Volume 14 of W. Ross Ashby's Journal
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1951



Volume 14



3232+03	3232+04

Information in machines Markov process / chain affecting a machine
Summary: Information when a stochastic parameter changes infrequently. Information in machines Information ways of loosing Markov process / chain affecting a machine
3233	3234

Summary: Ways of losing information. 3274 Information in machines
Information in machines
3235	3236

Summary: Wiring pattern of DAMS. Information in machines DAMS (Dispersive and Multistable System) [13]: Possible patterns for joining output and inputs, 3182, 3237.
Information in machines Resting state maximal number
3237	3238

Information in machines
Summary: Conditions that a machine shall have the maximal number of resting states. This can be specified further... Information in machines
3239	3240

Summary: Maximal number of resting states. (3308) Information in machines
Summary: Information when A drives B. Information in machines
3241	3242

Absolute system why not x=f^-1 (x')? Canonical equations why not [x=f^-1 (x')]? Information and canonical equations Information in machines
Summary: The inverse of the canonical equations. Experiment when it stops Information and experiment Information in machines
3243	3244

Summary: An experiment stops when the exchange of information has reached equilibrium. (3248, 3254, 3691) Information and experiment Information in machines Epistemology [5]: When does an experiment stop? 3245.
Independence and information Information and independence Information in machines
3245	3246

Summary: Independence does not in general cause loss of information. (3274) Information in machines
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3247	3248

Summary: Entropies in the parts do not sum to that of the whole. Entropy of a part may equal that of the whole. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Summary: Information and experiment. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3249	3250

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3251	3252

Summary: ~~Information in an absolute machine.~~ [deleted] Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3253	3254

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3255	3256

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3257	3258

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3259	3260

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3261	3262

Summary: Information and the experimenting on dynamic systems. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3263	3264

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Summary: This then is the maximal information obtainable in an absolute system of σ states by starting it at a state selected arbitrarily and then observing how it's behaviour goes from state to state. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3265	3266

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3267	3268

Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Summary: Information always decreases, step by step, as an unknown line of behaviour unfolds. Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
3269	3270

Summary: Uncertainty about the details within a line of behaviour is independent of whether that line, or some other, will occur. 3274 Experiment when it stops Information and experiment Information in machines Epistemology [6]: Passage of information from machine to observer, { 3248 - 3271 }
Latent roots distribution of
3271	3272


Information and machines, collected
3273	3274

Markov process / chain information from Parameter as source of information Resting state and information
Transition probability and information
3275	3276



3277	3278

Summary: Results collected from the last hundred pages (since 3164) on the subject 'Information in absolute systems.' 3297 3500 Personal notes [14]: New wiring avoids feedback within the 'cortical' part, 3279.
Summary: New layout for DAMS, and an unsolved problem. Summary: I have just reviewed the notes on the pages mentioned (2955, 2996, 3001, 3003, 3014, 3026, 3028, 3056, 3059, 3071, 3082, 3087, 3115, 3138, 3140, 3149 and the previous note), all dealing with the relations between environment, essential variables, and the 'red mass' of 2957, once the essential variables have been specialised and separated. Here are my conclusions...
3279	3280

Environment REVIEW Essential variables REVIEW OF Multistable system REVIEWED DAMS (Dispersive and Multistable System) [94]: Review of relations between multistable system, environment, and specialised essential variables, 3281.

3281	3282

Amplitude limitation postulated in some systems

3283	3284

Arc in 'cortex' of DAMS (Dispersive and Multistable System)

3285	3286



3287	3288


Summary: A review of essential variables. (3484, 3521)
3289	3290

The Multistable System [42]: Behaviour of systems having a state from which the transition probability is very small 3291.

3291	3292


Summary: Equations of density in phase of systems that tend to stick at certain states. 4153 The Multistable System [18]: Length of line of behaviour and chance that a step function will change, 3294.
3293	3294



3295	3296

Summary: The longer the line of behaviour, the higher the chance of step-function change. Information when only part is observed

3297	3298



3299	3300

Summary: Information in machines. Information in a continuous system

3301	3302

Summary: The continuous system can gain information though absolute. Summary: As soon as a sub-system is isolated it starts losing information. Information in a sub-system
Personal notes [13]: Discussion with Wiener, May '51. 3304.
3303	3304

Dispersion examples
Constancy and network Network cut up by constancies Part-function in network
3305	3306

Summary: Dispersion.
Resting state number of
3307	3308



3309	3310


Summary: How many resting states has DAMS? (Continued 3319)
3311	3312


Summary: Resting states in DAMS will be few. (Continued 3319)
3313	3314


Summary: Pneumatic controllers. Part-function approximate
3315	3316

Constant intrinsic stability properties
Summary: Measuring how much one variable affect another. The part-function as a limit.
3317	3318


Resting state maximal number
3319	3320



3321	3322

Constant intrinsic stability and resting states

3323	3324

Summary: Designing parts for a system with many resting states. (3333)

3325	3326



3327	3328



3329	3330



3331	3332

Summary: How to find the distribution of values in a system of many parts.

3333	3334



3335	3336



3337	3338



3339	3340



3341	3342



3343	3344


Summary: (1) I ignore resting cycles here, as they will probably be rare. (2) I treat only of parts of constant intrinsic stability with equations of form *x^-_i=K_i{Φ_i(x₁,...,,...,x_n)-x_i)}. (3) The variables in the parentheses (above) are the 'inputs' to the part, and Φ_i is the 'output'. (x_i merely follows Φ_i). (3323) (4) Just solving f(X)=0 is of little use, for an unknown, and large, number of roots may be complex. The total number of roots, real and complex, is the product of the degrees of the several f's regarded as polynomials (3308) (5) To get the real distinct resting states, find geometrically the real intersections of the surfaces, f(X)=0**. (6) Figure of 3322 shows that, if we want to get our resting states into a certain region of phase-space then the surfaces must waggle within it, and also across it. (3325 top).
3345	3346

Summary: (Continued) (7) If Φ_i tends to a form resembling ρ_i parallel planes (3334), then the number of resting states (stable and unstable) tends to Πρ_i. (3336) (8) (3340: a method, of little importance, for getting the sets of planes all orthogonal. (9) (3342: what happens when all parts are identical. (Not the case with DAMS) (10) To get the maximal number of resting states within a given region: (a) construct each part so that the output consists of many parallel planes, (b) join them so that the sets of planes are orthogonal. (12) If the number of resting states is increased, we can expect the number of stable states to be increased in about the same proportion. (3345)
Summary: How many resting states has a system assembled from parts of known properties? Also 3496 DAMS (Dispersive and Multistable System) [20]: Why DAMS was changed to give more resting states, 3348.
3347	3348



3349	3350

Summary: Modifying a stable field.
Stimulus measuring its size
3351	3352

Summary: Ways of altering a system's sensitiveness to disturbance.
Summary: Effect of richness of joining on the number of stable resting states. Equilibrium and coupling Joining and state of equilibrium
3353	3354

Arc needs no feedback Cortex needs no feedback Feedback not in cortex
Summary: The nervous system should not have internal feedbacks (unless for special reasons) But see 3396. Confirm 3425, 3521
3355	3356


Environment must be able to carry sufficient information
3357	3358

Summary: Information and adaptation. 3521
Summary: How many nerve calls has an earthworm? Personal notes [12]: Idea for new book: the building of DAMS, 3360.
3359	3360

Input control possible Parameter control by

3361	3362



Hover mouse here to display note
3363	3363+01


Summary: Conditions that one system may control another in detail.
3364	3365

Summary: Control in systems of Constant Intrinsic Stability. Constant intrinsic stability useful only for static questions Ergodism DAMS (Dispersive and Multistable System) is not ergodic
Summary: My machines are not ergodic. The Conditioned Reflex [39]: Anrep's experiment, 3367.
3366	3367

Entropy calculation of Information calculation of Markov process / chain information from Transducer theory of

3368	3369



3370	3371



3372	3373



3374	3375


Summary: Information going through a transducer. Information through a transducer Transducer destroying information
3376	3377



3379	3380

Summary: Solution of the paradox of 3379.
Summary: The essential variables must be able to send much information into the rest of the system. 3500 DAMS (Dispersive and Multistable System) [19]: Essential variables should be able to send much information in the rest of the system if they are specialised. 3382.
3381	3382

DAMS (Dispersive and Multistable System) [18]: Advantage to the Essential Variables, if specialised, of using noise 3383.

3383	3384


Summary: Designing an essential variable that works by emitting noise. 3521.
3385	3386

Summary: DAMS needs a complex environment, but a simple training-schedule. DAMS (Dispersive and Multistable System) [17]: Environment for DAMS must be more than minimally complex, 3387.
Oddments [33]: My work is the 'chemistry' of machines, 3388.
3387	3388


Summary: My work is the 'chemistry' of machines. Progress in it will be largely empirical. Review 4141
3389	3390

DAMS (Dispersive and Multistable System) [16]: For many resting states, the steps functions' behaviours should be almost uncorrelated. 3391.
Summary: That a set of step-functions should provide many resting states it is necessary that they should be uncorrelated. This can be achieved by many cross-connexions. 3521
3391	3392

Summary: If the number of resting states is increased by some change of design, take care that the number stable is not actually reduced.
Equilibrium proportion stable
3393	3394

Summary: Of the resting states, the number stable can be anything from none to all.
Feedback destroying information Transducer in a ring
3395	3396


Summary: A system joined in a circuit is likely to have very few resting states. Confirmed 3426 but see 3571. Summary: To retain information in DAMS Mark 13, use output 3 and either of 1 or 2 in all cases. DAMS (Dispersive and Multistable System) [15]: How to retain information in DAMS, 3398, 3428.
3397	3398

Summary: Thinking of the machine as having a finite number of states is the fundamentally sound method. Oddments [30]: Probabilities in continuous statistical systems should always be obtained as limits from finite countable states, 3399.
Summary: How to integrate step by step when parts have outputs. 4498 shows how it should be done. Constant intrinsic stability integration of equations Output integration and output
3399	3400

Resting state maximal number
Summary: Further data on what is required for many resting states.
3401	3402

Information necessary for resting states Resting state and information
Transducer destroying information
3403	3404

Summary: A machine's tendency to destroy or conserve information (as uncertainty of state) depends slightly on certain necessary factors in the parts but depends more on the holistic factor of assembly. Information destruction in machine

3405	3406

Summary: Two similar parts that will give many stable resting states.

3407	3408


Summary: Design of a part and the number of resting states. DAMS (Dispersive and Multistable System) [14]: Essential variable that will inject noise proportional to its deviation 3410.
3409	3410



3411	3412



3413	3414

Summary: My standardised vocabulary, collected. (Standard symbols, 2004)
DAMS (Dispersive and Multistable System) [21]: In DAMS, the other conditions have no 'usual' values; and this increases the difficulty we have in apprehending it. 3416.
3415	3416

Summary: With a new and complex system there are no 'usual' values for the parameters, and this increases the difficulty of getting to know it. 3514
Summary: Law of the Invariance of Distribution. The Multistable System [19]: Law of the Invariance of Distribution 3418. The Multistable System [20]: In a multistable system, counting the number of resting states is not practically possible, 3418.
3417	3418

Information destroyed by part function Latent roots distribution DAMS (Dispersive and Multistable System) [22]: DAMS must contain its information reduntantly 3419.
Cortex, sensory layering in DAMS (Dispersive and Multistable System) [23]: DAMS' variables must be arranged in layers at its sensory input, in order not to lose information, 3420.
3419	3420

Summary: How to stop part-functions from destroying information. 5291.9
Resting state maximal number DAMS (Dispersive and Multistable System) [24]: For DAMS to have many resting states it must have much independence internally 3423.
3422	3423


Summary: The conclusion is, then, that for many resting states we must have plenty of independence. Feedback not in cortex Independence to pattern DAMS (Dispersive and Multistable System) [25]: Main properties of DAMS at the 20-variable size, 3425.
3424	3425

Summary: Historical note.
Summary: Complex wholes are unstudiable. 3474, 3496, 3513 Epistemology [7]: Peculiarities in studying a large active system 3427.
3426	3427

Summary: Information in DAMS. DAMS (Dispersive and Multistable System) [15]: How to retain information in DAMS, 3398, 3428.

Hover mouse here to display note
3428	3428+01

Higher geometry of fields and matrix theory [14]: Determinant and latent roots of a matrix formed by adding a diagonal and a 'permutation' matrix, 3429, applied 3433.
Summary: Value of a determinant.
3429	3430

DAMS (Dispersive and Multistable System) [67]: Latent roots of a system arranged in a circle of levels, 3431. DIAGRAM

3431	3432

Higher geometry of fields and matrix theory [14]: Determinant and latent roots of a matrix formed by adding a diagonal and a 'permutation' matrix, 3429, applied 3433.
Summary: The latent roots of a system formed as a circular chain of levels. Cf. 3573
3433	3434


ECT (electroconvulsive therapy) explanation Fatigue explanation The Multistable System [21]: Some of the step-functions can be used only once, so the system will tend gradually to lose versatility, 3436.
3435	3436

Summary: A multistable system tends to lose reactivity, which will often be restored by applying some strong, but unrelated, stimulus, at the cost of some forgetting. ? Action of E.C.T. (Corollary 3464). ? Explanation of 'induction'. 3656, 4628, 4524. Induction (physiological) explanation of
Reynolds' number Step function laminar and turbulent flow The Multistable System [41]: Eddies resemble the subsystems of a multistable system. 3438.
3437	3438

DAMS (Dispersive and Multistable System) [26]: Look of for dimensionless numbers in DAMS, 3439. DAMS (Dispersive and Multistable System) [27]: Look for large-scale examples of step-function, resting state, absolute system, in DAMS, especially when it is large, 3439.
Quotations [3]: Ninety quotations, { 3440 - 3450 }
3439	3440

Quotations [3]: Ninety quotations, { 3440 - 3450 }
Quotations [3]: Ninety quotations, { 3440 - 3450 }
3441	3442

Quotations [3]: Ninety quotations, { 3440 - 3450 }
Quotations [3]: Ninety quotations, { 3440 - 3450 }
3443	3444

Quotations [3]: Ninety quotations, { 3440 - 3450 }
Quotations [3]: Ninety quotations, { 3440 - 3450 }
3445	3446

Quotations [3]: Ninety quotations, { 3440 - 3450 }
Quotations [3]: Ninety quotations, { 3440 - 3450 }
3447	3448

Quotations [3]: Ninety quotations, { 3440 - 3450 } Quotations [4]: Mataphysics is the finding of bad reasons for what we believe upon instinct, 3449.
Summary: Ninety quotations. Quotations [3]: Ninety quotations, { 3440 - 3450 }
3449	3450

Natural Selection [33]: In the cortex the relentless necessity for survival may lead to highly specialised properties in the patterns, having only that aim, i.e. useless to the individual, but not, of course, actually harmful, 3451. The Multistable System [36]: The subsystems should gradually develop highly specialised means for each others' disruption 3451.
Summary: In the cortex the relentless necessity for survival may lead to some interesting consequences. (See 3454) (Review, 4155)
3451	3452

Army organisation of Society [37]: Clausewitz on the principles of war 3453.
The Multistable System [34]: The struggle between subsystems as 'war' 3454.
3453	3454


Psychiatric applications [14]: Neurosis resembles two armies struggling, 3456, 3462.
3455	3456

Summary: The art of war - in the cortex. Review 4155, 4589
Inhibition retroactive Retroactive inhibition The Conditioned Reflex [40]: Neutral stimuli play a part in reactions, 3458.
3457	3458

Summary: The animal reacts to all its surroundings. Retroactive inhibition and the theory of interaction in a Multistable System. Dispersion control of Multistable reserve origin demonstrated The Multistable System [95]: Dispersion depends on 'neutral' as well as on the active variables, 3459.
Summary: Let DAMS keep moving. DAMS (Dispersive and Multistable System) [28]: When DAMS is being demonstrated, let it be increasingly slightly disturbed, 3460.
3459	3460

Natural Selection [45]: An objection to be answered, and its answer, 3461.
Summary: Society. Psychiatric applications [14]: Neurosis resembles two armies struggling, 3456, 3462.
3461	3462

Summary: How DAMS can be made neurotic. (See next section) (See 3480)

3463	3464

Summary: Neurosis by conflict must use up a system's resources of step-functions.
Statistical mechanics nature of DAMS (Dispersive and Multistable System) [29]: DAMS is a 'statistical' machine: what does this mean? Answer, 3466.
3465	3466



3467	3468



3469	3470



3471	3472


Summary: The question 'what is a 'statistical' machine?' answered.
3473	3474

Summary: What makes a complex machine 'statistical'? Review 4141
The Multistable System [43]: Probability of an absolute system whose variables are statistics of a much larger system, 3476.
3475	3476

Summary: How does a statistical machine work? DAMS (Dispersive and Multistable System) [30]: DAMS should show absolute systems of few variables, each a statistic from the larger system, 3477.
Summary: DAMS should demonstrate that it can manage the statistics of its environment as well as the exact details. The Multistable System [22]: The multistable system should react sucessfully to the probabilities in its environment, 3478. DAMS (Dispersive and Multistable System) [31]: Experiment to be tried on DAMS, 3478.
3477	3478

Summary: DAMS should tend to avoid activating variables with widespread effects. 4155 Society [38]: We would expect activities in time to avoiding impinging on amplifiers like the newspapers, 3479. The Multistable System [35]: Multistable system will tend to avoid activating variables that ramify widely or magnify much, 3479.
Psychiatric applications [15]: Nature of neurotic symptoms, 3480.
3479	3480

Summary: On neurosis. Money reduction of all social variables to Society [39]: Society, and the nervous system, reduce all variables to a common form, 3481.
DAMS (Dispersive and Multistable System) [32]: Practical method for DAMS' environment, 3482.
3481	3482

Summary: A more practical form of environment for DAMS.

3483	3484


Summary: Details of the Essential Variables.
3485	3486

Summary: How long should an arc be? 3511, 3514, 3557 Arc multiple arcs traversing environment Orthogonality of control
Equilibrium neutral equilibrium and part-functions Part-function neutral equation and
3487	3488

Summary: Part-functions are apt to lead to many useless neutral equilibria. 3491 - No they are acceptable; 3495
Summary: Variable of constant intrinsic stability as part-function. (See also below) Constant intrinsic stability when part-function Part-function if of constant intrinsic stability
3489	3490



3491	3492

Summary: Equilibria in systems of part-functions. (See below)
Essential variables and neutral equilibrium
3493	3494

Summary: Types of equilibria in DAMS. Conclusion: Neutral equilibria, with esential variables within limits, are acceptable.
Summary: How to study a complex system.
3495	3496

Resting state shift when one variable is shifted Stimulus reaction is proportional to size of
Summary: For a small disturbance, the effects everywhere tend to be proportional to the size of the disturbance.
3497	3498

Code inverse Environment as transducer Transducer inverse Transformation inverse Oddments [36]: If the environment is operator E, the brain must become -E^-1. 3499.
Summary: If the environment is E the brain must become -E^-1 4294 Environment must be non-singular Essential variables information from Step function carries information
3499	3500

Summary: Two new types of information in the multistable system. 3521 Natural Selection [10]: Example how apparently useless characteristics may be selected 3501. Natural Selection [16]: Haldane on natural selection, 3501.

3501	3502

Summary: Haldane's book. Summary: Example of dispersion. Dispersion in motor cortex
Equilibrium number of states DAMS (Dispersive and Multistable System) [33]: Number of resting states in DAMS, 3504.
3503	3504

Summary: How many resting states has DAMS?
Cause gene as Genes Grüneberg's Natural Selection [4]: Example of a gene that has many and far-reaching effects 3506.
3505	3506


Society [40]: Moreno's diagrams of the structure of social groups, 3508.
3507	3508

Summary: History of DAMS. Summary: Sex and the Multistable System Natural Selection [5]: There are two sexes so as to bring together all mutations. Each valve in DAMS has two inputs for the same reason 3509. DAMS (Dispersive and Multistable System) [34]: History of DAMS: taken down for uniformisation, 3509. DAMS (Dispersive and Multistable System) [35]: The two inputs to each value cause mixing just as do two sexes, 3509.
Information nature of Redundancy organisms prefer highly redundant information
3509	3510

Mathematics emphasis on linear and additive methods Natural Selection [36]: Evolution as a random search for solutions of problems, 3511. The Multistable System [23]: Multistable system as searcher for linear methods, 3511.

3511	3512

Summary: Testing DAMS. 4511 Design empirical Epistemology of complex systems Natural Selection [19]: How to improve a very complex system, 3513. Natural Selection [44]: Method for improving, 3513. DAMS (Dispersive and Multistable System) [36]: How to improve DAMS, 3513.
Arc in 'cortex' of DAMS (Dispersive and Multistable System)
3513	3514

Summary: Designing DAMS. Essential variables how many is optimal?
Summary: More complexity means more essential variables, which then have all to be satisfied.
3515	3516

Summary: Improvement by the purely empirical is as old as industry.
Environment relation to essential variables Essential variables relations to environment etc.
3517	3518


Receptors reason for multistable systems Signal multistable system responding to The Multistable System [24]: Multistable system responding to 'signal', 3520.
3519	3520

Summary: How to arrange DAMS. DAMS can react to a 'signal' or 'symbol'. Environment REVIEW Essential variables REVIEW OF
Summary: Index to Essential Variables since 3289. 3582 Summary: Reduction of all variables to a common form is of no importance. Money reduction of all social variables to
3521	3522

Learning must use step-functions Step function necessary in central nervous system

3523	3524


Summary: Fundamental theorem that the nervous system must contain step-functions.
3525	3526

Natural Selection [34]: The development of histological staining followed essentially Darwinian lines, 3527. Also pastry-making, 3527.
Chess how to play super chess Society [42]: Advanced society planned as a super brain 3528. Should be able to undertake super-chess, ibid.
3527	3528

Transformation random transformation
Summary: Fully developed form of the 'mechanical brain'; design for a chess-playing 'machine'.4563 Organisation frequency of trials Trial and error internal between trials Natural Selection [17]: Mutation rate must not be too high, 3530. Natural Selection [20]: Developing a super-chess playing system, 3530.
3529	3530

Summary: The brain should have some step-functions almost inaccessible to the environment. The Multistable System [25]: Some step-functions should be hardly accessible, 3531.
Intelligence magnification of
3531	3532



3533	3534

Summary: Definition of a system's 'intelligence' at a resting state. Resting state 'intelligence' at
Anatomy as constraint Environment relation to anatomy
3535	3536

Summary: Anatomical features gives quick success but lose generality. (Next page) 4563
Summary: Fixed qualities in a system. DAMS (Dispersive and Multistable System) [37]: Wiring counts as 'anatomy' in DAMS, 3538.
3537	3538

Summary: Neurofibrils exist. Instinct Tinbergen on Signal in instinct
Cause meaning of Hawk as stimulus Stimulus usually transformed Transformation of stimuli
3539	3540

Transformation and inate releasing mechanisms
Conflict between instincts Learning gets fixed
3541	3542

Natural Selection [18]: Instability of sexual signals, 3543.
Neon lamp
3543	3544

Summary: The neon in DAMS as absolute system. Natural Selection [35]: Natural selection of soap-bubble combinations, 3545. DAMS (Dispersive and Multistable System) [38]: Neon lamp as absolute system, 3545.
Natural Selection [12]: Grains of sand falling through a hole 3546. The Multistable System [39]: Crystals as multistable system, 3546.
3545	3546

Summary: Some statistical systems. Review 4141 Habituation in metal crystal
DAMS (Dispersive and Multistable System) [39]: Effect of joining by subsystems, 3548.
3547	3548


DAMS (Dispersive and Multistable System) [40]: Subsystems formed when many units are joined at random, 3550.
3549	3550


Summary: Joining at random and by sub-systems.
3551	3552


Summary: We think dynamically, not logically. Logic proof that we do not think with it Psychiatric applications [41]: We think dynamically, not logically, 3554.
3553	3554

Basic pattern various, on DAMS (Dispersive and Multistable System) Network some types tested DAMS (Dispersive and Multistable System) [41]: Various patterns of joining tried, 3555.

3555	3556


Summary: How to join DAMS. DAMS (Dispersive and Multistable System) [42]: Principle for eliminating oscillating circuits 3558.
3557	3558

Essential variables relations to environment etc. DAMS (Dispersive and Multistable System) [43]: Environment, Essential Variables, and network, 3559.

3559	3560

Summary: Joining up Essential Variables, Environment, and network. Review 3582 Arc length of
Independence testing for, in DAMS (Dispersive and Multistable System) Neuron numbers of DAMS (Dispersive and Multistable System) [44]: Method of testing for 'illegitamate' and wandering actions, 3562. DAMS (Dispersive and Multistable System) [45]: Number of neurone in monkey's nuclei 3562.
3561	3562

Summary: Representation of a typical environment. Arc multiply traversing Environment representation of

3563	3564



3565	3566



3567	3568


Summary: The number of circuits that passes through each valve in DAMS is large. Arc number of
3569	3570

DAMS (Dispersive and Multistable System) [46]: Effect of a 'bottleneck' in the levels 3571.

3571	3572



3573	3574



3575	3576


Summary: Proof that circuits of levels that include a one- variable level arc easy to stabilise. DAMS (Dispersive and Multistable System) [68]: Roots of a system joined in levels and with one level reduced to a single variable, 3578. DIAGRAM
3577	3578

Canonical equations simple examples
Summary: Processes for elementary study.
3579	3580

Summary: Infinitesimal displacements activate a unique set of variables. (3599) Environment reviewed again Equilibrium multistable system around state of The Multistable System [81]: In a system of part-functions, all infinitesimal displacements, in whatever direction, from a resting state activate the same set of variables, 3581, 3599.
Environment reviewed again Essential variables Reviewed again
3581	3582

Environment reviewed again Essential variables Reviewed again
Environment reviewed again Essential variables Reviewed again
3583	3584

Environment reviewed again Essential variables Reviewed again
Environment reviewed again Essential variables Reviewed again
3585	3586



3586+01	3586+02

These images are reproduced courtesy of The Estate of W. Ross Ashby. Copyright 1972, 2008 © The Estate of W. Ross Ashby