Beer’s ideas

Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 11/03/2018 00:30

Contents

Ashby (repeated from “Introducing systems thinkers”) 1

Ashby’s ideas (distilled from “Ashby’s ideas”) 2

Beer’s ideas. 4

The Viable System Model (VSM) 4

Variety and relative complexity. 5

Beer’s management control options: amplification and attenuation. 6

Beer’s management control options - revisited. 6

Beer’s thought experiment and big data. 7

Further reading. 8

 

Ashby (repeated from “Introducing systems thinkers”)

“Despite being widely influential within cybernetics, systems theory… Ashby is not as well known as many of the notable scientists his work influenced.

W Ross Ashby was one of the original members of the Ratio Club, who met to discuss issues from 1949 to 1958.”

 

W. Ross Ashby (1903-1972) was a psychologist and systems theorist.

For some, understanding classical system theory requires making a paradigm shift as radical as is needed to understand Charles Darwin’s evolution theory.

Many find it difficult to understand the implications of what Ashby said.

 

“At this point we must be clear about how a "system" is to be defined.

Our first impulse is to point at [a concrete entity] and to say "the system is that thing there".

This method, however, has a fundamental disadvantage: every [concrete entity has] no less than an infinity of variables and therefore of possible systems.

Any suggestion that we should study "all" the facts is unrealistic, and actually the attempt is never made.

What is necessary is that we should pick out and study the facts that are relevant to some main interest that is already given.” Ashby 1956

 

To apply Ashby’s system theory is to apply the scientific method

You observe or envisage a system and model it in an abstract system description - a theoretical system.

The state of the system is modelled as variables whose values can be measured (e.g. the positions of the planets).

The processes that maintain or advance the values of those variables (e.g. the orbits of the planets) are also modelled.

The model (a type) hides the infinite complexity of real-world actors and activities that act to realise (instantiate) the model

If and when the system runs in reality, the reality can be tested against what the model predicts.

 

Ashby focused his attention on control systems in particular.

His Law of Requisite Variety defines the minimum number of states necessary for a control system to control a target system with a given number of states.

In response, Conant (1970) produced his so-called "Good Regulator theorem" stating that "every Good Regulator of a System Must be a Model of that System".

 

Observation: The Good Regulator theorem was proved by biological evolution long before Conant articulated it.

Animal brains maintain mental models of things (food, friends, enemies etc.) they care about in their environment.

These mental models must be accurate enough to enable the animals to monitor and manipulate those things.

 

Unless otherwise stated, quotes below are from Ashby’s Introduction to Cybernetics (1956).

Ashby’s ideas (distilled from “Ashby’s ideas”)

“Ashby popularised the usage of the term 'cybernetics' to refer to self-regulating systems

The book dealt primarily with homeostatic processes within living organisms, rather than in an engineering or electronic context.” Wikipedia 2017

 

Information flows are central to cybernetics.

E.g. Ashby saw the brain as a regulator that maintains a body’s state variables in the ranges suited to life

The aim is homeostasis – to maintain the state of the body - and so help it achieve other desired outcomes.

 

Ashby’s ideas about regulatory or control systems include:

·         Variety: the number of possible states a system can exhibit (one way to assess its complexity).

·         The law of requisite variety: “only variety can absorb variety”.

·         Attenuator: a device that reduces variety.

·         Amplifiers: a device that increases variety.

 

Variety (a candidate measure of system complexity)

Ashby said that: “a system is any set of variables which he [the observer] selects”. 

“A system's variety V measures the number of possible states it can exhibit, and corresponds to the number of independent binary variables.

But in general, the variables used to describe a system are neither binary nor independent.”

 

In short, complexity = variety = the number of possible states a system can exhibit.

There are several difficulties with this definition of complexity.

There is not only complexity in the structure of system state - as in Ashby’s measure of “variety”.

There is also complexity in behavior - as in Mcabe’s measure of procedural complexity.

And complexity in the trajectory of system state change – which is an interest in System Dynamics.

Today, there is no widely agreed measure of complexity.

 

Law of requisite variety ("variety absorbs variety”)

Ashby's law of requisite variety applies to how a control system controls selected or essential variables of a target system.

"The larger the variety of actions available to a control system, the larger the variety of perturbations [in values of target system variables] it is able to compensate".

 

Ashby’s law defines the minimum number of states necessary for a control system to control a target system with a given number of states.

It is interpreted here as meaning:

·         A control system’s information state models only those variables in the target system’s concrete state that are monitored and controlled.

·         For a homeostatic system to be stable, the number of states of the control system must be at least equal to the number of states in the target system.

·         The more ways that a homeostatic system can deviate from its ideal state, the more control actions a control system will need.

 

The law says having enough variety is a necessary precondition to control selected variables in a target system.

 

The law does not say variety is all you need

The law does not say having enough variety is a sufficient precondition to control selected variables.

 

The law does not mean maximising variety

The law does not say having more than enough variety is a good idea.

Ashby emphasised the need to be selective.

we should pick out and study the facts that are relevant to some main interest that is already given.”

 

Which is contrary to the following “maximize internal variety” principle,

"Since the variety of perturbations a [control] system can potentially be confronted with is unlimited, we should always try maximize its internal variety (or diversity),

so as to be optimally prepared for any foreseeable or unforeseeable contingency." Principia Cybernetica.

This suggests redundant design effort, inefficient system operation and may result in data quality issues.

 

One real world entity may be subject to many control systems

In nature and in business, control systems may be distributed rather than centralised (as in a brain).

Our body does not maintain homeostasis purely by top-down command and control from the higher brain.

Instead, its many variables are maintained by several control systems, operating in parallel, which are distributed and not in direct communication with each other.

An oyster manages to maintain homeostasis without having any central brain and nervous system.

In large businesses, there is rarely if ever an overarching control body that monitors and directs all business processes.

There are in practice several (hierarchical and/or parallel) bodies that may compete for resources, and even have conflicting goals.

 

To different controllers, a real world entity is at once several target systems, a different one to each controller.

Suppose two control systems either compete or complement each other in seeking to control the same target system state?

Surely, this is the very stuff of relationships between people in a social system.

 

Read Ashby’s ideas for a futher elaboration of Ashby’s law and its implications.

 

Beer’s ideas

Stafford Beer (1926- 2002) was a theorist, consultant and professor at the Manchester Business School.

He regarded Ashby as a grandfather of cybernetics (I believe Ashby was a godfather to one of Beer’s children).

He respected Ashby, but was focused more on what might be called “management science”.

So, he set out to apply Ashby’s ideas to business systems.

Beer’s book title “Brain of the Firm” (1972) may well be a deliberate echo of Ashby’s “Design for a Brain” 20 years earlier.

The Viable System Model (VSM)

For general system theorists, the “organisation” of a system is how actors cooperate by playing describable roles in describable processes.

For some more sociological systems thinkers, the “organisation” of a business is its management (or command and control) structure.

 

In “Diagnosing the system for organisations” (1985) Beer detailed his “Viable System Model” for a business organisation..

He said the VSM was inspired by the structure of the human central nervous system.

 

Observation: the VSM doesn’t resemble the known structure or workings of the human brain or nervous system.

It cannot be the VSM, since many viable systems have nothing like a central nervous system (e.g. the solar system, a tree, a bee hive, an oyster).

And there is scant evidence of any business operating in a way you could say closely matches the VSM.

The VSM is primarily a tool for diagnosing human organization design issues, and generating change proposals.

 

Beer’s writing is not easy to follow and the VSM is complicated.

For a picture of the VSM, find “Diagnosing the system for organisations” on the internet and look at Figure 37.

Beer wrote: “There is no 'correct' interpretation of the VSM. We have spoken instead of more or less useful interpretations.”

Interpreting the VSM is a job in itself, which some management consultants enjoy doing.

Here is a possible interpretation.

 

The five systems in Beer’s Viable System Model

One interpretation

5: makes policy decisions to steer the whole organization and balance demands from different units.

Business executive

4: looks out to the environment and monitors how the organization must change to remain viable.

Business strategy and planning

3: establishes the rules, resources, rights and responsibilities of System 1 and interfaces with Systems 4/5.

Enterprise architecture?

2: information systems that enable primary activities to communicate and System 3 to monitor and co-ordinate those activities.

IT operations

1: the primary/core business activities (recursively, each is a viable system)

Business operations

 

Note that Beer (being a steel industry man) hugely underestimated the extent to which core business activities in many businesses are IT operations.

 

“Few organizations have adopted the VSM as their formal organizational structure.

But many consultants have used it to diagnose the way an organization is operating and where improvements are needed.” (Stuart Umpleby).

 

This paper is not about using the VSM.

It is about Beer’s use of four of Ashby’s ideas listed above.

·         Variety: the number of possible states a system can exhibit (one way to assess its complexity).

·         The law of requisite variety: “only variety can absorb variety”.

·         Attenuator: a device that reduces variety.

·         Amplifiers: a device that increases variety.

Assessing the complexity of a business system

In a phrase of the time, the four Ms were four structural resources: Men, Materials, Machines and Money

Ashby might have said thinking about structures is inadequate because it is behaviors we need to understand, monitor and control.

Beer said that thinking about structures is inadequate because managers need to think about managing complexity.

 

How does a manager assess the complexity of a business system to be controlled?

One might try to do this systematically, as follows.

1.      Choose your measure of complexity

2.      Identify the system elements to be described (roles, actors, processes, variables, whatever)

3.      Describe two real world entities in terms of those elements

4.      Demonstrate your two descriptions have been made to the same level of abstraction.

5.      Demonstrate by testing that the two real world entities behave according to your descriptions.

6.      Then apply the complexity measure and compare.

 

The process looks hopeless, fanciful and impractical

Beer knew that Ashby’s measure of complexity is incalculable in all business systems of interest

So he said a subjective assessment of relative complexity is valid.

Managing a complex business system

What if a control system fails to control key state variables of a target system?

What if a manager fails to control key variables of business system, such as production rate, profit etc.?

What if a government fails to maintain the happiness of its population (no joke here, follow the link at the end of this paper).

 

Beer proposed managers should use Ashby’s ideas to:

·         Amplify/increase variety in the control or management system, or

·         Attenuate/reduce variety in the target or operational system.

 

There are many other design options to consider.

You might consider also:

·         improving the information flow quality (speed, throughput, integrity etc.)

·         adding another control system in parallel to the existing one.

 

And if the actors in the system are self-determining (can choose their own response to a stimulus) two more options could be:

·         empower actors in the target system to determine their own actions, in the light of given goals

·         empower actors in the target system to find and deploy their own control systems.

 

What if turns out that a target system cannot or does not always respond appropriately to a control system?

The control system can detect this and pass responsibility over to some kind of exception handling processor.

 

I am not pretending these to be original ideas; Barry Clemson tells me you can find some of them in Beer’s writings.

Nor am I pretending that this is an exhaustive list of options.

Beer’s thought experiment and big data

Science demands that theories lead to predictions that are testable in experiments.

System design creates system descriptions whose behaviors should be testable in built systems.

If the experiments or system tests fail, then the theory or system design should be discarded or corrected.

 

In the 1970s. Ackoff and Beer predicted the imminent collapse of government institutions, if not the collapse of society as a whole.

What has happened in 50 years since, alongside ever increasing population?

The United Nations report huge advances in the health, life expectancy, education and welfare of people across the globe.

How did those institutions avoid their predicted fate, and succeed so well?

 

Was Beer’s advice sound?

Beer focus was on a particular aspect of management science.

For example, he didn’t address difficulties in recruiting or motivating people to perform business operations.

His focus was on information processing, and on the variety in the actors and activities that institutions seek to monitor and direct. 

To address this he proposed automating feedback loops to collect more information (“big data” we might call it now).

 

For example:

“Beer built a device that would enable the country’s citizens, from their living rooms, to move a pointer on a voltmeter-like dial that indicated moods ranging from extreme unhappiness to complete bliss.

The plan was to connect these devices to a network—it would ride on the existing TV networks—so that the total national happiness at any moment in time could be determined.

The algedonic meter, as the device was called (from the Greek algos, “pain,” and hedone, “pleasure”), would measure only raw pleasure-or-pain reactions to show whether government policies were working.”

See New Yorker reference

 

What would modern polling scientists (psephologists) make of that?

Four points occur to me.

 

Data quality issues

Remember the dubious “maximize internal variety” principle?

Beer favored maximising the internal variety of the control system, in case that information might be useful.

Consider for example, the information gathering practices of the former East German government.

And the business practice of dumping data in a “data warehouse” or collecting lots of “big data”.

There is considerable experience of businesses collecting more data than they need, then having data quality problems.

When they try to use that data, they find the data is out of date, not quite what they want, or inaccurate.

The feedback loop might be characterised as garbage in garbage out.

 

The best control systems are lean.

A lean control system is one that knows a minimal amount of what is going on in the controlled entity.

E.g. A thermostat knows nothing of a heating system bar the temperature of the environment.

Lean non-intrusive government is generally favoured over the practices of the former East German government.

And in business, management by exception is common.

Managers often ask us to minimise the variety they monitor by reporting only “traffic light” status information to them.

 

Humans are not machines

Beer promoted the “participative democracy” that many social system thinkers have long treated as a vision or mission statement.

Even so, his structured approach to management still looks like the top-down command and control you might find in government-run or state-controlled industry.

Collecting big data and directing activities based on analysis of that data was the basis of Beer’s “Project Cybersyn” in Chile.

That project didn’t end well; see further reading below.

 

Social entities and social systems are not the same thing, but may be aligned

Social entities behave in ways beyond description using the terms of general system theory and cybernetics.

Collecting data and directing activities based on analysis of that data is one part of managing business.

Motivating and helping people to reach goals is another part (along with rules that discourage aberrant behaviors).

A real-world business is a mix of social entities and social systems, which may be aligned.

Read Social cells for a longer discussion.

Further reading

You will enjoy this article about Stafford Beer http://www.newyorker.com/magazine/2014/10/13/planning-machine

“The Planning Machine: Project Cybersyn and the origins of the Big Data nation.”

By Evgeny Morozov, a Critic at Large, October 13, 2014 Issue of the New Yorker.

 

Apparently, Beer attempted to model parts of the Chilean economy (and his VSM) using Forrester’s System Dynamics.

It is one thing to envisage a large and complex social entity as a system, and model your theory.

It is quite another to test that model, prove it is right or useful in practice.

 

Noting that Beer was a socialist of a kind, you might be interested to read Marxism and System Theory

 

Sources read in the course of writing this paper include:

·         http://ototsky.com/khipu/lib/beer_diagnozingthesystem_en.pdf

·         http://digitalcommons.colby.edu/cgi/viewcontent.cgi?article=2829&context=cq

·         http:// www.hbcse.tifr.res.in/jrmcont/notespart1/node9.html (this link appears broken)

 

You can find a list of Beer’s works (1959 to 1983) in “Diagnozing

 the system

”.

They include for your interest: S. Beer (1983) A reply to Ulrich's "Critique of pure cybernetic reason: the Chilean experience with cybernetics". 1. appl. Systems Analysis 10.

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