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The art and science of the possible

Tag Archives: causality

Big idea 2014: the world still defenseless against mediocrity

05 Sunday Jan 2014

Posted by lnedelescu in democracy, future, human capital, society

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2014, causality, creativity, Credo, democracy, Frank Lloyd Wright, If-then, Mediocrity, Progress, Society, Steve Jobs

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From Ivy League scholars to country presidents, mediocrity permeates society’s highest echelons. Indeed, we remain defenseless against it in the 21st century. We are hard at work battling world financial crises, poverty and social inequality even as we produce mediocre leaders who proliferate populist, symptomatic, efficient but not effective “solutions” to these and other pressing issues. Democracy itself, the most advanced social construct to date, is no match for mediocrity. I propose keeping mediocrity in check is a much more effective way to go about our world’s progress. We would first need to understand how it evades society’s filters.

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The rising toll of the (still) predominant mechanistic mindset in a complex world

11 Tuesday Jun 2013

Posted by lnedelescu in complexity, Crisis, democracy, future, society

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causality, Cause and Effect, Charles Handy, complexity, David Hurst, democracy, Ecology, future, Mechanistic, Mindset, Resilience, Robustness, Society, Thinking

Mechanistic_Thinking_EA

Thesis: there is I believe a meta-societal, global shift from robustness to resilience (see this for an intuitive illustration of the difference). This is fueled by an underlying transition from a mechanistic (Industrial Revolution) to a complex-adaptive (Conceptual Economy) worldview.  We have managed to design robust systems (economy, air traffic, healthcare, energy), but not resilient. Robust systems are great for quasi-stable environments, but the price for not having resilience in highly dynamic, networked environments is staggering: $12 trillion for the 2008 financial crisis, and counting. Unless we learn how to design resilient systems, likely through the application of complexity principles, democracy itself may be at risk.

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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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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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