Forms of Reasoning in Pattern Management and in Strategic Intelligence
The contemporary world is full of information. It has been said that in the seventeenth century, an average person acquired the same amount of information in their whole lifetime about their world as we get from a single newspaper every day (Scholte 1996)
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Forms of Reasoning in Pattern Management and in Strategic Intelligence Tuomo Kuosa
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Introduction
The contemporary world is full of information. It has been said that in the seventeenth century, an average person acquired the same amount of information in their whole lifetime about their world as we get from a single newspaper every day (Scholte 1996). The amount of information flowing constantly around us is huge, but only a small fraction of it is useful or valid for us as such. Not so long ago, information and knowledge were scarce and therefore very valuable. Nowadays, most information is free and easy to access, but a rapid understanding of it is rare (Weick 2001). Sense-making requires more than just reading empirical data. It calls for valid expertise, good methods, time and resources (Kauffman 2000; Foreman-Wernet 2003). Strategic intelligence solutions involve services and consulting which help enterprises and consumers to always obtain the most valid and up-to-date information and strategic understanding of the issues in which they are interested. Why is strategic intelligence emerging? The world is not only full of loose information but it is also more complex, interdependent, hectic, nonlinear, coevolutive and less stable (Casti 2000; Kauffman 2000). The structures and processes of social systems involve increasingly large networks like the Internet (Kauffman 2000). There usually exists a whole network around a certain issue, which is called the network’s macro-level. On the meta-level, the whole network usually further self-organises into local clusters. Inside these meta-level clusters are clusters of micro-level agents that are, for practical reasons, more strongly linked with their ‘neighbouring’ nodes than the nodes in distant locations (Cilliers 1998). These micro-level agents, for instance, individuals, are often called complex adaptive systems (CAS), since they are able to share knowledge, change their behaviour or learn owing to their local interactions
T. Kuosa (*) Alternative Futures, YATTA, Eteläranta 6, Helsinki 01300, Finland e-mail: [email protected] M. Giaoutzi and B. Sapio (eds.), Recent Developments in Foresight Methodologies, Complex Networks and Dynamic Systems 1, DOI 10.1007/978-1-4614-5215-7_6, © Springer Science+Business Media New York 2013
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(Kauffman 1995). The complexity or unpredictability around the network clusters or individual CAS is revealed by their underlying relationships with other network systems, synchronous self-organisation processes, relevant feedback loops, coevolutions, etc. Eve Mitleton-Kelly (2003) uses the concept of complex evolving systems (CES) to describe actions and learning in this whole rugged landscape. Because the members of each network cluster share more knowledge in their local interaction, not all the clusters of the whole network have the same information. The dissonance of information increases as the whole network grows. At the same time, however, its ability to preserve information is increasing, thanks to the clu
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