Complexity in process consulting: a good thing?

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A colleague used the word “simplistic” in describing the 10 principles of process consulting offered by Ed Schein.

I interpreted his use of the word simplistic in describing shines 10 principles as a negative thing. There’s a part of me that remembers the 10 Commandments are simplistic too.

In my studies of complexity and chaos theory there is a belief among practitioners that to successfully adapt to or manage complexity requires an equivalent degree of complexity in the manager or leader or organization’s processes themselves. There is rarely if ever evidence offered to support this contention, but it seems to be intuitive. It is the very intuitive attractiveness of that idea that causes me to be skeptical and wonder what the evidence really shows about the need to be complex in order to manage complexity.

The other side of the argument is that a combination of very simple rules in a dynamic environment can cause very complex results, and so I’m not sure that complexity needs complexity to be managed.

If you believe the 10 principles are overly simplistic where would you add some additional nuance to his general advice?

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leadership in complexity: making the game simple

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this is a reflection on strategies of leadership under conditions of complexity

Michelle: as your excellent posting on CAS notes, at certain degrees of complexity we have to adopt simpler and shorter term behaviors, and act with care and humility, measuring results and applying the feedback more rapidly in order to evolve our practice to conform to the new demands of a changing environment.

I think your analogy of a football playbook to be right on target. They have the general principles of how they want to play, but they require this tightly organized group of teammates to adapt in split seconds to the opponent and to the players around them in order to be successful.

I’m reminded of Vince Lombardi‘s strategy of running to daylight in which simple zone blocking that was very physical allowed the line to gain a space advantage and then required the running back to simply find the hole and run through it. The flexibility built into the strategy was perfect for a highly complex and adaptive learning environment.

By rendering the strategy into a simple heuristic that was easy to communicate and understand, he was able to synchronize and simplify the complex situation in to manageable proportions. This is a different strategy than trying to decompose the football problem into smaller subsets, which is still by its nature a control strategy rather than an adaptive strategy.

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leadership in complex adaptive systems

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This is a reflection on Heifetz’ model of Adaptive Leadership, a practical prescription from HBS on becoming an adaptive leader in uncertain times

The Heifetz model of leadership adaptation requires an ability to develop a theory of action, rules of cause and effect that allow us to diagnose a situation and then select an appropriate action, suitably resourced, that incorporates others etc. in order to achieve a more predictable success.

The article you reference shows pretty clearly however that the skills traits and characteristics of leaders in a CAS are different than that of less complex organizations. The idea that simple behavior can create emergent phenomenon that are unpredictable defeats the idea of control systems and cause-and-effect. In the military, we are experiencing this phenomenon with respect to the complexity of nationbuilding in the land where the nationstate is not the natural organizational structure and yet it’s the one that we are trying to compel.

In many ways, culture seems to me to be one of those emergent phenomenon that takes on a life of its own in ways that nobody realized, understood or predicted.

It’s hard to reconcile the idea of independent agents with the usual models of leadership and even Heifetz is model of adaptive leadership since independent agents do not grant authority to others except on a transactional basis.

CAS with high degrees of volatility and uncertainty do not lend themselves to routine diagnosis since every situation may be a data point of one, never to be repeated. These kinds of systems can only be appreciated not understood, and certainly not controlled in the Frederick Taylor sense.

Re: Tamara’s question about decomposing the health care system and decomposing CAS more generally: the answer has everything to do with how tightly coupled the component subsystems are in the CAS.

Loosely coupled networks can allow for local subsystem management because there is a time delay between the changes you propose and the reflective effects from other subgroups affected by your change. The amount of interconnectedness, or the number of nodes that share a direct relationship with the subsystem under study, also influences how much the subsystem can be studied as a separate entity.

As an example, a house of cards can be said to be very tightly coupled and very interoperable with little to no slack. This is a structure that does not lend itself to subsystem management. A different example would be a set of dominoes in which you can create subsystems of dominoes so that if one falls over it doesn’t knock down the entire master system but only the subsystem that you have isolated by removing key connectors. This is how those giant domino structures are built and only at the end do they add the crucial connectors to create the master system.

Dr. Scott Page from the University of Michigan has done some excellent work in this area by identifying four different elements by which we can consider the complexity of a given system.

The elements of complexity that you described give caution to Heifetz is indicators of system underperformance which oversimplifies the challenge of anticipating future underperformance.

In fact we may be performing to the same level in the present while the environment changes around us and the changes have not yet manifested in our performance levels. If we wait until we have a series of underperformance as, it may be too late to begin the adaptive leadership that he prescribes. In general, I find Heifetz too reactive, which is an irony for a book on adaptive leadership.

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Complexity and planning for “endstates”

from a discussion on usefulness or not of the “end-state” concept and planning in general when faced with the challenges of nation building:

Complexity theory says that complexity arises from a combination of 4 attributes: moderate to high levels of interdependence, connectedness,  diversity and adaptation. Complex systems do not lend themselves to cause & effect analysis because thay are not computable, and yet we can act appropriately. For example, we can decide how and when to cross a snowfield that is in a critical state (prone-to-avalanche) to reduce risk, whereas we can plan to cross a snowfield that is not critically organize. Scott Page (2007) has some great insights into these ideas in “Complex Adaptive Systems: An Introduction to Computational Models of Social Life”

When I hear “end-state” with respect to complex situations, I hear a euphemism for “a state sufficiently changed from this current one in order to have a significantly new set of options for action available”.  In the case of an Afghanistan end-state, that could take the form of a narrative of the DIME conditions that allow a dramatically reduced  presence at a “normal” level of geo-political risk. That respects the essentially qualitative assessment of what that description might entail

Using “state” in that sense, the definition or vision of an “end-state” or for any number of “imaginable future states” does mean they have to be orderly, only describable, such that from the description, a strategy can be preferred.

You could conceivable move from complexity to chaos to complicated, with different prblem-solving/design/problem-managing strategies appropriate as the “state” changed.

Decisions for how to act under complexity could be framed in terms appropriate for managing complex networks; that is, acknowledging that strict rules of  cause & effect arent in play, but that simple, sound management principles that are values based can be applied.  Gribben (2004) has some thoughts on these lines in “Deep Simplicity”.

Since political decisions rule, and they are essentially social and therefore qualitative in nature,  I believe our conversations about policy and objectives should acknowledge the language of complexity and fuzziness, while retaining the power of doctrinal precision where it works for us

Design versus planning: what to do when you don’t know what to do

The scientific method has been responsible for the most extraordinary improvement in mankind’s standard of living. Since the Enlightenment and the Renaissance, it has been responsible for every major advance in human understanding and technology.

The scientific method relies on a positivists worldview, which can be said to value certainty, control, objective reality, and planning. Positivism asserts that the objective world exists independent of our senses and that we can reliably understand it through the action of our senses.

The scientific method and the rational analytical outlook on life has been so successful that it seems absurd to question it. In the last couple decades however, there have developed problems of such complexity that the normal response of planning and the scientific method have proven ineffective in solving.

This has given rise to entire new branches of science, such as chaos theory and complexity theory. These kinds of problems have become known as”wicked problems” in the academic literature. For these kinds of problems conventional planning is insufficient for achieving effective results. This is almost always a result of and improper identification of the problem prior to formal planning beginning.

Without the proper problem identification, it is as if planning cannot gain traction.

Another way to say this is that you plan when you already have a good idea about what to do.

But what do you do when you don’t know what to do?

This is where the idea of design comes into play. In the academic literature design is considered to be a pretty planning exercise in which problem identification is postponed until a deeper understanding of the situation in the context can be developed. Design would normally include multiple perspectives on the situation, with little regard paid yet to boundaries and outcomes. Both of these are normally found early in the planning process and design it would be premature.

Perhaps the most significant difference between the two processes of design and planning is this:

Planning proceeds from understanding to visualization to solution, while design begins with solution and proceeds through visualization to understanding. They are perfectly backwards.

Because we explicitly do not understand the situation and our normal means of understanding it has failed, design must proceed backwards. It starts with tentative solutions which are then compared to a situation to see if we can visualize how it might fit. From this visualization we find one of two things: if it seems like it might work than we can say we have begun to understand the complexity because of a solution which seems to fit. But if it doesn’t seem like it would work, we also understand more about the situation by ruling out possibilities.

In this way we can say that the solution comes prior to the problem identification. It seems like an anomaly but it turns out to be a routine part of design. Solutions in search of problems: the essential idea of design.

Profitable ETF Trading Strategies: Understanding regional correllation

Based on our market classification scheme, there are 9 market states derived from 3 price momentum measures combined with 3 volatility measures. There is an important phenomenon related to the Bull-Sideways-Bear market states that can give you both insight into current conditions as well as a decisive market edge in risk management.  The edge comes from observing and understanding the correllation in returns from geographical regions of the world with respect to US market returns.

Correllation is a basic statistic that examines the degree to which 2 streams of returns (in this case) move in tandem. At high levels of correllation and with a hypothesis in support, we may begin to look for cause and effect, but for our purposes simple correllation is sufficient for insight into favorable trading conditions.

When the world market is in Bull conditions, and the sun is shining, it is normal to find correllations diminishing, with some regions of the world achieving much higher than normal rates of return.  Simply based on the size of the US market it is usual to find different parts of the world leaving the US behind.

As we have seen lately though, when things turn south, correllations quickly converge and the whole world starts going south together. It is normal in these conditions for a flight to quality to occur and money seeks out the relative safety of the mature companies of the US. This sets up perfect conditions for a pair trade that goes long the US markets and short the rest of the worls, which can either be the mature economies of Europe and Asia (represented by ETF symbol EFA) or emerging market (represented by symbol EEM).

This strategy essentially will eliminate market direction and allow you to benefit from the persistent relative strength between regions of the world. This strategy allowed for you to benefit tremendously from the current market volatility, and it repeats itself with surprising regularity over long time periods.

Reflections on Ray Ison’s work in Systems Thinking in Action Research

Ison, R. (2008). Systems Thinking and Practice for Action Research. In P. Reason & H. Bradbury (Eds.), The SAGE Handbook of Action Research: Participative Inquiry and Practice (Second ed.). London: Sage Publications., Ltd.

My masters work is in systems management, and Ison’s work struck a chord with me as well. His mind map of the systems thinking domains is basically a road map of my practical intellectual travels since 1991. I have been reading and practicing in many of those domains he outlines, and the relationships and groupings he maps look like the wiring diagram inside my own skull.

As a systems thinker, my value add to the Army for the last 15 years has been as a problem-finding and problem-solving integrator, bringing together different domains on various projects related to Army change management. I have had training and applied it in the mapped areas of human performance engineering, operations research, soft and hard systems, total quality, statistical process control, lean 6 sigma and systems dynamics.

My current research area is educating leaders for uncertainty and complexity, but it’s actually a little more than that. I am attaching a short slide presentation on a model that stretches along the continuum of Simplicity<->Complicated<->Complexity<->Chaos<->Randomness. Each state seems to have distinct conditions, requirements, values, success strategies, and process. The world seems to shift dynamically, but not quite randomly between these states, and the boundary conditions appear to be fuzzy and the states are not mutually exclusive. Each state has a different set of cognitive, emotional, and conciousness related skills, which means our leaders need to be able to sense, adapt and respond mindfully. Rendering these notions into deliverable curriculum is my research area.

Ison’s work is directly related to my inquiry, and I have come across other works of his before and found him to be an excellent resource. He has a terrific blog and website and I will be sending him a note to see if I can get his take on where Complex Adaptive Systems, emergence, chaos might be arrayed on his mindmap. Weick’s ideas on High Reliability Organizations, and the work of Kahneman, Tversky and Gigerenzer on bounded rationality and cognitive heuristics and biases needs to be integrated as well.

I seem to be heading towards blending the content that includes Checkland’s soft systems methodologies (as a synthesis of best practices of dynamic human systems), systems dynamics for leaders, and Schon’s methods of educating reflective practitioners as the basis of developing education strategies for the 21st century.  Action research to get stakeholder participation in content selection and educational environment construction seems to be a natural fit.