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Posts Tagged ‘Control theory’

Please Do Not Disturb


When a control system is humming along, the gap between the desired and current states is so small that the frequency of command issuance by the Decision Maker component is essentially zero; all is well and goal attainment is on track. However, with the universe being as messy as it is, unseen and unpredictable “disturbances” can, and do, enter the system at any point of access to the structure.

CS plus disturbances

If the sensors and/or actuators can’t filter out the disturbances or are malfunctioning themselves, then true control of the production system may be lost. Perceptions and commands get distorted and the distance between goal attainment and “reality” will be perceived as shorter or longer than they are. D’oh! I hate when that happens.

Goal Setting

September 13, 2013 Leave a comment

Given the generic control system model below, how can we improve the performance of an underachieving production system?

Intact CS

One performance improvement idea is to make the goal directly visible to the production system; as opposed to indirectly via the policy/process actions of the actuators.

Goal Visibilty

A meta-improvement on this idea is to allow the production system constituents direct involvement in the goal setting process.

Goal Setting

It seems to make naive sense, doesn’t it? Well, it does unless the goal is some arbitrarily set financial target (“increase market share by 10%“, “decrease costs by 5%“, “increase profits by 25%“) pulled out of a hat to temporarily anesthesize Wall Street big-wigs.

Categories: management Tags: ,

Unobservable, Uncontrollable

September 11, 2013 4 comments

Piggybacking on yesterday’s BS post, let’s explore (and make stuff up about) a couple of important control system “ilities“: observability and controllability.

First, let’s look at a fully functional control system:

Intact CS

As long as the (commands -> actions -> CQs -> perceptions) loop of “consciousness” remains intact, the system’s decision maker(s) enjoy the luxury of being able to “control the means of production“. Whether this execution of control is effective or ineffective is in the eye of the beholder.

As the figure below illustrates, the capability of decision-makers to control and/or observe the functioning of the production system can be foiled by slicing through its loop of “consciousness” at numerous points in the system.

observe control

From the perspective of the production system, simply ignoring or misinterpreting the non-physical actions imposed on it by the actuators will severely degrade the decision maker’s ability to control the system. By withholding or distorting the state of the important “controlled quantities” crucial for effective decision making, the production system can thwart the ability of the decision maker(s) to observe and assess whether the goal is being sought after.

In systems where the functions of the components are performed by human beings, observability and controllability get compromised all the time. The level at which these “ilities” are purposefully degraded is closely related to how fair and just the decision makers are perceived to be in the minds of the system’s sensors, actuators, and (especially the) producers.

Who’s Controlling The Controller?

September 9, 2013 4 comments

The figure below models a centralized control system in accordance with Bill Powers’ Perceptual Control Theory (PCT).

CS-No Disturbances

Given 1: a goal to achieve, and 2: the current perceived state of the production system, the decision-making apparatus issues commands it presumes will (in a timely fashion) narrow the gap between the desired goal and the current system state.

But wait! Where does the goal come from, or, in cybernetics lingo, “who’s controlling the controller?” After all, the entity’s perceptions, commands, actions, and controlled quantity signals all have identifiable sources in the model. Why doesn’t the goal have a source? Why is it left dangling in an otherwise complete model?

Abstraction is selective ignorance – Andrew Koenig

Well, as Bill Clinton would say, “it depends“. In the case of an isolated system (if there actually is such a thing), the goal source is the same as the goal target: the decision-maker itself. Ahhhh, such freedom.

DM source tgt

On the other hand, if our little autonomous control system is embedded within a larger hierarchical control system, then the goal of the parent system decision maker takes precedence over the goal of the child decision maker. In the eyes of its parent, the child decision maker is the parent’s very own virtual production subsystem be-otch.

Usurped goals

To the extent that the parent and child decision maker’s goals align, the “real” production system at the bottom of the hierarchy will attempt to achieve the goal set by the parent decision maker. If they are misaligned, then unless the parent interfaces some of its own actuator and sensor resources directly to the real production system, the production system will continue to do the child decision maker’s bidding. The other option the parent system has is to evict its child decision maker subsystem from the premises and take direct control of the production system. D’oh! I hate when that happens.

No Middleman

Sensors AND Actuators

Yin And Yang

August 19, 2012 1 comment

In Bill Livingston’s current incarnation of the D4P, the author distinguishes between two mutually exclusive types of orgs. For convenience of understanding, Bill arbitrarily labels them as Yin (short for “Yinstitution“) and Yang (short for “Yang Gang“):

The critical number of “four” in Livingston’s thesis is called “the Starkermann bright line“. It’s based on decades of modeling and simulation of Starkermann’s control-theory-based approach to social unit behavior. According to the results, a group with greater than 4 members, when in a “mismatch” situation where Business As Usual (BAU) doesn’t apply to a novel problem that threatens the viability of the institution, is not so “bright” – despite what the patriarchs in the head shed espouse. Yinstitutions, in order to retain their identities, must, as dictated by natural laws (control theory, the 2nd law of thermodynamics, etc), be structured hierarchically and obey an ideology of “infallibility” over “intelligence” as their ideological MoA (Mechanism of Action).

According to Mr. Livingston, there is no such thing as a “mismatch” situation for a group of  <= 4 capable members because they are unencumbered by a hierarchical class system. Yang Gangs don’t care about “impeccable identities” and thus, they expend no energy promoting or defending themselves as “infallible“. A Yang Gang’s  structure is flat and its MoA is “intelligence rules, infallibility be damned“.

The accrual of intelligence, defined by Ross Ashby as simply “appropriate selection“, requires knowledge-building through modeling and rapid run-break-learn-fix simulation (RBLF). Yinstitutions don’t do RBLF because it requires humility, and the “L” part of the process is forbidden. After all, if one is infallible, there is no need to learn.

Bankrupt Models

August 17, 2012 1 comment

In his paper, “The Dispute Over Control Theory“, Bill Powers tries to clarify how Perceptual Control Theory (PCT) differs from the two main causal approaches to psychology: stimulus-response and command-response. In order to gain a deeper understanding of PCT, I’m gonna try to reproduce Bill’s argument in this post with my own words and pictures.

The figure below represents a PCT unit of behavioral organization, the Feedback Control System (FCS). An FCS is a closed loop with not one independent input (e.g. stimulus or command), but two. One input, the reference signal, is sourced from the output function of a higher level control unit(s). The second input, an amalgam of environmental disturbances, “invades” the loop from outside the organism.  Both inputs act on the closed loop as a whole and the purpose of the FCS is to continuously act on the environment (via muscular exertion) to maintain the perceptual signal as close to the reference signal as possible. As the reference changes, the behavior changes. As the disturbance changes, the behavior changes. Since action is behavior, the FCS exhibits behavior to control perception; behavior is the control of perception.

The figure below depicts models of the stimulus-response and command-response views in terms of the PCT FCS. The foremost feature to notice is that there is no loop in either model – it’s broken. The second major difference is that neither model has two inputs.

In the Stimulus-Response model, the linear, causal path of action is: Stimulus (a.k.a Disturbance) ->Organism->Behavior. In the Command-Response model, the linear, causal path of action is: Command (a.k.a Reference)->Organism->Behavior. Hence, the models can be reduced to these simple (and bankrupt) renderings of a dumb-ass organism totally under the control of “something in the external environment“:

So, you may ask: “How could our best and brightest minds in psychology and sociology gotten it so wrong for so long; and why don’t they embrace PCT to learn how living systems really tic?” It’s because they erroneously applied Newton’s linear cause-effect approach for the physics of inanimate objects to living beings and they’ve thoroughly crystallized their UCBs into cement bunkers.

When you push a rock, there is no internal resistance from the rock and Newton’s laws kick into action. When you push a human being, you’ll encounter internal resistance and Newton’s laws don’t apply – control theory applies.

Compounding the difficulty has been a surprising tendency for scientists who are normally careful to know what they are talking about to leap to intuitive conclusions about the properties and capabilities of control systems, without first having become personally acquainted with the existing state of theart. If any criticism is warranted, it is for promulgating statements with an authoritative air without having verified personally that they are justified. – Bill Powers

D’oh! BD00 takes major offense at Bill’s last sentence.

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