Managing the Transition of Ventures as Complex Systems
Part III

Bryan S. Coffman
July 23, 1997

This article summarizes Stuart Kauffman’s book At Home in the Universe. All of the principles that are discussed concerning complex systems are his, or if attributed to others, may be found in his book. The conclusions drawn with respect to venture management and transition management in organizations are a mixture of mine and Dr. Kauffman’s. The models from the Taylor Modeling Language are copyrighted, of course, by MG Taylor Corporation.

 

Related Articles:
Part I: Selection, Self-Organization, and Autocatalysis
Part II: Requirements for Order and Evolution on Fitness Landscapes

Coevolution and Coupled Fitness Landscapes

"We all swap our stuff… In doing so, we create niches for each other."

Now that the organization’s made it to the end game perched smugly on its peak, nature raises the stakes. No enterprise is an island. Species in ecosystems influence the evolution of other species. They all coevolve. Their fitness landscapes are linked. Therefore, a change in overall fitness by one species on its landscape may deform the peaks on the landscape of a different species.

A few notes here about building a community. We know from reading Kelly’s book Out of Control that the composition of stable communities is highly dependent upon the order in which species are introduced. We also know that when building a community, at first it’s easy to add species but this gets harder and harder as the community approaches stability. This is more than the food web and who eats who. Much of an ecosystem is caught up in cooperation—mutualism and symbiosis. Of course, there are host-parasite and predator-prey links as well.

And we also know that if we remove species at random, the cascade of changes through the community will follow the form of a power law curve. In other words, in a few instances, the removal of a species will be catastrophic, but most of the time the removal of a species will cause a little cascade of change that quickly dampens out.

It's easy to understand the linkage between species through a predator-prey model. Imagine a valley of grass inhabited by mice and lions (OK, well, I drew a mouse and a lion: forget about whether it's REAL or not ;-). The mice eat the grass and the lions eat the mice. As the population of mice increases, they provide more food for the lions, who use the extra energy to breed more lions. More lioins eat more mice and the population of mice begins to decline. Fewer mice cause the lions to die out and the overlapping cycles repeat.



However, the fitness landscapes of the mice and the lions are coupled together. If the mice mutate to develop better cornering ability to outmaneuver the lions, then the lions' fitness landscape will deform--what was once a peak now becomes a valley.

Perhaps now the lions develop a coloring that’s harder to see. Now the mice are in trouble and their new peak sinks to become a valley from which they must emerge.

To emerge from the valley, they'll begin with wild jumps around their new landscape to find the areas with higher peaks. Once near a peak, the jumps become shorter and once the new peak is gained the jumps will cease altogether--unless the lions have adapted in the meantime.

Again, this relationship can be modeled using the NK network (see Part I) model with a few variations. Each fitness landscape represents a species, and each node on the network under the landscape represents a trait. N is the number of traits per species, represented as nodes on the network. K is the number of couplings between traits in a species. C, a new variable, is the number of links to traits in other species that influence the behavior of the node. K represents internal connections and C represents connections between species. There’s a fourth variable, S, which represents the number of species that are interacting.

N = number of nodes or traits (genetic possibilities) on the target species' fitness landscape

K = number of inputs to each node from within the target species

C = number of connections to each node in the target species from traits of other species that influence the behavior of the node

S = number of other species interacting with the target species

Different types of behavior emerge while running the model.

If K is high, then there are lots of peaks on a species' landscape. It doesn’t much matter what other species do because if a species gets bumped off of a peak, it’s a short climb back up. This is an ordered regime for coevolution and one of average low fitness as well.

Or if C is low, there are few connections between species. As a result, each species climbs to its nearest peak and settles down there. The sparcity of interconnection means that species will rarely bump each other off of peaks. It's as if the different species contribute nothing to one another's well being. There is too much order in this regime to permit further coevolution.

In both of the two previous examples, a species can find an evolutionary stable strategy in the ordered regime, moving to local peaks and remaining there. If you perturb a system in the ordered regime, the disturbance is quickly damped out.

If K is low, there are few peaks to get trapped on but it takes a long time to climb to the top. So, even with modest values of C or S, the species will keep trying to climb peaks that move too fast to attain.

This is known as the Red Queen regime, or the arms race syndrome. Species are so tightly linked that they engage in a never-ending, chaotic war to outdo each other. If a system is perturbed in this chaotic regime, the changes amplify and cascade in waves throughout the ecosystem. This is the reverse of the power law.

In each of the three previous examples, the average level of fitness attained by any species is relatively low. Either there are a lot of modest peaks that are attained quickly and then held, or the landscape heaves beneath the species so that it can never reach a peak at all.

There's a fourth option. If the simulation of coupled fitness landscapes is run long enough, a more interesting phenomenon emerges. Red Queen regimes migrate toward the more ordered regimes and the evolutionary stable strategies migrate upward toward the more chaotic regimes. They meet in the middle, around the complex phase transition once again. It would seem that nature abhors too much chaos or too much order. In effect, the ecosystem manages the number of connections between species, between traits within a species and the number of species involved.

IMPLICATIONS FOR TRANSITION MANAGEMENT

Industries can be examined to discern the relative sizes of their K, C and S values. If there’s too much interconnectivity between organizational species, or between divisions and departments, a Red Queen war can break out. If there is not enough connection, then the organization or division will stagnate. Stagnation can be programmed if evolutionary stable strategies are desired for a period of time.

Over time, all industries naturally move away from overly chaotic or ordered regimes. If an organization comes to rely too much on complacency or aggressiveness for its success, it will be in trouble. Assuming a Red Queen strategy on a more docile landscape causes the organization to disperse away from local peaks (the self-imposed mutation rate is too high) instead of coalescing. Assuming an evolutionary stable strategy in a more agitated environment will leave the organization constantly inhabiting the basins.

 

Solving Hard, Conflict-laden Problems with Patch Theory

"As we approach the year 2000, the design of complex artifacts is plagued with nearly unsolvable conflicting restraints… Organisms, artifacts and organizations all evolve and coevolve on rugged, deforming fitness landscapes. How do we track peaks on deforming landscapes?"

This is all fine, but in the real world of enterprises, the values of K, C or S tend to be high. We need strategies for handling the dense interconnection and the inevitable conflict such connection brings. Kauffman uses the example of designing a modern jet. Everything seems to affect everything else in the design. The size and type of engines affects the size of the fuel tanks, affects the design of the wings or the center of gravity, and so on. The traditional method for solving such complex problems requires some of the variables in the equation to become constants. For instance, the plane may be designed around the engines, the organization may be designed around one particular product line or department. It's tempting to overlay a hierarchy or prioritization scheme over the elements of the problem to force an artificial resolution of the conflict.

Could there be another way of resolving conflict between components in design and implementation besides imposing a bureaucratic hierarchy on the system? Maybe.

We’ll start with the familiar NK network. Let’s say that a problem has N parts and K inputs to each part (connections between parts). This system is going to navigate on a fitness landscape. The diagram shows 36 components to the problem with each component receiving four inputs which influence its next state, or decision, or design iteration.

For now, we’ll treat the problem/system as one patch. This means that a part can mutate (change its state, design or answer to its part of the problem) only if the mutation is good for the entire system; if it moves the entire system up in the fitness scale. Most mutations will fail the criteria because of the dense interconnnectivity and the dampening of changes in the solution that attempt to move through the system. A part can't adopt a new solution unless it makes everyone else in the system happy. Therefore, the entire system will move slowly up to the nearest local peak and squat there. Once on the peak, all changes would take it down one of the slopes. Since this violates the criteria of accepting only increased fitness, there will be no movement off of that local peak. Chances are that local peak is at a relatively low level of fitness as well. This example illustrates the central government strategy and the total consensus strategy. No one can change anything unless you can prove it is good for everyone else. Once a local peak is attained, further evolution is impossible and the system is merely waiting to be fractured and destroyed by the competition and other forces in the ecosystem. Even if you can see a better solution you can't get there from here.

Suppose we now superimpose a patchwork grid over the network. We’re not going to disturb any of the N nodes or the K connections between nodes. But we’re going to overlay a grid of patches and now, if a node, N, mutates, it can do so if that increases the fitness for its patch. It no longer has to worry about increasing the fitness of the entire system. But since the nodes are still all interconnected, that accepted mutation may propagate across other patches and influence them. In other words, it changes the problem that those other patches have to solve and now there’s a chance for evolution. An improvement in fitness for one patch may mean a degradation in fitness for the system as a whole. Now the system can move up and down on the fitness landscape.

What if we divide the system into N patches, where each node is its own patch? This leads to chaos and the Red Queen regime where each node competes viciously against all of the nodes it’s connected to. No one gets anywhere. This is an example of total participative management and decision making.

What if the number of patches is something in-between? The system encounters a phase transition between chaos and order and just on the chaotic side of the boundary we find our comfortable land of complexity where there’s enough order to get the work done but enough variability to allow evolution as well.

IMPLICATIONS FOR TRANSITION MANAGEMENT

Patch theory is different than conventional management theory where you break problems down into departments and then manage the departments. Patch theory says you break problems down into departments and then let them coevolve a solution. Traditional management theory was on the right track. Organizations were divided into departments or divisions, each with its own unique but interconnected part of the problem. Where management theory went wrong was in severing the connections between nodes and wiring the nodes in each patch up to a single "manager" node through which all decisions and state change communication had to pass. This left the door wide open for Red Queen wars between divisions. It also reduced the efficiency of the organization--especially in times of rapid change--because most of the nodes in the enterprise had lost their connections to other patches. To overcome this limitation, people organized informal "grapevine" structures so that much of the real work could get done in spite of the formal structure.

If a solution does not coevolve, then one part of the problem will become fixed, and the rest of the elements will have to fall into line based on the ramifications of its specifications.

If an enterprise employs too many patches—if members of the network are too autonomous—Red Queen behavior and internal arms races may emerge. Many sales organizations suffer from this phenomenon where each salesperson tries to negotiate for a better deal with the company than his associates. Many companies have vicious internal politics that resemble this type of behavior as well.

When applying patch theory and coevolution, the first step is to break the problem into patches. Ensure that the connections between network nodes remain intact. The underlying network structure must remain whole. If, instead, the connections get severed, each patch either acts as an autonomous unit, or as a node. This risks Red Queen behavior.

For an example of how we use Patch Theory in DesignShop® events, click here.

 

Conclusion: Implications for Venture Management

The following ideas summarize the conclusions from each of the preceding sections in the light of the venture.

Selection

Selection is insufficient of itself to propel evolution. We cannot rely solely on the random selection of people, ideas or artifacts in or out of our organization as a mechanism to further our growth.

 

Autocatalysis

There are really no measurements we can make concerning the complexity of our organization. The science has not become that cohesive or precise. But we can think in general ways about the diversity of the people, ideas and artifacts in our organization and the probability that any or all of these actually act as catalysts. We can imagine whether our organization is indeed autocatalytic or not. I suspect that it is not yet. The knOwhere® stores are our next major test of an autocatalytic system.

I also suspect that for many small enterprises there is a tendency toward less diversity and more probability of catalysis. In other words, people, ideas and artifacts must facilitate each others work more in a small company than in a large one. The economics for having great diversity in a small company are just not there. Or if the diversity is present, it tends to cluster in certain individuals, making them cross-functional and many-skilled.

We also need to be aware if the catalysis that we have going on in MG Taylor is truly feeding back on itself and converging on autocatalysis, instead of diverging its energy. We may be catalyzing the production of lots of new "molecules" but are these pathways linked together in a net or do they dissipate and straggle off? These questions are related to the policy, logistics and task stages of our Vantage Points model.

 

Requirements for Order in Emergent Systems

Since we are a highly networked organization, we should pay attention to the degree that our network is interconnected in a causal way. This is tuning the K variable. Naturally we all communicate with one another but what is the nature of the catalytic connections that lead to decisions? I’m not sure myself how to think about this but it’s worth some effort. If there are too many conflicting messages in the system, it will oscillate in the chaotic regime of state space. If we put too much control on the system, it will settle down into the ordered domain out of which we will find it difficult to evolve.

If we choose to operate with a higher K value then we must tune the P value. Recall that P regulates the way nodes make decisions by canalizing the decision making function or rules. This means that regardless of the variability of input, the output of the nodes is fairly regular. Only a few out of many inputs play a large role in determining the output. A commander on the battlefield receives a great many inputs for decision making. In his synthesis, usually a few of the inputs can determine the outcome: an order from a higher command, a threat that must be countered immediately, a break in the weather pattern, etc. Decisions come down to paying attention to a few of the variables while not ignoring any of them. This is a strategy for resolving conflict.

 

The Evolution of Living Systems on Correlated Fitness Landscapes

There must be a level of redundancy in the system to stabilize its chances for success. This is particularly difficult in a small enterprise.

We need to use policy and task level tools to help restrict conflict if the K value is too high, creating a landscape with too many peaks. If we find ourselves quickly scaling peaks of mediocre fitness, we may have too high a K value.

Trying to mutate to new, innovative ideas too rapidly and in too rapid succession can cause the organization to lose focus and wander all over the fitness landscape.

The way to explore unmapped areas of terrain is to "mate" with a partner and watch the fitness of the offspring that necessarily occupies terrain in-between the parents.

If our N and K values become very high, we will be unable to make radical improvements. The complexity will tie us down and we’ll have to resort to spin-offs and other mechanisms to inject the necessary growth.

As we enter new arenas of the economy we should be aware of what games are being played there and what stages the games are in. If the game is new, we use long jump strategy to quickly seek more favorable position. This means trying out many innovations covering a broad arena. If the game is a middle game, we should not enter unless we can see a way through to making it a new game. If we are in the middle game or enter in a middle game in progress the strategy is cautious improvement step by step until we occupy a local peak. If we’re in the end game, we’re on a local peak and should stay there.

 

Coevolution and Coupled Fitness Landscapes

We know that building community is easy at the beginning when everything’s just simmering in the pot. As things crystallize out, it becomes harder for new ideas to take shape in the matrix.

We may find ourselves in one of three scenarios: playing the Red Queen arms race, stalled in evolutionary stable system, or on the complex edge of chaos where we have a stable ability to produce and an ability to evolve as the landscape deforms beneath us. If we’re playing the Red Queen scenario, then we may have scant internal connections but be over-connected to other species in our ecosystem. If we’re playing the evolutionary stable system scenario, we may be over-connected internally but not have enough connections to other species. We can also tell if we’re in either of those scenarios if our overall level of fitness seems mediocre.

 

Solving hard, Conflict-laden Problems with Patch Theory

Even if we are organized as a network we may be trying to solve problems with a single patch. If so, then no one can make a move unless it’s deemed good for everyone else in the system. The result will be a slow climb up to a modest level of fitness followed by stagnation.

If we’re behaving as if each of our network members is a patch unto themselves, then we’re in a chaotic zone playing an internal version of the Red Queen strategy. We’ll find that we can attain modest fitness peaks fairly rapidly but for some reason the peaks deform so rapidly that we’re forever climbing.

Or we may divide into patches and divide the network at the same time along patch lines. This forms factions or departments that can lead to another form of Red Queen behavior.

concepts concerning complex adaptive systems come from Stuart Kauffman's book At Home in the Universe, 1995, Oxford University Press
application to organizational theory copyright 1997, MG Taylor Corporation. All rights reserved
copyrights, terms and conditions

19970724072802.web.bsc

Copyright,
© MG Taylor Corporation, 1995 - 2002

iteration 3.5