It is easy to see, therefore, that, provided the same change is made



To all, change of parameter-value to the whole set cannot increase

The set’s variety. This is true, of course, whether D and E are states

Of equilibrium or not. Now let the system continue under P2. The

Two groups, once resting apart at D and E, will now both come to

B; here all will have the same state, and the variety will fall to

Zero. Thus, change of parameter-value makes possible a fall to a

New, and lower, minimum.

The condition that the change P1 -> P2 may lead to a further fall

In variety is clearly that two or more of P1’s states of equilibrium

Lie in the same P2 basin. Since this will often happen we can make

The looser, but more vivid, statement that a uniform change at the

Inputs of a set of transducers tends to drive the set’s variety down.

As the variety falls, so does the set change so that all its mem-

Bers tend, at each moment, to be at the same state. In other words,

Changes at the input of a transducer tend to make the system’s

State (at a given moment) less dependent on the transducer’s indi-

Vidual initial state and more dependent on the particular sequence

Of parameter-values used as input.

The same fact can be looked at from another point of view. In

The argument just given, “the set” was taken, for clarity, to be a set

Of replicates of one transducer, all behaving simultaneously. The

Theorem is equally applicable to one transducer on a series of

Occasions, provided the various initial times are brought into

138

Proper correspondence. This point of view would be more appro-

Priate if we were studying some very complex transducer, making

Fresh experiments on it each day. If it contained great numbers of

Rather inaccessible parts, there might be difficulty in bringing it

Each morning back to some standardised state ready for the next

Experiment. The theorem says that if its input is taken, in the early

Morning, through some standardised routine, then the longer the

Routine, the more certain is it that the machine will be brought,

Ready for the experimenter, to some standard state. The experi-

Menter may not be able to name the state, but he can be confident

That it tends to be reproducible.

It should be noticed that mere equality of the set’s parameter at

Each step of the sequence is not sufficient; if the effect is to be

More than merely nominal (i.e. null) the parameters must undergo

Actual, non- zero, change.

The theorem is in no way dependent for its truth on the size of

The system. Very large systems are exactly as subject to it as small,

And may often be expected to show the effect more smoothly and

Regularly (by the statistical effect of largeness). It may therefore

Be usefully applicable to the brain and to the social and economic

System.

Examples that may correspond to this process are very com-

Mon. Perhaps something of this sort occurs when it is found that

A number of boys of marked individuality, having all been through

The same school, develop ways that are more characteristic of the

School they attended than of their original individualities. The

Extent to which this tendency to uniformity in behaviour is due to

This property of transducers must be left for further research.

Some name is necessary by which this phenomenon can be

Referred to. I shall call it the law of Experience. It can be

Described more vividly by the statement that information put in by

Change at a parameter tends to destroy and replace information

About the system’s initial state.

139

T RA N SMISSI O N O F VA R IE TY

Chapter

8

Brief account mentions no less than sixteen major transformations

Through all of which something has been preserved, though the

Superficial appearances have changed almost out of recognition.

Complexity of coding. When considering such repeated cod-

Ings the observer may easily over-estimate the amount of com-

Plexity that has been introduced. It not uncommonly happens that

The amount of complexity is nothing like as large as a first impres-

Sion might suggest.

A simple example, showing how a complex coding may have

Hidden simplicities, occurs when a simple one-one coding of the al-


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