Civil Engineering September 2022 | Vol 30 No 8

46 September 2022 Civil Engineering of accelerated pavement testing (APT) with the heavy vehicle simulator (HVS) 4 on road research in South Africa. It has world-wide recognition and calculating the significant economic benefit of research with the HVS 5 has also been well published. Figure 1 demonstrates the broad systems approach of the full lifecycle of R&D. It is viewed from left to right and starts with blue skies or basic research, delivering often discrete packages of knowledge that literally drift, still unconnected, in a sea of non-knowledge or ignorance. The adaptation applied to the original Figure 1 to demonstrate the concept of true ignorance or areas of non-knowledge (shaded pink) in the basic research phase shows ignorance is much larger than the discrete pockets or islands of knowledge obtained. The further processing of discrete pockets of knowledge is like a bullhorn which very selectively attracts pockets of knowledge into the narrow entry area of defined technology field of application. By nature, the initial demonstration of applied technology tends to be small and frail. The application happens via tech- nology coupling of existing knowledge or technology and new knowledge. The areas of application or demonstration of tech- nology increasingly have greater success in coupling with other larger pockets of established knowledge. This all happens in the expanding bullhorn area or volume towards the right, defining the technology field of roads and transport R&D. In this defined field of knowledge, new knowledge gained and successfully coupled forms increasingly larger tasks or domains of confirmed knowledge, developing towards the right-hand output side of full-scale implementations and engineering tasks. The aforementioned HVS programme is a very good example of covering all phases of development and technology implementation which ulti- mately led to the outcomes on the right: products, services, processes, guidelines and policies. 6 The focus on the tasks and demonstra- tion of knowledge tasks are exposed to well-known preconceived and hindsight bias effects. This human trait tends to reconfirm and strengthen such limited technology tasks as the perceived total sum of knowledge. The limited nature of the acquired knowledge is illustrated by the pink highlighted background of ig- norance or lack of knowledge in between the areas of knowledge in this technology field. This field of ignorance is largely ignored due to institutional focus on the knowledge areas. Research in South Africa in roads and transportation had lost the funding and support for fundamental research in the late 1980s. During that time, the research focus at the country’s premier research in- stitution, the CSIR, switched to the open market in anticipation of dwindling gov- ernment support for such basic research. Limited support by semi-government in- stitutions involved in R&D tried to fill this gap of basic research at the start of the whole R&D lifecycle. The inevitable result is that the areas of ignorance increased both in the research phase as well as the implementation phase. IGNORANCE VERSUS THE PERCEIVED CERTAINTY OF EXISTING KNOWLEDGE Ignorance should not be seen as the darkness implied earlier for successful mushroom farming. It is rather that an awareness is needed to emphasise the vast area of lack of knowledge. There is also a need to indicate that it needs a different approach and method to address than with normal research activity. It is worth looking at the difference between knowledge and ignorance as por- trayed conceptually in Figure 2. Ignorance is not a mirror image of knowledge, but the unknown void around pockets of knowledge, as described in Figure 1. That ignorance around the discrete islands of knowledge is what the focus should be on during the execution of basic research. We now know from the composi- tion of technology trees, that without the influx of these discrete pockets of knowledge from the blue skies area no growth in the technology tree can take place. The ignorance is also present in the implementation phase. Even in this phase of R&D such tasks or demonstrations of gained knowledge are not always as definitive or carved in stone as may be implied by end products like specifica- tions and or guidelines. Ask any person doing forensic investigations whether specifications alone give enough cover for the actual variance in real life materials, environments, etc. when landing in arbi- tration courts. It is the positive aspect of recogni- tion of lack of knowledge that should be pursued via search and innovation (new inventions and creativity). Figure 2 states that it is hard to search for something if you don’t know what you are searching for yet. Conceptually, research activity, as per the real meaning of the word, implies searching again or repeating the search for basically existing knowledge. As previously indicated, the outcomes towards the end of the R&D lifecycle shown in Figure 1 are: products, services, processes, guidelines and policies. These are defined as codification practices. 7 “Such knowledge codification practices, by their very nature, lead to ignorance since they involve the reduction of often complex rich knowledge to those compo- nents that are central for the task at hand”. Of greater concern is when ignorance or limited knowledge is deliberately manipulated to produce agnotology. Agnotology 8 is by definition, “the study of culturally induced ignorance or doubt, including wilful acts to spread confusion and deceit. ” AGNOTOLOGY BLUEPRINT According to Pinot (2017), 9 the “father of the agnotology concept”, Proctor (1995), 8 analysed the tobacco industry and identified their blueprint to create doubt and confusion for political and socio-economic benefit. This blueprint was subsequently used by other major The study of Ignorance is seldom discussed, because studying the absence of something is incredibly difficult. Epistemology , or the study of knowledge. This field helps define what we know and why we know it. (Confirmation and hindsight biases) Search Research Figure 2 The conceptual difference between knowledge and ignorance