Author: perspicacityll

Lindblom (1979) wrote:

Perhaps at this stage in the study and practice of policy making the most common view... is that indeed no more than small or incremental steps - no more than muddling -is ordinarily possible. But most people, including many policy makers, want to separate the 'ought' from the 'is.' They think we should try to do better. So do I. What remains at issue, then? It can be clearly put. Many critics of incrementalism believe that doing better usually means turning away from incrementalism. Incrementalists believe that for complex problem solving it usually means practicing incrementalism more skillfully and turning away from it only rarely.

I am not sure that today is all that different. I think many people still see muddling (incrementalism) as a flawed approach to problem solving and decision making. It still seems to have a negative connotation and there is an implication that there is a better way to manage organizations or a better way to make decisions.

I think people equate muddling with a confused, aimless flailing. They have no concept of skilled muddling. However, for me skilled muddling is an apt description of how experts deal with complexity and uncertainty. It is not a confused, aimless process, but a carefully controlled combination of probing the world and utilizing the resulting feedback to make tentative steps in potentially satisfying directions.

The image I have is of skilled rock climbers - who are well tuned to the opportunities that different holds offer and who have a good understanding of the risks. They have developed strength and agility, so that they have a wide range of affordances in terms of what is reachable and graspable. They are careful about insuring that they have appropriate protection to guard against catastrophic falls. They are constantly establishing a firm base of support before reaching for the next hand or foot hold. And they are well-tuned to the information that allows them to identify the best supports and the viable paths toward their goal.

I see many parallels between muddling and  Peirce's concept of abduction - in which experts use their experience to make tentative hypotheses about the consequences of choices and actions, and then test their hypotheses through acting on them. And then utilize the feedback to adjust their assumptions and framing to reduce surprise (error). Further I see this in terms of a closed-loop triadic semiotic process which three sources of constraint. Each of these sources reflect relations between agents and ecologies.

The first of these relations is the now familiar construct of affordance, which represents the potential for action. These reflect the relation between the effectivities of an agent (or organization) and the properties of objects in the ecology.  In control systems terms this reflects the constraints of the plant dynamics in the forward loops.

The other two constructs are less familiar. The second construct, specifying, reflects the potential for perception or information pick-up. It reflects the relation between the sensor systems (e.g., eyes) of agents and structure in the related ecological mediums (e.g., optical array, or graphical interface). In control systems terms this reflects the constraints on the sensors in the feedback loops.

The third construct, satisfying, reflects the functional constraints on performance. This reflects the relation between the intentions, values, or preferences of agents and the actual consequences of particular choices or actions. In control terms this reflects the comparator where intentions are compared to feedback - resulting in a surprise or error signal.

These three components are ontologically independent. That is an affordance exists independently from the information to specify it and from the intentions of agents. Thus, a cup is graspable even though it is out of eyesight and you don't have a current use for it.

However, these three constructs are tightly coupled with respect to epistemology. Thus, for example, our experiences of affordances depend on how well they are specified, and on whether they help us to satisfy our intentions. You cannot actually grasp the cup unless you can sense it (e.g., it is in eyesight), and you are unlikely to grasp it if you don't have a use for it (e.g., to hold a liquid).

Thus, skilled muddling involves tuning the coupling of agents with their ecologies or situations - increasing the range of affordances, tuning the perceptual system and aligning your intentions with the activities that lead to satisfying consequences. The opening figure illustrates how these dimensions map into Rasmussen's intuitions about the design of cognitive systems that are capable of skilled muddling.

These ideas are developed more extensively in our book "A Meaning Processing Approach to Cognition" (Flach & Voorhorst, 2020).

Most organisms and essentially all organizations have multiple layers of interconnected perception-action loops. And it will be generally true that the loops all operated at different time constants, have differential access to information, and have differential levels of authority. One way to think about the coupling across authority levels is that higher-levels set the bounds (or the degrees of freedom) for activity at lower levels. In this context, a key attribute of an organization is the tightness of the couplings between different levels.

For example, the classic Scientific Management approach involves a very tight coupling across levels. With this approach, higher levels (e.g., management) is responsible for determining the 'right way' to work and the management levels have the responsibility to implement training and reward systems to ensure that the lower levels (operators - workers) strictly adhere to the prescribed methods. This is an extremely tight coupling leaving workers very little degrees of freedom, as they are typically punished for deviating from the "one best way" prescribed by management. In essence, the organization functions as a clockwork mechanism. This approach can be successful in static, predictable environments, where the assumptions and models of managers are valid. However, in a changing, complex world (e.g., VUCA environments) this approach will be extremely brittle - because the cycle times for the upper levels of the system will always be too slow to keep pace with the changing environment. This approach will surely fail in any highly competitive environment, with intelligent, adaptive competitors.

One way to loosen the coupling between levels is to replace the "one best way" with a playbook, or a collection of smart mechanisms that are designed to address different situations that the organization might face. Typically, higher levels develop the playbook and are responsible for training the plays and for rewarding/punishing workers as a function of how well they are at selecting the right play for the situation and implementing that play.

The coupling can be relatively tight if the playbook is treated as a prescription such that any deviations or variations from the plays are punished, especially when negative results ensue. Or the coupling can be looser if the plays are treated as suggestions, deviations (e.g., situated adaptations) are expected or even encouraged, and the lower levels are not punished for unsuccessful adaptations.

A still looser coupling is an approach of Mission Command. With Mission Command higher levels in the organization set objectives and values, but they leave it to lower levels of the organization to determine how best to achieve those objectives given changing situations. This approach requires high levels of trust across levels in an organization. Higher levels have to trust in the competence and motivations of the lower levels, and lower levels have to trust that higher levels will have their backs so they will not be blamed or punished when well-motivated adaptations are not successful.

These are simply some examples of different levels of tightness in the couplings between levels intended as landmarks within a continuum of possibilities. On one end of the continuum are tightly coupled organizations where the couplings across levels are like the meshing of gears in a clock. As couplings become looser the organization becomes less machine-like and more organismic. Metaphors that are often used for looser couplings include coaching and gardening. In organizations with looser couplings higher levels introduce constraints (e.g., guidance, suggestions, resources), but don't determine activities. The higher levels tend the garden, letting the plants (workers) free to grow on their own.

In an increasingly complex world - organizations with tight couplings across levels will tend to be brittle and vulnerable to catastrophic failures due to an inability to keep pace with changing situations. When couplings across levels are looser, the potential for bottom-up innovation and self-organization increases. However, if the coupling is too loose - there is an increasing danger for inefficiencies, conflict, and chaos. Thus, organizations must continuously balance the tightness of the couplings trading off top-down authority and control to increase the capacity for exploration and innovation (adaptive self-organization) at the operational level. In a complex world, loosing the couplings can allow the organization to muddle more skillfully.

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.