Social Species

Technology of Social System Behavior

Plan B and the route to get there from Plan A is a finished theoretical structure. It is generic, scalable, and can be implemented on any social system. You can refute the incontrovertibility claim for yourself by examining applications (Popper). From our experience in sociotechnology development, we suspect the reason Plan B eluded humanity until 2013, was the proficiency of social conditioning to delude the masses that Plan A is all there is. The handicaps infused by socialization include:

  • Authority and power are equivalent
  • Social change can only take place top-down
  • Obedience to authority
  • Declarations by the ruling class are infallible
  • The chain of command is absolute
  • Responsibility is separate from autonomy
  • Laws of Nature can be ignored or defied
  • The Golden Rule

The assumption that social behavior is altogether volitional is a bulwark of the ruling class constellation of delusions. The reality faced by implementers is that the mathematical physics of systems has a controlling say on how human organizations may or may not behave.  Nature’s veto power over your aspirations is absolute.

While these invariant laws of nature mean nothing to groupthink delusions, the foreman, vector, practitioner of implementation can use this scientific knowledge to great advantage in navigation and preparing for the arriving future. At the very least, knowledge of the laws of control and regulation of systems keeps you from making catastrophic errors.

You are not expected to understand this discussion of the applicable forces of nature from a once-through reading. There are several forces involved and their permutations are infinite. When you grasp some these profound concepts and realize the complexity of real-time interactions, you will appreciate what drove Starkermann into computer-assisted simulation of social system behavior. Rudolf Starkermann was the pioneer and the main producer of social system behavior determined by natural law. His work was aggressively rejected by every science and engineering discipline around the globe.

William Ross Ashby, the English polymath, assembled the principal laws of social system dynamics, entirely congruent with Starkermann’s work, and they are presented here. The more times you study Ashby’s ensemble, the more useful concepts and tools you will discover. The payoff is large and immediate. The rest of this selection is entirely from Ashby. Don’t freak out with the jargon. Grab the concepts.

Considering how many fools can calculate, it is surprising that it should be thought either a difficult or a tedious task for any other fool to learn how to master the same tricks… Being myself a remarkably stupid fellow, I have had to unteach myself the difficulties, and now beg to present to my fellow fools the parts that are not hard. Master these thoroughly, and the rest will follow. What one fool can do, another can. Silvanus P. Thompson

Ashby on social systems

The state of any material system is a function of the preceding state and nothing else. All processes in matter are state determined. A collective of humans is a system like any machine such that its state at one instant determines its subsequent behavior.

Processes conducted over an invisible network of subconscious minds, matterless, are not deterministic. You cannot foretell from the current state, based upon the current state, what state next will be.

The laws of nature acting at each point of tangible matter determines how the state will change. Since every state goes to some state and never goes to two, natural law specifies a mapping of the set of possible states into the same set. Restriction to a subset is the essential operation that generates properties, relations, patterns and structures. Repetition of one operator, like natural law, shows the special features.

The basic concept is the mapping from one set (domain) to a set (range). The rule or process or transition or change or correspondence gives for each element in the domain one and only one element in the range. Mapping represents a state-determined system.

The system is defined by its internal state and the state of its surroundings. What matters is the regularity of the behavior. Whether energy is gained or lost is irrelevant. The transfer function and the structure determine the entire repertoire of possible behavior.

William Ross Ashby provided many building blocks for the platform of Plan B

Control and Regulation:

Regulation and control are of the highest practical importance. The existence of a limit on the total quantity of information transmissible puts an absolute limit to the amount of regulation and control achievable. However good we think we are, there are limits we cannot exceed. We have an informational universe. What lies beyond is unknowable.

The aim is keeping certain variables within assigned limits and finding some set of dynamic linkages that will keep the system stable and stable within those limits. The terms coordination, integration and regulation are represented by a relation between the disturbance and the response so that the goal is met. Every good regulator is a model of the system. Its capacity as a regulator cannot exceed its capacity as a channel for handling variety.

To be survival-promoting by acts of regulation, a good regulator is isomorphic with the system being regulated. The control action is merely the systems actions as seen through a mapping. The brain models its environment.

The chief philosophical value of physics is that it gives the mind something distinct to lay hold of, which, if you don’t, Nature at once tells you – you are wrong. James Clerk Maxwell

Tidbits

  • Disturbances correspond to noise.
  • You control by what gives rise to the error – not the error.
  • Developing control processes that are really intelligent.
  • A system too complex to understand can still be controllable.
  • All regulators are information processors.
  • If the transfer function can appropriately select, the system can gain in intelligence from experience and input from the context.
  • A system that learns from the outside can do better than the designer.
  • A learning machine has a process for getting and using information from a teaching environment.

Goals:

  • The goal is a message of zero entropy.
  • Change the goal and you change the amount of design.
  • Getting goals implies selection.
  • When goals are set by introspection it is simply the output of the brain’s final verbalizing stage. It can give only a coded version of what happened earlier in the process.
  • A goal is a way of behaving.
  • If you know the goal and the starting point and use this information, the number of operations drops to the square root of the original number.
  • It is a focus in a stable dynamic system.
  • All state determined systems tend to preferred regions. Getting a machine to have some goal is no problem at all.
  • The trick is to get the system to seek some goal already specified – to design the transfer function so that the specified goal is reached in spite of disturbances.
  • Appropriate selection is crucial at every level and extension. Choices have to be made in parallel and in sequence.
  • The process of selection is limited by the information available. You gather information to narrow the field.

If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is. John von Neumann

General Methods:

The science of method is completely rigorous and of extreme generality. The method is practical, pragmatic and empirical. Methods that work on complex systems flow from their own collection of science. Any method that loses information is a pattern recognizer.

To define a system, start by specifying a set of states. To make unambiguous what is being talked about. The various states of the system set are its elements. The elements must have individuality and permanence of individuality. The set shows state-determined behavior when its succeeding state is a single-valued and everywhere defined function of its present state. The mapping corresponds to and is due to whatever natural forces are operating in real time to cause the change. The natural laws showing in the first set act to form the second. The repetition of natural law generates a sequence of states, a trajectory. The mapping specifies for every input trajectory, the resulting output trajectory – via the transfer function. Mapping reduces the original domain to a subset by performing the physical act of selecting.

A system or a brain can be intelligent only in relation to a defined goal, which may be complex and provisional. Next comes the identification of the factors that tend to prevent its being achieved. Next comes construction of the regulator that shall so process the information about the disturbances that the goal is reached in spite of them.

 

Tools

The method is general. Use what you know to narrow the field. Then, within it, make trials.

Our “hurdle” is a test to quantify a constraint. We choose the test variable and design a disturbance we think will evoke a telling reaction.

Bring the system to some state that is known.

The tests are operational. They can be brought to demonstration and owe nothing to plausibility. Demonstration is always the ultimate test, let plausibility say what it will.

This idea that there is generality in the specific is of far-reaching importance. Douglas Hofstadter

Black-box complexity

Black box methods are used to relate controllable causes to ultimate effects. Experimentation on complex systems has its own science.

Interaction. You act on it and it reports back to you.  Information is gathered from the protocol of events. The protocol is a message that contains information about the box’s nature. Your knowledge is essentially a re-coding of what is in the protocol. You win when you can tell what the system will do next.

Any hurdle of ours can only go as far in information as the protocol allows. You can test up to an isomorphism but no further – the box may not contain what you think it does. You can get the number of its degrees of freedom. It is the number of variables that must be observed to get determinate behavior – unique, single-valued and not subject to random variation.

Do I/O on a black box to deduce its construction. You need to be able to learn from prior tests to design later tests. Adaptive.

Organization and structure:

  • Constraints are the essence of organization and hence of structure in multivariate systems, measurable with information theory.
  • Organization and dynamics are independent.
  • Casting organization into a framework where natural law can be applied.
  • Organization represents a loss, restriction or constraint on what might have happened.
  • Organization only has meaning relative to an environment – purpose
  • Properties of the sequences of action quite transcend those of the mere unit.
  • Repetition with natural law brings out the system’s structure. The structure appears when the laws that govern the system are invariant in time.
  • Being too complex, you must lose the details, content and meaning of the relationships. You measure the quantity of the relationships only. Constraint and organization depend upon the relationship of the observer to the system.
  •  An organization corresponds to operations. As soon as the relation between two entities A and B becomes conditional on C value or state, a necessary component of organization is present.
  • The theory of organization is partly co-extensive with the theory of functions of more than one variable.
  • The presence of organization between variables is equivalent to the existence of a constraint.
  • Organization is concerned with properties that are not intrinsic to the thing but are relational between observer and thing.
  • The concepts of dynamics and organization are essentially independent.

There can be a sophisticated dynamics of a whole as complex and cross connected as you please that makes no reference to any parts. Thereby bypassing the concept of organization – which is mostly in the eye of the beholder. The organization seen in a system is dependent on the observer who sees it. Conditionality between the parts and regularity in behavior. First organization, yes-no, then good or bad. Good must be defined explicitly in every case. The organization is good if it makes the system stable around an assigned equilibrium.

Since good depends on the circumstances and on what is wanted, the good organization/method is then of the nature of a relation between the set of disturbances and the goal. Change the disturbances and not the organization and it flips to bad.

Intelligence as appropriate selection:

  • You achieve appropriate selection by law, not by magic.
  • The rule of appropriate selection applies to the final goal and all the sub-goals found on the way to it. Nested.
  • Selection to be rational and defensible must be based on information. Shrinking the range of possibilities to their minimum.
  • The goal of intelligent action is an appropriate selection from a set. Any system that achieves appropriate selection does so as a consequence of information received. The amount of cutting down the processing load is absolutely bounded by the amount of information received.
  • The brain is a system of remarkable inflexibility. Intelligence is as intelligence does. Intelligence is a process that uses information and processes it with high efficiency so as to achieve high intensity of appropriate selection.
  • You cannot achieve appropriate selection in excess of the information available.
  • Intelligence implies the ability to solve problems, which implies the ability of making proper selection among the totality of candidates.
  • The intelligence amplifier is a selection amplifier is a form of regulation amplifier. It is information-based amplification.

Any process that achieves appropriate selection does so as a consequence of information received. The selector must first receive some quantity of information. Then this information is used to narrow down the field of uncertainty among the various possible answers to its minimum. The amount of narrowing is bounded by the amount of information. Trial wins information to help a further appropriate selection.

The opposite of courage in our society is not cowardice, it is conformity. Rollo May

Stability and Equilibrium

Business as usual is an example of a well-defined operator (rules) which drives on toward equilibrium. In doing so it automatically selects those operands that are especially resistant to its change-making tendency.  The progression towards the specially resistant form is of extreme generality demanding only that the operative laws are determinant and unchanging. Natural law meets that specification. In any isolated system, life and intelligence inevitably develop.

The developed stable organism will show its stability by vigorous reactions that tend to preserve its own existence. To itself, its own organization will always be good. What emerges depends simply on the laws and from what state it started. Intelligence is an adaptation to and a specialization towards a particular environment with no implication of validity for any other environment. The intelligent system will be directed toward keeping its own essential variables within limits. Our trick is to turn these fundamentally selfish processes to our advantage.

All large complex dynamic systems show the property of being stable up to a critical level of connectance (13%) and then to go suddenly unstable. The time before the terminal cycle can be detected is the extent to which the elements act as information transmitters.

The world reveals itself by its transitions. Information tends to force its way in. Pattern in the environment inevitably tends to diffuse into the system. Every isolated determinate dynamic system obeying unchanging laws will develop organisms that are adapted to their environment. Every social system going to equilibrium is selecting.

When the system moves to equilibrium it may cause the emergence of all sorts of properties by no means having an obvious relation to the equilibrium. To have status quo some coordination is essential – displacements by disturbances still have to be compensated.

Invariance is the core of equilibrium. History must be used in memory exact or it is itself a disturbance. The final details are postponed until the information necessary has been supplied by the environment. The necessary features of the learning process.

The fixed pattern in complex affairs comes from the fact that what is regarded as a state at one level of view may be found to have a rich internal structure. Thus the Roman Empire remained recognizably the same entity over hundreds of years in spite of many disturbances, while a closer examination shows a vast number of personal activities and changes were contributing to the stability of the Empire as a whole.

Systems with unchanging laws and contexts change towards states in which they linger. By showing a convergence towards such states they generate relations between the law and the system’s state.

Cooperative action is quite general. At any state of equilibrium, the parts always interact so that the action of all is to regenerate the state of each. The state of form is always organism plus environment. Adaptation is always to some property of the environment. Change the environment and what was adaptive behavior becomes grossly inappropriate.

Below Every Tangled Hierarchy Lies An Inviolate Level. Douglas Hofstadter

Definitions:

Noise-tight means with no input disturbance, it does not gain information by itself. When at equilibrium, it loses the information about which initial state it came from. Information from history tends always to decay. Final states depend more on the sequence than the initial state. Later learning destroys earlier learning.

The power to veto stability means that the goal of he who can veto will be included in the stable state.  The design trick is to get the basic drive of nature to do the work.

n-tuple means n components. Estimate the number of states to be searched by the number of states in each component, assuming independence, and then get the product for combinations. Every relation that holds reduces the search.

The mapping is a correspondence, rule, method, process, construction, algorithm, computation, machine, device, drive, force, reflex, instinct, command or any other cause whose effect is that given any element in set E, one and only one element in set F results.  It shows whatever natural laws are operating in real time to cause the change. Directive correlation refers to what the system parts do. The many natural laws concerning system behavior are specifications of mappings. A binary relation is that both single-valued and everywhere defined.

Temporary behavior is a state which is not in a cycle. Once produced, these states no not recur. This trajectory which precedes a cycle is called a run-in. A cycle is terminal behavior. Run-in plus cycle is a disclosure – that repetition is about to occur. The system discloses that it is recycling.

Activity is the measure of the extent the system is doing things or communicating internally. If the naïve observer is to understand the system, to distinguish temporary behavior from its terminal behavior, his capacity to record and compare states (called an aperture) must exceed the longest disclosure length.

Cybernetics is a technical method for tacking practical problems otherwise too complex for solution. Methods for simplifying complexity. The science of causes and effects when they occur in great numbers and the processes at work are complex and lengthy. We can understand the properties of long chains of cause and effect, acting round and round the same circuit.

Coordination is deviation from statistical independence. Total transmission can be measured and it can be related to the transmissions between parts to give insight to the nature of the processes going on in the system. If a quantity that increases exponentially can be treated in k stages, the branches fall to the exponent divided by k.

Adaptation means dealing with an invariant of some dynamic process. A system is a specialized means of adaptation to a specialized class of disturbances.

Kurt Gödel’s achievement in modern logic is singular and monumental – indeed it is more than a monument, it is a landmark which will remain visible far in space and time. … The subject of logic has certainly completely changed its nature and possibilities with Godel’s achievement. John von Neumann

Control theory in implementation

The amount of regulation that the brain can achieve is absolutely bounded by its capacity as a channel. Professionals are expected to respect this law or be marked as futile even before starting. It is how you can focus your activities on the problems that are properly realistic and actually solvable. Life and intelligence result from the persistence over time of the action of any transfer function that is single-valued and unchanging.

Reproduction is a phenomenon of the widest range tending to occur in all dynamic systems of some complexity. Change inducing actions.

The range of phenomena is so broad is cannot be dealt with on the parts level. General principles alone give the bird’s eye view that enables us to move about the vast field without losing our bearings:

  • When the system is complex and combinatorial the information processing load greatly exceeds a practical limit.
  • Model making is better than the study of raw facts. The transfer loses information. Use the laws that are present.
  • The set of stuff that is led by natural law into one basin, which it occupies thereafter, is a confluent. The basins are not final.
  • A property is identified with the subset of elements that possess the property
  • You do operations directly on the sets, the elements being out of sight.

There are, of course, mathematical physics that reign over networks of entangled minds, i.e., social systems. The mathematics of synchronized networks of autonomous nodes describe the behavior of the system of self-organizing behavior where each node is capable of exhibiting all sorts of unique behavior as an individual. In 1600, the Dutch scientist, Christian Huygens, started the drama when he first noticed that two pendulum clocks hung from a common support would eventually come to tick in unison. The mathematical theory of this synchronization developed late in the 20th century, applies to any network system, metal or flesh.

In small social systems, where everyone is tightly personalized, strongly coupled with every other member, the synchronizing connection is by productivity and effectiveness in attaining group goals – mutually observed performance signaling in unison. You have been a node in these transient high-performance, self-organizing groups several times. Team sports is just one manifestation of high-value coupling. It engenders group resistance to disturbance.

In large social systems of identical oscillators, assured identical by invariant human nature, the controlling mathematical physics is substantially different. In the 1980s the designers of complex communications systems first discovered that having identical elements in the network is no guarantee of predictable network behavior. History is filled with examples of large social systems spontaneously flipping out of sync and evolving into exotic, complex, unpredictable patterns of behavior. In fact, many distinctive, complex states can display from the same system. They are neither selectable nor controllable. Don’t count on replication either.

The reality of large network dynamics is part of what makes understanding dynamics of the human brain, 200 billion neurons connected to each other typically by thousands of synaptic edges, a pursuit of the ridiculously impossible.

In large social systems, individuals are in sync, entangled only with neighbors they can observe. Coordination may or may not occur with the neighbor’s neighbor. The connecting force in large organizations where depersonalization is unavoidable, is not performance but groupthink, held to be infallible. Low-value couplings between nodes lowers the threshold where desynchronization takes place and groupthink takes over.

The only way to keep large prosperous organizations in sync is by detecting signals of emergent instability and “tickling” things back intelligently to the synchronous state. For healthy social systems, detection of incipient unrest must always be “on.” For dysfunctional organizations, spontaneous synchronization is impossible. When it is forced by authority, cooperation gets worse. The mathematical physics, the natural laws that drive networks of entangled minds, like all natural laws, are undefiable. Anything held as infallible is destined to die on the rack of the Second Law.

It is a remarkable fact that the second law of thermodynamics has played in the history of science a fundamental role far beyond its original scope. Suffice it to mention Boltzmann’s work on kinetic theory, Planck’s discovery of quantum theory or Einstein’s theory of spontaneous emission, which were all based on the second law of thermodynamics. Ilya Prigogine

Some History of Intelligence Amplification

The distinction lies essentially in the facilities available to the dynamic simulationist. Until 1940, every model maker had little more than the resources of pencil and paper and perhaps sixteen hours in the day. Every model he made was subject to these restrictions and to the fact that his brain, as a material dynamic system, could not get through more than a certain quantity of information processing in one life time.

Before 1940, models of really complex structure were both unconstructible, because of the labor involved, and unusable for the same reason, large quantities of information processing being impossible. Today, dynamic simulation is a routine matter carried on inside computers. The basic process of search goes on equivalently whether the mechanism is made of silicon or protein in a living brain.

A model is a dynamic archive. New facts can be added to the existing store and old ones corrected. Running scenarios on a model provides facts that are logical consequences of the direct observations. The distinction between model and computation has ceased to be significant. The worker can use hundreds of variables in numbers truly appropriate to the actual richness of the system. Instead of being forced to replace every actual function by some fictitious form, usually the linear, which was chosen solely for its suitability for algebra, he can now compute with the actual data with all its idiosyncrasies.

Quantity of information refers to the number of effective or distinguishable causes. The process must be carried through for information used in the making of models, finding laws, and finding constraints. This is recoding the information in a more compact form that can be processed alike by all law-abiding mechanisms. It has a logically irrefutable solution.

In a complexity, the possibility of adaptation occurring in any reasonable time is fundamentally dependent on the presence of simplicities – Method. It must improve its ability to react intelligently in ever more complex ways to the disturbances and threats of the environment. Adapting is a system of two subsystems interacting, seen from a particular point of view.

We grow in direct proportion to the amount of chaos we can sustain and dissipate Ilya Prigogine

 

Entanglement and control theory

There are, of course, mathematical physics that reign over networks of entangled minds, i.e., social systems. The mathematics of synchronized networks of autonomous nodes describe the behavior of the system of self-organizing behavior where each node is capable of exhibiting all sorts of unique behavior as an individual. In 1600, the Dutch scientist, Christian Huygens, started the drama when he first noticed that two pendulum clocks hung from a common support would eventually come to tick in unison. The mathematical theory of this synchronization developed late in the 20th century, applies to any network system, metal or flesh.

In small social systems, where everyone is tightly personalized, strongly coupled with every other member, the synchronizing connection is by productivity and effectiveness in attaining group goals – mutually observed performance signaling in unison. You have been a node in these transient high-performance, self-organizing groups several times. Team sports is just one manifestation of high-value coupling. It engenders group resistance to disturbance.

In large social systems of identical oscillators, assured identical by invariant human nature, the controlling mathematical physics is substantially different. In the 1980s the designers of complex communications systems first discovered that having identical elements in the network is no guarantee of predictable network behavior. History is filled with examples of large social systems spontaneously flipping out of sync and evolving into exotic, complex, unpredictable patterns of behavior. In fact, many distinctive, complex states can display from the same system. They are neither selectable nor controllable. Don’t count on replication either.

The reality of large network dynamics is part of what makes understanding dynamics of the human brain, 200 billion neurons connected to each other typically by thousands of synaptic edges, a pursuit of the ridiculously impossible.

In large social systems, individuals are in sync, entangled, only with neighbors they can observe. Coordination may or may not occur with the neighbor’s neighbor. The connecting force in large organizations where depersonalization is unavoidable, is not performance but groupthink, held to be infallible. Low-value couplings between nodes lowers the threshold where desynchronization takes place and groupthink takes over.

The only way to keep large prosperous organizations in synch is by detecting signals of emergent instability and “tickling” things back intelligently to the synchronous state. For healthy social systems, detection of incipient unrest must always be “on.” For dysfunctional organizations, spontaneous synchronization is impossible. When it is forced by authority, cooperation gets worse. The mathematical physics, the natural laws that drive networks of entangled minds, like all natural laws, are undefiable.

Ashby zingers

  • No matter how large the corporation and how small the alteration made to it, if the corporation is not restricted there is no limit to the size of the impact on behavior.
  • Taking into account earlier events in what he can observe. This is using memory to add information for appropriate selection. Memory is the existence of a transmission having a correlation between events appreciably separated in time.
  • High internal communication density means a long learning cycle.
  • Every act of coordination requires transmitted information
  • All social relationships are governed by information theory. The situation of the compound therapist and patient is a system subject to the basic laws of cause and effect. The therapist is restricted in that he cannot become knowing except as the actions of the patient make him so. Adaptive treatment is based on information gathered during progress.
  • A set of states and an operator such that unlimited repeated action by the operator on the state cannot generate a state outside the set. (rules)
  • The logic of mechanism, method, action and behavior rests on your basic ways of thinking about cause and effect.
  • Large systems either show no change in output to a change in input or an abrupt step change. The system goes either to total inactivity or total activity.
  • Method science sprawls irregularly over many disciplines.
  • The achieving the correct final form repeatedly in spite of a stream of disturbances is homologous with the correction of noise by a correction channel.
  • All truth is of finite range and reliability.
  • A relation is a mathematical object subject to mathematical operations. A system is a relationship among nominal variables.
  • Darwin showed that quite a simple rule acting over a great length of time can produce design and adaptation far more complex than the rule that had generated it.
  • Natural laws are not goal seeking. They are simply processes that the laws of nature provide. This indifference is dependable as the processes will work in any case.
  • A dynamic system is one actively changing in time.
  • Information theory has no direct tie to real time. It is based on the correspondence of events. System think is a way to find the hidden simplicities in a very complex system.
  • No possible rearrangement of parts can make the amount of information in the machine greater than that used by the designer.
  • As humans ascend the scale of intelligence, it displays precisely by their power of regulating their environment in spite of greater ranges of stress coming to them. In man, his primary goals are what evolution and natural selection have built into them.
  • History shows that commerce will become sterile until the limitation is made part of its working conceptual structure.
  • Separating the real issues from those that are mere survivors of the questions that agitated the dark ages.
  • Natural selection is an operator.
  • Information flows into you during induction, within you during deduction and out from you at decision making.
  • Every coordinated activity requires an internal flow of information between the parts being coordinated.
  • Use methods to get what you actually can get.
  • The collection of truth is futile, for it will not keep.
  • The truth is the whole system and not any extract from it.
  • Life is not a chaos of special cases.
  • Living organisms do not react to the actual future, are not affected by it and do not receive information from it.
  •  The brain cannot deal effectively with the really new. It must wait until the new has slid into the past. Let the world produce something really new and the brain is helpless. All wisdom is wisdom after the event.
  • If the scene is a random intellectual walk, there is no method possible here.
  • In the act of invention, science has little to say.
  • All products of accurate thinking can be based on set theory.
  • The context always does something and it cannot do two things at once.
  • X’ = f (X) State next is a function of the previous state.
  • Man has always been sure that he knows how he thinks so that every new fact is received into a morass of old speculations and pseudo-facts, most pre-Darwinian. Inquiry into his understanding is commonly regarded as a personal insult.
  • The meaning of a message depends on the set that the message comes from.
  • Risk is the transition probabilities.
  • Every describable behavior can be produced by an infinite number of machines (Shannon in 1938)

 

Complementary and Opposite Dynamic Systems

Plan A and Plan B are in a yin/yang relationship – complementary and opposite. It is impossible to confuse the two social operating systems. If it takes you more than 15 minutes to certify a dysfunctional organization, you haven’t been paying attention to the working environment. A prosperity-seeking organization takes even less time to authenticate.

People in OD can’t help grumbling about the multidimensional stupidity drowning them in angst. People in Plan B can’t help showing you all the good stuff going on and inviting your questions.

 

Trademarks of organizational dysfunction dynamics

Simultaneous indicators
  • Prominent Great Divide: Delusion-speak and implementation-speak
  • Depersonalization, demonization, extermination
  • Authentically-reckless regarding operational-results responsibility
  • Delusion-informed, GIGO, dictatorial decision-making
  • Management by crisis, hindsight, event-driven
  • Ca’canny, grumbling
  • Defense of infallibility, undiscussables enforcement
  • Leadership focused, drive and force (punishment)
  • Rule-based behavior, Au rule
  • Dependency
  • Authority, pulling rank
  • Hidden suspicion

Missing

  • Feedback, lessons-learned
  • Prominent suspicion
  • Audition learning, ground truth
  • Appreciation, gratitude
  • System think
    • Authentic Franceschi Fitting
    • Audition, testing, proving grounds, R&D
    • Entropy extraction

 

Plan B operational trademarks

Simultaneous indicators

  • Prominent Great Divide
    • Delusion speak
    • Implementation speak
  • Personalization, democratic, Pt rule
  • Actionable-quality information (AQI) emphasis
  • Unambiguous outcome responsibility
  • Risk-informed decision-making, effectiveness-focused, prevention
  • Experimental – lab, test situations, RBF
  • Prominent feedback, lessons-learned
  • Prominent Franceschi Fitting
  • System-think, prominent suspicion
  • Autarky, viability husbandry, entropy extraction
  • Audition-learning emphasized
  • Recognition, appreciation

Missing

  • Undiscussables
  • Criticism, accusation, drive, punishment
  • Hierarchical authority

It is an inherent property of intelligence that it can jump out of a task which it is performing and survey what it has done. Douglas Hofstadter

 

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