure, for example, in which the factual sentences in language L would be inserted. This embedding of L in T, as well as the necessity to distringuish the syntax of T from that in use already in L, leads either to more complicated sentence structures, or to oversimplification of the causal assertions. Neither of these features is desirable: the first leads to cognitive complexity, and the second leads to information loss.

However, a non-textual representation of the causal interrelations and other identified structure, such as taxonomies of failures, facilitates the cognitive distinction of this structure from that of the facts themselves. The causal and taxonomic structure is perceived visually, and the incident facts linguistically, and these two cognitive capabilities are largely independent. The complexity of the understanding of the facts remains at the level of the language L; the visual structure is at the level of the language T (with the embedded L-expressed facts treated as atomic), and the cognitive complexity of the representation remains at the level of L added to T, rather than L embedded in T, as with a purely text-based representation.

Anecdotal experience with text-based and graphical representations of the results of causal analyses has indicated that a representation of causal information as a directed graph allows both experts and non-experts alike to interpret the results more easily, as well as to see structural features that could well be hidden in a textual representation. Examples of such renderings are WB-Graphs (see the WBA Home Page at http://www.rvs.uni-bielefeld.de) and the causal graphs of Pearl (Judea Pearl, Causality: Models, Reasoning and Inference, Cambridge University Press, 2000).

The desirable properties of a graphical representation are:

In pursuit of the second property, it is desirable to display the results of a causal analysis as a unit, say a page in some standard format. The practical limit of page size is A0 in the international standard sizes. If the causal relations can be displayed graphically on a page of size less than or equal to A0 while remaining readable, then there is good reason so to do. If this cannot be accomplished, some method of factoring the results must be applied to render them over multiple pages. A factoring method is desirable even if results may be rendered on one page, for different page sizes are used for different purposes: an A0 rendering of a graph is adequate for comprehension and discussion, but is inappropriate for a report, for which A4 is the standard size.

One method of factoring which has proved relatively helpful for causal graphs in particular is graph-theoretic factoring, in which graphs are separated at their places of least width and each "chunk" is rendered separately, with the cut-set nodes identified in each chunk through color. The entire graph is rendered small, to exhibit the overall graph structure including the (colored) cut-set nodes while rendering any text unreadable. The individual chunks are rendered on separate pages. An example is the WBA of the 1993 A320 Warsaw accident (Ladkin, Höhl), available through the Publications page at http://www.rvs.uni-bielefeld.de This factoring, however, has limited application to situations in which the width of the graph is large (say, greater than five nodes), or in which a large proportion of the graph lies in one chunk. An example in which this factoring would seem to be poor is the WBA of the 1995 Cali accident, also at http://www.rvs.uni-bielefeld.de. This type of factoring obviously does not apply to representations of the results in forms other than that of a graph.

Another method of factoring consists in identifying subsystem involvement and rendering factors which concern an individual subsystem as one unit, with different units for different subsystems. Such a method was used for the WBA of the Ladbroke Grove accident (de Stefano) to factor a WB-graph whose minimal readable rendering is likely A2 as separate A4 units. (Contact the author, Ernesto de Stefano for a copy of his report, in German).

Some representational difficulties may be caused by orthogonal features of causal explanations, such as identifying causal factor relations on the one hand, and classifying them (say, according to latency/immediacy features; according to time of occurrence, which may well involve long intervals or recurrency for latent factors; or according to some taxonomy of human error or of organisational behavior). Attempts to represent all these features in one unit may lead to visual clutter and thereby to cognitive complexity. Some form of factoring is required in such cases.

Ideally, a graphical representation could also display the history of the analysis. For example, the method SOL and its tools (contact Dr. Babette Fahlbruch) infers the existence of causal factors in the structure of the organisations involved in an incident (called "indirect causes") from the ostensive facts concerning the progression of the incident (a "situational description") using a checklist/questionnaire style approach based on phenomenological and structural taxonomies, designed to control for the "heuristics" which lead to bias in using such "checklist" approaches to causal information gathering. (SOL is to our knowledge unique among causal anaylsis methods in attempting to control for heuristics.) Not all heuristics are known, and not all known heuristics have accepted controls. It might be judged that a visual rendering of the history of the analysis would allow one more easily to identify and control for heuristics whose features may not have been fully accounted for in the version of the method being used.