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Managing complexity is managing abstraction
The Kaminsky method is adept at resolving complex challenges by addressing two hidden pitfalls that leaders encounter. Due to this, it provides a broader perspective for navigating complex scenarios.
1. Traditionally, companies overlook the crucial distinction between complicated and complex problems. They tend to address complex tasks as complicated ones despite their distinct nature.
* The Kaminsky method recognizes these differences and focuses specifically on the complex issues that underlie major challenges (Learn more >)
2. Companies typically encourage teams to make precise decisions, a practice conflicting with the Incompatibility Principle of fuzzy logic: "As complexity increases, precise statements lose meaning, and meaningful statements lose precision".
* Fuzzy logic of Lotfi Zadeh - a branch of maths, that generalizes classical logic and set theory, providing a broader framework for managing complexity.
COMPLICATED is "layperson's complication".
The term 'COMPLICATED' suggests the presence of numerous elements, connections, and interdependencies within the system, all of which nevertheless can be identified and the rules of their interaction can also be determined precisely. It's crucial to note that solely an abundance of parts doesn't inherently make understanding or analysis difficult. These details may be organized in a way that is 'clear' or even 'very simple' for professionally trained experts.
* For instance, repairing an airplane or organizing accounting without the necessary knowledge is undoubtedly complicated. However, once you've acquired the knowledge, you can do it and replicate the solution algorithmically as needed. To address such complicated problems, companies assemble teams of professionals who possess the necessary 'know-how.'
COMPLEXITY is when nothing is accurately determined.
Complexity arises when a system also includes many interrelated elements, but determining exactly how they relate or influence each other is impossible. In some cases, even identifying all the elements is an insurmountable task. Complexity is not just a set of parts and also not their sum. It is a chain of dynamic interactions that give rise to new, often unpredictable phenomena—emergent properties with minimum specific predictability, indicating the absence of any concreteness.
* Thus, the nature of such complexity is inherently abstract, reflecting the abstract characteristics of the elements involved.
This abstraction conceals primary challenges, both the macro and micro "black swans" - risks that, by definition, cannot be entirely mitigated through expert knowledge alone.
Consequently, addressing the origin of this complexity requires active engagement with abstraction, whether it aligns with your preferences or not.
Identify or designate an individual responsible for managing or overseeing a particular abstraction within a given context.
Assigning an owner to the abstraction implies responsibility for understanding, maintaining, and possibly making decisions related to that particular abstraction. This involves ensuring the relevance, and effective use of the abstraction within the organization or project. The owner of the abstraction may play a key role in guiding the team or stakeholders in utilizing the abstraction to achieve specific goals or objectives.
In subsequent system descriptions and solution searches, we employ fuzzy logic and fuzzy semantics to incorporate unquantifiable data. This involves expressing such data through linguistic variables and allows the transfer of fuzziness to clarity. To amplify the impact, when it's possible, we introduce the Foreign Language Effect, determining variable values in a language not native to teams and decision-makers. This strategy aims to avoid the fuzziness of communications, contributing to a more comprehensive understanding of the complex system under consideration.
Before anything can be managed, it must be recognized for what it is. This is especially important for complex tasks. You need to ascertain which elements of the situation are complex, which are complicated, and which are combined. Each type of issue needs to be managed in a way that is consistent with its characteristics.
To obtain significant conclusions about the behavior of a complex part of the system, it is necessary to abandon the high standards of accuracy and rigor that are characteristic of complicated systems and to use approximate approaches.
This action seeks to bring contextual awareness to the intended range of outcomes, ensuring that all stakeholders share a common understanding of the range of the purpose they are working towards.
Also, we always iterative refine core goals: recognizing that in complex systems, a range of purposes may evolve or need adjustment over time, due to changing circumstances, new information, or emerging insights.
By scanning the behavior we aim to comprehend the system's responses to different stimuli, changes, or inputs. This understanding is crucial for developing effective strategies to navigate the complexities inherent in the system. Here we also necessarily consider Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure" to avoid diagnostics mistakes.
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