Perspicacity

At the heart of any consideration of questions of controllability and resilience is the question of degrees of freedom.  This was a central question of concern for Nikolai Bernstein in his explorations of perceptual-motor skill. In the context of motor skills, degrees of freedom refer to the constraints/possibilities for moving our bodies. But more generally - one might think of the degrees of freedom in a system as setting bounds on the potential for action.  When degrees of freedom are high - then there will be many different ways to accomplish the same task or goal.

Generally, the more degrees of freedom a system has - the more resilient it can be. This is because when one route to a goal is blocked, there will be other paths for reaching that goal.  However, degrees of freedom pose a challenge for control - because the more degrees of freedom a system has - the more variables have to be taken into account in the control logic.  In essence, increasing the degrees of freedom increases the potential for disturbances that have to be managed by the controller.

So, the degrees of freedom problem refers to a trade-off between flexibility (resilience potential) and control complexity. More degrees of freedom means there are more ways to be successful in accomplishing a goal, but also more ways to fail. In the context of perceptual motor control, the constructs of coordinative structure and smart mechanism were introduced as hypotheses of how to effectively manage this trade-off.

A coordinative structure or smart mechanism is created by constraining or locking out a subset of the degrees of freedom to create a simpler 'mechanism' (less complex control problem) that is specialized for a particular task or situation. This is somewhat analogous to the smart heuristics described by Gerd Gigerenzer in relation to ecological rationality.

The skill of golf provides a good example of how skilled athletes manage the degrees of freedom problem. Trying to drive a golf ball is a difficult control problem because of the many degrees of freedom in the body. For example, there are many potential disturbances to the path of the club head during a swing - a bend at the elbow of the leading arm, a twist of the head or shoulder, the positions of the legs, etc. It is simply impossible for a person to control all of the different degrees of freedom in real time. So, learning to drive a golf ball consistently involves learning to lock out many of the degrees of freedom (e.g., locking the elbow of the leading arm, holding the head in a fixed position with eyes on the ball, etc.). Thus, pro golfers learn to become a simple 'driving machine' that involves realtime control of only a few degrees of freedom that are specifically chosen to generate power to hit the ball great distances in a specific direction.

So, what good is having a lot of degrees of freedom if we are going to lock them out. The high degrees of freedom allows golfers to become many different kinds of simple mechanisms.  They can lock out a different set of degrees of freedom to become a chipping mechanism, designed to hit a ball with greater control over distance and direction. Or they can lock out another set of degrees of freedom to become a putting mechanism. Thus, a pro golfer will learn to become many different simple control mechanisms that are specialized for different situations they will face on a golf course. Successful golfers not only have different types of golf clubs - but also have specialized control mechanisms that have been 'designed' through extensive practice for specific situations.

Thus, to be resilient it is desirable for organizations to maximize degrees of freedom to increase the space of potential possibilities or to increase the possible routes to satisfying ends. However, the organizations also need to develop (train) smart mechanisms tuned to the demands of different situations - so that they will not be overwhelmed by the need to control too many variables in realtime. And they have to learn to choose the right mechanism for the right situation.  Just as smart heuristics reduce the computational burden, while maintaining high levels of effectiveness with regards to decision making, smart mechanisms reduce the computational demands on control, while maintaining high levels of effectiveness with respect to coordinated action.

Have you ever been in a situation where you are looking for one person and another person, who you didn't expect approaches and remarks "Aren't you going to say hi?"

To which you reply, "I didn't even see you."

And they respond, "But you were looking right at me!"

Many years ago Ulrich Neisser illustrated this phenomenon experimentally and it has since been replicated in numerous experiments in which salient stimuli (e.g., people carrying umbrellas, gorillas, etc.) in the field of view are not 'seen.'

For me, this illustrates the intimate coupling between sensation, perception, cognition, and action or using the OODA Loop framework, between Observing, Orientating, Deciding, and Acting.

I wonder if the use of representations that illustrate the different functions as if they are separate isolated stages of processing might lead to misconceptions about the nature of human experience.  Frankly, I believe that these are not independent stages of processing. Each stage blends (harmonizes) with the other stages such that the dynamics within each function are in part shaped by what is going on in the other stages.  So - what we observe is shaped by our orientation (or framing of the problem), and by the options we are considering, and by our capacity for action. While simultaneously what we are observing is shaping the other functions.

A simple example is research by Dennis Proffitt that shows that our perception of the steepness of a slope is different depending on the weight we are carrying in our backpack.

The bottom line is that when we break human information into a sequence of discrete stages it is likely that we can lose sight of some of the relations from which human experience emerges. The functions are not like a sequence of dominoes, but more like a barber shop quartet, in which each voice (i.e., function) is blending with the others to create a sound that has unique emergent properties.

I wonder if we redrew the OODA loop as overlapping circles that blend together to produce our experiences - it would lead to different intuitions about the nature of cognition and expertise.  What do you think? Does it strike a chord with you?

It seems to me that much of the hype about Artificial Intelligence (AI) reflects the potential power of AI to control things that had traditionally been controlled by humans.  If not replacing humans, the expectation is that humans will be displaced from lower levels of control to higher supervisory levels. Thus, the AI systems are framed to be autonomous control systems (e.g., autopilots).

I think this vision might be reasonable for simple and maybe even complicated domains of operation. However, I think this is the wrong vision when thinking about applying AI to complex or chaotic domains of operation. An alternative that I suggest for applications in domains that are more than complicated is to think about AI as being analogous to a telescope or microscope. That is, the function of AI is to enhance observability.  The function is to use the power of advanced computations and statistics to pull patterns out of data that humans could not otherwise perceive.

In this context, the control problem is framed as a joint cognitive system (rather than as an autonomous system). The role of AI in this joint cognitive system is to enhance observability to shape how humans frame decisions. In terms of Boyd's OODA loop framework, the value of AI is to enrich Observations in ways that constructively shape how humans Orient to a problem or frame the Decision processes.  Thus, humans are engaged and empowered (not displaced), and the ultimate quality of control is enhanced.

I was listening to the No Way Out podcast hosted by Brian Rivera and Mark McGrath talking to Johan Ivari and Annette Nolan about teamwork and they briefly mentioned the distinction between Synchronization and Harmonization and a light bulb went off.

I have long been interested in coordination both in relation to perceptual motor skill and in relation to teamwork and polycentric control. However, in thinking about coordination I tended to think primarily about the need to synchronize activity. In terms of motor skills, this was about synchronizing muscle assemblies, and in organizations, it was about synchronizing actions across and within teams. But I had not thought to frame coordination in terms of harmonization. Though it was obvious that high functioning teams needed to 'blend' their skills together in ways not fully captured by the term synchronization.

Synchronization emphasizes the timing of activities. For example, in making a pass to a teammate (e.g., in basketball, soccer, or American football) the release and pace of the ball has to be in synch with the motions of the receiver.

Harmonization, however, is not just about timing, but it is about blending diverse individuals to create a satisfying result. For example, this might reflect the ability of teammates to move together to create the opportunity for a successful pass. In group problem solving - success can often depend on the ability of people to share diverse experiences in order to discover/create an innovative solution. Harmonization seems to be consistent with other descriptions of team functioning such as 'building common ground' or 'organizational sensemaking.'

Now in thinking about polycentric control I suggest that synchronization is a necessary, but not sufficient requirement for successful coordination. In addition, effective polycentric control requires harmonization. That is, it requires that individuals blend their skills and their diverse perspectives in ways that complement the skills and perspectives of their teammates AND that satisfy the demands of their domains of operation.

To be clear, harmonization, is not an answer. It's practical meaning can only be defined relative to the demands of situations or domains of operation. Rather, it is just a subtle, but perhaps important shift in how to frame questions about coordination. Maybe this is just word play - but I find comfort in having a word that fills in some of the gaps with respect to understanding how teams work.

 

When talking about cognitive experiences people often refer to a sense of 'flow.' This concept does not fit easily into conventional cause-effect narratives based on billiard ball collisions (or string of dominos) metaphors.  It suggests the need for a new narrative. I wonder whether it would be possible to follow the lead of physics and consider the possibility of a new narrative where instead of talking about cause - we talk about constraints; and instead of talking about effects - we talk about possibilities.

The physicist, John Wheeler describes the motivation that led physicist to adopt a field narrative:

As the classical story about the blind men and the elephant illustrates - the complexity of natural systems generally exceeds our grasp.  That is, from any one perspective we can only grasp a part of the elephant. Thus, to get a sense of the whole elephant it is necessary to walk around to explore each of the parts and then to mentally stitch the parts together to get a sense of the whole elephant. The different possible positions of the blind men are analogous to the different disciplines in science. Each discipline owns a part of the elephant and the challenge is to combine the various perspectives into a coherent understanding of the elephant.

However, for those who can see, there is another approach for getting a sense of the whole elephant.  We can move away, increasing our distance from the elephant. As we move farther away, we can see less detail, but we can now see relations among the parts that were not visible from up close.  Still, even when the whole elephant is visible, we can only see one side at a time. So, there is no single perspective that allows us to see the whole elephant. There remains the need for some cognitive work to stitch the different perspectives together into a complete understanding of the whole.

Thus, there are two ways that we can change perspectives in exploring the elephant. One way, illustrated by the blind men is through aggregation of the parts. The second way, available to those with eyes, is to change perspective through increasing distance from the elephant. This is a metaphor for abstraction.

One of the key insights of Jens Rasmussen is that to make sense of complex organizations or complex work domains it is necessary to explore through both decomposition/aggregation and through abstraction.  Further, he suggests, based on observations of experts trouble shooting faults in complex technologies, that in terms of thinking productively about complex systems some regions in the abstraction/aggregation space are privileged. In particular, he suggests that at high levels of abstraction the details become less important. For example, there is limited return from reducing a functional purpose into goals, and then further reducing them into sub-goals, which can be further divided into sub-sub-goals. On the other hand, at low levels of abstraction details become more and more important. For example, the shapes of the different components have to fit together in the space available. The threads of one part have to mesh with the threads of another part.  The diagrams below are intended to illustrate an exploration space for exploring complex domains jointly by abstraction and decomposition/aggregation.

The key point is that to better understand the whole elephant we have to use multiple senses and take multiple perspectives.  John Boyd uses the analogy of building a snowmobile to illustrate the importance of combining an analysis mindset (e.g., exploring the components of different vehicles - motors, steering mechanisms, traction), and a synthesis mindset (e.g., recombining those components to satisfy a different purpose - driving over snow). Decomposition/aggregation reflects an analysis mindset, and abstraction reflects a synthesis mindset. The hypothesis is that to think productively about complex problems one needs to be able to move along the diagonal in this abstraction/decomposition space.

I am still struggling to find the right way to represent the multiple layers of constraint that shape the dynamics of organizations. The image above is framed in terms of a nuclear power plant. As with previous depictions, I include three layers of constraint - recognizing that there may be other layers within layers. However, I think these three layers represent important distinctions that are common across many different organizations.

In this representation - the intention is to emphasize that the layers are nested - such that outer layers set the context or the degrees of freedom for operations at inner layers. Each layer is monitoring operator actions and plant performance - but they are filtering the information to reflect different spheres of action, different degrees of granularity, and different time spans.

At the Industry Management level the focus is on standards associated with regulation, investment, and training. At the Plant Management level the focus is on anticipating events, planning, and supervision. At the Operator level, the focus is on operating, monitoring, and maintaining the equipment.

A key aspect of the coupling across the layers is the degrees of constraint. That is, how loosely or tightly the inner loops are constrained by outer loops. The stability or success of an organization will be a function of finding the right balance of constraint relative to the demands of the work environment (e.g., the pace of change, the degree of complexity/uncertainty). The balance is reflected in attributes such as subsidiarity and trust - which describe whether organizations give more or less discretion (degrees of freedom, decision authority) to inner layers.

A general principle is that when the pace of change or the degree of uncertainty is high, organizations that give more discretion/flexibility to people in the inner layers to directly adapt to situations will be more stable. This is largely due to the longer time constants required for sensemaking at the outer layers. In other words, when events on the ground are rapidly changing in surprising ways, there is no time to wait for direction from upper levels of management. The shifting of military organizations such as the US Marines toward more Mission Command styles of leadership is motivated by a desire to be more effective in a highly dynamic, uncertain domain.

On the other hand, when the work domains are relatively stable, the broader perspective and longer integration periods (i.e., broader experience) of outer layers are less likely to lead the organization to over-react to transient changes (e.g., chase fads). Thus, more top-down control or regulation leads to stable, more efficient performance. For example, commercial aviation and nuclear power domains typically benefit from more top-down regulation and constraint that reflect extensive experience in relatively stable domains of operation.

It is important to recognize that the effectiveness of a polycentric control system (or any control system) can only be judged relative to the functional demands of specific work contexts. So, be skeptical of any models that propose to set the 'ideal' or 'optimal' general recipe for success. Solutions that may have been well tuned to the past, may become unstable when the demands of the work domains change.

 

 

In the previous post - I offered a model for a cognitive system in which I attempted to integrate the three dimensions of the Strategic Game described by Boyd (Moral, Mental, and Physical) as nested layers in a polycentric control system. A unique and critical aspect of this model is that each layer functions effectively as an OODA loop closed through the ecology. That is - each layer is involved in directly interacting with the ecology. The difference between layers is the time span over which they are integrating information. The outer layer in the model is integrating across events - to derive general principles that have broad implications for stability. The middle layer is integrating over a specific event - typically taking into account patterns that allow it to recognize possibilities, to apply heuristics, to plan, and to steer activities toward a satisfying outcome. Finally, the inner layer is controlling actions and reacting to stimuli and situations in the context of the beliefs and intentions specified by the outer layers.

However, I think this is difficult for many to grok, because it is difficult to get past conventional assumptions about information processing. Information processing models have typically been formulated as a series of information processing stages with a precedence relation such that later stages in the process only "see" the products of earlier stages. Thus - the deeper stages are required to utilize inference to construct an 'internal model' of the actual ecology from the 'cues' provided by the earlier stages. In other words - there is typically assumed to be a gulf (blanket or filter) that separates our mental experiences from our direct physical interactions with the ecology. Many people can't imagine that abstract principles or beliefs such as "do unto others as they would do unto you" could be actually tested directly as a result of the consequences of actions in the ecology.

However, I hypothesize that this cognitive interplay between moral, mental, and physical dimensions is scale independent and that it applies equally to individuals and to organizations as cognitive systems. Thus, the figure below frames exactly the same model - but in terms of organizational dynamics. The hope is that the organizational perspective will be a little easier for people to grok, because the interplay of the different levels is more observable in an organization than in an individual organism.

In this diagram the inner loop (green) represents the front line workers who are directly acting in the ecology. For example, these might be the first responders (fire, police, and medics) who are directly involved in managing the emergency response.

The next outer layer (orange) represents the incident command center. This includes the incident commanders and managers who are monitoring the situation on the ground, who are managing the logistics, and who are developing plans to allocate responsibility and coordinate the actions of the first responders on the ground. Note that the incident command is getting information from the responders on the ground, but they also have access to information about the ecology (e.g., the availability of external resources) that is not available to the people who are immersed in the details of local situations.

The outer most layer (blue) represents the organizational homes of the various first responders (e.g., the fire department, the police department and the medical system). This outer system is less active in dealing with the specific immediate emergency event - but this loop will be responsible for after action analysis of the event, where it will have information about the ecology that might not have been available to either of the inner layers during the event. This layer is responsible for evaluating the response in relation to other events (e.g., after action reviews and historical analyses of prior events) and to extract the lessons and general principles that will potentially improve responses to future events. These lessons will then be integrated into training processes that will shape how planning and emergency responses will be conducted in future events.

A key aspect of the dynamic is that the (beliefs, intentions, and actions) at the different levels are shaping and being shaped by each of the other layers - though the changes are happening at different time scales. Each layer is operating at a different tempo or bandwidth than the other layers, and most importantly this means that each layer will be able to 'see' or 'pick-up' patterns in the ecology that cannot be seen in the other layers. And it is important to emphasize that these patterns are not 'in the head' but are emergent properties of the direct physical interaction with the ecology. Each layer is an abductive system in that it is forming hypotheses that are then directly tested by future actions in the ecology (not by inferences on a mental model). Every loop is 'tested' against the practical consequences it has toward achieving stable (i.e., satisfying) consequences in the actual ecology.

In sum, I am proposing that the classical partitioning of cognitive systems into a sequence of processing stages is wrong. Yes - information is being processed at multiple different levels - but each level functions much like an OODA loop and the stability of the complete system is dependent on whether the various loops shape interactions with the ecology in productive or satisfying directions.

The figure below is an attempt to illustrate three important dimensions of any cognitive system as layers in a polycentric control system. The three layers are designed to reflect John Boyd’s dimensions of the strategic game: Physical, Mental, and Moral. Note that all three layers are closed loops (effectively OODA loops) that get feedback as a result of the consequences of acting on the ecology. Further, the outer layers set up constraints or determine the degrees of freedom available to inner layers. Finally, it is important to recognize that each layer is working continuously and simultaneously with the other layers, but at a different time scale (i.e., these are not sequential operations).

The inner most layer (green) in this diagram represents the direct coupling with the ecology that reflects perceptual-motor skills. This “represents the world of matter-energy-information all of us are a part of, live in and feed upon” (Osinga, 2007, p. 210). This type of coupling was the focus of J.J. Gibson’s research and constructs such as affordance and optical invariant play important roles. Much of the action at this level is ‘automatic’ requiring little conscious awareness.

The next outer layer (orange) in this diagram reflects conscious thinking related to problem solving and decision making. At this level an intention is formed that will frame subsequent actions in the layer below. This “represents the emotional/intellectual activity we generate to adjust to, or cope with, that physical world” (Osinga, 2007, p. 210). The functioning of this layer is the focus of researchers such as Gary Klein (Recognition-Primed Decision Making, Naturalistic Decision Making) and Gerd Gigerenzer (Ecological Rationality). Important constructs to consider at this level include abduction and heuristic. The intention provides the framing for attention, recognition, and the possible actions at the skill-based level.

The most outer layer (blue) in this diagram reflects the influence of long-term experience and learning. It “represents the cultural codes of conduct or standards of behavior that constrain, as well as sustain and focus, our emotional/intellectual responses” (Osinga, 2007, p. 210). This layer reflects the values, principles, assumptions, and beliefs that shape performance at the other layers. This layer reflects the largely ‘implicit knowledge’ that shapes analysis and synthesis at the conscious thought level below.

This system is a multilayered, adaptive control system that is continually tuning to achieve a stable relation with the ecology. This tuning is happening simultaneously at all three levels, but at different time scales. Ultimately, stability depends on coordinated interaction across the three levels. The general notion is consistent with C.S. Peirce's logic of abduction, Piaget's constructs of assimilation and accommodation, EJ Gibson's construct of attunement, and Friston's Free Energy Principle. Thus, the dynamic is essentially one of minimizing "surprise" (i.e., reducing uncertainty) relative to the continuously changing Ecology.

The unique feature of this model relative to other models is that most models envision the interactions between levels as sequential. That is, there is a precedence relation between the three layers so that the outer layers need to infer the 'ecology' based on cues provided by the inner layer. In contrast, the model presented here suggests that all three levels have direct feedback of the consequences of action - though this feedback is integrated over different time constants. In essence - each layer is tuned to different bandwidths (in terms of the patterns that matter most).

Three paramedics are driving along a rural road in central Ohio when a truck in front of them suddenly seems to go out of control causing several other cars to crash, then hitting some pedestrians who were skating along the road and eventually hitting a tree. The paramedics get out to survey the scene and two of them each go to different victims and begin treating them, the third paramedic surveys the situation and heads back to the car, where he remains throughout the event.

What is he doing in the car?

In fact, after the event was over, the other two paramedics were puzzled, and ask him why he did not immediately begin treating the other victim? His response was "which other victims?" It turns out that neither of the other two paramedics realized that there were more than three victims. 

What was the third paramedic doing in the car? He was taking incident command. He realized that for some of the victims to survive it would be essential to get them to trauma hospitals, where they could get more extensive treatment, within the "golden hour." He realized, that to ensure that all the victims were saved they would need to get ambulances and a care flight helicopter into the scene as quickly as possible. He was on the radio calling for support and providing them directions so that they could get to the rural scene as quickly as possible. This included identifying a potential landing site for the care flight helicopter.

While he was in the car, a number of other people stopped to offer help, including a nurse. He was able to direct these volunteers. Asking the nurse to attend to one of the injured and directing another person to attend to the truck driver who was wandering from his truck in a daze. 

After the incident, this paramedic explained to the other two, "Yes, I could have started to treat one of the victims, but I wanted to make sure that all the victims survived. And I realized that to do that, we needed more resources and somebody would have to take command and coordinate these resources." 

This story illustrates the functions related to three different layers in a polycentric control system.

• At the mutual adjustment level, two of the paramedics, the nurse, and other volunteers were directly acting to address the immediate demands of the situation.
• At the planning level, the third paramedic was functioning as an incident commander. He wasn't directly treating patients, but he was attending to larger patterns and trends in order to anticipate needs and to coordinate the resources that would ultimately be critical for achieving a satisfying outcome.
• And the reason this paramedic took this role was because he had been trained in the National Incident Management System which had been developed based on generations of fire fighting experience with large forest fires. This has become a national standard and many fire and police departments are required to have NIMS training. This establishment and training of a standard reflects contributions at the Standardization or Knowledge-based layer of the emergency organizations. Additionally, the idea of a 'Golden Hour' is a principle based on extensive experiences in emergency medicine. The Standardization Layer reflects a capacity to integrate across an extensive history of past events to pull out principles that provide guideposts (structure, constraints) for organizing operations at the other levels. 

The figure below is an alternative way to represent a polycentric control system to the layered diagram used in prior essays. This representation was developed based on observations and interviews with emergency response personnel. Here the layers are illustrated as nested control loops. These loops are linked through communications (the thin lines) but also through the propagation of constraints (the block arrows), in which outer loops shape or structure the possibilities within the inner loops (e.g., standard practices, command intent, a plan, distributing resources).  The coordination between layers is critical to achieving satisfactory results and this coordination depends both on communications within and between layers and the propagation of constraints (sometimes articulated as common ground, shared expectations, shared mental models, or culture).

Note that neither the previous representation showing layers or the present representation showing nested control loops is complete. Each representation makes some aspects of the dynamic salient - while hiding other aspects of the dynamic and perhaps carrying entailments that are misleading with respect to the actual nature of the dynamic. An assumption of general systems thinking is that there is no perfect representation or model. Thus, it is essential to take multiple perspectives on nature in order to discover the invariants that matter - to distinguish the signal from the noise. 

Three important points:

• First - the power of a polycentric control system relative to addressing complex situations is that each layer has access to potentially essential information that would be difficult or impossible to access at other layers. However, without effective coordination between the layers some of that information will be impotent.
• Second - it is easy for people operating within a layer to have tunnel vision, to take the functions of the other layers for granted, and to underestimate the value that the other layers contribute. For example, it is easy for me and Adam to take Fred's art work for granted. However, when Fred is replaced by someone less skilled or with a different style - suddenly the gap becomes clearly evident. 
• Third - be careful not to get trapped in any single perspective, model, or metaphor. Be careful that your models don't become an iron box that you force the natural world to fit into. Be careful not to fall prey to the illusion that any single model will provide the requisite variety that you need to regulate nature and reach a satisfying outcome. 

Now, the rest of the story: All the victims from the accident above survived.