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The art and science of the possible

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The art and science of the possible

Tag Archives: models

On why the pursuit of truth is an asymptotic affair

21 Thursday Nov 2013

Posted by lnedelescu in complexity, knowledge, learning, paradox, philosophy, society

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Absolute, Cosmology, divinity, George Box, Knowledge Funnel, Leonardo Da Vinci, models, Murray Gell-mann, Progress, Reality, Roger Martin, Singularity, Sistine Chapel, Truth, Universe

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Roger Martin’s “knowledge funnel” is a very useful model for understanding the human pursuit of knowledge. Man contemplates a new mystery using intuition to infer causality, by trial and error arrives at an inexact approach that somehow seems to tame the new mystery before finally framing the new phenomenon with the objective precision of a rigorous formula. This is the process by which the vast unknown is distilled into bits of knowledge that our puny minds can manipulate.

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Management consultants as educators

24 Thursday Oct 2013

Posted by lnedelescu in business, consulting, management

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Amit Goswami, Best Practices, business, Clay Christensen, complexity, Consultants, Cybernetics, Dave Snowden, Distinctions, Educators, Gurus, management, management consulting, Management Thinkers, Methods, models, Peter Checkland, Peter Drucker, Recipes, Roger Martin, Russell Ackoff, Systems

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I have heard my share of reservations about management consultants. Many see business consulting as a shallow field devoid of real substance. As the old saying about no smoke without fire goes, there is likely good reason for the distrust of this modern profession. The great Russell Ackoff himself distinguished between two types of consultants: self-promoting gurus and educators.

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Post-causality: a quiet global revolution in the making

05 Wednesday Jun 2013

Posted by lnedelescu in business, capitalism, complexity, consulting, democracy, future, human capital, innovation, knowledge, management, philosophy, problem solving, society, taxonomy, technology

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Big Data, business, Categorization, causality, Cause and Effect, complexity, creativity, Cynefin, Daniel Pink, Dave Snowden, Drucker, Drucker Forum, Emergence, future, Imagination, Innovation, Knowledge, management, Methods, models, Motivation, Peter Checkland, Resilience, Revolution, Roger Martin, Russell Ackoff, Safety, Sense Making, Social Systems, Society

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If one were to cut a global cross-section through social classes, nationalities, ethnicities, ages, professions, genders, and so forth, very few commonalities would emerge. And yet, there is I propose just such a common thread: a shared causality mindset, a globally predominant belief in the supremacy of cause and effect.

Since it is people who run our institutions, this belief continues to shape our modern society and even influence to a large extent the technological outcrops of our knowledge economy. From business strategy to macroeconomic models, and from political debates to Big Data, causality is pervasive and its implications profound.

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Frameworks, trade-spaces, matrices: engineering thinking in management results in big, stagnant bureaucracies

25 Thursday Apr 2013

Posted by lnedelescu in complexity, consulting, human capital, management, Organizational Development, science, strategy, taxonomy, technology

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Analytic Thinking, bureaucracy, business, complexity, effectiveness, Engineering, future, management, models, philosophy, Validity

The most important function of management, particularly executive management, is setting future direction. That implies decisions and choices about the present and future.

Because engineering thinking or more broadly speaking analytic thinking predominates in many executive and consulting circles, it is believed that decisions require a degree of rigorousness similar to that of the scientific method in natural sciences. And so, it is firmly believed that analytic tools empower managers to make sound decisions. The result is a myriad of tools reminiscent of engineering speak – frameworks, trade-spaces, matrices – packaged in neat Power Point slides.

This all very good, but, as philosopher of science Isabelle Stengers remarks  “tools are demanding – they do not confer the power of judging, they ask for the choice of the right tool for the right situation; in other words they oblige us to think and wonder”. The danger that Stengers cautions against is the rigid interpretation of the power of tools. Tool power should never be situated above human judgement. And when it does, this results in the tools getting a life of their own, and embedding the human element which is helpless to escape their hold. This ultimately results in a bureaucratic construct as the purpose of humans becomes not the seeking of meaning and validity, but rather the maintenance and upgrading of the tools. This also results in a proliferation of enforcer types at the expense of creative types, reducing the number and quality of choices about the future.

A more progressive view of management tools is as “enlightening abstractions, precious new tools for thinking” rather than “ready made instruments”. Also, in Stenger’s view, the relationship between user and tool is not one-directional; rather, “tools modify the ones who use them; to learn how to use a tool is to enter a new relation with reality, both an aesthetic and practical new relation”. In my experience, this dual directionality can also unfortunately work backwards: rigid tools can have a limiting effect on thinking.

Source of Isabelle Stengers quotations is “The Challenge of complexity: Unfolding the ethics of science – In Memoriam Ilya Prigogine”

Also check out Dave Snowden’s related blog entry.

How to approach wicked, ill defined problems

17 Sunday Feb 2013

Posted by lnedelescu in complexity, problem solving

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causality, complexity, creative thinking, critical thinking, models, problem solving, relationships

I have been dealing with ill defined problems for quite some time.  In this post, I will try to capture the essence of my approach to wicked problems in a five step process.

1. ELEMENTS – THE BUILDING BLOCKS: mentally walk through or discuss scenarios associated with the problem and note key elements that pertain to the problem space as they emerge. At this point, elements can be loosely defined: it is better to have a larger list of elements that are vaguely defined than a smaller list of clearly defined elements.

2. LOGICAL CLARITY: conceptually strengthen the problem’s elements so as to reduce logical ambiguity. Explore and resolve overlaps, hierarchy. Categorize as much as possible. Reduce the original list of elements to the smallest possible set.

3. CAUSAL RELATIONSHIPS: walk through problem scenarios again, this time exploring the causal relationships between elements. This represents the “static” structural characterization of the problem.

4. MODEL: architect a model to capture the problem’s dynamics (the main difference between a static and dynamic characterization of the problem is the time dimension that exposes the evolution of the problem’s elements and their relationships). Validate the model by exercising a few “what-ifs” derived from the original scenarios. Based on the type of model that fits, categorize the problem type if possible.

5. EXPLORE: apply initial questions pertaining to the problem to the model and explore non-obvious insights. Exploring implications should increase the understanding of the problem.

In summary, the approach to wicked problem solving is: ELEMENTS – RELATIONSHIPS – DYNAMICS.  The more complex the problem, the more sophisticated the model, and more iterations likely required.

A key trick is that the process may not be followed sequentially; that is, one could jump between the various steps as necessary (for example, while working on the model in step #4, one may need to jump back to step #2 to additionally clarify a particular element, or to step #3 to re-evaluate a particular relationship).

Finally, the entire process requires a diversity of mental skills: creative thinking for scenario walk-through; critical thinking for logical strengthening; design thinking for model architecture.

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