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The Science Frontier Express Series 

I .S45 

The Dripping Faucet 

as a 

Model Chaotic System 

by Robert Shaw 

ISBN Number 0942344-05-7 

Copyright Aerial Press, Inc., 1984. Reproduction of this volume in any form is prohibited (except for 
purposes of review) without the prior written permission of either Aerial Press, Inc. or the author. 

Aerial Press, Inc. RO. Box 1360, Santa Cruz, CA 95061. (408) 425-8619. 


Water drops falling from an orifice present a system which is both easily 
accessible to experiment and common in everyday life. As the flow rate is 
varied, many features of the phenomenology of nonlinear systems can be seen, 
including chaotic transitions, familiar and unfamiliar bifurcation sequences, 
hysteresis, and multiple basins of attraction. 

Observation of a physical system in a chaotic regime raises general ques¬ 
tions concerning the modeling process. Given a stream of data from an experi¬ 
ment, how does one construct a representation of the deterministic aspects of 
the system? 

Elementary information theory provides a basis for quantifying the predic¬ 
tability of noisy dynamical systems. Examples are given from the experimental 
data of computations of the two dynamical invariants: a) the information stored 
in a system, and b) the entropy, or rate of loss of this information. 


1. Introduction 

2. The experiment 

3. Modeling strategy, some expectations about the data 

4. Some phenomenology 

5. A naive analog model 

6. Information theory and dynamics __ 

7. The minimum information distribution P(x) 

8. Quantification of predictability 

9. Entropy of purely deterministic systems 

10. The noise problem 

11. Information stored in a system 

12. Examples from the data 

13. Rate of loss of stored information 

14. Example from the data 

15. The entropy in the limit of "pure determinism" 

16. "Observational noise" 

a. Measuring instrument as channel 

b. Observation rate 

c. Complete and incomplete measurements 

17. Symbolic dynamics 

18. Example from the data 

19. Limitations of this modeling procedure 

20. More general models, how "good" is a model? 

21. The undecidability of optimum modeling 

22. Conclusion, "ideas" as models 

Appendix 1 - Continuous time calculation 
Appendix 2 - The dimension question 


In a lecture entitled, "Is the end in sight for theoretical physics?", 
Steven Hawking considers the possibility that all fundamental physical laws may 
soon be known[l]. He comments: "It Is a tribute to how far we have come already 
in theoretical physics that it now takes enormous machines and a great deal of 
money to perform an experiment whose results we cannot predict." If a descrip¬ 
tion of Nature is to consist solely of elementary particles and the forces 
between them, this statement might be true. However systems made up of a 
number, even a small number, of subunits can behave in ways which completely 
transcend our present understanding. The motion of fluids provides a good exam¬ 
ple of how a vast phenomenology can arise which is to a considerable degree 
independent of microscopic physics. We live in a whirl of moving structures, 
swept by social, economic, and personal currents whose dominant theme is one of 
unpredictability. Yet laws, constraints of some sort seem to be operating, as 
evinced by our ability to function. The central Issue of physics, that of pred¬ 
ictability , is in fact addressed as a practical matter by each newborn Infant: 
How do we construct a model from a stream of experimental data which we have not 
seen before? How do we use the model to make predictions? What are the limits 
of our predictive ability? Simple experiments, as well as the experience of 
daily living, still have much to teach us. Here, as a case In point, is an 
experimental study of a dripping faucet. 

Jl* Introduction 

A common complaint of insomniacs is a leaking faucet. No matter how 
severely the tap is wrenched shut, water squeezes through, the steady, clocklike 
sound of the falling drops often seems just loud enough to preclude sleep. If 
the leak happens to be worse, the patter of the drops can be more rapid, and 
irregular. A dripping faucet is an example of a system capable of a chaotic 

transition , the same system can change from a periodic and predictable to an 


aperiodic, quasi-random pattern of behavior, as a single parameter (in this 
case, the flow rate) is varied. Such a transition can readily be seen by eye in 
many faucets, and is an experiment well worth performing in the privacy of one^s 
own kitchen. If you slowly turn up the flow rate, you can often find a regime 
where the drops, while still separate and distinct, fall in an irregular, 
never-repeating pattern. The pipe is fixed, the pressure is fixed; what is the 
source of the irregularity? 

Only recently has it been generally realized that simple mechanical oscil¬ 
lators can undergo a transition from predictable to unpredictable behavior 
analogous to the transition from laminar to turbulent flow in a fluid. A random 
component in a physical variable is not necessarily due to the interaction of 
that variable with a great many degrees of freedom, but can be due to "chaotic 
dynamics" among just a few. A system in such a regime is characterized by 
short-term predictability, but long-term unpredictability. The system state at 
one instant of time is causally disconnected with its state far enough into the 
future. The study of simple nonlinear models capable of chaotic behavior has 
become an active area of physics and mathematics research, see references [2] 
for an introduction. 

The existence of dynamical chaotic behavior places severe limits on our 
ability to predict the future of even completely deterministic systems. The 
question soon arises, if a system is unpredictable, how unpredictable is it? 

Can we quantify a degree of predictability? Also, can we tell the difference 
between randomness arising from the interaction of many degrees of freedom, and 
randomness due to the chaotic dynamics of only a few? 

Limits on predictability imply a loss of information , and motivates a look 
at the formalism and concepts of information theory. Information theory, as 
developed largely by Shannon, often provides a natural and satisfying framework 


for the study of predictability. A main goal of this paper is to demonstrate, 
by example, how one can take a stream of data from a physical experiment and , 
compute quantities such as a) the amount of information a system is capable of 
storing, or transmitting from one instant of time to the next, and b) the rate 
of loss of this information. The paper will conclude with a more general dis¬ 
cussion of the modeling process. 


2. The experiment 

The dripping faucet has been used as an example of an everyday chaotic sys¬ 
tem in lectures by Rossler[3], Ruelle, myself, and others, but to my knowledge, 
this is the first experimental study. Experimental work is being performed at 
UCSC, in collaboration with Peter Scott. Figure 1 illustrates the simple 

Water from a large tank is metered through a valve to a brass nozzle with 
about a 1 mm. orifice. Drops falling from the orifice break a light beam, pro¬ 
ducing pulses in a photocell signal. The pulses operate a timer, yielding the 
basic data of this experiment, time intervals between successive drops. A Z80 
based microcomputer, built by Jim Crutchfield, controls the valve via a stepper 
motor, reads and resets the timer, and collects data. Timer resolution is 4 

Chief sources of external noise are vibration and air currents, to which 
the system is quite sensitive, and drifts in the flow rate, probably due to the 
strong temperature dependence of both viscosity and surface tension. However 
the system is easy to operate, with a little care, and produced interesting data 
almost immediately. Drop rates of interest were in the range of 1 to 10 per 
second, corresponding to flow rates of about 30 to 300 gpf [4] . 

^3. Modeling strategy , some expectations about the data 

A traditional physics approach to modeling the dripping faucet might be to 
try and write down the equations of motion, coupled differential equations 
describing fluid flow and the forces of surface tension, and solve them with 
appropriate boundary conditions. But this is an exceedingly difficult problem. 
The calculation of the shape of even a static hanging drop is at present a 
state-of-the-art computer calculation[5]. A numerical foray into the largely 

unexplored world of three-dimensional coupled nonlinear PDE"s is probably 

Another, more tractable, approach is to try and build a model directly from 
the data, without attempting a model from Newton"s equations and first princi¬ 
ples . Here we forget the physics entirely, and view the system as a black box 
producing a stream of numbers , T^> ... in this case the drop intervals. 

All our questions about predictability are posed abstractly, in terms of these 

Is there a causal relation between T and T ,,, between one drop interval 

n n+1 

and the next? A common method for testing a stream of numbers for determinism 
is to construct a "return map”, the numbers are taken in pairs and a 

point is plotted for each n. 

Figure 2 shows how such a plot would appear for two extreme types of data. 

Fig. 2 


a) If the drops are absolutely periodic, then T^= T 2 = T^, etc, and each pair of 
numbers plots the same point. 

b) Suppose each number was selected at random from some distribution of possi¬ 
ble intervals P(T ), with no correlation between one number and the next. 

In the latter case, the plotted pairs would produce a scatter plot, 
reflecting the absence of any "history" in the number stream. The distribution 
of points on the scatter plot would be uniquely determined by the rule for sta¬ 
tistical independence, PCT^jT^^) = P(T n )P(T n+ ^). This provides the definition 
for a "purely stochastic", or Markov, number stream. 

In general we expect, on physical grounds, something in-between. Systems 
are usually causal, with the information stored in the dynamical variables pro¬ 
viding continuity through time, but we also know that every physical system is 
subject to noise, which limits the total amount of information it can carry. 

Of course, we cannot expect to construct a model of the entire drop dynam¬ 
ics from a series of drop intervals. First, the discretization of the continu¬ 
ous system effected by looking only at drop intervals performs, roughly, a 
"Poincar£ cross-section" of the flow in the system state space. For example, a 
regular oscillatory dropping motion becomes a single point in the return map. 

One could imagine many different oscillations with the same period. Even worse, 
we are projecting the presumably infinite-dimensional dynamics onto a single 
time variable. The fact that this procedure can yield any structure at all 
makes a statement about the ability of continuous systems to behave in a "low- 
dinensional" fashion. 

Nevertheless we can hope, if the system is "low-dimensional" enough, to 
compute the same measures of predictability that we would obtain if we had 
available much more complete data, for example the position of the whole surface 
of the drop as a function of time. In order to be useful, quantities such as 
the "information stored in a system" must be properties of the system, and not 
the particular type of measurement. This implies an invariance under coordinate 
transformations, a property which appropriately defined measures of information 

possess. Thus we can hope that, in some cases, a "good enough" measurement of 


any dynamical variable will serve to characterize the predictability, 

4. Some phenomenology 

The behavior of our dripping faucet system exhibits a rich phenomenology as 
the flow rate is varied, one which we have not yet systematically investigated. 
The qualitative type of behavior at a given flow rate, e.g. periodic or chaotic, 
can depend on initial conditions, indicating multiple "basins of attraction”, 
and changes in behavior as a function of flow rate are often sudden and hys- 
teretic. The drop rate is not a monotone function of the flow rate. An over¬ 
view of the faucet behavior will appear[6], but at the moment only preliminary 
results will be presented, consisting of examples of the different types of 
behavior which can occur. 

At low flow rates, the system is in its "water clock" regime, quite strict 
periodicity is typical* At somewhat increased flow rates, corresponding to 
about 5 drops per second with our nozzle, variations in the drop intervals on 
the order of 5*10% start to appear* These can be difficult to detect by eye, 
the drops still appearing periodic, but the time vs* time maps show that varia¬ 
tions are present, and quite structured. 

Figure 3 illustrates one type of behavior, every other drop interval is 
somewhat longer than its predecessor, corresponding physically to a pairing up 
of the drops, as schematically illustrated to the right of the figure. This is 
an example of a "period doubling” bifurcation, two drops have to fall before the 
system behavior repeats itself, and successive points on the time vs. time map 

alternate between two locations. 


Another flow rate might yield a picture such as Fig. 4a. Here no periodi¬ 
city is apparent, but the system clearly is not totally random either, a consid¬ 
erable degree of determinism connects one drop interval with the next. The time 
vs. time map could be approximated by a parabola with some ’’pure stochastic” 
noise added. In fact, considerable study has been devoted lately to simple 
difference equations of the form = f(X^) + ^ * where fCX^) is a parabola, 

and ^ is a noise term, hopefully modeling a system with mixed deterministic and 
random elements[7]. For comparison, the plot resulting from iteration of such a 
one-dimensional map with added noise is shown in Fig 4b. 

data 1-d map 

.180 T n (sec) .193 

Fig. 4 - Data modeled by the 1-d map with noise: 
x" = 3.6x(l-x) + .02 £ 


Still another type of behavior is shown in Fig. 5a. This is a very fami¬ 
liar picture to those of us with experience with driven nonlinear oscillators, 
it is a Poincar§ cross-section of a "two-band” attractor, which occurs in the 
driven Van der Pol oscillator, the driven Duffing oscillator, and many other 
systems. The two-band attractor is part of the complete period-doubling bifur¬ 
cation sequence, as described originally by Grossman and Thomae in the context 
of one-dimensional maps[8], and Shimada and Nagashimaf9], LorenzflO], and 
Crutchfield et al[ll] in the context of continuous flows. One should imagine 
trajectories moving along a ribbon which loops through the cutting plane twice, 
the ribbon folding into itself at some point along its length. This is an exam¬ 
ple of a system with a mixture of periodic and chaotic aspects, points strictly 
alternate between the two islands in the time vs. time map, but move chaoti¬ 
cally within each island. This situation has been termed "noisy periodicity" by 
May[12], and "semiperiodicity" by Lorenz[10]. Similar pictures occur in 
iterated maps of the plane such as the H§non mapping[13], as illustrated in Fig. 

data Henon map 

Fig. 5 - Data modeled by the H§non map: 
x' = y + 1 - 2.07x 2 

y' 88 

-. 33x 


At a certain flow rate, one starts to see pictures like Fig. 6. The data 
still has a one-dimensional character, but the map is tangled, one drop interval 
no longer uniquely determines the next. There apparently still exists a single 
coordinate which more or less specifies the state of the system, but this coor¬ 
dinate is no longer projected in a one-to-one fashion onto the drop interval. 

One could resolve the ambiguities in the time vs. time map with various condi¬ 
tional probability schemes, but a more entertaining method is to add another 
coordinate, namely T^^, t0 che display, and view the resulting time vs. time 
vs. time map as a stereo plot. In this fashion, we let the visual cortex do the 
work, and those able to view stereo pictures with crossed eyes can verify that 
the strings which appear to cross in the two-dimensional figure are in fact dis¬ 
tinct, and one could in principle lay more reasonable system coordinates along 

Fig. 6 


All of the examples presented so far display a nearly one-dimensional char¬ 
acter, that is, the value of a single coordinate, often just the drop interval, 
determines to within fairly narrow limits the value of the next interval. As 
far as the data is concerned, the state of the system can be specified by a 
value along a single coordinate. Furthermore, the behavior is familiar from the 
phenomenology of simple low-dimensional nonlinear systems, despite the fact that 
the dripping faucet is a continuous fluid system, with, in theory, an infinite 
number of degrees of freedom. We can only expect that eventually more of those 
’’degrees of freedom” will assert themselves. 

Figure 7 illustrates a type of behavior which appears at somewhat increased 
flow rates. Much of the data falls along one-dimensional curves, but there are 
regions of the time vs. time map which appear to be higher dimensional. Appen¬ 
dix 2 discusses the interesting possibility of the common occurrence of attrac¬ 
tors of what might be called ’’mixed dimensionality”; in some regions of the 
state space, the state of the system can be optimally specified by the value of 
a single coordinate, but in other regions, more coordinates are required. 

Fig. 7 


At still higher flow rates, higher dimensional behavior becomes more and 
more evident. The variations in drop interval increase to 20% and more, and one 
obtains pictures like Fig. 8. There is still plenty of structure present, but, 
as the phenomenology of higher dimensional nonlinear systems is nearly com¬ 
pletely unknown, studies are postponed to the future. 


Further work will be directed toward illuminating the new nonlinear 
phenomenology which is lurking in this system. A comforting feature of the non¬ 
linear jungle is that certain landscapes tend to reappear; whereas dynamical 
detail can vary endlessly from system to system, certain themes become familiar. 
For example, in the dripping faucet system we have recognized forms and bifurca¬ 
tion sequences common to low-dimensional models. Perhaps by studying faucet 
phenomena that is less familiar, we can build metaphors which will be of use in 
comprehending more complex behavior in other systems. 

5. A naive analog model 

The term "model** is commonly used in physics to describe a representation 
of a physical system not involving all of its relevant variables, but rather a 
much simpler set of variables and a dynamic between them which still manages to 
preserve some aspect of the qualitative behavior of the complete system. 

Clearly some degree of predictability will usually be lost in the simplifica¬ 
tion, but a simple model often has value in its conciseness and explanative 
power, as well as as a starting point for a more complete description. 

Several years ago, I constructed a model of this type for a dripping fau¬ 
cet, and implemented it on an analog computer[14]. This simple, one-dimensional 
model is so crude as to have perhaps little or no predictive power, but it may 
be useful in describing to first order why the dripping faucet 

behaves as it does. When the water drop data collection system was constructed, 
it became a matter of curiosity to plug the analog computer in place of the phy¬ 
sical system, and see if any comparisons could be made. 

The model is sketched in Fig. 9. A mass, representing the drop, grows 
linearly in time, stretching a spring, representing the force of surface ten¬ 
sion. When the spring reaches a certain length the mass is suddenly reduced, 


representing a drop detaching, by an amount dependent on the speed of the mass 
when it reaches the critical distance. We thus have a driven nonlinear oscilla¬ 
tor, the nonlinearity arising from the sudden change in mass, and with position, 
velocity, and mass providing the three variables required for the occurence of 
chaotic behavior in a system evolving in continuous time. 

Fig. 9 - Analog model for water drop at low flow rates. 

A drop detaches at a distance x indicated by 
the dotted line. ° 


Sure enough, as a parameter is increased, the analog model enters into 
periodic motion, then period-doubles, then goes chaotic. The time between 
“drops", or the sudden reductions in mass of the analog simulation, is the 
discrete model variable corresponding to the time intervals observed in the phy¬ 
sical system. Physical faucet data can be found which closely resembles time 
vs. time maps obtained from the analog simulation, see for example Fig. 10. 

80 (msec) 90 

Fig. 10 - Comparison of experiment and "theory". 


The analog simulation makes available continuous variables, which are not 
obtained from the physical experiment as it is presently conducted. By plotting 
the variables of the analog simulation, we can get an idea of the geometry of 
the ,, attractor ,, describing the motion of the fluid system. Fig. 11 plots two of 
the three continuous model variables, for the same parameter values yielding the 
time vs. time map of Fig. 10. This structure can be recognized as a Rossler 
attractor in its "screw type" or “funnel” parameter regime[15]. The close 
correspondence of model and experiment of Fig. 10 argues that such a structure 
is imbedded in the infinite-dimensional state space of the fluid system. Fig. 

12 plots the position of the model drop as a function of time. The interaction 
of the phase of the natural drop oscillations with the discrete events of the 
drops detaching give rise to varying degrees of "rebound" of the remaining mass, 
and the resulting motion can be nonperiodic. 





Fig. 11 - Attractor reconstructed from the analog model. 

Note the rapid rebound when a drop detaches at x q . 


Fig. 12 - Analog model in the nonperiodic regime corresponding to Fig. 11. 

No strong claims are made about the accuracy of this particular model, as 
nearly any driven nonlinear oscillator will behave this way in some parameter 
range. Nevertheless, a few points are well illustrated by this example. 

a) A model of only a few dimensions can sometimes adequately describe the 
chaotic behavior of a continuum system, the high dimensionality of the fluid 
system is not required. This is in line with the original discussions of 
Lorenz[16], Ruelle and Takens[17], and Gollub and Swinney[18]. 

b) There exist fundamental geometrical structures which reappear in many dif¬ 
ferent nonlinear systems. The equations of the original Rossler system are 
quite different from those of this model, yet the same attractors appear. 
Furthermore, Rossler's original work, as well as work in this laboratory and 
elsewhere, indicate that the changes in topology of the various Rossler 


attractors as system parameters are varied is much more general than any one set 
of equations- Yet the parameter space of even the Rossler system, which pro¬ 
duces the simplest chaotic attractor, is largely unexplored. Much work remains 
to be done to determine "typical" paths through parameter space, and the result¬ 
ing changes in the behavior of a physical system. 

. c) Analog and special purpose hardware could play a leading role in the 
development of our understanding of nonlinear systems- General purpose digital 
machines are admirably suited for accurate calculations in particular cases, but 
often do not serve well for rapid scans of large classes of equations where 
qualitative understanding is totally lacking. The model-data correspondence of 
Fig. 10 was obtained by a search through a three-dimensional parameter space 
which took only a few moments on an analog machine, parameters are varied simply 
by turning knobs, and feedback is immediate. The time and expense required for 
the same job on a standard digital machine is horrendous to contemplate. The 
radical reduction in price and increase in capability of integrated digital cir¬ 
cuitry in the past decade enable the construction at low cost of special purpose 
and hybrid devices specifically designed to rapidly increase our general 
knowledge of dynamical systems theory. Alas, we have found this viewpoint dif¬ 
ficult to communicate to scientific officialdomf19]. 





6^ Information theory and dynamics 

We now turn to the question of describing and quantifying the predictabil¬ 
ity of a stream of numbers obtained from repeated measurements of a physical 
system. Before treating the water drop data, a brief general discussion of the 
relevance of information theory to studies of dynamical systems is perhaps in 
order. Although only well-known results in information theory will be 
described, a general appreciation has been lacking of their usefulness in 
describing the predictability of physical systems, particularly in the presence 
of noise. The viewpoint and vocabulary used have been developed over the past 
few years at Santa Cruz in collaboration with Jim Crutchfield, Doyne Farmer, and 
Norman Packard. 

The concept of M information" as a physical entity, with a p log p type 
measure, was perhaps first appreciated by Boltzmann[20]. Gibbs, Szilard, and 
others addressed the concept with varying degrees of directness, but it was not 
until Shannon's work "The Mathematical Theory of Communication" that a clear and 
explicit discussion of information and its properties appeared[21]. Shannon 
carefully restricted his discussion to the transmission of information from a 
"transmitter" to a "receiver" through a "channel”, each with known statistical 
properties. Some version of the following figure appears in the early pages of 
most books on information theory: 

transmitter receiver 

Fig. 13 

p(x) P(y) 


P(x) describes the distribution of possible transmitted messages x, P(y) the 
distribution of possible received messages y, and the properties of the "chan¬ 
nel" connecting the two distributions is contained in the conditional distribu¬ 
tion P(ylx), the probability of receiving message y given that message x was 

The set of possible transmitted and received messages labeled by x and y 
may assume a number of forms# If the messages are selected from a finite set, 
for example the letters of the alphabet, then we can label them with an integer, 
and the conditional distribution becomes a finite matrix P^ = PCy^lx^), con¬ 
necting {x^} with {y^}. The set of x and y might be continuous, describing for 
example voltage levels, or they may be multidimensional. 

If the distributions P(x), P(y), and P(y|x) are available, quantities such 
as the "information" passing through the channel are well-defined and easily 
computable. In the case of communication systems, these distributions are, as a 
practical matter, readily available: transmitter, channel, and receiver 
hardware are man-made devices with known properties, and the first-order sta¬ 
tistical properties of the English or other language used (which partially 
determine the "transmitter" distribution) can be estimated by constructing his¬ 
tograms from a large amount of text, as demonstrated by Shannon. From an 
economic point of view, Shannons analysis was required at that point in his¬ 
tory, to enable the development of modern communication networks. 

The mapping of Shannon's communication metaphor onto the problem of predic¬ 
tability in dynamical systems theory Is a small conceptual step. A dynamical 
system "communicates" some, but not necessarily all. Information about its past 

state Into the future: 


P as t future 

Observable past and future system variables are described by x and x', all the 
other time independent system properties are lumped into the conditional distri¬ 
bution P(x'|x) which describes the causal connection between past and future 
given by the system dynamics. 

Present theory usually treats only autonomous, non-evolving systems, hence 
x and x' describe the same set of coordinates at two different times, 
x » x^t^), x 0 (t^), ... and x' * x^Ct^), x 2 (t 2 ), ... etc. This provides a sim¬ 
plification over the more general case in communication theory, where the 
transmitted and received symbols may be different. Again, the symbols x and x' 
should be thought of as general labels for the coordinates, which might be 
discrete, continuous, or multidimensional. 

Knowledge of the past and future system states is represented by probabil¬ 
ity distributions P(x) and P(x^) over the variables. If this correspondence can 
be carried out, the problem of prediction, formally at least, becomes simple, 
given P(x) and P(x'|x), compute P(x^): 

PCr')- ^ PCt‘|x) PO) Jpc 

In this picture, the "system dynamics" P(x^|x) defines a map from distributions 

to distributions: 


Fig. 15 - Distributions to distributions. 

To study the behavior of the system farther into the future than t^» we 
simply reapply the mapping F to the distribution P(x'), producing a new 
predicted distribution at t^. Repeating this process models changes in 
knowledge of the system variables as it moves through time. Much of the modern 
work in dynamics involves studies of these sorts of iterated mappings. Classi¬ 
cally, we can discuss systems evolving in continuous time by considering map¬ 
pings which move the system forward in time by an infinitesimal amount. 

In the case of the water drop system, the measured system variable Is the 

drop interval T , and the dynamics are given by the conditional distributions 

P(T It ) which relate one drop Interval to the next. The time variable is 
n+1 n r 

discrete, and is labeled by the drop number n. 

Intuitively, we expect highly peaked probability distributions to represent 
fairly definite knowledge about the variables, and broad distributions relative 
ignorance. Thus, widening of a distribution under the dynamics will indicate a 
loss of knowledge, or predictive ability. Our task will be to quantify this 



7. The minimum information distribution P(x) 

Of all the possible distributions P(x) over the variables x representing 
various guesses about their positions, there is one special distribution P(x) 
representing a minimum knowledge of the system state * This is our best guess 
about the variable positions assuming that we have total knowledge of the time- 
independent part of the system, the "equations of motion'* P(x"|x), but zero 
additional knowledge about the variables. 

Usually, the best representation of minimum knowledge of the position of 
some variable is a flat distribution over the range of that variable, but if the 
variable is part of a known system, our expectation may be otherwise. For exam¬ 
ple, consider an observation of the position of a harmonic oscillator with known 
energy but unknown phase. Our expectation is that we are more likely to find 
the oscillator near the ends of its travel, as it moves slower there. The pro¬ 
bability of finding the oscillator at some position is proportional to the 
inverse of the velocity at that position. 


Fig. 16 - Minimum information distribution for the simple harmonic oscillator. 


The minimum information distribution P(x) is thus a property of the system 
P(x^|x), or of a particular "basin" of the system, if the system state space can 
be decomposed into independent dynamical regions. The distribution T(x) is the 
baseline against which all more sharply peaked distributions will be measured. 

As we shall see, if a system has any tendency to lose information, all input 
distributions P(x) will move toward P(x) in a quantifiable sense. 

If the system is incapable of transmitting information into the indefinite 
future, then any initial distribution P(x) will relax in time to the asymptotic 
"equilibrium distribution" P(x). The final minimum state of knowledge will be 
independent of any initial information. This is the original and intuitively 
correct concept Boltzmann described by the word "ergodic". The harmonic oscil¬ 
lator is clearly not ergodic in this sense, as it can preserve phase information 

P(x) also has the property that it is unchanged by the system dynamics, 
that is, it is invariant under the map F. Clearly F(x) must be the same distri¬ 
bution as P(x'). Otherwise, a prediction could be made about a change of state 
of the system at a particular time, and our knowledge is not at a minimum. 

Hence the term for P(x) found in the mathematics literature, "invariant meas¬ 

If a system can assume only a finite number of states x^, the dynamical 

rule connecting states becomes a finite transition matrix P . In this context, 

the unique asymptotic state vector is described by a classical theorem of 

Frobenius: A matrix consisting of all positive elements has only one positive 

eigenvalue, furthermore, any positive vector will approach the direction of the 

unique positive eigenvector under repeated application of the matrix P . This 

fact allows an easy computation of the invariant distribution of some map, one 

simply starts with any distribution P^^ and repetitively applies the map until 


the "power method" for finding the largest eigenvalue of a matrix, and is dis¬ 
cussed in the literature under the label "Frobenius-Perron operator"« Its use 
will be demonstrated later in this paper, and it was explicitly applied in the 
dynamical systems context in Ref. [22]. 

The division of seamless reality into "dynamics" P(x'ix) and "variables" x 
is to some extent arbitrary, no physical system is truly independent of time. 
But once such a model has been constructed, a single P(x) is uniquely defined 
for any time-invariant system by the requirement that it describe a minimum 
knowledge of the system state. 

8. Quantification of predictability 

The appropriate measure to quantify an amount of knowledge contained in a 
distribution P(x) is the Boltzmann Integral 

I = J pCx) \»J Pfr) <lx 

This measure yields zero when applied to a flat distribution P(x) - 1, and 

If the logarithm is taken to the base two, the units of this quantity are "bits". 


The number resulting from this integral quantifies the amount of informa¬ 
tion gained when we learn, for example by experiment, that a variable is distri¬ 
buted as P(x), relative to a flat distribution, representing no particular 
knowledge of x before the experiment* It is important to realize that "informa¬ 
tion” in this sense is a relative concept, the information in a distribution 
P(x) is always measured relative to an a priori expectation P(x). The informa¬ 
tion measure is thus a function from a pair of distributions to the real 
numbers* If we know something about the distribution of x even before we learn 
P(x), our a priori expectation ^(x) Is not flat, and the appropriate formula is 

I (■ p, p3p J ^ Io j ^ 

This Is the "Information gain", a sort of distance from the expectation F(x) to 
the new information P(x), which is Independent of the particular coordinates 
used to describe the variables x. The Information gain is then invariant under 
smooth coordinate changes on x. 

The information gain is a concept familiar in the context of statistical 
mechanics[23]* Here the distribution P(x) characterizing minimum information, 
or "thermal equilibrium", is the Boltzmann distribution. If the states of a 
system have an energy distribution differing from the Boltzmann, then there is a 
"free energy" which In principle can be extracted from the system without lower¬ 
ing its temperature. The knowledge of the system which allows us to extract 
"work" from it is the distance from the actual distribution of energies to the 

d Vai I a ioj ^ 

Boltzmann, measured by the information gain. The A free energy Is simply the 
information gain multiplied by kT, Af » I(P,P) kT ln2. 

The informational description of dynamical systems is somewhat less 
straightforward than the static situation of statistical mechanics. For exam¬ 
ple, the Boltzmann and other familiar distributions characterizing static 


equilibria are obtained directly by minimizing the information integral P log P 
subject to appropriate constraints[21]. But in the dynamical case, the "con¬ 
straints” are given by equations of motion, and P(x) is not so easy to deter¬ 
mine. Typically, P(x) is experimentally measured, or computed from equations of 
motion via an iterative procedure, examples will be given in a later section. 

Any actual measurement of a physical variable x, corresponding to a sample 
from the distribution P(x), must be to finite precision , typically we know only 
that x is within some interval x Q to x^ + dx. This motivates modeling the meas¬ 
urement process by creating a "partition", that is, breaking up a continuous 
variable x into a finite number of subintervals x^. Repeated measurements, or 
samples from P^x), gives a string of whatever symbols we are using to label the 
{x^}, with probabilities determined by how much of the probability density 
P(x) falls in each little bin x^. 

The degree of unpredictability, or entropy of the sequence of symbols is 
likewise quantifiable: 

H ='? 1°; ^ 


This measure increases with the degree of randomness, if the sequence simply 
repeats a single symbol x^, then p^ = 1 and all other probabilities are zero, 
the sequence is totally predictable and the entropy per symbol is zero. The 
maximum value is obtained when all possible symbols are equally .likely to occur. 

The entropy, as simply defined above, is clearly not a coordinate invariant 
quantity. It varies as the partition changes, and generally will increase as 


the partition is made finer, creating more possible symbols. To see the rela¬ 
tion between the information I(P,F) of P(x) with respect to P(x), and the 
entropy of a finite partition {x^} of the continuous variable x, consider the 
special partition of P(x) which cuts it into N equiprobable elements: 

The entropy of P(x) with respect to this partition is simply log(N). Now the 
entropy of the narrower distribution P(x) with respect to the same partition is 
approximately H = log(N) - I(P,P). That is, the information provides a con¬ 
straint on the entropy, lowering it from the maximum value it could have with a 
given number of elements in the partition. The equality of this relation 
becomes more exact as the number of elements grows. Here the entropy measures 
the degree of unpredictability of a series of discrete symbols, and information 
the degree of predictability of events arising from a continuous distribution, 
with respect to a background expectation. Entropy and information defined this 
way are both positive quantities. 

9^ Entropy of purely deterministic systems 

The most common treatment of classical mechanics has been purely deter¬ 
ministic. One considers the past state of a system as a "point” x, and computes 
its future position, another "point" x', using a dynamical rule x' = F(x,t), 
usually obtained from Newton's equations. Physical systems governed by linear 
forces, central forces, and a few others respond well to this treatment, 
eclipses are predictable far into the future, and the world is full of clocks 


and gyroscopes. But generalizing from this experience contributes to a false 
world view. Poincar§ realized that even in celestial mechanics determinism did 
not imply unlimited predictability, and the lesson of recent work in nonlinear 
dynamics is that in even the simplest of ‘'purely deterministic" dynamical sys¬ 
tems our predictability may be, as a practical matter, extremely limited. 

Difficulties in predictability arise because, in many systems, nearby 
“points" are spread apart exponentially fast by the deterministic dynamics, 
"errors" grow uncontrollably. The motion of the macroscopic variables becomes 
dominated by microscopic fluctuations, supplied by the heat bath for any physi¬ 
cal system, and the system displays "chaotic behavior”. The average rate of 
spreading becomes an important parameter of a given system, its "entropy", 
governing its degree of unpredictability* The existence of this quantity, and 
the utility of Shannon's information theory in dynamics, was shown by Kolmogorov 
in a 1959 article, "Entropy per unit time as a metric invariant of automor¬ 
phisms" [25] . 

Although the variables of a purely deterministic description are usually 
considered to be continuous, attempts to compute measures of predictability by 
performing continuous integrations quickly run into a practical problem. Here 
the function governing the dynamics takes "points" to "points": 

Fig. 19 - Points to points 


The conditional distributions P(x^|x) are delta functions, and terms such as log 
P(x'|x) appearing in the integrals diverge* This corresponds to the fact that a 
"point'* represents an infinite amount of information, to specify its position 
completely would require an infinite number of digits* The concept of "point", 
while useful, is thoroughly unphysical, being a convenient name for "arbitrarily 
sharp probability distribution". 

Kolmogorov and Sinai found a way around this difficulty, while partially 
preserving the fiction of "pure determinism"* They applied a "partition" to the 
domain of the continuous variables x and x% breaking it up into small elements- 
The deterministic dynamics can now map an image of each little block, conserving 
the underlying probability: 

Fig. 20 

As the above familiar picture shows, the image of a little block in x may fall 
over several blocks in x". With a certain probability a "point" starting in the 
block in x may arrive at any of these. By labeling the little blocks {x^ and 
{x^}, one can construct a finite matrix of transition probabilities p^* 

To compute the entropy, or degree of unpredictability, of a given system 
with respect to this partition, one now uses directly Shannons formula for the 
information received from a discrete channel when the input is known, H(x"|x). 
Anything coming out of the channel that could be predicted from the input is not 
"information" in this sense, there is no "surprise" associated with it. 


The quantity H(x"|x) correctly measures the unpredictability. To compute 
H(x"*|x) from the finite matrix one first considers the entropy of the possi¬ 
ble symbols x^^ received given that a particular symbol x Q has been transmitted, 
as given by the matrix entries 

The average information produced by the system is obtained by weighting this sum 
by the probability P(x^) that the various possible actually occur: 


The number so computed will depend on the partition chosen, but one can 
now, conceptually at least, pass to the limit of an infinitely fine partition, 
or the "refinement ft of the partition obtained by considering higher and higher 
interates of the map. Kolmogorov, Sinai, and others were able to show that, if 
one considers the set of all possible partitions, and selects the one which 
gives the largest numerical value for the entropy, the number one obtains is a 
topological invariant of the system, independent of the coordinate system used 
to describe the original continuous variables x* It should not be too surpris¬ 
ing that the optimum partition is one whose number density of microscopic parti¬ 
tion elements is proportional to the minimum information probability distribu¬ 
tion P(x). 

A very simple deterministic map which exhibits the main features of a 
deterministic system with a positive entropy is graphed below (for the n 

When viewed from a "point to point" perspective, the map is purely determinis¬ 
tic, each "point" x yields a unique image "point" x" * F(x). However, if one 
partitions the interval into small subintervals, representing the uncertainty 
which must exist in any measurement of x and x", one finds that, because the 
slope of the map is two, any element in x will shadow two on the average in y, 
as indicated on the right-hand part of the figure. Because the uncertainty is 
doubled, there is an unpredictable element present in x' of log(2)» 1 bit, 
regardless of how accurately we might know x. Thus the "purely deterministic" 
map acts as an information source [ 22 ]. It should be noted that the partition¬ 
ing, no matter how fine, is a conceptual necessity. 

The positive entropy of this map can also be seen by considering a point in 
x" and trying to determine its preimage point in x* Because the map is two- 
onto-one, the inverse is non-unique, to go backwards in time, we must replace at 
each step the one bit which the system produced in the forward time direction. 
Entropies for these sort of deterministic "one-dimensional maps" can be computed 
this way by looking backwards in time, see work by Oono[26]. Considerable com¬ 
puter studies of the properties of one-dimensional maps have been performed at 
Santa Cruz[27]. 


Entropy measures are nothing more than the result of a simple counting pro¬ 
cedure, in this case we are determining the ratio of "paths" to "states": 

Fig. 22 

As the above figure, adapted from Shannon, illustrates, a partition divides the 
variable domain into a number of "states", and the deterministic dynamics con¬ 
nects them with a certain number of "paths". The entropy is simply the log of 
the ratio of "paths" to "states", where both are understood to be appropriately 
weighted by their probability of occurence. It is a property of the "purely 
deterministic" approximation that this ratio converges, regardless of how many 
"states" are considered. 

A more physical interpretation of the entropy was suggested in ref.[22]. 

Any physical system will be imbedded in a "heat bath", producing random micros¬ 
copic variations of the variables describing the system. If the deterministic 
approximation to the system dynamics has a positive entropy, these perturbations 
will be systematically amplified. The entropy describes the rate at which 
information flows from the microscopic variables up to the macroscopic. From 
this point of view, "chaotic" motion of macroscopic variables is not surprising, 
as it reflects directly the chaotic motion of the heat bath. 


10* The noise problem 

Difficulties arise in the definition of entropy discussed above when 
"noise"* or a stochastic element, is allowed to enter a purely deterministic 
description of a system* Typically the noise smears out the dynamics at some 
length scale* The function governing the dynamics can now be described as tak¬ 
ing "points" to "distributions": 


If we consider a sequence of input functions P(x) of increasing narrowness cen¬ 
tered around some value of x, we will reach a size below which the output dis¬ 
tribution P(x') won^t change, we are "in the noise"* We can, if we like, then 
pass to the limit where P(x) is a delta function, or "point", with no further 
change in P(x^). It might be noted that this reasonable physical assumption 
implies that the map F describing a system with noise is a linear functional* 

The image of any P(x) must be the same as that obtained by breaking up P(x) into 
weighted "points" x^, and superposing the resulting weighted distributions 
P(x'| Xi ). 

if we try to apply the "partition" definition of entropy in this situation, 
we will have trouble- As, during the limiting process, the partition becomes 
finer than the length scale defined by the noise, the number of elements in 
{x'^} accessible to a given x.^ increases without bound: 


noise level 

Fig. 24 

The ratio of "paths” to "states” does not converge, and the entropy diverges 
with the log of the number of elements in the partition* 

This in fact makes physical sense* If an experimenter with a good amplif¬ 
ier turns the gain way up, he will see plenty of "unpredictable information”, in 
the form of thermal noise, no matter how well-behaved the input signal is. 
Further increasing the gain will not help him to resolve the input signal 
better, nor will it help him characterize the amplifier. 

Apparently the quantity H(x'|x) is not an appropriate measure of the pred¬ 
ictability of a system with a noise component, as it actually measures the 
unpredictability, which will diverge as resolution is increased* The correct 

measure is obtained by an even more direct application of Shannon's communica¬ 
tion channel image* 


11 . Information stored in a. system 

A "system" might be considered a body of information propagating through 
time. If there is any causal connection between past and future system states, 
then information can be said to be "stored” in the state variables, or communi¬ 
cated to the immediate future. This stored information places constraints on 
future states, and enables a degree of predictability. Again, ±f_ the system 
dynamics can be represented by a set of transition probabilities P(x'|x), this 
amount of predictability is directly quantifiable, and corresponds, in Shannon's 
vocabulary, to a particular rate of transmission of information through the 
"channel" defined by P(x'|x). 

Imagine two observers with access to the same system, at two different 
instants of time. Suppose they both have complete knowledge of the system 
dynamics P(x'|x), but that the later observer knows nothing of the system state 
at the earlier time. The earlier observer could attempt to "communicate" to the 
later by putting the system in a particular one of its allowed configurations x. 
The dynamics would then carry the system through time to the later observer who 
would read off the resulting system state x' and, perhaps with the help of a 
code book, discover the intended message. 

Figure 25 illustrates two possibilities. Suppose the earlier observer 
attempts to communicate by restricting the system state to a narrow Interval In 
x, described by P(x), but that, at the later time, the distribution has relaxed 
back to the minimum knowledge state F(x') . If this Is true regardless of the 
input distribution then no communication Is possible, and no Information is 
stored in the system. If, on the other hand, the output distribution varies 
with the input, some Information is passing into the future. 


no stored information 


information stored 

Fig. 25 

Notice that, under the rules of this game, an unchanging and completely 
known "variable" communicates no information into the future. Although a static 
structure is certainly a predictable element, establishing continuity through 
time, it becomes a part of the fixed time-independent system description. The 
"information" measures described here are a property of the system dynamics. In 
mechanics, as in the human sphere, the transmission of information requires the 
possibility of change. 


To quantify the average amount of information passing from past to future, 
first consider the earlier observer transmitting a particular x^ and the later 
receiving some from the distribution P(x"|xq). The information, or 
"surprise" associated with this event to the later observer will be measured 
only with respect to the asymptotic distribution p’(x'), as he does not know x Q . 
The mean over all emanating from some x rt is v 

LO'I*.)-- | PCx'lx.) kj -Jgf 

and, finally, the average information transmitted using an ensemble of input 
messages P(x) is: 


This quantity Shannon calls the rate of transmission of information for a con¬ 
tinuous channel* He comments that this rate can vary up or down depending on 
the statistics of the input messages P(x), some will be more suitable than oth¬ 
ers to the particular properties of the channel* The channel capacity he 
defines as the maximum rate one can find by varying P(x) over all possible input 
ensembles, and using the optimum. 

In the dynamical systems context, a particular input ensemble is selected 
by the properties of the system itself, namely the minimum information distribu¬ 
tion P(x). As was mentioned earlier, this distribution has the property that an 
input P(x) produces an identical output distribution P(x"), and it will automat¬ 
ically be generated by an autonomous dynamical system operating recursively. We 
can thus define the information stored in a system as the Shannon channel rate 

for an input (and output) distribution given by the equilibrium distribution for 
the system: 


SUJ LferU;** = J Poo j Pfr'M Io j ^0) 

This formula quantifies the average increase in our ability to predict the 
future of a system when we learn its past. It appears in a number of guises and 
Interpretations, written with joint probability distributions P(x,x") ■ 
P(x)P(x'|x) it assumes the symmetric form 

>■ rr \ i FC^/^) j*s 

I(*V)= J] ?<*■*'> * * 

In the more general case of a communication channel described by a map F(y|x), 
where the output symbols y need not be the same as the input symbols x, the 
channel rate retains its symmetry, I(y|x) = I(x|y). This is not true of the 
conditional entropies, R(y|x) £ H(x|y) if H(x) ^ H(y). 

If the dynamics P(x'|x) is specified in the form of a finite-dimensional 
transition matrix P the stored information appears as 

I (*/l=0 = L R p, i„ Vp. 



and terms of entropies 

IM?) - HCx 1 )- HCx'lx) 

The latter form makes It clear that the stored information is the difference in 
the expected randomness of a system with and without knowledge of its past. 

Note that both H(x') and H(x')x) will diverge as we proceed to the continuum 
limit with a noise element present, but that their difference remains finite* 


The stored information is a function of the dynamics P(x"|x) and the 
invariant distribution P(x) which is also determined by the dynamics. Thus the 

stored information is a map which takes a dynamical system, or a particular ^ 
basin of a dynamical system, to a positive real number. It is, as Shannon 
notes, another invariant quantity, independent of the particular coordinate sys¬ 
tem used to describe the dynamical variables. It correctly goes to zero when 
P(x J '|x) - P(x') meaning the future and past are statistically independent, also 
notice that it diverges in the case of "pure determinism". A perfectly deter¬ 
ministic system, if such a thing existed, could store an infinite amount of 
information, even if it had positive entropy, because it would be able to pro¬ 
pagate a "point** from the past into the future. If we rewrite the relation 
between stored information and the entropies: 

HCx'lx) = HC/) - iMx) 

we see how Kolmogorov was able to define a finite conditional entropy in the 
noise-free continuum limit. Both terms on the right diverge as the partition is 
taken to be indefinitely fine, but their difference H(x^|x) remains finite. 
However, the measured information storage capacity iCx^lx) of any real system 
will be finite. 

12 . Examples from the data 

At last we are able to return to the dripping faucet, and attempt some data 
analysis. Figs. 26 and 27 display two sets of data which represent, to coarse 
resolution, periodic drops. The upper right-hand panels show the data on a 
greatly expanded scale. We can see that the drops are in fact not strictly 
periodic, there is what appears to be stochastic noise admixed. It might be of 
interest to know whether there are correlations In the fluctuations from drop to 
drop or whether the noise is "purely stochastic". To determine this quantita¬ 
tively we compute the drop-to-drop stored Information. 



0 T n (msec) 

Fig. 27 - Same display as Fi 
internal structur* 



An approximate model of the dynamics of the observed variable is con¬ 
structed by accumulating the transition matrix P(T |T ) as a histogram. The 

iiTl n 

observed variation in the drop interval is split into fifteen bins, so each pair 

of intervals (T ,T ..) will increment one of about 225 matrix entries. The only 
n n+1 

other quantity required to compute the stored information is the equilibrium 
probability density P(T) which is also accumulated as a histogram. 

The first "periodic” regime of Fig. 26 is seen to store virtually zero 
information, fluctuations in the drop intervals are indeed "purely stochastic". 
The forward image of one bin in the histogram reproduces the shape of the entire 
equilibrium distribution, and this is true for every bin. The second string of 
data, shown in Fig. 27, stores a small but significant amount of information 
from drop to drop, interval variations are not completely independent. This can 
be seen qualitatively by the pattern of points in the time vs, time map, the 
system might be said to be in a "period two" regime obscured by noise. 

A third example, illustrated in Fig. 28, is supplied by the system in a 
clear period-two regime. Here long and short drop intervals strictly alternate, 
but an observer who had just started to look at the system would not be able to 
tell which would be next. Thus, knowledge of the system past is worth one bit, 
allowing him to correctly predict whether a long or short drop interval will be 
next. A check for further structure in the pattern can be performed by accumu¬ 
lating histograms as above, the results are negative. The system stores only a 
single bit, the rest of the variation is "stochastic". 

However, this system differs from the one described in Fig. 27 above in 
that the information stored perseveres indefinitely , allowing predictions to be 
made about times arbitrarily far into the future. The period-two drop regime is 
thus not ergodic, a localized probability distribution does not relax to the 
distribution P(T) representing minimum knowledge of the system state. A single 
observation determines the long-short sequence for all time. 

T n (msec) 


Fig. 28 - 

200 171.2 T n (msec) 

two" orbit. Here 


A careful reader may have objected to the statement that the nearly 
periodic example of Fig. 26 contains no stored information. What about the 
phase? The system is a rather good clock, with scatter in the drop intervals of 
well under 1%. An early observer could clearly communicate to a later one via 
the system by adjusting the phase degree of freedom, which the later could read 
off by comparison with a reference clock. 

This situation is an artifact of our description of the dripping faucet 
with a single state variable, the drop interval, in discrete time , parameterized 
by the drop number. This description correctly describes the predictive value 
of the knowledge of one drop interval to the determination of the next, but 
neglects the imbedding of the system in continuous time. Within the discrete 
description the "purely deterministic" and "purely stochastic" examples of Fig. 
2a,b are identical in that they store zero information. 

A complete description of the state of the dripping faucet at an "instant" 
of time requires (at least) two state variables, the current drop interval T, 
and a phase variable t, which could be, for example, the time since the preced¬ 
ing drop. States of knowledge will be represented by two-dimensional distribu¬ 
tions P(T,t). 

Computations of the stored information are still performed with the same 
formula, though, using the higher dimensional distributions. The conditional 
distributions involving both T and t can, in this case, be computed from distri¬ 
butions involving only T, the details are relegated to an appendix. The situa¬ 
tion, though more complicated, is straightforward. So, for clarity of presenta¬ 
tion, much of the discussion of the next sections will consider only the predic¬ 
tability of one drop interval from the previous, that is, a one-dimensional sys¬ 

tem in discrete time. 


The total stored information of the nearly-periodic example in continuous 
time can be estimated simply by considering the ratio of the scatter in drop 
intervals to the average interval length* The clock can be "set" to about this 
accuracy, and the informational value of this knowledge is about 

This number is more in line with estimates of the "signal-to-nolse ratio" 
one makes from, say, the widths of lines in Fig* 10a. The dripping faucet is a 
very “clean” system, with a high degree of determinism. The total stored infor¬ 
mation varies considerably and not monotonically, however, with the flow rate. 
This reflects presumably changing relationships between the few measured vari¬ 
ables and the background continuum. Studies of these interesting questions 
remain for the future. 

13 . Rate of loss of stored information 

We have been describing the time evolution of the variables of an auto¬ 
nomous system by a map which takes distributions over the variables at time t^ 
to time t^. We have seen that, under a few reasonable assumptions, associated 
with each such map is an invariant which can be identified physically as the 
average amount of information stored in the dynamical variables. This number 
measures the degree to which an observer at t^ can predict events at t^. 

Predictions farther in the future than t^, without intervening observation, 
can be studied by reapplying the same map to the distributions at t^» yielding 
the distributions at time t^. Associated with this compound map is the invari¬ 
ant Information stored between t^ and t^. Further iteration of the map produces 
a sequence of numbers 1^, 1^, . describing the ability to predict farther 

and farther into the future. We expect that often our ability to say anything 


about the future system state from an observation at will decline as we con¬ 
sider the more distant future, and that, if the map is ergodic, the sequence I 
will converge to zero, as all distributions eventually map to a single minimum 
information distribution P(x). The effect of having made a particular observa¬ 
tion at is lost. 

Now we will examine properties of the rate of loss of the stored informa¬ 
tion, that is, the difference between succeeding terms in the series 
1^, I^ y . This quantity h n = 1^ - wi3 - 1 156 seen to correspond to the 

M entropy” of a deterministic map, although there are difficulties in passing to 
the deterministic limit. 

The expected general form of the curve I(t) describing the stored informa¬ 
tion remaining at time t from a measurement at t * 0 is schematically illus¬ 
trated in Fig. 29. 

Fig. 29 - Small squares represent possible measurements 

Two properties of this curve can be shown from "convexity" properties of loga¬ 
rithmic measures of information: 

a) I(t) monotonically decreases, I < I . This property is intuitively 

Ttri n 

obvious, our information about a system cannot increase in the absence of 

b) The curve I(t) is concave, h^^ < h^* This is also intuitively clear, 
sharper distributions representing greater stored information will spread 
faster than broad distributions. 

The maximum entropy, or loss of predictability, will thus occur when the 
stored information is at a maximum. In the case of a discrete map, taking the 
variables over an interval ^t, a reasonable definition of the entropy is then 
the loss of Information between the first and second iterations of the map. 

Under this definition both the stored information and entropy of a "purely sto¬ 
chastic" map will be zero. 

It is tempting to extend this definition to the continuous case by consid¬ 
ering the slope of the curve I(t) at t ■ 0. But we should remember that an 
"instant" of time is just as unphysical a concept as a "point" in space. As the 
intervals between measurements become shorter, the rate of information obtained 
by observation from the system is increased. In general we can expect that the 
rate of loss of predictability may depend on the measurement process, the more 
"severe" the measurement, the greater the perturbation of the original state. 
Thus the limit process necessary to define a slope cannot be performed. A more 
complete discussion awaits a quantum mechanical treatment. 


14 * Example from the data 

Figure 30 displays the time vs. time map for some water drop data in a 
"fuzzy hump" regime, together with an approximation to the minimum knowledge 
distribution P(T) constructed by distributing 4000 drop intervals into 50 histo¬ 
gram bins. A 50 x 50 transition matrix P(T - ,T ) is likewise assembled. Fig. 
31 shows what happens when we place all the "probability fluid" initially in the 
bin whose location is marked with the arrow in panel (a), and then apply the 
matrix P(T^ + ^,T^) several times. 

After the first iteration, the initial "delta function" assumes a width 
characteristic of the stochastic noise level of the data* We can now compute 
the stored information associated with the map* Its value for this particular 
initial bin is 2.3 bits, and the average over all bins, 1.8 bits, approximates 
the invariant 1^. Under only a few applications of the matrix, nearly all the 
stored Information is lost, and the equilibrium distribution closely approaches 
the experimentally observed distribution of Fig* 30b. The entropy of this map 
is the maximum loss of stored information per Iteration, which occurs between 
iterations one and two, about *83 bits per drop. 

To summarize, we collect our experience with the water drop data as a tran¬ 
sition matrix, which becomes our model for its dynamics* This model of course 
gives good predictions for additional data, if the system is autonomous. The 
invariant stored information associated with this matrix and its iterates is 
then computed, this measures the predictability of the system. 

As discussed earlier, this computation of the stored information considers 
only the predictability of the length of one drop Interval given knowledge of 
the length of the preceding interval, without considering that the actual state 
space is much larger, as the intervals are imbedded in continuous time* 

Evolution of t: 
Average stored 
is listed in tl 


When the total stored Information is computed, as described in the appendix, the 
number obtained is much larger, some 9.7 bits. As Fig. 32 illustrates, the 
first few bits are lost rapidly, but the remaining phase information persists 

Fig. 32 - Stored information as a function of continuous time 

for the data set of Fig. 30a. Computation was performed 
for multiples of the average period, as indicated by 
the small squares* 

In this connection Fig. 33 is instructive. Plotted is the probability of 
observing a drop fall at some "instant” as a function of time. In the absence 
of any observation, this distribution is flat, as indicated by the dotted line. 
But now let us suppose that we know that a drop has fallen at t ■ 0. Our proba¬ 
bility of a drop falling at time t, in the absence of any further knowledge, is 
shown, for the data of Figs. 30-32. Because the variation of drop intervals Is 
only about 5% of the average interval, considerable phase information is 
preserved. Successive distributions spread roughly only linearly, as would be 
expected from repeated convolutions of a narrow gausslan distribution, and the 
stored information declines at a slow logarithmic rate[28]. 

T n (msec) 

drops 1-3 


log P 

.6 t (see) 

drops 52—54 

log P 




10.6 t (sec) 

Fig* 33 - (top) Time vs. time plot for a data set whose variation 
is a small fraction of the average period. 

(center) Relative probability of observing a drop 

given that one has occurred at t = 0, for drops 1-3. 
(bottom) Same, for drops 52 - 54. 


This statistical effect, resulting in the long-term preservation of infor¬ 
mation in a chaotic system, has been termed '’phase coherence*', for more discus¬ 
sion, see refs- [10,29]. For comparison, consider the same plot for data at a 
much higher flow rate, where interval variations are comparable to the average 
interval* Here the knowledge that there was a drop at t ■ 0 is of predictive 
value for only a short time into the future. 

Fig- 34 - (top) Time vs. time map for a data set with variations 
a large fraction of the average period. 

(bottom) Relative probability of observing a drop at 
time t given that one occurred at t ■ 0. 


15 * The entropy in the limit of “ pure determinism ” 

In section 9, the entropy of a purely deterministic system was discussed in 
terms of a limit process involving the partitioning of the domain into infini¬ 
tesimal elements. In the preceding section, the entropy of a map with a noise 
component was defined as a maximum rate of loss of stored information. Here it 
will be shown that the singular noise-free case cannot in general be approached 
smoothly as the limit of a mapping with a stochastic element, as the stochastic 
element tends to zero. The property corresponding to the deterministic entropy 
of a mapping with a very small stochastic element will be made clear. 

The example we will study is the parabolic map with noise added, 
x' = rx(l-x) +£ , which can generate a string of numbers resembling faucet data, 
as illustrated in Fig. 4. Again, ^ is a random variable of some small width, 
modeling, for example, thermal noise. 

Figure 35 shows the invariant distribution P(x) of this map at r - 3*7 for 
* 0, the no-noise case, and for £ « .IX. 

Fig. 35 - Invariant distribution of the parabolic map 
with and without added noise. 


Here the "noise" uncertainty is modeled for convenience by a simple truncation 
of the accuracy of the computation* (The experience of Crutchfield and Packard 
and others has been that the outcome of numerical experiments such as this is 
usually unaffected whether one takes £ to be of gaussian, uniform, or other 
form[27]. Only the effective width of £ is important.) Note that this is a 
rather small noise level, much smaller than that illustrated in Fig 4b. The 
noise would be invisible on the scale of that figure, although finer features of 
the P^x) distribution do become smeared out. 

In the deterministic case, ^ “ 0, the information storage capacity of the 
map diverges, as mentioned earlier. But it becomes finite with the addition of 
noise, and the series X^, *•* of the Information stored by higher iterates 

of the map has the concave behavior discussed in section 13. This series is 
graphed for three small noise levels in Fig. 36. As expected, the initial value 
of the stored information becomes higher as the noise is reduced. 

The rate of loss of stored information, h « I - I . , is graphed for the 

n n n+l 

three cases in Fig. 37. The rate of loss starts off in each case at the same 
value, then falls off to a plateau. The length of the plateau becomes longer as 
the noise level decreases. The value of the rate of loss of Information in the 
plateau region is near the value of the entropy of the map in the no-noise case, 
which is indicated by the dotted horizontal line. 

The fact that the entropy of a map with even a low noise level is initially 
higher than the corresponding deterministic map is due to partition effects, and 
to regions of the mapping where intervals are contracted, that is, where the 
slope is less than one. In a purely deterministic description, a small interval 
representing an uncertainty can be shrunk and then expanded back to the same 
level without any further loss of information, as schematically illustrated in 
Fig. 38a. But if there is noise present in the mapping, no interval can be 


contracted to below the noise level. An attempt by the deterministic part of 
the dynamics to contract and then reexpand a small uncertainty interval will 
result in a larger uncertainty, and loss of information, as indicated in Fig. 

As the noise level is decreased, the stored information will increase 
without bound, but the initial part of the entropy curve will remain the same, 
even at arbitrarily low noise levels. Thus the entropy of the deterministic 
approximation to a physical system will be relevant only to observations con¬ 
ducted at intermediate length scales, that is, coarse enough not to resolve the 
noise level, but not so coarse as to smear out large-scale structures. Although 
more careful studies might be needed to clarify the effect of paritloning on the 
entropy calculation, it seems that the Initial values of the entropy can be 
estimated from the deterministic map simply by ignoring the contracting parts of 
the map. 

In summary, this definition of entropy, in terms of a rate of loss of 
stored information, does not suffer the difficulties which the standard defini¬ 
tion in terms of partitions finds in the presence of noise. However, the two 
definitions are equivalent only in a particular intermediate set of length 



16 * "Observational noise " 

In the discussion up to this point, we have assumed that the variables x 
and x', representing the results of measurements of a system before and after a 
time interval At, are the "true" variables, that is, knowledge of these vari¬ 
ables will give the best possible account of the predictability of the system. 

We have relied on the coordinate invariance of the various measures of informa¬ 
tion to justify the use of an arbitrary measurement scheme, we hope that a "good 
enough" measurement of any set of dynamical variables will equivalently charac¬ 
terize the predictability. 

More generally, though, we might expect that a measurement procedure often 
will not reveal the complete state of the system. The measuring Instrument may 
suffer from a lack of resolution, or there may be "noise" in the measuring 
instrument independent of any noise in the system itself. Or perhaps the system 
possesses more "degrees of freedom" than the measurement apparatus, and ambigui¬ 
ties are Introduced in the projection onto the measured variables. A more gen¬ 
eral situation is sketched below: 

Fig. 39 


The "true” before-and-after variables x and x" and their causal connection 
P(x'|x) are not observed directly. Instead, the states y and y" of the measur- 
ing instrument are recorded, and only the causal relation P(y"ly) between these 
variables is directly accessible. 

The properties of the measuring instrument are specified by conditional 
distributions connecting the "true" and observed variables. The vertical data 
paths of Fig. 39 illustrate two interpretations of the effect of a measurement. 
The distribution P(x|y) describes a state of knowledge of the "true" variables 
P(x) given an observed distribution P(y). This is a key step, P(x|y) is a "best 
guess", which, acting onFCy), yields ?(x). The Inverse of this conditional 
distribution, P(y'|x^), can be interpreted as the projection onto the observed 
variables P(y^) of the "true" distribution P(x^). 

The process whereby the complete system dynamics P(x'|x) generates the 
observed causal connection P(y^Iy) might be described as follows: An observed 
distribution P(y) constrains the possible values of the "true" variables x to a 
distribution P(x). This distribution is acted upon by the "true" dynamics 
P(x^|x), yielding the distribution P(x"), which can be interpreted as a state of 
knowledge of the "true" variables after a time At, given an earlier measurement 
of the accessible variables y. Finally, this state of knowledge is projected 
onto the observable variables by a second measurement, yielding the distribution 


If the "true" system dynamics P(x^|x) happen to be available, and the pro¬ 
perties of the measuring instrument are known, the resulting observed dynamics 
can easily be computed by composition of the conditional distributions: 

Pa'h)= f[P0i1x-)KxMP(xij)JxJx' 


This expresses the equivalence of the two paths between y and y' in Fig. 39. 


This description of the measurement process might be given by an omniscient 
observer, possessing a total overview, including the true dynamics P(x'ix). 
However, the usual case is that we know only the observed dynamics P(y'ly)> and 
perhaps something about the properties of the measuring instrument. The static 
image of Fig. 39 cannot describe the mysterious process whereby we generate suc¬ 
cessively better models of a presumably unique true dynamics from limited sense 
data. Yet hopefully communication theory can be useful, providing both a frame¬ 
work for describing imperfect measurements, and a formalism for quantifying the 
"degree of imperfection”. The communication metaphor suggests a few basic con¬ 
cepts which may survive the future development of our understanding of the 
modeling process. 

a) Measuring instrument as channel: 

Objective information about the state of a system is obtained via measure¬ 
ment. The measuring instrument is thus a channel between the system and the 
observer, and if its properties can be represented by conditional distributions 
P(x|y) and P(y^lx'), the channel can transmit information at a particular rate , 
given by I(x|y) and I(y'|x'). This rate places limitations on what can be known 
about the system. 

We are assuming that both the system and the measurement process are time- 
independent, and that the same instrument is used in both the before and after 
measurements. Thus there will be identical equilibrium distributions P(y) and 
P(y') defined on the measured variables, and the transmission rate both to and 
from the primed variables will be the same: 

I(x|y) = I(y|x) - I(y'|x') = I(x'|y') 

The domain of the measured and "true” variables may well be different, and the 
measurement may not be being conducted in an optimum fashion, thus this number 


is a channel rate, and not a channel capacity* Its units are "bits per observa¬ 

If the measuring instrument is "perfect", there will be a one-to-one map¬ 
ping between the measured and "true" variables, and clearly the distributions 
P(x'ix) and P(y^ly) will have the same stored information and entropy. Here the 
rate of transmission through the channel is infinite, if the variables are con¬ 
sidered continuous. For any physical measurement, though, the rate is finite, 
which implies that the stored information associated with the observed model is 
necessarily less than that of the true dynamics 

Ky'ly) < Kx'lx) 

The observed mapping P(y'ly) is in this picture a composite of the maps assocl- 
ated with the "true” dynamic and the measurements, and it can be shown mathemat¬ 
ically (exercise) that the rate associated with any composite mapping is less 
than or at best equal to the capacity or rate of the smallest of its parts, 
equality holding only in the singular case where everything else is one-to-one. 
This is clear physically, the information flow through a series of devices is 
restricted by the slowest among them. 

The measurement Instrument acts as a "filter" between the observer and the 
system, and both the stored information and the entropy associated with the 
observed dynamic P(y^ly) will be less than or equal to the stored information 
and entropy of the "true" dynamic P(x^|x), for predictions any distance into the 
future. Thus both the absolute value and the slope of the curve I(t) will be 
less for the observed variables, as sketched below: 


t -> 

Fig* 40 - Stored information and entropy reduced 
by passage through measurement channel. 

Although in general the true dynamics P(x^|x) cannot be fully known, usu¬ 
ally we have a greater or lesser confidence in the measurements. For example, 
the water drop dynamics takes place on a time scale of tenths of seconds, 
whereas measurements are performed with a crystal clock, with a resolution of a 
few microseconds. Thus we expect that, if the dynamics is indeed low dimen¬ 
sional, we will be able to construct a good model of the true dynamics. This 
intuitive feeling can be numerically justified, for the "fuzzy hump" data of 
Fig. 30, by comparing the information the system is capable of storing, about 10 
bits, with the measurement channel rate. The latter can be estimated by consid¬ 
ering that the timer can resolve the true drop time to 4 microseconds out of an 
a priori expectation that is roughly flat over the average drop interval, about 
.1 second. Thus about 14 bits are available for measurement. The fact that the 
measurement precision is much greater than the measured system storage capacity 
provides a good basis for the belief that the system is well characterized by 
the measuring instrument, but whether this is actually the case or not is an 
undecidable question. This point will be discussed later. 


b) Observation rate: 

A single observation of a physical system will yield a certain amount of 
information about the state of the system, the amount is as we have seen quan¬ 
tifiable in bits and limited by the resolution of the measuring instrument. By 
considering a series of observations, repeated at a given rate, we can define an 
average ’’observation rate”, the product of the average bits per observation and 
observations per second. The units of this quantity are thus "bits per second", 
the same as the entropy. 

One task of an experimenter studying a new system is to characterize the 
determinism of the system, which can be described as the information stored 
through time in the dynamical variables. If the system has a positive entropy, 
some or all of this causal connection will be lost in the passage of time. If 
succeeding measurements are too far apart, they will not be able to resolve the 
determinism, even if they are of high precision, this situation is sketched 


t —> 

Fig. 41 - Causally disconnected measurements. 

Here the observer is only taking random samples out of the minimum information 
distribution P(x). In order to resolve a given degree of determinism, or stored 


information, the experimenter must observe at a rate greater than the entropy at 
that level of stored information. If we assume that the average rate of loss of 
information is roughly Independent of the amount of information stored, as in 
the figure, then a given degree of determinism can be resolved with either high 
precision measurements at a low rate, or coarse measurements at a high rate[30]. 

c) Complete and Incomplete measurements: 

An autonomous system can be described as a dynamical rule acting on some 

. . The 

dynamical rule which we have been writing P(x^|x) thus will also appear below as 

space of states {X}, producing a sequence of states X » X . ,,, X .^, 

n n+l n+2 

If a system is completely specified by its "state” X^, then there will be 
no increase in predictive ability upon learning its history X^ etc. 

Thus we can define a "complete measurement” as one which allows the maximum pos¬ 
sible predictability with a single measurement. With a "complete” set of vari¬ 
ables, the dynamics becomes a first-order Markov process, the evolution of the 
system so described is independent of its history, that is, 

P(X n+1 IX n ) - P(X r+ i^ n *^n-lThis is like a "nearest-neighbor interaction" 

between successive observations X , X . „. 

n* n+l 

The "true" variables are by definition "complete", but this property can be 
lost in an imperfect projection down to the measured variables. Some unmeasured 
aspect of the system may persevere through time to be recorded by a later obser¬ 
vation. Then a better prediction can be made by considering the past history of 
measurements, we have, in the language of communication theory, the more compli¬ 
cated situation of a "channel with memory". 

There are at least two general ways in which this situation can arise. 
First, the "true" dynamic might be capable of storing considerably more 


information than the measurement channel can transmit* Examples of this case 
will be presented in the next section. Second, the dimensionality of the 
"true" dynamic might be higher than that of the measured variables, or that the 
two sets of variables are not mapped in a one-to-one fashion. This case is 
exemplified by Fig. 6, even simple functions can become multivalued. Note that 
in the second case, the measured variables cannot be made complete by just 
increasing the accuracy of the measurements, an actual change in dimensionality 
or mapping of the observation channel is required. A complete set of variables 
can of course be constructed by assembling a larger dimensional variable con¬ 
sisting of the present measurement plus as many past measurements that increase 
predictability. The process of reconstructing a higher dimensional state space 
from lower dimensional observations has been termed "Geometry from a time 
series" by Packard et al [31]. 

17 . Symbolic Dynamics 

The result of a physical observation is typically recorded in the form of a 
number of finite length. Thus, from a measurement point of view, an experiment 
can have only a finite number of outcomes. Each outcome can be labeled by a 
symbol, and the recorded time behavior of the system then consists of transi¬ 
tions between these symbols. Measurement, in principle, reduces a continous 
system to a discrete one. If the underlying geometry of the dynamics is simple, 
a rather coarse measurement with a small number of symbols may extract a consid¬ 
erable portion of the determinism of the system. The description of a continu¬ 
ous system by a small number of symbols is often referred to in the mathematics 
literature as "symbolic dynamics” [32]. 

Repeated coarse measurements of a chaotic dynamical system typically will 
produce a partially correlated string of numbers. We will begin by discussing 
the predictability and entropy of such a data stream. Then we will consider the 


case where a measurement can have one of only two outcomes. This provides a 
particularly simple arena to examine, in a preliminary way, a few rather general 
questions. Given a data stream, how much can we infer about the system that 
produced it? What sort of measurements should we make to optimize the predicta¬ 
bility of a system? Do we use the same criteria to best determine the past 
states of a system? This section builds on the recent work of Crutchfield and 
Packard [33]. 

First a bit of notation, a string of symbols generated by repeated measure¬ 
ment of some system will be written s^, s^, s^, ... and a history of n such sym¬ 
bols s 1> s^, ••• , will be denoted S n . Now because the measurement process 

has produced a discrete set of symbols, we can directly apply the definition of 

entropy, for a single symbol we have 

Hfs) =-S fcMyKO 

where the sum is taken over the possible values each symbol s can take on. The 
entropy of a string of length n is given by 

HCS") = -5 P^)I 7 P«V 

where the sum is taken over the much larger set of all possible patterns of 
symbols of length n. Already we can see that practical problems will arise in 
computing directly the entropy of long strings of symbols. 

If successive symbols are totally uncorrelated, as are for example the 
outcomes of a repeated coin toss, then the entropy of a string of n events is 
simply n times the entropy or "surprise" of the single event: 

H(S n ) « nH(s) 

But if the events are correlated, the "surprise" of later events is reduced by 


expectations build up by the earlier history. The classic example, due to 
Shannon, is written language. The longer a string of text, the easier it is to 
guess the next lette. 

The expected general form of the total entropy as a function of the number 
of events or symbols is illustrated in Fig. 42 below. The total entropy 
Increases monotonically (we never get ’’unsurprised"), but the rate slows as 
expectations are built up, leading to the convex curve shown. 

(number of symbols) 

Fig. 42 - Total entropy of a string of symbols as a function of its 
length* The asymptotic slope of this curve is the entropy 
per symbol, and the y intercept of this asymptote, indicated 
by the dotted line, is the stored information. 

In a real chaotic system with noise present, initial data has predictive 
value for only a limited time, events which are too far apart are causally 
disconnected, as argued in Fig. 41. Correlations extend over only a finite 
length of the string of symbols, old experience ceases to have predictive value. 
The effect of this on the graph of Fig. 42 is that the curve approaches a 
constant slope, that is 

H(S n+1 ) - H(S n ) - H(s|S n ) - const. -EE h 


for large enough n. Shannon refers to the asymptotic value h as the "entropy 
per symbol" of a discrete signal source. 

Now how does one compute the stored information associated with this string 
of symbols? We know that this number will be finite, if correlations extend 
only over finite lengths of the string. We also know that, if the curve of Fig. 
42 is initially convex, the measurements which produced the symbol string were 
"incomplete", in the sense discussed in the preceding section. Predictability 
of the next symbol is increased, up to a point, by considering longer and longer 
past symbol histories. 

A reasonable way to approach this computation is to consider the total 

predictability of the future symbol string given the past symbol history, 

l(s ,_,s ,„,•••|s ,s or I(S n |S m ) if we indicate past and future strings 

n-f-l tirrZ n n-1 

by S m and S n . We can now argue that this number will be independent of m and n, 
if both are larger than the correlation length. A few manipulations of the 
definitions of entropies and stored information given earlier yield: 

I(S n |S m ) - H(S m ) + H(S n ) - IKS® 411 ) 

This can be seen to be the _y Intercept of the tangent to the flat part of the 
curve of Fig. 42, if m and n are large enough* This nice geometric picture 
tells us, for example, that 

I - H(S n ) - nh 

for large n, where h is the asymptotic entropy HCS^ 1 ) - H(S n ). It is clear as 
well that the y intercept of the tangent to any portion of the curve gives a 
lower bound to the stored information, and that a criterion for the maximum 
correlation length is that the y intercept no longer Increase with increasing 
string length n. 


If the resolution of the measurement process which produces the symbol 
stream is Increased, the number of values each symbol s can assume increases, 
and the number of possible patterns S n Increases exponentially. As the resolu¬ 
tion approaches the noise level, the asymptotic slope of Fig. 42 will increase 
without bound. But the y intercept indicated will remain bounded, presumably 
approaching the stored information of the underlying dynamical system. Please 
note here the distinction between the entropy of a particular number stream 
obtained by measurement, and the entropy of a map or system , defined in section 
13 to be the rate of decrease of stored information or predictability as future 
times are considered. The latter "entropy" remains bounded as resolution is 

Now we will take a look at the effect of coarse measurements on the 
familiar parabolic one-dimensional map. This example, the writer hopes, will 
provide a useful and not overly technical illustration of the ideas suggested 

The single dynamical variable in a one-dimensional map is a value along the 
real line between (usually) zero and one. A simple way to model a coarse meas¬ 
urement of this variable is to divide the interval in two, and to report as the 
measured variable only whether the value of the "true” dynamical variable falls 
on the left or right half of the interval. This measurement scheme is illus¬ 
trated for the map X “3.7X (1-X ) in Fig. 43a, where the dividing line is 

Hrri n n 

placed at X ■ .5. Fig. 43b shows the minimum information distribution P(X) for 
this map. 


Fig. 43 - Graph and Invariant distribution for the map X .. **3.7X(1-X). 

n-t-i n n 

The results of a sequence of repeated measurements of the system are no 

longer a string of high-precision real numbers between zero and one, but rather 

a string of the two symbols "0" and "1**, labeling the left and right side of the 

interval. If we denote the string of symbols so generated as s^, s^* s^, 

then the transition matrix of measured quantities P(y'ly) can equivalently be 

written P(s ,, |s ). 

n+1 n' 

The information stored in the continuous variable over an iteration of the 
map is arbitrarily large, as discussed in section 11. But under the dynamic 
induced by the single symbol measurement, clearly no more than one bit can be 
stored over a single iteration, as only one bit is being observed. We will now, 
in Fig. 44, trace the information stored around the schematic data paths of 
Fig. 39, where P(x'|x) is the "purely deterministic" dynamics defined by the 
one-dimensional map, and P(y'ly) is the dynamic induced by the coarse measure¬ 



As mentioned above, I(x^|x) is infinite, as are also H(x) and H(x'), but 
I(x|y) and I(y"|x') can be no more than one bit, the maximum capacity of the 
measurement channel. I(x|y) is in fact somewhat less, as the partition at 
X = .5 does not divide P(x) evenly, the iterates of the map fall on the right 
about 78% of the time. Thus the symbol "1" occurs more often, this constitutes 
prior knowledge which reduce the average new information obtained in each single 
measurement to .766 bit. A "best guess" of the state of knowledge of the "true" 
variable x given that the symbol "1" has been observed is illustrated in the 
top-left panel of Fig. 44, it is simply the right side of the minimum distribu¬ 
tion P(x). Given that the channel is noise-free, the knowledge we gain about 
the system is simply equal to the entropy or "surprise" of the measurement, that 

I(x|y) - H(y) 

This is the informational value of the inference we make about the "true" system 
given the limited measurement. This state of knowledge P(x) is now iterated 
under the "true" dynamics P(x^|x), yielding the distribution P(x^) illustrated 
in the top-right panel of Fig. 44. This distribution represents our knowledge 
of the variable after an iteration of the map, given that we knew only that the 
variable was to the right of .5 in the interval before the iteration. The 
information stored to this point (averaged over both possible initial measured 
symbols "0" and "1") has been reduced to .254 bit. The difference between 
I(x|y) and I(x v |y) is, for this partition position, H(x'Ix), the entropy of the 
deterministic map, as computed for example in Ref. [22]. Finally, the distribu¬ 
tion P(x') is projected down onto the measured variables "0" and "1", as shown 
in the lower-right panel, further reducing the stored information to .094 bit. 
Now we have completed our trip around the diagram, and this last number is the 
stored information associated with the induced dynamic between symbols, P(y^|y). 


Thus by transforming to and from the "true*' variables, we can compute the 
transition matrix between the symbols "0" and "1", and the stored information 
associated with this matrix. However, in writing the induced dynamic P(y'ly) as 
a two-by-two matrix p ( s n +]J s n ) we assume that the observationally accessible 
state of the system is completely specified by a single measurement of the 
primed variables. That is, the single symbol "0" or "1" describes all we can 
know about the system. But in this case we can make a better prediction as to 
the next measured symbol than is possible with just the transition matrix by 
considering the history of observed symbols, not just the single prior symbol. 

A single symbol measurement is thus not "complete”, that is. 

P( Vl , VVl*- ) ,tp(s rri-l |s n ) - 

The stored information associated with the single-symbol matrix P(y'ly), 
I(y' |y) ** .094 bit can be improved upon by constructing higher-order transition 
matrices, considering the previous two, three, or more measured symbols to 
predict the next. The more of the past which is considered, the better the 
prediction of the next symbol; the stored information as a function of the 
number of past symbols considered is graphed below in Fig. 45 for the map of 
Fig. 43. 

‘n+ll 0 



Fig. 45 - Predictability of the next single symbol 
given a history of length n. 


This curve is simply related to the slope of the total entropy curve through the 

I(s|S n ) - H(s) - H(slS n ) 

Note the extremely slow convergence of this curve, even after we take a 
history of twenty symbols, there is more predictability to be gained by looking 
back to the twenty-first. This is because our model is noise-free, with corre¬ 
lations extending over indefinite lengths. The tangent to the asymptotic curve 
becomes, at least, difficult to compute. As we will see in the next section, 
real data behaves more reasonably. 

An upper limit to the predictability of s^^ obtainable with any amount of 

knowledge of the past is supplied by the value I(y'|x), or I(s ,, lx ). This is 

n*T*i n 

the predictability of the next single symbol s^^ given knowledge of the com¬ 
plete continuous variable X n , clearly the knowledge resulting from a set of past 
measured symbols can only approach this value. Knowledge of X^ reduces the next 
symbol s^^ to a certainty, that is, H(y^|x) * 0, and I(y^|x) * I(y^lx^) because 
I(x"|x) is infinite, and P(y^lx^) is a projection. However, the entropy of the 
deterministic map Itself remains finite, here H(x^[x) ** .512 bit. 

A second and stricter limit on the predictability of a series of coarse 

measurements is obtained by considering the value I(x"|y), or I(X ,, Is ), indi- 

nrrl n 

cated by the dotted line in Fig. 45. This is the amount of information an 
observer with full ability to observe the complete continuous variable gains 
when he learns which side of the interval the variable originated from, 
corresponding to the upper-right of Fig. 44. This quantity limits the increase 
in our ability to predict the future symbols s^_^, ••• given the single 

symbol s^, that is, the average increase in our ability to predict the future we 
obtain with each new measurement. 


Note that although I(x^|y) and I(y"|x) describe the information stored 
along symmetric paths in Fig. 39, they are not equal. The measurement process 
has introduced a time asymmetry into an autonomous system. In this case, a sin¬ 
gle measurement gives us more information about the past system state than the 
future. Because the system is presumed to be autonomous, any number stream 
derived from it by measurement must be equally predictable forward and backward 
in time, that is, I(y^ly) must equal I(yly^), and likewise for higher order com¬ 
binations of symbols. But once we assume a model P(x~|x), in this case expli¬ 
citly nopinvertable, a single measurement may allow us to infer more about the 
state of the model in one time direction. 

For the case of the noise-free one-dimensional map of Fig. 43a, the com¬ 
plete state of the continuous variable can be determined by considering the 
sequence of the coarsely measured variable s^, e rt ^i* etc * it: generates. This 
however is a very special case, and is only possible because i) the system is 
"perfectly deterministic”, with an infinite storage capacity, thus the present 
state of the variable X determines all future states of the system, and ii) the 
partition dividing the interval is in exactly the right place to allow a one- 
to-one mapping between points on the continuous variable X and symbol histories. 

We will now study the effect of moving the partition from x - .5, changing 
the projection of the system onto the observed variables y and y'. Figure 46 
demonstrates that the effect depends on which informational measure is being 
considered. H(s), the simple one-symbol entropy ignoring symbol to symbol 
correlations, is a maximum at the partition position which makes the two possi¬ 
ble symbol values equiprobable. H(s|s n ), the "surprise” of the next symbol 
given a symbol history, is a maximum at x« .5, the peak of the map. Its value, 
in a noise-free model, is limited by the deterministic entropy of the underlying 
map. I(s|S n ), the predictability of the next symbol given a symbol history, has 



Figo 46 -The top left panel shows the attracting part of the graph 
for the map x" = 3.7x(l - x). The identity point 
of the map is the point in x where the identity line crosses 
the graph. In the other panels appear the single symbol 
entropy, the entropy of a symbol given its history, and 
the predictability of a symbol given its history, all as a 
function of partition position. A history of 15 symbols 
was used in these calculations. 


a sharp maximum at the identity point of the map, x * x', here simple 
alternating sequences like 010101 are most likely* 

The lesson of Fig* 46 seems to be that, if we have to perform a measurement 
with constraints, the "best" measurement of a system will depend on the use to 
which the system is put. If we want to use the system as a recording device, 
and determine the past states, we may want to optimize the entropy H(y"|y) 
coming from the system. If we want to send signals into the future, and exer¬ 
cise control over future measurements, we will maximize I(y'ly). An extreme 
example of the difference between these two measures is given by the full two- 
onto-one parabolic map x^ » 4x(l-x), or by the binary shift map of Fig. 21, with 
the measurement partition at x = .5 in both cases. Here the entropy generated 
by the map, H(x'|x), becomes equal to one bit per iteration, which is the max¬ 
imum measurement channel rate I(y^fx^). Thus no predictability of the future is 
possible, yet an initial condition in the continuous variable x can be read off 
to arbitrary accuracy if we wait long enough. 

The considerations guiding optimal partitions in one dimension seem 
straightforward, but even in two dimensions the situation is unclear [34]. 

Also, the problem of fuzzy partition boundaries, or noise in the measurement 
channel, seems open. The whole question of the matching of a measurement 
channel to a particular system seems largely unexamined in a physics context. 
"Here be dragons", and a lot of interesting geometry. 

18 * Example from the data 

The effect of a coarse two-symbol measurement on the stored information and 
entropy will now be illustrated, for the "canonical" data of Fig. 30. As must 
be clear by now, this data set was chosen as an example due to its similarity to 
the one-dimensional map with noise added of Fig. 4. 


The stored information associated with the "true” dynamic PCT^^lT^) is 
1.80 bits, as was shown in section 14. The maximum capacity of the observation 
channel is only one bit, leaving us to suspect that the coarse measurement is 
not ''complete", that is, the quantity I(y^ly) can be improved on by considering 
the past history of the primed variables. 

Taking a cue from Fig. 43a, we define the two symbols of the coarse meas¬ 
urement by placing the partition at the peak of the distribution. Fig. 47 below 
shows that there is indeed a slight convexity in the total entropy curve as the 
number of past symbols is increased. Here, though, the curve reaches an asymp¬ 
totic slope after only a few past symbols are taken into account. This is 
understandable in view of Fig. 31, the stored information associated with the 
"true" dynamic P(x'|x) falls to near zero after only 4 iterations. There is no 
point in looking farther back into the past than this, the system state at that 
point is causally disconnected from the present state. 


H(S n ) 



Fig. 47 - Entropy as a function of number of symbols of the "fuzzy 
hump" data set, for a partition taken through the peak 
of the hump. The asymptotic slope is maximal for this 
partition, but not the y intercept. 



Fig. 48 - Figure corresponding to Fig. 46, for real data. Note 
the general correspondence In form of the various 
informational measures. A history of 7 symbols was used, 
longer than the correlation length of about 4 symbols. 


The effect of varying the partition position is shown in Fig. 48. The 
predictability and surprise of the next symbol given a past symbol history vary 
pretty much as in the deterministic parabolic map of Fig. 46. Apparently, some 
of the geometrical considerations governing the selection of optimum partitions 
carry over from the deterministic to the noisy case. 

The reduction in predictability resulting from the coarse measurement is 
quantified by comparing the stored information of our best idea of the "true" 
variables, I(T n+ ^iT^), with the total predictability of the future symbol 
string given the past symbol history, ICs^^s^^* • • • ^n^n-l* * # Because 
correlations extend only over a few measurements, a calculation of the latter 
quantity is feasible, and is shown as a function of partition position in Fig. 
49. The optimum position for best total predictability is, as for the best 
prediction of the single next symbol, somewhere near the identity. This best 
value is about .7 bits, thus the reduction due to the coarse resolution of the 
measurement is at least 1.8 - .7 = 1.1 bits. 

Fig. 49 - Total stored information of the data string as a function 
of partition position. This figure corresponds to the 
movement of the y intercept in Fig. 47 as the partition 
is varied. 


We have demonstrated, at perhaps excessive length, methods of characteriz¬ 
ing data originating from a chaotic system, with a stochastic noise element, 
passing through a limited measurement channel* The next section mentions some 
of the difficulties in applying the methods presented here* 

19 . Limitations of this modeling procedure 

The treatment of experimental data discussed in this paper can be summar¬ 
ized as follows* First, an appropriate set of variables x are selected, and 
their values discretized into a finite number of possibilities* Next, the tran¬ 
sition matrix P(x^|x) is constructed by taking a large quantity of data and 
accumulating the individual matrix elements as histograms. Finally, numerical 
measures such as the stored information are computed, to characterize the pred¬ 
ictability which this empirically constructed model enables* 

An immediate practical difficulty with this procedure is: how do we know 
that we have selected an "appropriate" set of variables to describe the state of 
the system? For example, the number of observed variables might be less than 
the actual "dimensionality" of the system. Measurements will then be "incom¬ 
plete" in the sense discussed in section 16, and a reconstruction process will 
be required to generate a more complete characterization of the system state. 

Even the question of a practical definition of the "dimension" of a dynami¬ 
cal system is a research problem, this question is briefly discussed in appendix 
2. Low-dimensional model systems, as well as experimental data presented here, 
can have complicated geometrical structure, with what corresponds to an intui¬ 
tive notion of "dimension" varying locally. The selection of variables in this 
situation clearly requires the judgement of the observer, implying the prior 
existence of a model of a more general sort in his mind. 


The computation of the stored information associated with a model P(x^|x) 
amounts to a simple counting procedure, all the dimensionality and topology of 
the system is projected down to a single number. Much more than this will be 
required to describe even the gross qualitative geometric features of a system 
dynamic. In fact, the predictability of the immediate future may vary radically 
from point to point on the system attractor, this property would not be 
reflected in the average quantities we have discussed here [35]. Nevertheless, 
the stored information will be very useful as it does provide an objective meas¬ 
ure of the improvement of an observation; as the model or the measuring instru¬ 
ment is improved, the numerical value of the stored information will increase. 

Another serious practical problem is the amount of data required to con¬ 
struct a good model. The dynamic transition probabilities P(x"|x) are a physi¬ 
cal property of the autonomous system which can only be approximated by a data 
string of finite length. If there is not enough data, statistical fluctuations 
in the histogram approximation to P(x^|x) will add false structure to the model, 
resulting in an artificially high value of the stored information. 

The approach to the data taken in this paper was to use histogram bins wide 
enough to get good statistics, but narrow enough to be below the noise level, 
and thus capture all the deterministic dynamics. The data records taken so far, 
typically of lengths of four to eight thousand drop intervals, are in fact only 
marginally able to satisfy both these conditions simultaneously, even for the 
simple attractor used as the main example. Also, the noise level below which no 
causal structure exists is to an extent an assu m ption , a judgement on the part 
of the experimenter. 

The accumulation of histograms is actually often a poor way to measure a 
physical probability. Statistical convergence is slow, proceeding as the square 
root of the number of samples, and the sheer size of the transition matrix for a 


high-dimensional system renders the histogram method completely infeasible. Yet 
there must exist more efficient methods for constructing models, or representing 
expectations, as demonstrated by the simple fact of our ability to function suc¬ 
cessfully in a multidimensional world. 

Bayesian methods may aid in treating some of the problems of insufficient 
statistics. Here typically the physical probability distribution is estimated 
on the basis of a limited amount of data, using certain assumptions about some 
properties of the distribution to be measured. The consistency of the observed 
data with these assumptions is checked, and the assumed prior properties of the 
distribution can then be modified to provide a better fit with succeeding data. 
The problem of estimating the size of a space of N states from limited data pro¬ 
vides a simple example of this approach. If a random process selects out the 
states at a rate of one per time unit, we would have to wait a time on the order 
of N log N to see all the states. But duplications in observed states will 
start to occur at times on the order of the square root of N, and this can be 
the basis of an estimate of the size of N. This situation is analogous to the 
"birthday problem": given a room full of people, how many have the same birth¬ 
day? See work by Erber[36], Ma[37], and others. To improve our ability to con¬ 
struct models with limited data, further research is indicated into describing 
the expected properties of the probability distributions of various sorts of 
systems, and the effects of insufficient statistics on entropy-like measures. 

Some studies of this sort are underway[38]. 


20• More general models , hov " good " Is £ model ? 

There is a large conceptual difference between the simple physical model 
described in section 5 and the histograms accumulated from the data. The latter 
is just a representation of the data, every transition is explicitly described 
(although as mentioned above some thought enters into the very act of setting up 
the measurement channel). The data histograms in a sense can't be wrong, but 
despite their exhaustive description of the data they in themselves have no 
explanative value. 

Physical models such as the mass-spring example are not constructed 
directly from the data, but rather by analogy. They consist of algorithms to 
predict transition matrix entries, in the absence of the data. There are two 
considerable advantages of this sort of model over the complete transition 
matrix. First it is much more concise, only desired matrix elements need be 
actually generated. Second because it was generated by an analogy, it often 
suggests generalizations beyond the particular data, allowing for example pred¬ 
ictions about the effect of varying system parameters. 

The raw histograms, despite their limitations, have the nice feature that 
there exists a measure of quality, the stored information, which is independent 
of external auxiliary measures such as "utility functions". This measure is a 
property of any string of numbers derived from a time-invariant process. But 
once a model of the second type has been constructed by the observer, there are 
two streams of numbers produced, one by the model and one by the reality, and a 
measure of the "goodness" of the model will be some sort of "distance" between 
the two number strings (Fig. 50). 

The value of a model in the real world depends upon its use, and thus in 
general rests on an external evaluation function. Shannon, in the context of 


communication theory, introduces a "fidelity criterion” of fairly general form 
to measure a distance between an input and output number string. This measure, 
dependent on the value to the user of a particular degree of accuracy in channel 
transmission, determines an effective rate of transmission through a noisy chan¬ 
nel. In the biological world, the effective "evaluation function” judging the 
utility of a model is often severely binary. A fox and a hare carry with them a 
rather detailed model of each other^s behavior, yet in their interaction all the 
subtlety is collapsed to two outcomes, the hare is eaten or the fox goes hungry. 

If one is willing to simply count distinguishable states and transitions 
between them, without judging the value of particular items of information, and 
if a large amount of data is available, a "distance" between model and observed 
data strings can be defined, independent of an evaluation function. This can be 
done by computing the predictability of one string given the other. Typically a 
physically observed initial condition is supplied to the model, and the subse¬ 
quent physical and model data streams compared. A certain number of digits will 
correspond between the two number streams, and a certain number will differ, see 
Fig. 51. The difference between the two number streams is, to the observer, an 
unpredictable element , and thus represents a source of information . For a noisy 
system (or model), the amount of information so generated will diverge for meas¬ 
urements of arbitrary precision, as discussed earlier. However, the average 
number of matching digits, and the average rate of loss of matching digits as 
predictions farther into the future are attempted, can be recorded. It is 
unclear to the writer whether such measures are nicely coordinate independent, 
but they seem operationally well-defined in a given setting, and capable of 
measuring improvement of a model. An improved model will result in a lower rate 
of divergence between the two number strings, less "surprise" per unit time to 
an observer making continuous measurements at fixed resolution. 










These ideas have already appeared in practical use. In a recent talk[40], 
Cecil Leith discussed a large computer model used for weather prediction. An 
observed weather pattern is given to the model as an initial condition, and the 
evolution of the model and the actual weather system compared. The exponential 
rate of divergence of the two paths through the space of variables is computed, 
and used to indicate improvement in the model. 

It should be noted that predictive errors of fixed constants, or fixed 
drift rates do not reduce the "goodness" of a model under this sort of measure. 
Eclipse predictions which are always late by exactly twenty-four hours are of 
equal value as correct predictions, once the fixed delay has been recognized. 

In principle, a "table of corrections" can be prepared over a short time to com¬ 
pensate for fixed errors, and linear and even polynomial tendencies for the two 
strings to drift apart. Only exponential rates of separation will give finite 

"observed data" 









Fig. 51 


contributions to the logarithmic measures of information. 


A second sort of measure of the "goodness” of a model arises from con¬ 
siderations of the conciseness of a model. To actually generate a prediction 
from a model requires a computation, and one can give a length in bits for a 
computer program to implement the calculation. Or one can measure the length of 
the message which communicates the model to another person. If the same "dis¬ 
tance” under some measure between model and reality can be achieved with a 
shorter model, we usually feel that the shorter model is "better". These ideas 
have been developed in the field known as "algorithmic information theory", by 
Kolmogorov, Chaitin, and others. A very readable introduction is Chaitin's 
Scientific American article[41], as well as Joe Ford^s article, "How Random is 
a Coin Toss?"[42]. Chaitin and Kolmogorov define the "randomness" of a number 
string to be the length of the computer program required to generate it. A 
"completely random" number can only be specified by explicitly listing each 
digit, thus the corresponding computer program must be of at least equal length. 
The asymptotic ratio of program length to output length will be one "bit per 
bit" for a completely random number, and zero for a completely determined number 
string, e.g. 1010101010... . 

A highly concise model is useless if says nothing about the real data 
string it is supposed to predict. Thus a reasonable measure of the "goodness" 
of a model should compare the length of the model to the predictability it 
enables. For example, one might measure the total size of the body of informa¬ 
tion comprising the model, including the length of the algorithm, the size of 
any "table of corrections", and the amount of initial data used, and compare it 
to the total predictability it enables, that is, the total number of nonredun- 
dant "matching digits" integrated over the future. 

The chief limitation of the modeling schemes described in this paper, and 
indeed of all of information theory as it presently exists, is the limitation to 


autonomous systems. In any hydrodynamic system of reasonable size, as well as 
In dally life, the space of possibilities is so huge that all that can be 
observed are unique events * Only a tiny fraction of the possible distinguish¬ 
able states are visited in a reasonable time, and the accumulation of average 
transition rates between individual states is out of the question. But we know 
that our brains can organize experience, and represent expectations about real¬ 
ity in a way that allows considerable prediction. Despite our constant use of 
this facility, we have virtually no exterior understanding of it, as demon¬ 
strated by our inability to incorporate even a rudimentary form of this process 
in any man-made machine. 

A "good model" in physics participates in this unknown interior organizing 
process, and is said to be explanative . At this point a "good explanation" can 
only be defined in terms of the subjective feeling of satisfaction it produces, 
but before subjective sensation is deemed out of place in a discussion of the 
construction of models, it should be remembered that such feelings or instincts 
guide all creative work, scientific or otherwise. A model can have "explanative 
value" even when it has no predictive power, the outstanding example being the 
theory of evolution. 

21 . The undecldabllity of optimum modeling 

As a model is improved, the discrepancy between its predictions and data 
from the system under study becomes less and less. Under an older view of 
things, this process could be continued indefinitely; successive improvements to 
the model could bring the two number strings into coincidence to as great an 
accuracy as desired. However, if the given system is chaotic, a minimum rate of 
divergence between the two number strings is guaranteed. Even if the model were 
an exact physical duplicate of the original system, and was set to the same ini¬ 
tial condition, the two would diverge at a rate given by the entropy of the 


system. In this situation it becomes reasonable to ask, how are we to know when 
we have reached this intrinsic limit to predictability? How are we to know when 
we have constructed the optimum model? 

A similar question arises in an observational context. It is a common 
experience for those of us who conduct physical experiments that data improves 
over time, often for no apparent reason. For example, the faucet which produced 
the data of Fig. 52a below might, several weeks later, yield the data of Fig. 

52b at the same flow rate. The improvement in the data is quantifiable as a 
measured increase in the information storage capacity of the system, computed as 
described in this paper. How are we to know that further improvements in equip¬ 
ment or technique will not result in even better data, and increased predicta¬ 
bility of the system? 

Fig. 52 

The purpose of this section is to make the fairly obvious point that we 
don't. Furthermore, it will be argued, the question of whether a model is 
optimum is undecidable In the sense of Godel. 


From a classical perspective, It Is clear that an experimenter's claim that 
he Is "In the noise", and that there Is no point In attempting to Increase reso¬ 
lution, Is an assumption. If the observation rate is less than the entropy of 
the system at some resolution level, the system will appear stochastic, even If 
It were "completely deterministic". Boltzmann recognized the logical necessity 
for this sort of assumption. It appears as an "assumption of molecular chaos" In 
his treatment of the kinetic theory of gases. In the more modem view this 
assumption becomes labeled a fact of Nature, and the experimenter will point to 
"thermal kT noise", or even "basic quantum mechanical indeterminacy" as justifi¬ 
cation for not expending more effort on resolution. Still, the question of 
ultimate resolution remains a practical one for chaotic systems with measured 
noise levels far above the kT level, as Is probably the case for the water drop 
system, and certainly for weather systems. 

The concept of "undecidability" has been developed in the context of 
discrete state logical systems and computing machines, and has not, to my 
knowledge, been generalized to continuous systems or systems with a noise ele¬ 
ment. Nevertheless let us usurp some vocabulary and results for use In dynami¬ 
cal systems theory, noting that a large discrete state system can accurately 
mimic the behavior of a continuous system, and that the addition of noise to a 
system is unlikely to decrease any "undecidability" it might have. 

The discrepancy between the predictions of a model and observations of a 
physical system is, to the observer, a random element. If the model embodies 
the observer's complete knowledge of a system (including perhaps the addition of 
a noise element), he will be unable to distinguish the two resulting number 
streams, should he get them mixed up. A "proof" that the model was the best 
possible would involve a proof that the random element, or discrepancy, was 
intrinsic to the system, or "truly random", not susceptable to any further 


deterministic description. However, as Chaitin and others argue in the context 
of the theory of algorithms, one cannot ‘prove” a number to be random, to pos¬ 
sess such a proof would imply a logical contradiction. Thus, given a set of 
seemingly random numbers, there is no way in general to show that there does not 
exist an algorithm shorter than the list itself to generate the numbers, and 
thus reduce the randomness. 

This result makes a certain amount of intuitive sense. A "proof" is a 
manipulation of the fixed axioms of a closed logical system, whereas "random 
numbers" are exactly those entities which come from outside the logical system, 
that is, cannot be generated within the system. To "prove" the number random, 
the system would have to recognize the number as coming from outside. As there 
is no representation of the number inside the system, the comparison required by 
a recognition process cannot occur. 

The process of divining a deterministic aspect to a string of data, thus 
reducing its randomness, might be likened to "breaking a code". Man-made codes, 
where the apparent randomness is deliberately maximized, illustrate difficulties 
which in principle can arise in attempts to model natural phenomena. For example, 
a type of cipher called an "open key" code has been studied recently[43]. Here, 
even when the algorithm for generating encrypted text is known, the text cannot 
be decoded by a simple algorithm (it is believed). Only an exhaustive search of 
impractical length can find the determinism and reduce the randomness of the 
text string. So we can at least Imagine a situation where even if we had 
guessed a correct model for a physical process, we could not demonstrate that 
the observed data came from that process. 

It is on this basis that one might argue that a description of theoretical 
physics in terms of particles and interactions between them can only be partial. 
Even if one finds the elemental rules, determining their qualitative 


consequences on a macroscopic scale might require a computation of indefinite 
length, with a non-unique outcome. Oh, well. Maybe Godel's theorem has nothing 
to do with chaos, and there is only a desert where these two questions meet. 

Then again, study here might address one of the larger Issues; what is the limit 
to man's ability to predict and thus control his own evolution? 

22 . Conclusion , " ideas " as models 

This paper has presented, by means of a physical example, preliminary work 
on the problem of predictability in the presence of noise. Computation of the 
stored information and rate of loss of stored information for data generated by 
the water drop system provided examples of the usefulness of these statistics to 
describe a degree of predictability, and to distinguish a difference between 
unpredictability arising from deterministic and stochastic sources. A more gen¬ 
eral discussion of the problem of modeling was attempted. 

The limitations of the procedures illustrated above point out the nearly 
total lack of understanding of the neural modeling system heired to each of us 
as our evolutionary birthright. Even an ant possesses capabilities which we are 
unable to incorporate into our machines. Any general theory of modeling must 
explain our ability to move easily through a high dimensional world, and, above 
all, to generate new ideas. Incremental learning, which occurs for example in 
acquiring a skill, might be described by some sort of conditional distribution 
which is continually updated in a Bayesian fashion, but an idea, as Drake 
notes[44], seems a more abrupt occasion. An "idea" can be described as the 
sudden construction of a model for sense data. Some kind of connection is real¬ 
ized, often increasing the predictability of the perceived world. 

The occasion of "having an idea" seems a curious combination of the active 
and the passive. One can actively accumulate knowledge, and actively exclude 


from the local environment influences which deaden creative thought, but one 
cannot actively make an idea. Instead, one has an idea, a passive process which 
often involves some amount of waiting around. The delay is probably due to a 
process more akin to a random search than a computation, as the brain"s ordinary 
recall is usually rapid, taking only a few seconds. 

This suggests that there are parts of the brain optimized for making rapid 
searches through a very large space of possibilities, together with a signal to 
the "operator" when a connection has been made. This signal takes the form of a 
pleasant subjective sensation, often described in the psychological literature 
as the "aha" experience. It can even happen that one experiences the sensation 
of an idea about to occur, before the logical content of the idea becomes 

To the extent that we are materialists, we must believe that underlying 
such events is a physical process . How Is it that we can accumulate, and even, 
to use Jim Crutchfield"s phrase, anticipate knowledge? What are the operating 
principles of the dizzying jewel from which the natural world turns back to gaze 
upon itself? Perhaps more can be learned from a study of this mechanism from an 
interior, as well as an exterior perspective. Perhaps there is experimental 
physics still to be done without even getting out of bed. 

Ludwig Boltzmann wrote the entry entitled “model” for the 1902 edition of 
the Encyclopaedia Britannica. Here he states: 

“...Long ago philosophy perceived the essence of our process of thought to 
lie in the fact that we attach to the various real objects around us par¬ 
ticular physical attributes - our concepts - and by means of these try to 
represent the objects to our minds... On this view our thoughts stand to 
things in the same relation as models to the objects they represent. The 
essence of the process is the attachment of one concept having a definite 
content to each thing, but without implying complete similarity between 
thing and thought; for naturally we can know but little of the resemblance 
of our thoughts to the things to which we attach them. What resemblance 
there is lies principally in the nature of the connexion.♦ 


Boltzmann lived too early to learn the physical and topological "nature of the 
connexion". We may be more lucky. 

I hope the reader can forgive the loose tone of some of this discussion, 
and that if it is not science, it is at least entertainment. Perhaps, though, 
contemplation of a dripping faucet can supply a few rungs in the ladder of meta¬ 
phor which must be built to understand modeling in a larger sense. When the 
falling drop is perceived, it falls in the brain as well, outlined in sparkling 
surreal intensity. The world swarms with life forms, many bearing an individual 
neural net. Each thin web is capable of directing a complex entity, and in each 
is mirrored the passion of the world. Yet this instrument sprang from the slime! 

Finally, I hope this work suggests that one does not need a particle 
accelerator to step to the frontiers of physics. The tapestry of reality 
flutters with moving causal structures, both preserving and generating informa¬ 
tion at all length scales. This brew of freedom and constraint generates a 
complexity that renders a simple faucet an enigma, let alone consciousness and 
evolution. Thus even if the particle physicists were to succeed, and all the 
laws governing the fundamental building blocks of matter were known, we would 
still walk down the street without knowing how or why. The mystery remains, 
as always, as close as our forehead. 

Acknowledgement s 

This work was made possible by the support and understanding of the 
Research Committee and others at the University of California, Santa Cruz. 
Special thanks are due to Jim Warner for hardware and software advice. 


Appendix 1_ - Continuous time calculation 

Section 12 describes the computation of the predictability of a time 

interval T obtained from a measured time interval T , given the conditional 
n+l n 

distribution p ( T n+ ^l T n ) an< * the equilibrium distribution of intervals P(T). In 
fact, although the water drop system produces data in the form of discrete time 
intervals, these intervals are imbedded in continuous time. A complete descrip¬ 
tion of the measureable state of the system would include the time elapsed since 
the preceding drop. The additional phase degree of freedom increases the 
storage capacity of the system* This larger amount of stored information can be 
computed from the same data set used to compute the interval-to-interval stored 





The figure above schematically represents the situation at a particular 
instant of time, indicated by the dotted line, during a sequence of drop inter¬ 
vals. The time elapsed since the preceding drop is , the time until the next 
is t 2 * Knowledge of the state of the system requires knowledge of the length of 
some drop interval, and its phase. For an observer at the dotted line to be 
informed of the system state, he must be told the numbers T^ and t^. In the 
absence of prior observation, he will have to wait until the end of the interval 
T^ before a system state is determined. His a priori expectation of observing a 
pair of numbers t^ and T^ is described by a distribution P(t 2 ,T^) which, by time 
symmetry, is the same as the distribution of prior values 


The stored information is defined in this paper to be the value of the 
knowledge of the immediate past state in determining the immediate future state. 
Thus, we must compute the value of the knowledge of the numbers T^ and t^ in 
predicting t^ and T^• These four numbers are connected by a conditional distri¬ 
bution P( t 2 » T 31 t i t an< * full stored information is given by the expres¬ 




Now in this case, we do not need to compute the full four-dimensional distribu¬ 
tion, because 

^VVW " I(t 2 ,t l* T l ) 

This can be shown algebraically, using P(T 3 1tj, t 2 » T 1 ) ■ PCTjIt^tp, or appreci- 
ated by noting that the predictability of T^ is completely determined by 
T^ ** t^ + t^* Thus there is no additional stored information associated with 
higher order distributions involving T^. So the full stored information simpli¬ 
fies to 


p(t,l t,T.) 


Jit JUT, 

The problem then is to express P^,^), P(t 2 > and P(t 2 lt 1> T 1 ) in terms of the 

measured distributions P(T) and P (T |T ). The subscript has been added to 

o n+i n 

P(T |T ) to distinguish this particular measured distribution in what follows, 
n+i n 

First, we will determine the equilibrium distribution PCt^T^). Now 

p(t, t.) = Pam pcx) 



f P,(T,1I) 

' l T > 

This last step is made clear by examining the figure below 

U T 
t> T 

If we divide T at random, all subintervals from zero up to the full length of T 
are equiprobable• By a similar argument. 



The remaining step is to observe that 

P(t 2 | ti > T i) - P 0 (t 1 +t 2 |T 1 ) 

Thus all parts of the full continuous information integral can be reduced to 
sums over the measured distributions, as claimed* This integral is approximated 
by a discrete sum via a binning procedure as before. 


In the case where the variation in drop Intervals is only a small fraction 
of the average drop interval, as is the case in the "fuzzy hump" data set, the 
computation of the stored information simplifies further. 



As the above figures suggest, P(t) is well approximated by a constant. Also, 

P(t,ro ~ pm 

again because of the small relative variation in T. The two approximations 

were used in the full stored information calculation illustrated in Fig. 32. 


Appendix 2_ - The dimension question 

The central idea behind the Lorenz, Ruelle-Takens picture of turbulence is 
that the random element of a turbulent flow can be described, at least in some 
limited situations, by a model with only a few "dimensions", or "degrees of 
freedom”. It is clear that a large amount of information is required to 
describe the state of a fluid system in "fully developed" turbulence, but the 
experimental evidence indicates that near the transition to aperiodic behavior, 
a "low-dimensional" model often adequately describes the behavior. But how does 
one define the "dimensionality", "number of modes", or "degrees of freedom" of a 
physical system? Although the concept of "dimension" is intuitive, operational 
definitions are less clear. The dimension of a mathematical model is defined in 
its construction, but in a physical system, the "dimension" becomes a quantity 
to be determined by observation. One approach, employed by a number of workers, 
is to examine the dimensionality of an attractor in a state space reconstructed 
from the time series data of a physical experiment. For example, if some system 
is undergoing a stable oscillation, its motion can be represented by a limit 
cycle in an appropriate state space, a one-dimensional closed curve. 

There has been considerable recent work in an effort to extract statistical 
measures from experimental data, and relate them to formally defined measures of 
dimension. A review cannot be attempted here, suggested references are [45], 
especially the article of Farmer, Ott, and Yorke. Here will be given only a 
beginner's dimension calculation for some water drop data, a short general dis¬ 
cussion, and a few criticisms and suggestions for future experiments. The 
writer has benefited from discussions with some of the principles, including 
Doyne Farmer, John Guckenheimer, and Jim Yorke. 


The dimension of an object should describe how its volume scales with 
increasing linear size. Proceeding informally, let's think of an attractor as a 
compact object with a "volume" of N equiprobable distinguishable states, local¬ 
ized in state space within a region of radius R. If the "radius” of a single 
distinguishable state is r, then the dimension of the region containing N states 
is given by: 

N - (R/r) d . 

Taking the log of this relation, we have: 
log(N) - d log(R/r). 

The left-hand side is simply the stored information, or number of bits resolv¬ 
able on the attractor, while log(R/r) Is the resolution obtainable from a meas¬ 
urement along a single linear attractor coordinate. Thus the dimension of the 
attractor, roughly speaking, is the minimum number of independent measurements 
required to localize a single system state. If our view of the attractor is 
limited by the measurement, that is, the stored information intrinsic to the 
attractor is much larger than the measurement channel rate, or in the notation 
of this paper: 

I(x'|x) » I(x|y) 

then we can expect an improved total log(N) as measurements are improved. The 
dimension d could then be independent of resolution over some range, and its 
value would indicate the ratio of increased predictability to increased resolu¬ 
tion (as pointed out by Farmer et al). 


The number of states "nearby” a particular state should be a function of 
the dimension of an attractor, and this provides the basis for most of the 
dimension calculations. Typically, the number of states within some length L is 
determined as a function of L, N(L) - L d where d is the dimension. This 
approach will now be used to attempt a "dimension" measurement for three sets of 
water drop data. The algorithm below was chosen on the basis of simplicity, it 
is I believe closest to the "correlation exponent” of Grassberger and Procaccia, 
and the "pointwise dimension" of Farmer et al, and Guckenheimer[46]. 

First, a point is picked at random out of the data and its "distance” from 

all other data points along the one-dimensional drop interval coordinate deter¬ 
mined. The resulting list of distances is then sorted in order of distance to 

nearest neighbor, distance to next-nearest neighbor, etc. This process is 

repeated for a number of other randomly selected data points, then a mean dis¬ 
tance Co nearest neighbor, next-nearest neighbor, etc. is computed. This dis¬ 
tribution of distances is inverted, and plotted as log(N) vs. log(L), where N is 
the number of data points within a distance L [47]. 

Next the data is taken in pairs of successive points, and distances to all 
successive pairs in the resulting two-dimensional state space computed using, 
for example, a standard euclidian metric. Mean distances to nearest neighbor, 
next-nearest neighbor and so forth are computed as before, and log(N) vs. log(L) 

Successive curves are generated by taking the data in successive triplets, 
4^s, 5's t and 6's. Each step corresponds to increasing the dimension of the 
state space by one. In principle the observed dimensionality of the attractor 
generated by the data should also increase, until the dimensionality of the 
imbedding space is larger than the actual dimensionality of the attractor. Once 
there is room enough in the imbedding space for the full attractor, the observed 


attractor dimension should arrive at is "true" value. Thus, in theory, all one 
has to do is measure the slope of successive curves, when it stops changing as a 
function of imbedding dimension, one has determined the dimension of the physi¬ 
cal system. 

In practice, things are not so simple. Figures A1 - A3 below displays the 
results of applying the above program to three data sets. For the first data 
set, the dimension calculation seems rather successful. The attractor of this 
water drop regime seems to be a gauzy assemblage of two-dimensional ribbons and 
sheets, if the data is viewed as a stereo plot. The calculated curves become 
more or less parallel after an imbedding dimension of two is reached, and a fit 
to the slope of the later curves near their center yields a value surprisingly 
(to the writer) close to two. However, the second data set demonstrates the 
problems which quickly arise if the probability distribution on the attractor is 
not particularly uniform. This set is generated by a somewhat noisy period-two 
drop regime. The purely stochastic part of the data generates dimension curves 
whose slopes continuously increase with imbedding dimension, as expected. But 
there is a radical break in the curves at length scales corresponding to the 
distance between the two periodic points. 

One might learn to usefully interpret a set of curves such as these, but 
the more usual situation seems to be exemplified by the third data set. To the 
eye, the attractor seems simple enough, it's a bent one-dimensional string with 
a little noise on it. The calculated dimension curves, however, seem rather 
uninformative, there is no clear place to fit a slope, and no apparent way to 
see that these curves were generated by a quasi one-dimensional object. 


The good news is that sets of dimension curves, even if not particularly 
useful, are apparently coordinate-independent. The writer tried three different 
metrics in computing "distances" in the embedding spaces: euclidian, sup norm, 
and the "manhattan" metric (sum of absolute values of coordinate differences). 
The dimension curves were translated horizontally in the figures by varying 
amounts depending on metric, but their general form even in detail was well- 

Armed with this limited experience, we will now present a few criticisms of 
the dimension industry, as it currently operates. Implicit in most of the 
recent work on dimension is the assumption that there in fact exists a single 
number which characterizes the dimension of a chaotic attractor. Discussions 
often begin by considering Cantor sets, and other self-similar "fractal" 
objects, where the "dimension” describes scaling relationships as finer and 
finer length scales are considered. In a purely deterministic treatment, 
chaotic attractors possess such a Cantor set structure; as the information 
storage capacity is Infinite, the complete past and future history of the dynam¬ 
ics is determined by the infinitesimal structure to be found at any part of the 
attractor. Thus a dimension calculation using any part of the attractor would 
yield the same number. 

But the base length scale which all physical systems, including digital 
computers, possess limits the degree of structure the underlying attractor can 
have, and likewise limits the history it can embody to a finite and computable 
length of time. So It is to be expected that the "dimension" will usually vary 
across the attractor, if by "dimension" we mean the number of coordinates 
required to maximally localize a state. As a simple example, consider a system 
which in its Poincar€ section periodically changes from a fixed point to a limit 
cycle and back again: 


Each time the limit cycle shrinks to a “point” beneath the noise length scale, 
its phase is lost, and each time it reemerges, its phase is a random variable, 
determined by the noise. Thus two coordinates are required to achieve maximum 
predictabilty in the lower part of the figure, and only one in the upper. 

Objects such as this whose actual structure is supported by noise form an 
interesting new class, see recent work by Farmer[35] and Deissler[48]. 

The usefulness of a “dimension" number as a scaling exponent depends on the 
number of orders of magnitude between the smallest and largest length scales. 
This range is in fact severely limited for most physical data. The scaling 
structures which have received so much recent theoretical attention are notably 
absent in most of the fluid systems so far examined, due to the limited dynami¬ 
cal range, and high dissipation. The experience in this laboratory is that the 
more low-dimensional structure there is in the reconstructed attractor, the less 
well-defined is a single "dimension" number. 

This is not to say that the set of dimension curves may not have a use as 
indicating an overall character of a data set, much as a power spectra curve 
does. But there seems little reason at this point to prefer one dimension algo¬ 
rithm over another as "more fundamental". 


The most serious limitation of existing "dimension” measurements of physi¬ 
cal systems is their total lack of any consideration of the effect of spatial 
variation in the system. In the work described here, and in all other work of 
which the writer is aware, data is supplied by only a single probe of the fluid, 
and an attractor supposedly characterizing the entire system is obtained by the 
time delay reconstruction method. Even in the best of circumstances, such a 
reconstruction necessarily takes place over an interval of time, and for a sys¬ 
tem with a positive entropy, the causal connection between successive measure¬ 
ments is reduced. In this case, two measurements at different points in the 
fluid will always yield greater predictability than one. This is intuitively 
obvious, and corresponds to an increase in the "rate of measurement”. 

Any discussion of the "dimension” or "degrees of freedom” of a fluid flow 
must address the flow of causality in a spatial sense. Fluid motion is con¬ 
strained on the smallest scales by viscosity, and at the largest, by boundary 
conditions. A fluid moving nonperiodically has managed to gain a degree of 
independence from these constraints. Experiments have indicated that this can 
happen in several different ways. If boundaries are close, compared to the typ¬ 
ical roll or wave length scale, the fluid motion tends to be collective. The 
same attractor is obtained by reconstruction of measurements made at any point 
in the fluid, subject to the resolution limitations discussed above. If the 
boundaries are far apart, dislocations or defects in regular flow patterns tend 
to occur (Donnelly's "turbators"), and the transition to turbulence is more akin 
to the melting of a crystal. At the onset of nonperiodicity in the latter case, 
the space of possible fluid flow patterns is apparently impossibly large, with a 
complicated connectivity, and the path of the fluid system through this state 
space tortuous, even if the entropy, or rate of divergence from any one path is 


It should be possible to distinguish collective coherent nonperiodic fluid 
motion from non-coherent motion using the same stored information statistic dis- 
cussed earlier in this paper* Two measurements taken simultaneously at points 
as close as possible in space will give the same stored information, have the 
same degree of causal connection, as two measurements close in time taken from a 
single probe . Thus the loss of information as it flows through the fluid can be 
measured, at least in an average sense, by mapping the information common to 
both probes as a function of distance* For a collective fluid motion, we would 
expect a rather slow drop-off of the mutual information as two probes are 
separated, with significant correlations remaining throughout the fluid* A 
quicker drop-off might well be associated with certain fluid flow features pass¬ 
ing between the two probes, and might indicate that information transmission 
through such features is reduced- It may also be possible to quantitatively 
characterize more fully developed turbulence, measuring the size of typical 
coherent volumes, as well as the *entropy per unit volume”, or "degrees of free¬ 
dom per unit volume”, or "number of positive characteristic exponents per unit 

Lack of funding has precluded the early experimental test of these ideas* 


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