Any firm can consist of two elements. The first part is called "run," and it is defined by a limited level of uncertainty; in this part we can use processes and models that we already have, i.e. they are ready-made. The second part, dubbed «change,» is marked by a high level of uncertainty; here, we generate assumptions and hypotheses, as well as construct new processes, models, and products.
How do we act in the face of uncertainty? What can help us make the most effective decision? In a complex context where cause and effect relationships are unclear, there is a significant level of uncertainty, and the external environment is constantly changing, we need to constantly push the boundaries of our vision.
The complexity theory proposed by Dave Snowden can help broaden our comprehension of the world around us. Complexity theory is a semantic framework that can form the basis for decision-making in a variety of situations and contexts. It was developed by the author in 1999, when he worked at IBM to improve the effectiveness of intellectual capital management.
The key points of this theory are the following:
- Cynefin in Welsh means habitat, a place to store your property;
- Cynefin is a sense-forming framework, not a categorization framework;
- In categorizational models, frameworks embody the data;
- In sense-making models, by contrast, the data embody the frameworks;
- All systems can be represented by agents of interactions and cause-effect relationships;
- Depending on what we know, they can be assigned to one of the following domains: Disorder, Simple, Complicated, Complex, Chaotic.
Let now consider the provisions of this theory in more detail. So, as noted above, in accordance with the provisions of complexity theory, systems are composed of agents of interaction and causality, and depending on what information we have, they can be assigned to one of five domains: Disorder, Simple, Complicated, Complex, Chaotic.
Simple systems are characterized by the fact that the number of interacting agents and cause-effect relationships are obvious and repeatable, so the decision-making model looks as follows: Sense -> Categorize -> Respond.
In Complicated systems a different model of decision-making is applicable, because the information about cause-effect relations and the number of interactions between agents is not clear, and we need expert support to make decisions, so the model looks like this: Sense -> Analyze -> Respond.
As for Chaotic systems, the following can be said about them. With respect to these systems it is impossible to determine the number of interaction between the agents and cause-effect relations due to the lack of necessary information, so in this case the following decision-making model is applicable: Act -> Sense -> Respond.
In Complex systems, interactions and cause-effect relationships are characterized by unpredictable results and the decision-making model looks as follows: Probe -> Sense -> Respond.
The Disorder domain is characterized by a lack of clarity as to which of the other four domains are applicable here , so it is necessary to divide the problem situation into separate elements and assign each of these elements to one of the four domains and make decisions on the appropriate model.
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Thus, based on the key points of the theory of complexity, we can more correctly assess the problem situation and, accordingly, make the best decision.