Finding 3b: The effectiveness with which an organisation can represent

If the memory of an organisation emerges through a combination of documents, data, and interactions, organisational learning therefore depends on the ability of an

organisation to represent dynamic interactions as well as documents and data. Representation in this context can be understood as ‘a formal system for making explicit certain entities or types of information, together with a specification of how the system does this’(Marr, 1982, p.20). Finding 3a revealed the difficulty in storing and making available the knowledge that exists in an organisation, with the

implication that there is a need for organisations to capture and make searchable the dynamic interactions between employees in addition to capturing data and documents. One interviewee provided an example of how this was attempted at a large

organisation in which he had worked:

‘It was like a knowledge management tool, I can’t remember what it was called. But essentially it contained articles, credentials, or examples of deliverables that _________ consultants deliver, so projects that are complete – there’s probably hundreds of

thousands now. But also, people are on that forum, and you can just type in a question, and it’s linked in to your email, so you get a question pop up in your email – you join a particular group, so I would have been in the customer experience group, and a

question pops up, and the 10,000 people around the world who are members of that group get that question, and then you respond if you’ve got something to say. So technology there is a huge enabler.’ Interviewee 10

A key property of complex adaptive systems is the way in which knowledge is represented across each node in the system (Cilliers, 1998). A distributed

understanding of representation is comparable with Derrida’s post-structural argument that meaning results from a dynamic interplay between all of the words – or ‘signs’ – in a system. Building on this perspective, Cilliers observes how in a complex system, ‘meaning is conferred not by a one-to-one correspondence of a symbol with some external concept or object, but by the relationships between the structural components of the system itself’ (p.11). Importantly, the survival and success of the system is largely dependent on how effectively it can exploit its knowledge in in order to inform how it interacts with its environment (March, 1991).

Theme 2 established that dynamic interactions in a human organisation generate a range of information. The findings indicated that the ability of artefacts to support effective learning and decision-making is limited, as artefacts lack much of the observational, performative, context-dependent data, and an understanding of how that data was shaped through discussion. Theme 1 also established that interactions are rendered more problematic by digital tools due to the reduction in information that is required for transmission across a digital network. Cilliers (2000, p.86) shows how our entire approach to designing computer systems is based on semiotics and the use of symbols to represent information. However, the problem with such an approach is it assumes that complex information can be reduced to specific symbols and then represented in a machine, and it is for this reason that Cilliers argues how attempts to store and manage knowledge in a computer system are problematic. The use of any visual symbol to codify and represent information requires interaction from the

observer to decode the information, and this interaction and subsequent reconstruction leads to emergent meaning. Attempts to replicate knowledge visually are guided by the same reason that email has become the dominant way of exchanging information

in an organisation – it is convenient. However, using the perspective of post- structuralism and complexity, it can be argued that any symbolic representation of knowledge will increase the diversity of meaning emerging through reconstruction.

While diversity of ideas is essential for the survival of a complex adaptive system, consistency of meaning during interactions is also important in order to minimise unintended complexity (Espejo, 2003). The data indicates that while documented knowledge ‘opens up’ opportunities for learning, it is only through interaction and discussion that this knowledge can be interpreted and effectively applied in a new context. This reflects the intersubjective view of learning (Hollan et al., 2002) and the idea that learning consists of interactions (Koschmann et al., 2005). Cilliers (2000) notes how it is all too easy to fall for a general theory of representation because ‘a text may have to be interpreted, but an image speaks to us directly, or so we believe’ (p.82 – emphasis in original), and observes that post-structural theory in fact strongly rejects this approach to representation because an image is just as open to

interpretation as text. From such a perspective, attempts to represent knowledge using documents and data that ignore the ways in which people interact with this

information will hinder the effectiveness of organisational learning.

A key problem with studying CAS is that effectively modelling such systems requires a system of equal, if not greater, complexity. This problem has been defined as the ‘Law of Requisite Complexity’ (McKelvey & Boisot, 2003), and can be understood as meaning that ‘a system must possess complexity equal to that of its environment in order to function effectively’ (Uhl-Bien et al., 2007, p.301). As there is currently no system in existence with the ability to equal or surpass the complexity of the human

brain, the only truly effective way to capture all the information resulting from dynamic interactions is using another human brain – in other words, people are the best way of capturing and storing the complex information emerging through interactions. If distributed representation is an effective response to storing and accessing dynamic interactions in an organisation, leveraging distributed

representation is possible through a more intentional use of adaptive space, and this is explored further in the Discussion chapter.