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Ashby's Law of Requisite Variety

In discussions about the quality of control (or consistent with the themes in this blog the quality of muddling), Ashby's Law of Requisite Variety is often raised. Basically, this Law states that

for full control, the variety of the controller must me at least equal to the variety of the process or situation being controlled.

In this context, the opposite of variety is constraint. So, an alternative statement of Ashby's Law is that:

 for full control the controller must not be more constrained than the process or situation being controlled.

A synonym of variety that is typically used in the motor control literature is degrees of freedom. Thus, a third statement of Ashby's Law is that:

for full control, the degrees of freedom of the controller must be at least as large as the degrees of freedom of the process or situation being controlled.

The gist of Ashby's Law is that if the controller is more constrained than the process being controlled (i.e., has less variety or fewer degrees of freedom), then there will be states of the process that cannot be reached by the controller.  In other words, the controller will not be free to access all process states.

Note that failing to satisfy Ashby's Law does not mean that the controller can't achieve satisfaction with respect to controlling the process (e.g., achieve certain goals or avoid catastrophe). It simply means that there are limits to where the controller can take the process.  In other words, there are some states of the process that cannot be reached due to constraints in the controller. So, if the controller is more constrained than the process being controlled, then it cannot do anything or everything - there are limits to what states can be achieved and or limits to what process changes can be countered (or maybe even observed).

Typical reasons that a controller might not satisfy Ashby's Law might reflect constraints on perception (observability) or constraints on action (controllability). The control system might not be able to discriminate certain process states from other process states. Or the control system's motor coordination may be too gross to perform the precise moves required to achieve certain state transitions.

Scaling up Variety to meet the challenge of Complex Problems

The natural world is complex or messy and many of the problems that humans or sociotechnical systems must solve in order to survive are ill-structured or wicked. Relative to Ashby's Law, the implication is that the variety associated with these problems can be extremely high.

So, it is quite fortunate that humans are also complex (e.g., the brain has a high degree of freedom), that humans are diverse, and also that we have the ability to use complex technologies. Thus, the variety of an organization of diverse humans and technologies  (i.e., a sociotechnical system) can also be extremely high. Although the demands of many complex problems may exceed even the large capacity of the human brain, it will generally be possible for organizations of diverse humans and technologies working together to meet the challenge of Ashby's Law with respect to complex problems. And in many cases the variety of the sociotechnical system may exceed the variety of many complex problems.

On the positive side, exceeding the demands of requisite variety opens up the possibility of full control and also the possibility of redundancy and flexibility offering multiple solutions to control problems. On the negative side, the excess variety within the sociotechnical system may also be a source of 'noise.' That is, the variety within the sociotechnical system may reflect conflicts (e.g., differing values) that make it difficult to coordinate actions to achieve skilled control of the situations. An organization with many degrees of freedom is difficult to manage - like herding cats.  Thus, the excess degrees of freedom within the organization (e.g., differing opinions) can add variety, increasing the computational demands on the control system.

While satisfying or exceeding Ashby's Law means that complete control is possible, it does not guarantee that complete or even satisfying control will be achieved. The variety of the controller (or the degrees of freedom or constraints) must be structured to reflect the variety of the process being controlled. In other words, the constraints or structure of the controller must be organized in such a way that it can meet the demands associated with the process. Another way of saying this is that the controller must have a valid internal model of the process. This does not necessarily mean a conscious mental model, but it means that the degrees of freedom in the controller must be tuned appropriately to the demands of the process being controlled.

Degrees of Freedom Problem

In motor control, the tuning of degrees of freedom in the human body to the demands of physical control problems (e.g, playing winning golf) is typically referred to as the degree of freedom problem. While playing winning golf is a high variety problem, each of the different shots required for winning golf are fairly low dimensional problems.  But different shots are associated with different types of demand. The requirements for driving a golf ball long distances and staying in the fairway are different than the requirements for chipping a golf ball to a nearer smaller target, which are different than the requirements for putting a ball into a hole.

While the human motor system has adequate degrees of freedom to satisfy the demands of each different shot, it is necessary to use different degrees of freedom (or different constraints) for each type of shot. To be successful at the highest levels, a golfer needs to be able to organize the degrees of freedom of the motor system into different smart mechanisms. Each mechanism reflecting the requisite variety of different situations (e.g., driving, chipping, or putting). In creating these smart mechanisms, different degrees of freedom are 'locked out' (constrained) to reduce the complexity of the control problem.

Thus, for the golfer a key to skilled performance is to lock out unnecessary degrees of freedom (potential sources of noise), leaving a few degrees of freedom that are well matched to the demands of a particular type of shot.

In an analogous fashion, in designing sociotechnical systems focus needs to be on identifying the situational demands of various work functions and creating constraints (e.g., locking out degrees of freedom) to create smart mechanisms for addressing the demands of those functions. This involves setting lines of authority and communication and designing appropriate procedures and representations so that the the organization is well tuned to the problem constraints (or requisite variety). In work domains where the demands are changing - it also becomes necessary to support organizational learning, so that the organization is capable of self-tuning the degrees of freedom to adapt to the changing problem constraints.

Identifying and Designing Constraints

For cognitive systems engineering - an important implication of Ashby's Law is that the focus of work analysis is on identifying the problem constraints. Understanding the problem constraints is a first step toward designing organizations that can achieve satisfying control of complex situations. From the perspective of design - satisfying Ashby's Law is achieved by matching constraints or degrees of freedom. Rasmussen's Abstraction Hierarchy (AH) is one way that cognitive systems engineers try to visualize the constraints in a work domain. Each level of the AH is associated with different classes of constraint (e.g., values, physical laws, regulations, organization, physical function, and physical form).

Diversity within an organization is critical to meeting the requisite variety demands of complex work domains. However, this diversity can also be a source of noise that can make skilled control difficult. Design thinking involves introducing the appropriate constraints to channel this diversity along productive paths reflecting the requisite variety of the problems to be solved (e.g, the shots to be made).

Social Friction

Have you ever had a fantastic idea that got crushed in discussions with peers and colleagues? Perhaps, the idea was not completely crushed, but it became necessary to compromise and to modify or delay implementation of your idea in order to reach enough consensus for the organization to act on your idea. Or has it ever happened that you later conclude that an idea that you thought was great at one time, turns out to be not so wise and you thank your lucky stars that resistance from within the organization prevented you from making a big mistake. Almost any idea that requires an organization to change course or to try something new or different will come up against resistance (will experience social friction).

In some cases, the social friction will alter or delay implementation of an innovative good idea. But in other cases the friction will result in constructive improvements to the initial idea, and in other cases the friction will prevent the organization from implementing a change that might appear to be a good idea to some, but that would have actually led the organization down a risky or dangerous path.

Essential Friction

Imagine trying to walk or stand on a surface with minimal friction (e.g., on very slippery ice). Under such conditions, maintaining stability can be problematic. On the one hand, friction or drag is considered to be a an obstacle or cost when it comes to movement (e.g., a waste of energy). However, on the other hand, friction can be essential to controlling motion (e.g., maintaining balance and being able to walk to a goal and stop without sliding past). Thus, zero friction is not a desirable condition when it comes to controlling locomotion. As Gene Rochlin has noted:

Without the damping effect of friction, we would live in an impossibly kinetic world in which the consequences of every action would persist and multiply to the point of insanity (p. 132)

Of course, there can be too much friction, such that the energy costs of motion are prohibitive. The bottom line, however, is that some friction is essential for stable, controlled locomotion.

As in the physical world, Rochlin suggests that friction may also be essential to sanity in the social world.

In the realm of the social and political, morals, ethics, knowledge, history, and memory may all serve as sources of "social friction," by which gross motions are damped, impetuous ones slowed, and historical ones absorbed. Such friction is essential to prevent the persistence and multiplication of social and political movements once their driving force is removed (p. 132)

In the social context, an analog to friction might be the opposition and second guessing that tends to arise with any new idea or prospect of change within an organization. A new idea that might at first appear as an innovation, will gradually lose steam in the face of opposition and critiquing. Thus, ideas that are not continually pushed or infused with energy will dissipate, perhaps without ever being implemented. Some of these ideas might have been positive innovations and others might have been simply ill-formed or bad ideas.


If the abduction or adaptive control logic illustrated in the previous blog (#7) is representative of everyday sensemaking, and if the underlying dynamic is essentially muddling through, then a natural question to ask is: What does skilled muddling look like?

Lindblom's term 'muddling' suggests a messy, chaotic process - a kind of meandering with little chance of convergence. This term stands in stark contrast to the term 'control' and the image of a 'servomechanism' that is the typical image of a control system used in the social sciences. In the servomechanism metaphor there is an implication of a well defined goal and well-defined criteria for comparing the current state to the goal state to yield a well-defined error signal for guiding activity.

However, Lindblom notes that for many public policy decisions there is no single, well-specified goal. Rather there will typically be many competing goals or value systems. In many cases these will be incommensurate relative to each other and only tenuously  linked with actions or outcomes, making it difficult to even know when you are on the right track. Thus, with regards to public policy and many important personal decisions (e.g., buying a home, choosing a profession, wooing a mate, voting for a president) there is no a priori well-specified goal or performance standard to specify the right path to a satisfying end.  In fact, one might claim that the only reliable metric for judging the quality of a decision or action is the degree of satisfaction with the result. At best, we can recognize a satisfactory solution when we get there - but even that might be questioned (e.g., sour grapes).

However, Lindblom's term incrementalism does suggest something about what quality muddling might look like. This term suggests that quality muddling results from making small (incremental) changes. This suggests a conservative approach that tends to progress through small tweaks to policies that have worked in the past. This strategy progresses through small changes to existing policies, rather than through dramatic innovations.

Stability in Closed-Loop Systems

For the social sciences, the simple servomechanism (e.g., thermostatic control of room temperature) is the prototype of a  control system. However, from the perspective of control theory, the simple servomechanism is only one of many solutions for regulating processes. In regulating complex processes (e.g., multi-dimensional), complex control strategies are necessary (e.g., multiple sources of feedback that must be integrated in ways consistent with the process dynamics). In many cases (e.g., when there are long lags in the process or when there are uncertainties about the process dynamics) simple compensatory control (i.e., based on current error feedback) may not yield a stable control solution.

In assessing alternative solutions - particularly for complex processes - the first priority of control theory tends to be stability or robustness. Typical ways to increase the stability or robustness of control solutions is to lower the gain or add damping.  In many respects this is analogous to adding friction. The lower gain or damping makes the system more conservative - less responsive to error or deviations from a goal or ideal. It makes the system resistant to change, and reduces any tendency to follow the 'noise' down a garden path to catastrophe.

With respect to gain, good designs trade off speed for accuracy and stability. Lower gain means slower responses - but it also reduces susceptibility to noise or over shooting the target. With respect to robustness, good designs typically trade-off local optimality for stability. That is, a robust controller may not be optimal for any situation, but it will typically be satisfactory for a wider range of situations than a controller that is tuned to be optimal for particular situations.

The prototypical example of a case where a control system is not conservative enough is pilot induced oscillations. This is a situation where the pilot's gain is too high. The pilot overreacts to the errors and the result is that his actions actual result in divergence from the intended target state - often with calamitous results.

Skilled Muddling

Thus, Lindblom's intuitions about incrementalism as a good strategy for dealing with complex sociotechnical problems is consistent with principles derived from control theory.  The point is not for organizations to be rigid or completely adverse to change. Change is necessary to keep up with the demands of a changing ecology.  However, skepticism and checks and balances with respect to radical new ideas can be essential to skilled muddling. In other words, the friction associated with building consensus within an organization of diverse people with conflicting opinions and values can be essential to the long term success (stability) of the organization.

In order for a control system to be robust in a complex world, a conservative approach to change is generally a good strategy. This helps to ensure that the actions of the organization will generally be responding to the signals (i.e., actual changes in the ecology) rather than the noise (i.e., imagined changes), and that the system will result in satisfactory performance over a wider range of situations. This strategy allows good ideas that are persistently advocated to eventually influence the direction of the organization, while protecting the system against the risks associated with bad ideas and misplaced enthusiasm.  The bottom line is that slow and steady progress (i.e., the turtle's strategy) is what usually wins the race in a complex risky world. 


Rochlin, G.I. (1998). Essential friction: Error-control in organizational behavior. In The necessity of friction (ed.) N. Ackerman,  Boulder, CO: Westview Press. 132-163.