Posts Tagged ‘Chaos and Complexity’

What makes complexity complex? Why the fuss? Here’s a brief review of the key components and how they relate. It should remind you of your own experiences – your own sense of complexity and what you go through in bringing people together (or separating them, as the case may be) in solving problems and creating solutions.

As has been stated in previous blogs, the hallmark of complex systems is emergent behavior; behavior that flows bottom-up and is different in kind, creating something novel. A flame is a good example. The chemistry of burning carbon and hydrogen (wood) gives no indication of how a flame would appear. Here is another example. Knowing children want to play is one thing, predicting what games they will invent is in a “whole ‘nother ballgame” (pun intended).

The Building Blocks of Complexity

Four components and how they vary are at the source of complexity:

  • The ability to learn and adapt;
  • Connectedness;
  • Interdependency;
  • Diversity


Critical to emergence is the ability to learn and adapt. Novelty is what comes from complexity. This means having a team that learns, uses what works, and creates what is needed.


This is about being engaged, connected to the situation and people, and being fully present.  If you’ll allow a little hyperbole, the engineer and problem are one and this mythical engineer is in touch with everyone else in the situation.


In addition to connection there needs to be flexing. There needs to be the ability to influence one another (power) for emergence to occur. In complexity, Thomas Merton’s statement, “No man is an island,” is quite appropriate. One person on high does not dictate emergent structures – they evolve from the group in a situation where everyone and no one can take credit. It is the team that gets the credit.


For thorny, complex challenges to be taken on and fruitful results generated multiple frames of mind are needed. Healthy challenges from everyone involved – the conflict of diversity is needed.

The Interesting In-Between

How do these attributes, these variables relate in a complex system. “The Interesting In-Between” is a phrase John H. Miller, PhD, and Scott Page, PhD, use in their book, Complex Adaptive Systems in discussing how “settings” of the four variables are critical if emergence is to occur. The key trait is no one variable must either disappear or dominate. They each must be at the “in-between” setting.

If learning is at zero then obviously no adaptation will occur. Whatever set of rules are being used right now is how it will be. If it is at 100% everyone will know everything about everyone else and equilibrium will set in. Novelty will disappear.

Similarly, if connectedness is at zero novelty will be absent since there will be no influence on the system. On the flip side, if everyone is connected to everyone else then, once again, equilibrium sets in and novelty disappears.

Interdependency at zero would give us a bunch of hardheads with no interest in listening to others. If you have an adolescent child worried about what others think you are familiar with the paralysis that occurs with complete interdependence.

Diversity also influences novelty and emergence. If there is no diversity then groupthink occurs. If everyone is completely diverse then no common ground exists upon which a successful solution can emerge.

In addition to showing attributes that go into complexity the need for complexity shows when looking at these variables. Imagine a situation where same-old, same-old just doesn’t make it. Things can get very tiring and frustrating. It is like the vanity plates I see on a car routinely driving around the neighborhood, “SS DD.” If you don’t know what that means and want to find out, send me an e-mail.

Maintain a Balance Point

What all of this boils down to is the responsibility of the leader to maintain a balance among all four variables at a mid-point which has a positive tension.  To borrow a term from astronomers looking for earth-like planets, Goldilocks positions must be held for each variable, not too dampened and not too wild.

“How stable is the schedule?” is a question that came to mind when preparing to be interviewed by Carl Pritchard for Project Manager Today (based in the United Kingdom) regarding scheduling packages and the benefits and obstacles end-users experience.  (The path to complexity, chaos, and game theory began when introducing distributed project management systems via LANs and WANs.)

The deeper question is, “Can credible earned value calculations be performed?” Earned value is one of the most elegant concepts to come out of project management. The core comprises two simple equations:

Cost Variance = Earned Value (what’s installed) – Actual Cost (what was paid)

Schedule Variance = Earned Value (what’s installed) – Planned Value (what was supposed to be installed by end-of-day today)

So what role do chaos and complexity play when working with schedules? The answer is, “A large one.” Some background will help.

Schedule Stability, Chaos, and Complexity

One thing clients and students will always hear is:

All good schedules sit on a solid foundation the building blocks of which come from quality- and risk management.

Combining this with the earlier question, “Can credible earned value calculations be performed?” leads to an interesting approach for determining if earned value is possible. The approach determines where a project sits on a series of sliding scales. The rule governing the scales is: the more the project and environment are to the left the better the odds of creating a credible schedule. Here is a sampling of the scales:

  1. Closed scope — R&D project
  2. Formal project — Ad hoc, tactical approach with excessive number of workarounds
  3. Stable environment — Dancing terrain
  4. Sufficient resources — Insufficient, multitasking
  5. Risk neutral — shame-based environment with high level of fear and frustration
  6. Synergistic relationships — win-lose relationships
  7. Realistic expectations — greed
  8. Clear reporting — “spin” reporting
  9. High quality, defined acceptance test — disconnected wish-list

Earned Value versus Sunk Cost

With projects solidly to the left of each scale traditional project management tools can be applied. If one is lucky a definitive estimate can be created leaving everyone with a clear understanding how deliverables will be created along with the associated time and budget. Earned value can be calculated.

This begs, almost screams, another question, “What about projects that slide towards the right?” The more movement to the right, the greater the chaos, the more the project crumbles. A hard reality takes shape. Earned value is less and less viable and the team enters the world of sunk cost.

Sunk cost is making an investment without assurances the desired outcome will be reached. It is a gamble.

The inverse of the previous quote regarding good schedules becomes true.

As quality- and risk management suffer, uncertainty increases creating the potential for increased variances. The variances can eventually exceed the initial baseline.

The ability to forecast disappears and deciding what to do with the project becomes a crap shoot.

The point of all this?

Simple. Avoid confusing intention with reality. Wanting to do well and having the ability to do so are two different things. Pay attention to both the strategic components and the tactical nuts and bolts. Have a clear audit trail top-to-bottom and back up. The devil and success sit side-by-side in the details. To the extent you can neutralize the former and amplify the latter earned value can be realized.

Very good project managers have been trashed over the misuse of best practice. The example I have in mind is a client financial firm (Firm X) that wanted to buy another firm in order to grow and lower the probability of being bought themselves. The chairman was able to win the battle but lost the war. It happened by his leveraging the firm’s reputation and applying spin to “best practice.”

Strategic Positioning

A little background will help. This happened when consolidation was occurring in the financial world. Firm X had a very positive, understated reputation on Wall Street. They usually exceeded their performance predictions. Consequently, the chairman’s word had a great deal of cache.

In order to gain leverage in buying the other firm the value of Firm X’s stock needed to increase. Here is where spin comes into play. The chairman cashed in on Firm X’s reputation. Wall Street analysts were told that plans were underway to improve operational efficiencies in credit card processing – a large area of operations for Firm X. Also, there would be economy of scale by applying the improvements to the merged entity.

The chairman simply made empty promises. No one down the food chain was consulted (the critical nature of which we will look at later). The organization was simply told, “Make it so if you want to survive.”

Win the Battle

As predicted, the value of their stock increased. The other firm couldn’t compete with this and was purchased by Firm X. All seemed well and good.

A Blood Bath

Everything was fine until it was time to publish the results of the methods of improvements – those best practices that were to be put into place. Not only were there no improvements in operations, costs actually soared tens of millions of dollars.

Inside Firm X it was a combat zone. In IT they went through project managers like little kids eating M&Ms. As the reality of the actual numbers began to surface desperation set in. Bonuses were offered to anyone who would sponsor a project that would give the desired results. Imagine a Greek trireme going into battle and the captain promises a bonus to the piper (the guy who beats out the rowing rhythm) if the ship could just go faster.

Lose the War

The chairman got what he wanted – the merger. He also got something else – the boot. When the numbers were published Wall Street told the chairman in the future he would have a hard time borrowing even a dollar. The board had to react and did so by removing him from office except for overseeing the credit card operations debacle. His title became, “Chairman of Special Projects.” As in any other organization, one might as well have leprosy as have “Special Projects” as one’s title. Three months later, the chairman resigned. The top two tiers of IT were replaced with people from the firm that was bought. They were conservative in practice and a more stable organization.

Chaos and Best Practice

Most mergers fail. One possible reason being spin, i.e., propagating the belief that if one knows the rules better than anyone else then a highly reliable model can be generated that will predict the outcome. The blindness associated with this approach and how it can backfire was addressed earlier in the Black Swan blog.

It is important to remember chaotic systems are rule-based. The difficulty lies in the fact they are unpredictable and can turn on you in an instant. Knowing all the rules does not guarantee the desired outcome will be achieved. The chairman in this case thought he could dictate top-down what the results would be. The reality is solutions emerge from the bottom-up.

Wanting to buy a competitor or merge for some perceived gain is fine. The trick, though, is to be humble, realize the realities of chaotic systems, and strive to work together to dampen the distractions and amplify the opportunities through a bottoms-up approach while leading the way towards the goal.

Through his hubris the chairman blinded people to the reality of the situation by spinning best practice in a chaotic situation. “Doomed” is too small a word.

To my knowledge, none of the sacrificed PMs were rehabilitated or reinstated to there former positions.

“Best practice” is one of the most popular buzz phrases today in project management and other professions. It IS valuable but, in my experience, it misses a much larger mark, i.e., the heavy lifting required to get to the point where best practice can be applied. The fact is work is typically a real joy when the best practice stage is reached. So what else is there? What comes before best practice? Let’s explore.

Framing the Problem

The underlying issue regarding whether or not best practice methods will work can be viewed in terms of problem framing for systems that are rule-based. In terms of chaos and complexity theory systems that are rule-based can be divided into two broad categories based on potential outcomes: predictable and unpredictable. Within each of these there are two subcategories.

For the predictable outcomes the systems can be either simple or complicated.

For the unpredictable outcomes the systems can be chaotic or complex.

Predictable Systems

Simple systems have rules that work correctly all the time. These systems are called “symmetrical.” A wristwatch is a good example of a simple system. The more symmetrical the watch is the more it is prized for its accuracy. Another way to view simple systems is to think of a coin separator. The coins are put in at the top and the predictable get slotted into the correct silo.

Complicated systems have rules that work correctly all the time but have multiple rule-sets from which to choose. Here imagine several different types of coin separators and having to choose which one to use. This is the realm of best practice. Many consulting firms sell themselves as providing best practice. That is fine but, again, that isn’t where the heavy lifting is needed.

Before going on one important point to make is predictable systems have a top-down command-and-control structure.

Unpredictable Systems

Chaotic systems are rule based but only show intelligible patterns at the macroscopic or statistical level. Hurricanes are good examples of chaotic systems. Patterns can be discerned from outer space. Trying to make sense of what happens on a particular street in a particular town in the middle of a hurricane is insane.

Reorganization is a good example of a chaotic system. The directives make sense when looked at from on high in the organization (strategic) but all Hell breaks loose at the individual, day-to-day level (tactical). Relationships are cut and people are left to free float and figure out how best to form new connections.

Complex systems lie somewhere between complicated and chaotic systems. An outstanding characteristic of complex systems is emergence. This is where new patterns emerge bottom-up. (For more on this read my blog series on change management.)

Emergence is the appearance of entirely new traits, properties, or modes of behavior that could not be predicted using the rules that started the system. A classic example of emergence is consciousness. One can study neurology and the interaction between nerves without ever “seeing” the rule that says, “And now we will have consciousness and the ability to self-reflect.”

In the business world the work to reconnect during and after a reorganization is an example of working in a complex way, i.e., having the beginnings of order emerge from the chaos.

The Myth of Best Practice

And now it is time to destroy the myth of best practice. In my experience best practice teams are put together in situations that are at best chaotic or, if one is lucky, complex. These situations suffer tremendously when a top-down approach is attempted.

As previously mentioned, the biggest problem is the team becomes disembodied – floating out past Pluto. Why? A common problem comes from the application of spin.

Experiences only make sense when we can place them in the correct context. If management naively insists (spins) situations are complicated (best practice) or simple when they are actually complex or chaotic it is impossible for the team to connect and build a truth system appropriate for the situation at hand. The project drifts helplessly while the team practices what I call “frenetic procrastination,” i.e., burning hours with nothing to show for the efforts – just pieces and parts.

In the next article we shall dig deeper into this issue.

One of the biggest challenges in chaotic situations is managing time, or should I say, finding enough time. A sign of time is critical and a change in approach is needed is spontaneous overtime that steadily increases. The probability of failure increases proportionally. What to do? The trick is to switch from management to governance.


The trap in such situations is trying to be the Flash – a comic book character who could race around so fast it appeared he was multitasking. With this approach each person, event, work package, etc., are approached on an individual basis. Wrong!

Management is fine when the situation is sufficiently stable for rules to be enforced and management by exception can be used. The challenge is getting to that spot. This is where governance comes into play.


Governance is applying management efforts across a boundary rather than on an individual basis. In chaotic situations the leader will die the death of a thousand razor cuts when attempting to shoulder all the individual challenges and difficulties people will bring to the table. At the boundary level the number of interactions decreases, returning some of the leader’s time.

What works is leaving everyone in the caldron to stew and figure out, as a group, how best to stabilize the situation. They all sink or survive as a group. This has the effect of cutting down petty behavior and pushing people to think. It is summed well in a quote from Mary Case, “No pressure, no diamonds.” A key characteristic of complex systems not only surviving but also thriving is the presence of a pressure that will not let up until an adaptive solution has been created.

Power and Survivability

The first most important attribute to gauge in a complex situation is how much power you or the sponsor above you has. Power is simply the ability to influence. This was covered in Managing Expectations. The power umbrella must be sufficiently broad to cover enough stakeholders and resources for an adequate solution to be generated. Once the power has been gained it is then critical to avoid a major pitfall – excessive preoccupation with the design specifications and their implementation.

Functional versus Design Specifications

Design specifications ARE important since there needs to be a testable deliverable for successful completion of the project. Success and the devil are in the design details. At the leadership level, though, one’s focus should lean towards the functional specification. Leave the team to solve the design problems. If the leader gets pulled into design problems two things happen: there is no leader, and the dynamic among team members gets upset because a powerful person has stepped into the design effort. People play to the highest power present – they address the powerful person’s presence rather than focus on solutions.

“How do I focus on the functional specification?” is the question. The answer brings us back to the boundary between individuals and groups. By insisting on the expected performance at the boundaries between subsystems along with the boundary between the overall system and the outside world the leader keeps perspective. Once teams learn decisions and judgments will be made at the boundary level the healthy pressure is on to work with other team members and stakeholders.

When the desired responses are achieved the leader then can reward all the individuals who contributed to the success. Obviously, this is something of a paradox because the individual learns to succeed by cooperation with other team members and contributing to client success. When this frame-of-mind permeates the project flexible relationships develop, amplifying the power present in the situation. Why? Customers can feel it emanating from the team and want more of it. The odds of success go up.

Chaos and Complexity #6: A Checklist that works!

by Gary Monti on October 19, 2010

Let’s put the theory covered so far in this series to work. Good leaders ask the right questions and listen to others for the answers. When dropped into a new situation one of the first, best questions to ask is, “Is the situation complex and, if so, what degree of complexity is present?” This sounds good but it can run into problems very quickly. One of the most common ones is the fact people are feeling the urge to focus and get to work. This can be a waste of time if the right frame-of-mind is lacking for determining what tasks need to be accomplished and how resources should be allocated. This was brought home in a previous blog dealing with the need to decide when to push on versus regroup.

Below is a checklist that helps facilitate a qualitative assessment of the level of complexity. It is in everyday language to facilitate use by a broad range of stakeholders and team members. In other words, it stays away from jargon, which can be the kiss of death when requesting information from people.

The Checklist

  1. Not sure how the project will get done;
  2. Many stakeholders, teams and sub-teams;
  3. Many vendors;
  4. New vendors;
  5. New client;
  6. Team members are geographically dispersed;
  7. End-users are geographically dispersed;
  8. Many organizations;
  9. Many cultures (professional, organizational, sociological);
  10. Many languages (professional, organizational, sociological);
  11. High risk;
  12. Lack of quality best characterized by lack of acceptance criteria;
  13. Lack of clear requirements;
  14. Many tasks;
  15. Arbitrary budget;
  16. Arbitrary end date;
  17. Inadequate resources;
  18. Leading-edge technology;
  19. New, unproven application of existing technology;
  20. High degree of interconnectedness (professional, technological, political, sociological).

But, If That Were True Then…

One of the most common responses when giving someone this list is, “If this list is accurate it would mean all my projects are complex!” It’s usually said with some astonishment and a degree of wonder as to whether or not it is overblown.

This brings us back to the blog on managing expectations. It is extremely important to establish the right expectations as to how much regrouping versus pushing forward is required in order to get to a realistic baseline that is executable.

Everything Is Simple

The real power of this checklist is based on the following:

  • It is principle-based with each question reflecting key categories that, if addressed correctly, will reduce complexity and lead to a stable deliverable;
  • It is simple. The list is designed to fit on one side of an 8.5 x 11.0” sheet of paper. It fits in your pocket. It is also simple in the sense the 20 items listed are interconnected. An amplification of effort can be achieved by addressing one area and seeing its positive impact on the others, e.g., requirements;
  • It helps re-orient when the confusion caused by greed, fear, ignorance, or indifference is present and obfuscates the conversation. Having this list gets the conversation back on track;
  • It is obvious. This might be the most important point. There is no special education or jargon required. Which reminds me of a quote from Einstein. He was asked, “When do you know you understand relativity?” His answer, “When you can explain it to the waitress at the diner.” This list taps into the reality that we all experience.

The thing to keep in mind is everything is simple when viewed from the correct perspective. If there is failure to address the checklist then the project will be complex. Period. If the items are addressed then things will get as simple as they can be under the circumstances and within the limits of the situation.

Chaos and Complexity #5: Chaos vs. Complexity

by Gary Monti on October 12, 2010

What is the difference between chaos and complexity? Many of the previous blogs have referred to both terms. While related, they are distinct. Here they will be differentiated.

Chaos vs Random

First, let’s look at what chaos is and isn’t. In everyday language chaos and randomness are considered synonyms. In chaos theory they are very different.

Random refers to a lack of structure at any level. No intelligence or pattern can be discerned.

Chaos does have observable patterns present. Chaos refers to the unpredictable behavior a deterministic (rules-driven) system displays. Chaotic systems are non-linear. This means small changes might produce a large change at certain times (tipping points). At other times a chaotic system can display remarkable robustness and remain intact when being hit with many, substantial impacts. There are other characteristics associated with chaotic systems, which we will explore in later blogs. For now, one more characteristic will be addressed which leads into the development of complex systems – emergence.

Emergence and Adaptation

Emergence is the appearance of patterns or intelligence arising from the interactions of components at a granular level. The most important distinction with emergence is the bottoms-up rather than top-down development of patterns. The resulting patterns can’t be predicted but they can be capitalized upon, amplified, and used to push adaption.

Adaptation is a transformative modification of the initial system, i.e., the system one ends up with can be different from the one started with. A good example of this is the map of Europe before and after World War II. The war began with England and France’s response to Germany’s invasion of Poland. The initial goal was the preservation of the sovereignty of Poland. In the end the German’s were defeated but Poland was lost behind the Iron Curtain. Notice how the adaption can have beneficial effects but may not necessarily result in the desired goals being met. This is a good example of the riskiness associated with working in the realm of chaotic systems. It still is better than trying to work in a deterministic fashion on a dancing terrain. Do you remember CompuServe? It had a chance to buy AOL, felt satisfied with being the big dog in business computing, stuck to a linear model, failed to adapt, got bought by MCI and now is a part of Verizon’s network.


Complex systems are a special type of chaotic system. They display a very interesting type of emergent behavior called, logically enough, complex adaptive behavior. But we are getting ahead of ourselves. There’s a need to back up a bit and describe a fundamental behavior that occurs at the granular level and leads to complex adaptive behavior. It is self -organization.

Self Organization occurs when the individual components in a chaotic system come together to work as a team to achieve the desired goal. Remember the non-linear component of chaotic systems? This applies during self-organization and means teams may form, work for a while then fall apart and reconstitute in a different form when an obstacle is met to keep on moving forward.

Complex Adaptive Behavior is the name given to this forming-falling apart-reforming-falling apart-… behavior. Specifically it is defined as many agents working in parallel to accomplish a goal. It is conflict ridden, very fluid, and very positive. The hallmark of emergent, complex adaptive behavior is it brings about a change from the starting point that is not just different in degree but in kind. In biology a good example of this is the emergence of consciousness. Another example is the Manhattan Project and the development of the atomic bomb.

Back to Linearity

The development of a complex system within a chaotic situation has a big plus. Complex systems can cross over into predictability where the newly developed rules work, e.g., the actual development and delivery of the atomic bomb. Remember the equilibium-disequilibrium talked about in the previous blog?

We now have a good basis for moving forward. In future blogs we will draw upon both the vocabulary and frame-of-mind presented here to look at how one leads in chaotic situations.

Chaos and Complexity #4: Push on or Regroup?

by Gary Monti on October 5, 2010

One of the most brutal forms of punishment is solitary confinement. At the other end of the spectrum is multitasking. Stating the obvious, what works best is finding a balance point. This means keeping it simple, something that sounds nice but can be quite challenging. Why? A hallmark of a complex environment is unpredictability. In other words, one doesn’t know where things are leading. In fact, they aren’t leading anywhere – that’s why the situation is called “complex” or, worse yet, “chaotic.” (We will look at the similarities and differences between these two terms in a later blog.)

This creates an interesting conundrum: Where does one place their focus when the environment provides no clues as to whether it is best to push on or regroup? Let’s explore.

Pay Attention

A really short answer to dealing with this situation is, “Just look and decide.” Okay. Given that, where do you look and what do you decide? The answers lie within the team. Determining just how far the team can go sets the limits in the situation. The specific concern in working within those limits is seeing whether the team should experiment (regroup) or continue with what is in place (push on). The goal is working in a predictable situation, one where a schedule can be developed.

Success and Failure

An honest appreciation of the possibility of failure is essential. Now, this isn’t something to dwell upon. In fact, the project manager’s challenge is paying attention to how close to the edge of the cliff the project is but then having the courage to stay focused on working effectively to back away from that cliff and stabilize the situation.

Achieving that stabilization usually requires some experimentation. This goes beyond best practice where alternatives are present. Rather, the experimentation is the team making calculated stabs at what they think might work and accepting that some of those experiments may fail but still yield good information.

For example, a common contributor to complexity is lack of clear requirements. This puts an unfair burden on the team since they are probably being held to an end date and maybe a budget. So, what do you do? Some experimentation is needed where the PM and team create what they believe the requirements to be and then offer this up for approval. Now, the odds of this working the very first time are usually small since stakeholders are probably pulling the project in different directions. If this weren’t true there would be clear requirements to begin with. So, the team must think of alternatives and have more than one arrow in their quiver.

Professional Gambling

With a sense of the team’s limits the project manager will do best being a professional gambler. The gamble is this: Can the team come up more than one set of requirements and can the PM push to get one set, or a variant, accepted. This means allocating time for the team to go out to the edge, almost being a little bit wild, and come up with options as to what might work, e.g., looking at what the best that could happen is, what the worst is, what could be expected, etc.. It is up to the PM to decide how to play the politics and push for acceptance of some realistic set of requirements. This is a very fluid situation requiring focus and discipline.

Come Back From the Edge

The PM’s main goal is to get to some set of requirements allowing the team to move, at the very minimum, to a best practice situation where they can choose from several realistic options how best to proceed with the project in a linear fashion, i.e., creation of a realistic schedule. In other words, the team needs to get to a spot where they can start predicting outcomes based on a given set of requirements. Without this a schedule is impossible and the situation will collapse.

Let’s recap. We see that a certain amount of time is spent trying to establish order from the bottom up and getting buy-in from power brokers and senior managers so that top-down support is established. Unified commitment from senior management is what is needed.

The PM must subtract the time the team spends experimenting plus the time the PM spends politicking from whatever time is left in the schedule.

The PM and team then need to regroup and determine how much of the approved scope can be accomplished in the remaining time. This leads to another potentially difficult decision point.


Typically, what happens is by the time senior management gets behind the scope of work there is insufficient time left. The PM is then faced with the distasteful task of offering some combination of the following options:

  • Cut scope;
  • Ask for more resources but not so many that the project is pushed into confusion;
  • Extend the schedule, and;
  • Worst case, cancel or delay the project

The PM has to watch and not get caught in the middle, i.e., potential team burn-out on one hand and senior management having unrealistic expectations on the other.

A Solution

The best way I known of to handle such situations is simply working to avoid them. The odds of successfully avoiding a train wreck of a project go up when the above realities are applied at project inception. Ask the question, “Why shouldn’t I believe this is a complex project?” If there’s insufficient information to create a network diagram then the situation is complex and the team is going to have to go to the edge and do the experimentation mentioned. The PM does best by staying with that reality. It’s not a good formula for winning a popularity contest but it does help develop the reputation for being honest about what the organization can do under the circumstances.  In the long run working this way in a respectful manner is what brings about the most growth possible under the circumstances.

Why do chaotic systems behave strangely? The previous blog (Coyotes) touched on this. What’s behind this behavior? In a word, it is ”attractors.” More specifically it is a special type, a strange attractor. Understanding strange attractors helps appreciate the unpredictability. Let’s take a look.


What is an attractor? It is an influence, a tension, or force that affects a situation. There are three types:

  • Point
  • Cyclical
  • Strange

Point attractors bring a system to a distinct, predictable end point. You throw a stone off a cliff and gravity pulls it down to the beach below.

Cyclical attractors cause a system to oscillate. Education programs provide examples of cyclical attractors. Infrastructure grows in a country increasing the demand for civil engineers. Colleges and universities increase the number of engineering students until the market is flooded. Enrollment is then cut back and attrition sets in. The next upswing in the economy increases the demand for more civil engineers.

Strange attractors distort systems. If not addressed they can damage or destroy the system.

Fun House Mirrors

A good visual metaphor for strange attractors is a fun house mirror. Part of what makes them so hilarious is our ability to see the near-impossibility of imagining actually being built that way and trying to function. (Would you really want hips that big?! Could you get into a car if your head was truly that elongated?)

Daily Living

Daily living would be thrown off track if we suddenly were transformed in that manner. This is exactly why strange attractors must be addressed. Failure to address them can lead to the death of or damage to a system. It gets even stranger when the fact is thrown in that strange attractors can be positive, negative, or both.

Take marriage or any committed relationship. It is both a positive and negative strange attractor. Don’t think so? Do you live with someone who squeezes the toothpaste from the middle of the tube? Are you the one who squeezes from the middle? Is there a “right” way to put dishes in the dishwasher?

Railroads, Telephones, and Denial

What can really kill a situation is trying to force a situation that is truly chaotic into one that is cyclical- or point-attractor based. The railroads attempted to do this as did AT&T. Industries lobby for regulation and monopolies as an attempt to stifle any distortion in their business model. In the end, it doesn’t work. It backfires and stifles economies.

In the 19th and early 20th Century railroads in the USA lobbied to get powerful right-of-way controls. When automobile roads improved and trucking came on line in a big way railroads ended up with useless lines and were stuck with a high tax burden plus excessive operating capital requirements to maintain the lines.

If you are old enough you might remember when one had to pay extra for each extension phone in their house. The phone company could monitor the voltage drops to see if anyone was “stealing” from them. The situation was ripe for the anti-trust action against AT&T in 1984.

The Project Management Challenge

Capitalistic economies thrive because of strange attractors. Something new and different is always coming out.  In other words, the world revolves around projects. You know, those temporary endeavors that provide a unique product or service. While projects can be exciting there is a darker flip side. A team can be working well and following project principles when – bang! – a competitor with a new product shows up and changes the game. Think Yahoo vs Google. ROI models get tossed out the window and the underpinning for the project becomes unstable or disappears completely.

The next blog will look at the challenges facing the project manager when working in such a situation. Why is this important? If you’ve been a Project- or Program Manager long enough you can recall the project where the threat or actuality of the rug being pulled out from beneath you has occurred.

Chaos and Complexity #1: Coyotes, Chaos and Complexity

by Gary Monti on September 14, 2010

In this series we will dive into the complexities of…well…chaos and complexity. Why? A possible first thought is they are the “in” topics today – flavors of the month. The answer and the reality are far simpler. We have to deal with them on a daily basis. And, we have to get good at it if we are to survive and thrive.

There is another important reason. It has to do with the uniqueness of the theories. Chaos and complexity have broad application across many apparently different aspects of life from heart arrhythmias to children playing on a playground to counterinsurgencies. The list goes on-and-on.

So, let’s get started. But where? A good place is basic definitions along with how the two are connected.


A common misconception regarding chaos is viewing it as synonymous with “random.” While that can be true in everyday use the two words are quite different when looked at in terms of chaos theory.

In chaos theory “random” refers to complete lack of structure and patterns. A classic example is the motion of gas molecules at the microscopic level. Newton would be driven crazy trying to predict their trajectories. (If you’ve had some physics you might recall the challenge of working with three-bodied problems or a double pendulum.)

Chaos on the other hand is quite different. Specifically, it applies to any system which has definitive rules of operation but shows nonlinear behavior. Assuming that is about as clear as mud some explanation may help.

“Nonlinearity” has some specific criteria which appear when looking at the elements of a chaotic system :

  • A chaotic system comprises components connected through deterministic rules;
  • With a given starting point in time a chaotic system ends up behaving in ways the rules cannot predict. This is the nonlinearity. It is rather strange. In every day terms what this means is a given system can be started at the exact same point two different times and the results will be both unpredictable and different. Multiparty, parliamentary systems reflect this well. The rules for operating the system are fairly constant. Forming a coalition government can be quite the example of nonlinearity as can be seen in modern day Iraq.

This can be maddening. The rules are clear, the components of the system are thoroughly understood by everyone and yet it’s impossible to get consistent results. What makes it even crazier is a third component:

  • The rules work and outcomes can be predicted in the immediate vicinity of a few components when a very short time span is used.

An example might help right now.

All of the above is observable in efforts to eliminate coyotes. Individual coyotes have been killed. However, efforts to do this on a mass scale has produced some interesting results. In the mid-eighteenth century coyotes were west of the Mississippi in about 11 states. Efforts to eliminate them have failed spectacularly with coyotes being in all 48 contiguous states. They have  lost their shyness of man and now live in urban areas. (Ironically, while I was conducting a workshop on complexity in downtown Chicago, on the floor below a coyote walked in off the street, jumped into an empty juice cooler inside the hotel’s quick stop store, rested for 20 minutes and then took off! The security video made the national news.)

This ability to thrive in chaotic situations leads to complexity.


Four characteristics define complexity:

  • Adaptability
  • Connection
  • Interdependence
  • Diversity

Complex systems are a subset of chaotic ones in that they are nonlinear. Decisions are made on a microlevel and bubble up to the macro. This is in stark contrast to social engineering where everything is top-down. Coyote social systems reflect all 4 components quite well. Faced with annihilation the coyotes branched out geographically and socially and tried new behaviors (diversity). If the changes worked they stuck (adaptability). The lesson spread quickly through the social structure (connection) with individual behavior graduating to coordinated social behavior (interdependence).

Packs now go through some neighborhoods hunting for pets. In some areas attacks on young children have been reported. The latter behavior is not as successful (adult supervision) as the former so there is less of it. Regardless, the coyotes keep changing their game plan at a tactical level to simply find out what works and the change migrates up the social ladder to become a pack strategy.

This bottom-up approach to change has been alluded to in a previous blog (Executive Map) and is a hallmark of a type of organizational structure essential for success in chaotic situations – complex adaptive systems.

That is enough for now. As the series progresses we will go through the looking glass and see things from a very different perspective: one that is both familiar and strange: familiar because, after all, you’ve made it this far; strange because it cuts against the grain of some commonly held beliefs taken as truth.