Category: Change

“Scaling up” and “change” are two topics whose relationship is preoccupying me these days.  I was delighted to discover an article that presents important insights by tying the topics to “complex adaptive systems”. 

From what I’ve seen, “scaling up” strategies can be characterized in two extremes (with some mixing, of course):

• the linear, top-down, roll-out, adopt-the-model form; 
• a systems, growing from the bottom up, action learning, stimulate-the-change form.

My recent reading clarifies that the “roll-out” form is good in “simple” and “complicated” situations;  the action learning form is appropriate for complex situations as distinguished by David Snowden.  The roll-out top-down format requires the stable, predictable situations with few unknowns and linear power structures that are associated with simple and complicated situations;  the action learning format is for situations where there are many unknowns, great variation in contexts and rapid change as is associated with complexity.

With the burdensome but clear name, Understanding pathways for scaling up health services through the lens of complex adaptive systemsis a great article that synthesizes others’ work, rather than drawing from first-hand research.   In fact, it makes the point that has not found complex adaptive systems (CAS) thinking actually applied to health issues.  The authors set the scene by describing problems with applying simple/complicated perspectives to CASs:

“People’s understandings of systems that are actually CAS are often over-simplified or erroneous, which creates problems for decision-makers who cannot control such systems through conventional means, often while being vulnerable to sudden changes in public opinion.  It is not unusual for systems to show little response to many attempts to control them, or to change suddenly when a tipping point is reached.  For example, many high-cost health investment projects have had little impact on people’s behavior or health status, in contrast to sudden changes that can occur in public opinion about smoking bans or in the demand for contraception.”

The real value of the article is that it makes two contributions to facilitate application.

Key Complex Adaptive System Phenomena

One of the article’s key contributions is its summary of five models of phenomena that are behind CASs. These demonstrate the problems with a roll-out approach taken in such circumstances.

1.  Path dependence:  history, culture and other contextual features can produce different outcomes even when starting from the same point.

2.  Feedback:  You can start from the same point, but during the process there are responses that affect the precursors of success to produce differing trajectories

3.  Scale-free networks:  In many issue arenas, some networks have hubs connecting many sub-groups of people/organizations, other hubs connect only a few (hence “scale-free”);  large nodes can facilitate both connection and collapse of a system (think of the financial system).

4.  Emergent behavior:  new connections create new possibilities that are greater than the sum of their parts.

5.  Phase transition:   this is the “tipping point” factor:  small changes and connections can suddenly “gell” to become dominant.

While people commonly focus on one or two of these phenomena — feedback and phase transition are, in my experience, the most popular — they all require consideration. Collectively these five phenomena produce a confusing array of action that is associated with complex adaptive systems and emergence; understanding them individually helps immensely in understanding complexity dynamics.

Application of Insights

The second contribution of the article is that it begins connecting these definable patterns (complexity being distinguished from chaos by such patterns) to both an action logic and methodologies with tools.   The action logic is a play on the plan-act-learn one of traditional learning cycles and development strategies of simple/complicated issues. 

Planning in CASs is predicated on the understanding that new, unforeseen opportunities and events will emerge that should be treated as an asset rather than a disruption in a planned set of outcomes mapped in advance.  The process of learning, envisioning, clarifying and experimenting will “emerge” the outcomes, in contrast to engineering outcomes as in a roll-out processes. 

Planning tools the article references include stakeholder analysis, network mapping, agent-based modeling and scenario planning. These “…deepen the understanding of path-dependent actions and consequences;  …test their abilities to anticipate and adapt to changing conditions; (identify) feedback loops and emergent behavior;  (and) identify neighborhood or place effects…”

The authors comment with respect to the next step of the action cycle: 

“During the implementation and monitoring of scaling up ,,. an understanding of CAS would emphasize the importance of adaptation, learning and flexibility to emerging issues rather than the rigid following of initial plans.”

Tools identified for this stage include small-area statistical variation analyses, facilitating and analyzing dialogue, and time series analysis to identify patterns.

The last step, evaluation, is handled in an unsatisfactory way in my opinion.  The authors recognize that unintended consequences must be addressed and point to problematic approaches such as creating unsustainable islands of excellence (is anything really an ‘island’?).  However, they make no reference to the developmental evaluation of Michael Patton and others’ works that emphasize that in CAS situations “evaluation” and “learning” must be integrated – initiatives cannot wait for evaluations after-the-fact, given CAS interventions are typically lengthy and new learning must be integrated into actions on a very short cycle.  Of course this does not deny the value of periodic reviews and assessments, but traditional simple-complicated evaluation methodologies in this situation are highly inappropriate in comparison to a more broad-looking assessment. 

The authors conclude with particular reflection on the health field, but their remarks can be broadly applied:  “The old assumptions have led to disappointed expectations about how to scale up health services, and offer little insight on how to scale up effective interventions in the future. The alternative perspectives offered by CAS may better reflect the complex and changing nature of health systems, and create new opportunities for understanding and scaling up health services. “

While working with Ashoka to scale transformational change, I recently met a most remarkable man with a most remarkable insight.  Allan Savory is associated with environmental land management; his most remarkable insight is to integrate a physical science observation with a governance one. In 2003, he was awarded the Banksia International Award for extraordinary contributions to improving our environment on a global level.

Allan was born in Zimbabwe when it was still named Rhodesia and part of the British Empire.  He has been a game ranger, biologist/researcher, soldier, farmer, rancher, politician and international consultant working on four continents;  he is President and Co-Founder of The Savory Institute.

Allan’s physical science observation is about the critical role of “large herbivores” (cattle, sheep, elk, elephants, bison, etc.) in the health of grasslands, rangelands and savannas of the world.  Rather than being the cause of degradation, they are critical to maintaining and improving the quality of land.  Their hooves break up the soil, their excrement fertilizes, and their grazing spurs new growth.  Allan’s particular physical science insight is about the massing, movement and timing of herbivores on a particular piece of land.  Through proper management even the most devastated land can be revitalized to support many more herbivores than traditional management practice.

Holistic Management: Physical and Social

However, his genius is realizing that this is a necessary, but not sufficient, insight to produce the paradigm shift he refers to as “holistic management”.  You also need change in decision-making processes, for which he has developed a “holistic framework” that he compares to a traditional or “core framework”.  He explains that: 

“Conscious, as opposed to instinctive, decisions by humans (at all levels from household to government or international policies) are made toward the achievement of an objective. The only tools with which to manage the environment at large considered in any government’s (or development agency’s) policies or projects fall under the categories of technology, fire or rest (of the environment).  And all actions to achieve the objective are based on one or more of many factors, such as past experience, expert opinion, research results, public opinion, cost, compromise, expediency, cultural beliefs, intuition, peer pressure, fear, propaganda, cost, cash flow, profitability, and so on.”

This core framework is good in simple and complicated issues, such as application and development of hard technologies – as he points out, with everything humans “make” from buildings, computers, genetic engineering to space exploration vehicles.  However, the core framework is counter-productive with complex issues – everything humans “manage” – agriculture, desertification, economies, species, environment – where there are significant unknowns and uncertainties that require an on-going strategy for learning and exploration.  These are sometimes called soft social systems or natural systems.

Allan points out that complex issues cannot be addressed by objectives and goals or through them visions or missions.  That is because these are emergent in complex situations, and defining them in advance denies their complexity.  With a complex issue, without a holistic context you cannot address the social, environmental and economic aspects of a situation simultaneously, and both short.

Applied to desertification, Allan’s holistic framework enhances the core one with three main additions:

1. Holisticgoal or holistic heading that ties what people value most deeply in life to their life-supporting environment.
2. The addition of two tools that make reversal of desertification possible in the world’s seasonal rainfall environments – grazing and animal impact from large herbivores such as livestock.
3. A set of filtering questions that ensure all decisions, policies, projects or actions are leading toward the future people desire.

This is further described for Holistic Management:  A New Framework for Decision Making.  I don’t agree with it all.  For example, he includes seven “testing questions”; I don’t agree answering them thoughtfully will “take minutes rather than hours”, particularly when participatory collective analysis take significant time.  I was surprised to see a system thinker like Allan refer to the “root cause of the problem”, but then saw his description of a “mesh of factors”.  There seems to be some contradictory qualities here.   Nevertheless, it is a useful framework and I much appreciate his assertion of the importance of on-going testing.  Allan comments that:

“I actually enjoy using the holistic framework in so many situations – it helped me decide to give away valuable land and so retain all I valued in life during Zimbabwe’s tragic land grab (while family and friends told me how foolish I was), it enabled me to refuse to support people in my village when all were signing a petition protesting a new bridge across the Rio Grande that would destroy the culture of our village, it enabled me recently to show our staff the folly of grading the roads on our Zimbabwe property rather to use pick and shovel – for social, economic and environmental reasons (where everyone would have graded).  And as you know we are reversing desertification perhaps the oldest problem faced by city-based civilizations using the framework.”

Allan responds:

Some tests take time in some situations, but once those are done – like gross profit analysis of a possible enterprise for example, or structured diagnosis of a natural resource problem – the rest of the testing is very rapid deliberately. In the textbook I use a picture of Lincoln’s face to make the importance of this point. Look at it for hours and one cannot make out what it is – squint your eyes and you see Lincoln. Take hours over each testing question and you see nothing – rush through them fast not making a decision and you get a “feel” and the final decision is “how do you feel?” A little training and people do it very quickly and well.

Lack of sustainability is, to a significant degree, the product of governance – our decision-making processes and structures favor non-sustainable outcomes.  What does “sustainability governance” look like?  The book Dynamic Sustainabilities (DS) has expanded my thinking about this.

The authors characterize traditional governance as “closing down” questions to conform to “planned equilibrium” thinking and an artificial world.  What they propose is governance that “opens up” questions and actions to embrace more fully the challenges of a dynamic world.  Well functioning Global Action Networks are governance arrangements that do this.

Particularly useful is a diagram the authors concocted titled “The Dynamic Properties of Sustainability”. Their basic argument is that we need governance structures that can address all four of the quadrants.

Most governance systems are designed to address shocks.  Shocks come in two forms:  ones with controllable (tractable) drivers such as fires that destroy buildings, that can be managed through “stability” responses such as building codes and fire response systems.  Then there are intractable shock issues – ones that cannot be controlled.  Some of these are episodic, such as floods that can evoke responses such as constructing dikes and restricting building on floodplains. The DS authors label these types of situations as warranting increased “resilience”:  the problem can’t be eliminated, and ways must be found to live with it.

Our governance structures are much poorer at addressing stress issues – ones that arise over a longer period of time – in part because governments have election-cycle horizons.  The DS authors distinguish between two types of action styles here, as well.  One is with issues that require a durability response: they are controllable chronic changes, but where there is insufficient knowledge about how to control them.  This is smog circa 1950.  And then there’re the most challenging of all situations:  those that require robustness.  The development of an ice age is a good example of this – its drivers cannot be controlled, and responses require more fundamental change such as evacuating colder regions and changing crops.

This diagram very much reflects David Snowden’s one that describes “Sense-Making Domains” that distinguishes between simple-complicated-complex-chaos.  Simple is associated with stability, complicated with durability, complex with resilience, and robustness with chaos.

The table arises from another one below that looks at the concept of “risk”.  The insurance industry basically works in only one of the quadrants, labeled “risk”, where they have lots of data and well-developed frameworks to predict the occurrence of an issue such as a death (hence life insurance).  We see that the government gets involved in flood insurance in part because the predictor models are weak although there’s lots of knowledge about (and experience with) floods.

To address smog required the development of conceptual frameworks to connect “dirty air”, “poor health” and emission sources.  It required the development of impressive data sources and ways of measuring, tracking and controlling emissions.  In the case of the ice age, there are enormous unknowns – theories abound (indicated by lack of consensual frameworks) and data (and experience) are sparse.

This all suggests that issues can evolve:  for example, the smog issue moved from ignorance up to risk (hence lawsuits).  The climate change challenge is facing this evolutionary crisis:  can it move up to “risk” quickly enough?

The Role of GANs in Resilience, Durability and Robustness

So this is where Global Action Networks (GANs) come in. One of their seven core characteristics is “entrepreneurial action learners”. They work to “open up” issues by engaging diverse participants who have mutually challenging perspectives on an issue. Indeed, they reflect the five empowering design principles of the DS authors:

1. Diverse participatory engagement
2. Extend scope, enable choice
3. Take a dynamic perspective, accept incomplete knowledge
4. Attend to rights, equity, power
5. Be reflexive

In the language of the DS authors, GANs are developing resilience and durability, and trying to transform robust issues into ones that can be addressed through one of the other action styles.  Transparency International has taken the issue of corruption, and added both data and conceptual frameworks;  the Global Reporting Initiative, the Global Compact and the Forest Stewardship Council are doing the same with their issues.

The DS authors work with four case studies, but they never do draw out governance lessons to the point of operationalizing.  They take a bit of a detour into descriptions of “adaptive, deliberative and reflexive” governance – nice academic concepts, but again they don’t readily produce good organizing models.  As well, I feel like their focus on “policy” solutions inhibits them.

Nevertheless, the book makes an important contribution.  Its major point – that we need governance structures to address all types of knowledge situations if we are to address sustainability – is right on target.  As they write:  “’Sustainable solutions’ are thus those that offer stability, durability, resilience and robustness in specified qualities of human well-being, social equity and environmental quality.”  Governments can’t do this, but the right governance arrangements can.

For over a year I’ve been working with an emerging academic research center – business network called GOLDEN.  It has produced a very powerful strategy to realize the depth, speed and focus necessary to transform business into sustainable enterprises.

GOLDEN starts from the position that capacity for realizing, and clarity about, the needed changes is a major inhibition in current corporate sustainable efforts.  This is a distinctly unique niche, different from those who are measuring triple bottom line impact, developing initiatives in the CSR tradition, or founding new social enterprises.  GOLDEN is about core strategic questions regarding the future of organizing wealth generating entities, and developing a broad change platform supported by the vision, discipline and rigor of academic thought leaders.

GOLDEN aims to stimulate capacity for corporations to change and to identify key change initiatives required to realize the “sustainable enterprise of the future”.  Behind this is the observation that there are many embryonic changes occurring – such as new ways of corporations and NGOs to work together – that collectively suggest that the iconic corporate model is transforming.   GOLDEN aims to dramatically speed up its definition and emergence globally.

GOLDEN’s strategy has three interacting components that work on the three critical levels of change that I identified in my 2005 book:  Societal Learning and Change: How Governments, Business and Civil Society are Creating Solutions to Complex Multi-Stakeholder Problems.  The societal learning and change “matrix” points out that many transformational change strategies are doomed because they do not tackle individual-, organizational- and societal-level change.  Classically, Americans tend to emphasize individual change, drawing on heroic leadership traditions.  Europeans in contrast tend to focus on social structures and institutions, seeing them as key to inhibiting or facilitating change.  Of course both of these views are true.

GOLDEN is addressing these three levels of change by developing three types of labs

Decision-makers (individuals) labs:  These will “map” the distance between current mental state and envisioned future and identify strategies for to develop the behaviors and aptitudes necessary for sustainable enterprise leaders.

Enterprises (organizations) labs:  These will experiment with innovative policies, programs and actions.

Industry eco-systems (societal) labs:  The collection of stakeholders in an industries’ operating environment (see Figure 1). 

These are called “labs” because they involve real-time experiments with innovations that can be broadly disseminated throughout the GOLDEN network. They are connected to two other key activities

• The Observatory which is a data-base to initially assess corporate capacity to identify the (a) relevance of sustainability issues (set direction), and (b) effectiveness of functional change initiatives; and
• Multi-level simulation modeling, that will use the Observatory data to forecast the impact of specific corporate strategy and policy choices.

Obviously this is a hugely ambitious undertaking.  It is led by the indefatigable (necessary quality) Maurizio Zollo, Director of the Center for Research on Organization and Management at Bocconi University, a leading European business school in Milan.  He is incredibly well-networked (necessary quality) in academia on both sides of the Atlantic, and President Elect of the European Academy of Management.  Microsoft and Telecom Italia are key corporate investors.

I’m particularly interested in the early stage development process of these types of networks. I’ve found that typically it takes 3-5 years from initial meeting around the idea of a Global Action Network, to doing something like the initiators envisioned. The first meetings around GOLDEN started in early 2010. This latest formulation is a huge leap forward that happened with a January meeting in Vienna, and saw the addition of the eco-system and decision-maker activities. Pilots are now being organized with companies around The Observatory and corporate participants for a broad 2013 engagement are being recruited.