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© 1997 Paul E. Plsek & Associates, Inc. All rights
Updated 23 September 1997. Uploaded to Internet 5 November 1997.
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"It sounded an excellent plan, no doubt, and very neatly and simply arranged.
The only difficulty was, she had not the smallest idea how to set about it."
Lewis Carrol, of Alice in Alice in Wonderland
New thinking from the field of complexity science will radically alter our views on the management of organizations and other human social systems. These brief notes are intended as an introduction to this field and to its potential implications for the management of organizations. For a more thorough overview of this field see Waldrop (1992).
(click to move to a specific section, or just scroll through to read the entire paper)
What is a complex adaptive system (CAS)?
- summary of key points
The stock market: An example of a complex adaptive system
Some emerging principles of complexity
|Definition: A system of individual agents, who have the freedom to act in ways that are not always totally predictable, and whose actions are interconnected such that one agent's action changes the context for other agents. Examples of complex adaptive systems include: the stock market, a colony of ants, the human body immune system, and just about any collection of humans such as an industry, a business organization, a department within an organization, a team, a church group, a family, or the Rotary Club.|
In a CAS, agents operate according to their own internal strategies or mental models (the technical term is "schemata"). In other words, each agent can have its own "rules" for how to respond to things in its environment; each agent can have its own interpretations of events. These rules and interpretations need not be explicit. They do not even need to be logical when viewed by another agent. These are clearly characteristics of humans in just about any social system.
Agents can share mental models, or be totally individualistic. Further, agents can change their mental models. Because agents can both change themselves and share mental models, a CAS can learn; it's behavior can adapt over time. Again, we clearly know that human organizations change over time and are capable of progress.
The behavior of a CAS emerges from the interaction among the agents. A CAS can, and usually does, exhibit novel behaviors. Because of the interaction, the behavior of the system is also non-linear; seemingly small changes can result in major swings in system behavior. If you reflect on this, you can probably recall many examples of these behaviors in human systems. We are usually surprised when they happen. However, when we learn to view systems through the lens of CAS, these behaviors become expected, not surprising.
Because of this novelty and non-linearity, the detailed behavior of a CAS is fundamentally unpredictable. It is not a question of better understandings of the agents, better models, or faster computing; you simply cannot reliably predict the detailed behavior of a CAS through analysis. You have to let the system run to see what happens. The implications of this are that we can never hope to predict the detailed behavior of a human system. While this seems obvious to say, note how often managers and leaders (we ourselves!) act as if we know or can be sure about how others should act in response to our actions.
Still, despite this lack of detailed predictability, it is often possible to make generally true, practically useful statements about the behavior of a CAS. For example, while we cannot predict the exact temperature in Atlanta at 4:49 pm on August 4, we can say that it is pretty likely that a traveler there will not need a heavy coat. This gives us some hope in human systems, we just need to be careful not to over-estimate our ability to predict what will happen. Over-estimation is the usual mistake that we all make; if you have ever been surprised by how something has turned out, you have fallen into the trap of over-estimation.
Stuart Kauffman, Ilya Prigogine, and others have shown that a CAS is inherently self-organizing. Order is an inherent property of the system; it does not need to be imposed from outside. Further, in a CAS, control is dispersed throughout the interactions among agents; a central controller is not needed. Yet, most of traditional management theory is about how to establish order and control through the actions of a few people at the top of an organizational hierarchy. This management instinct, one that we have all learned, may be the biggest factor holding back progress in our organizations.
In summary then, the key points to take away from this brief description of the theory of complex adaptive systems are:
The stock market is a good illustration of these properties of a CAS. Buyers, sellers, companies, and regulators each have their own mental models and are free to take many different actions. The specific actions of each agent are somewhat unpredictable, and can often be construed as illogical by other agents observing the action. Each action changes the environment that others within the system face. These others may take their own actions, which in turn further changes the environment. The detailed movements of the system (whether the market is up or down today and by how much) is fundamentally unpredictable. Furthermore, relatively small things, like the off-hand remarks of the Federal Reserve Chairman, can have a large impact on the market (i.e., there is non-linearity in the system). But, despite what seems to be total chaos, there is an underlying order that allows us to make generally true statements about the system (this is the basis of both the fundamentals and technical analysis approaches to the stock market). Finally, no one "controls" the stock market. It "happens."
Most organizational systems are a CAS. Substituting terms such as employees, co-workers, bosses, outcomes, performance, and so on into the stock market illustration above yields a pretty good description of what goes on every day in most organizations. (Try this substitution and see if it resonates with your experience in organizations. This is referred to as "sense-making;" when the emerging understanding of complex adaptive systems helps people make sense of what in the past has seemed a sometimes chaotic and non-sensical world.)
Curt Lindberg of the VHA has proposed the following principles of management that emerge from an understanding of organizations as CAS. In the spirit of the subject matter, Lindberg calls this list "Eleven, Emerging, and Connected Organizational and Leadership Principles." There is nothing sacred or permanent about the list. But these principles do begin to give us a new way of thinking about and approaching our roles as leaders in organizations. (I have added the text in parenthesis to further explain the principles by contrasting them against traditional management thinking.)
1. View your system through the lens of complexity (rather than the metaphor of a machine or a military organization).
The predominant metaphors in use in organizations today are those of a machine and a military operation. If an organization is a machine, then we just need to specify the parts well, and make sure that each part does its part. If an organization is a military operation, then command, control, and communication needs to be hierarchical; survival is key; and sacrificial heroes are desired (although no one really wants to be one themselves). Most of today's organizational artifacts -- job descriptions, organization charts, root-cause analysis, "turf battles," and so on -- emerge from these largely unexpressed and undiscussed metaphors. If you buy into these metaphors, then the traditional actions of management make sense and should work.
The basic problem with these metaphors when applied to a CAS is that they ignore individuality of agents and interaction effects among the agents. Or worse, they simply assume that these interactions can be tightly controlled through better (read: more) specification. While there are many situations in health care where the machine and military metaphors might be useful-for example, a "code blue team" or a surgical process -- there are also many situations where these metaphors are grossly inadequate. When we "view our system through the lens of complexity" we are taking on a new metaphor -- that of a CAS-and, therefore, are using a different model to determine what makes sense to do as leaders.
2. Build a good enough vision (rather than trying to plan out every little detail).
Since the behavior of a CAS emerges from the interaction among the agents, and since the detailed behavior of the system is fundamentally unpredictable, it does little good to spend all the time that most organizations spend in detailed planning. Most organizational leaders have had the experience of participating in very detailed planning, only to find that assumptions and inputs must be changed almost immediately after the plan is finalized.
Complexity science suggests that we would be better off with minimum specifications and general senses of direction, and then allow appropriate autonomy for the individual agents to self-organize and adapt as time goes by. This would suggest, for example, that intricate strategic plans be replaced by simple documents that describe the general direction that the organization is pursuing and a few basic principles for how the organization should get there. The rest is left to the flexibility, adaptability, and creativity of the system as the context continually changes. This, of course, is a frightening thought for leaders classically trained in the machine and military metaphors. But the key questions are: Are these traditional metaphors working for us today? Are we able today to lay out detailed plans and then 'just do it' with a guaranteed outcome? If not, do we really think that planning harder will be any better?
3. When life is far from certain, lead from the edge, with clockware and swarmware in tandem (that is, balance data and intuition, planning and acting, safety and risk, giving due honor to each).
"Clockware" is a term that describes the management processes we all know that involve operating the core production processes of the organization in a manner which is rational, planned, standardized, repeatable, controlled, and measured. In contrast, "swarmware" are management processes that explore new possibilities through experimentation, trials, autonomy, freedom, intuition, and working at the edge of knowledge and experience.
In an informed approach to complexity, it is not a question of saying that one is good and the other is bad (as some have suggested). The issue is about balance, thoughtfulness, and appropriateness. Where the world is certain and their is a high level of agreement among agents (for example, the need to administer TPA near the time of the onset of AMI) clockware is appropriate. In a clockware situation, agents give up some of their freedom and mental models in order to accomplish something they have agreed upon collectively. The CAS ceases to display emergence, creative behavior and begins to act more like a machine. There is nothing wrong with this.
However, where the world is far from certainty and agreement (the terms in the complexity literature are "far from equilibrium" or "near the edge of chaos") swarmware is needed with its adaptability, openness to new learning, and flexibility. Swarmware is also needed in situations where the old clockware processes are no longer adequate for accomplishing the purpose, or in situations where the purpose has changed, or in situations where creativity is desirable for its own sake. (The example of strategic planning in principle 2 above is potentially one such situation.)
4. Tune your place to the "edge of chaos" by fostering the "right" level or degree of: information flow, diversity and difference, connection among agents, power differential, and anxiety (instead of controlling information, forcing agreement, dealing separately with contentious groups, working systematically down all the layers of the heirarchy in sequence, and seeking comfort).
Theoretical studies of complex adaptive systems suggest that creative self-organization occurs when there is just enough information flow, diversity, connectivity, power differential, and anxiety among the agents. Too much of any of these can lead to chaotic system behavior; too little and the system remains stuck in a pattern of behavior.
Of course, the trick in a human CAS lies in gauging the "right" amount of each of these. Since the predominant metaphors of organizational life are those of a machine and military operation, most organizations today have too little information flow and diversity, and too much power differential. The degree of connectivity and anxiety can go either way. This is a general observation which, of course, could be different in any specific context. If you are in a CAS, you will have your own mental model about such things, as will the other agents in the system.
But since the detailed behavior of the system is fundamentally unpredictable, there is no way to analyze your way to an answer. You can have more or less correct intuitions, and some sense of general direction, but that's inherently the best you can do. You'll just have to try tuning up or down the various factors and reflect on what happens.
"Reflection" (the slightly jargon-y term from complexity literature that simply means observing closely and really thinking about what is going on) is a key skill for anyone in a CAS. Good "leaders" in a CAS lead not by telling people what to do; rather they lead by being open to experimentation with the above factors, followed-up by thoughtful and honest reflection on what happens.
5. Uncover and work paradox and tension (rather than shying away from them as if they were unnatural).
Because the behavior of a CAS emerges from the interaction among agents and because of non-linear effects, "weird" stuff seems to happen. Of course, it is only weird because we don't have a way to understand it yet.
In a CAS, creativity and innovation have the best chance to emerge precisely at the point of greatest tension and apparent irreconcilable differences. Rather than smoothing over these differences -- the typical leadership intuition from the machine metaphor -- we should focus on them and seek a new way forward. So, one group wants to hold on to the status quo while another wants radical change. Mix them into a single group and take on the challenge of finding a "radical way to hold on to the status quo." This is a statement of a paradox; it makes no sense according to the prevailing mental models. But working it sincerely places the group at the "edge of chaos" where creativity is a heightened possibility.
6. Go for multiple actions at the fringes, let direction arise (rather than believing that you must be "sure" before you proceed with anything).
As we have already noted, in a CAS it does little good to plan the details. You can never know exactly what will happen until you do it. So, allowing the flexibility of multiple approaches is a very reasonable thing to do. The "fringes" that we are referring to here are those issues that are far from the zone of certainty and agreement. The management advice here is to quit agonizing over it, quit trying to analyze it to certainty. Try several small experiments, reflect carefully on what happens, and gradually shift time and attention toward those things that seem to be working the best (i.e., "let direction arise").
These multiple actions at the fringes also serve the purpose of providing us with additional insights about the larger systems that every system is inevitably buried within.
A concrete example of this principle might be the healthcare organization that is trying to come up with a new financial incentive plan for associated physicians. There are many options and there are success and failure stories in the industry for each one. Therefore, we are far from certainty and agreement. Rather than meeting endlessly over it trying to pick the "right" approach, experiment with several approaches. See what happens, see what seems to work and in what context. Over time, you may find a "right" way for you, or you may find several "right" ways.
7. Create strategy and new sources of value through generative relations (you can never tell what will happen when agents come together).
Interaction among the agents in a CAS is the "stuff" from which system behavior emerges. A "generative relation" is an interaction that is set up on flexible mental models that seek to work collaboratively toward some common, "good enough" aim. In a generative relationship, all agents are open to honest reflection on their mental models and are open to change.
The point here lies in the contrast to the alternative; which are interactions that are based on the mental models of winners and losers, competing ideas and agendas, argument and point-winning. In a generative relation all agents are seeking to build collaboratively a new mental model, in the alternative approach the agents are seeking to win others over to their existing model.
In a CAS, you can never tell what will happen when agents come together. Especially when the agents come together in a collaborative context; not needing to know beforehand exactly what they will do or how they will do it, but knowing that they want to do something new together.
8. Listen to the shadow system (that is, realize that informal relationships, gossip, rumor, and hallway conversations contribute significantly to agents' mental models and subsequent actions).
Complexity theorist Ralph Stacey points out that every organization actually consists of two organizations: the legitimate and shadow systems. The legitimate system consists of the formal hierarchy, rules, and communications patterns in the organization. The shadow organization lies behind the scenes. It consists of hallway conversation, the "grapevine," the "rumor mill," and the informal procedures for getting things done. Most traditional management theory either ignores the shadow system, or speaks of it as something leaders must battle against (as in, "overcome resistance to change;" it's that military metaphor again).
Stacey further points out that the shadow system is often the place where much of the creativity resides within the organization. While the legitimate system is often focused on procedure, routines, and the like, the shadow system has few rules and constraints. The diversity, tension, and paradox of these two organizations that co-exist within one can be a great source of innovation if leaders could just learn to listen to rather than battle against the shadow.
When we see our organizations as CAS, we realize that the shadow system is just a natural part of the larger system. It is simply more interconnections among agents; often stronger interconnections than those in the legitimate system. Leaders who lead from an understanding of CAS, will not have a need to discredit, agonize over, or combat the shadow systems in their organizations. Rather, they will recognize and listen to the shadow organization, using the interconnections it represents as another avenue for tuning information flow, diversity of opinion, anxiety, and power differential (see principle 4).
9. Grow complex systems by chunking (that is, allow complex systems to emerge out of the links among simple systems that work well and are capable of operating independently).
Complex systems are... well, complex. They are not easily understood nor built in detail from the ground up. "Chunking" simply means that a good approach to building complex systems is to start small. Experiment to get pieces that work, and then link the pieces together. But, when you make the links, be aware that new interconnections may bring about unpredicted, emerging behaviors.
Applying this principle to team-building in a mid-sized organization, for example, would suggests that leaders should look for and support small natural teams. We might provide coaching and training for these teams. Then, when these teams are functioning well, look for ways to get the teams to work together and involve others. But beware, these new links may result in weird behavior. With a CAS, this is to be expected. The leaders should be open to doing some adapting of their own. Rather than insisting on pressing on with the training, groundrules, or procedures that worked so well in the first teams, the leaders should understand that the interconnections among teams has resulted in a fundamentally new system that may need new approaches.
Continual reflection and learning are key in building complex systems. You cannot reflect on anything until you do something. So start small, but do start.
10. Nice, forgiving, tough, and clear guys finish first (that is, balance cooperation and competition via the tit-for-tat strategy).
Throughout this list of principles we have seen the theme of balance as a key to successful outcomes in a CAS. Here, we are talking about the balance between cooperation and competition among agents.
Studies of competitive situations (such as the famous "prisoner's dilemma") have shown that a "tit-for-tat" strategy results in a consistently good overall outcome for a system of agents. Maybe not the absolute best for any one agent, but consistently good overall for the system.
In the tit-for-tat strategy, the first person offers cooperation on the initial interaction with another, and then practices reciprocity in subsequent interactions. If the other person cooperates back, continue cooperation; if the initial cooperative offering is met by competition, return the competition. The idea is to illicit cooperation in the long run; to co-evolve.
Commenting on this strategy, Waldrop (1992) says "Consider the magical fact that competition can produce a very strong incentive for cooperation, as certain players forge alliances and symbiotic relationships with each other for mutual support. It happens at every level of and in every kind of complex adaptive system, from biology, to economics, to politics."
From the complexity perspective, a good leader would be one who knows how to, and prefers to, cooperate; but is also a very skillful competitor when provoked to competition (that is, a nice, forgiving, tough, and clear person).
11. Build a space-a community-to convey, concentrate, create and learn together (rather than always being a lone ranger).
In the world of complex adaptive systems, progress emerges from interaction among agents. These innovations are more likely to emerge in a community where the agents can feel safe to expose their mental models, experiment, and learn together. By virtue of their role in the existing "legitimate system" (see principle 8), organizational leaders play a key role in getting such communities off the ground. Unfortunately, most leaders got to their present positions by standing out from their peers, by standing alone. Many times, "lone ranger" leaders are the key barrier preventing higher levels of performance from emerging from within organizations.
Astute readers will observe that CAS theory has much in common with general systems thinking, the learning organization, total quality, gestalt theory, organizational development and other approaches. It has much in common with these, but it is not any of these. CAS theory clarifies and pulls together lots of good thinking from the past. An understanding of CAS is an understanding of how things work in the real world. That others in the past have also understood these things and put them into various contextual frames should not be surprising. An understanding of CAS simply provides a broader, more fundamental, potentially unifying framework for these ideas.
These notes are not intended to be a comprehensive overview of complex adaptive systems science. Rather, the goal has been to create curiosity and to provide a good enough background to enable people new to the concepts to begin further study. The reference by Waldrop (1992) listed below is a good, journalistic introduction to the field. I am currently working with Brenda Zimmerman (York University) and Curt Lindberg (VHA) to compile additional learning aides into a resource kit for healthcare leaders. Contact me for more information (paulplsek@DirectedCreativity.com or call 770-587-2492).
Waldrop, MW (1992) Complexity: The Emerging Science at the Edge of Order and Chaos. New York; Simon and Schuster. (Back)
© 1997 Paul E. Plsek & Associates, Inc.
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