**THE ‘TSALLIS’ ENTROPY **

In 1988 Tsallis^{[1]} published a study containing an expression for the entropy which differed from the usual one used in statistical mechanics. Previous to this, the Rényi entropy was used as an interpolation formula that connected the Hartley-Boltzmann entropy to the Shannon-Gibbs entropy. Notwithstanding the fact that the Rényi entropy is additive, it lacks many other properties that characterize the Shannon-Gibbs entropy. For example, the Rényi entropy is not subadditive, recursive, nor does it possess the branching and sum properties^{[2]}. The so-called Tsallis entropy fills this gap, while being nonadditive, it has many other properties that resemble the Shannon-Gibbs entropy. It is no wonder then that this entropy fills an important gap.

Yet, it appears odd, to say the least, that information scientists have left such a gaping void in their analysis of entropy functions. A closer analysis of the literature reveals that this is not the case and, indeed, a normalized Tsallis entropy seems to have first appeared in a 1967 by Havrda and Charvat^{[3]} who introduced the normalized ‘Tsallis’ entropy

for a complete set of probabilities,
and parameter q>0, but q ≠ 1. The latter requirement 6 is necessary in
order that (1) possess the fundamental property of the entropy, that is, it
is a concave function. According to Tsallis^{[4]}, only for q>0 is
the entropy, (1), said to be expansible^{[2]} [cf. (6) below].

**PROPERTIES OF ADDITIVE ENTROPY OF DEGREE-q**

The properties used to characterize the entropy are^{[2,5]}.

**Concavity**

where the nonnegative n-tuple, (p) = (p_{1},...., p_{n}), forms a complete probability distribution. For ordinary means, the n-tuple, (x) = (x_{1},...., x_{n}), represents a set of nonnegative numbers which constitute a set independent variables. What constitutes the main difficulty in proving theorems on characterizing entropy functions in information theory is that the ‘independent variables’, (x), are not independent of their ‘weights’, (p)^{[6]}.

Coding theory, derives the functional dependencies in a very elegant way through optimization. The entropies S(x_{i}) represent the costs of encoding a sequence of lengths x_{i}, whose probabilities are p_{i}. Minimizing the mean length associated with the cost function, expressed as the weighted mean of the cost function, gives the optimal codeword lengths x_{i} as functions of their probabilities, p_{i}. Consequently, the entropies that result when the x_{i} are evaluated at their optimal values by expressing them in terms of their probabilities, p_{i}, constitute lower bounds to the mean lengths for the cost function.

**Non-negativity**

**Symmetry**

where, [ ] denotes any arbitrary permutation of the indices on the probabilities. For the entropy, the symmetry property (4) means that it should not depend upon the order in which the outcomes are labelled.

**The sum property**

where,
is a measurable function on ]0, 1[.

**Expansibility**

meaning that the entropy should not change when an outcome of probability zero is added.

**Recursivity of degree-q**

asserting that if a choice is split into two successive choices, the original entropy will be the weighted sum of the individual entropies. Recursivity implies the branching property by requiring at the same time the additivity of the entropy as well as the weighting of the different entropies by their corresponding probabilities^{[7]}.

**Normality**

**Decisivity**

**Additivity of degree-q**

for any two complete sets of probabilities, (p) and (q). As late as 1999, Tsallis^{[4]}
refers to (10) as exhibiting “a property which has apparently never been
focused before and which we shall refer to as the composability property”. Here,
composability means something different than in information theory^{[2]},
in that it “concerns the nontrivial fact that the entropy S(A+B) of a system
composed of two independent subsystems A and B can be calculated from the entropies
S(A) and S(B) of the subsystems, without any need of the microscopic knowledge
about A and B, other than the knowledge of some generic universality class,
herein the nonextensive universality class, represented by the entropic index
q...”^{[4]}.

However, the additive entropy of degree-q, (1), is not the only solution to
the functional equation (10) for q ≠ 1. The average entropy:

also satisfies (10), with the only difference that (1-q)/q replaces the coefficient
in the multiplicative term^{[8]}. Since the weighted mean of degree-q
is homogeneous, the pseudo-additive entropy (11) is a first-order homogeneous
function of (p), S^{A}_{n,q} (λp_{1},... ,λp_{n})
= λS^{A}_{n,q} (p_{1},... , p_{n}). It
can be derived by averaging the same solution to the functional equation
(10), in the case q ≠ 1, as that used to derive the Tsallis entropy,
except with a different exponent and normalizing factor, under the constraint
that the probability distribution is complete^{[9]}. Although the pseudo-additive
entropy (11) lacks the property of recursivity, (7), it is monotonic, continuous
and concave for all positive values of q. Weighted means have been shown to
be measures of the extent of a distribution^{[10,11]} and (11) relates
the entropy to the weighted mean rather than to the more familiar logarithm
of the weighted mean, as in the case of the Shannon and Rényi entropies.

Tsallis, in fact, associates additivity with extensivity in the sense that for independent subsystems

According to Tsallis^{[4]}, superadditivity, q<1, would correspond to superextensivity and subadditivity, q>1, would correspond to subextensivity. According to Callen^{[12]}, extensive parameters have values in a composite system that are equal to the sum of the values in each of the systems. Anything that is not extensive is labelled intensive, although Tsallis would not agree [cf. (30) below]. For instance if we consider black-body radiation in a cavity of volume V, having an internal energy, U and magnify it λ times, the resulting entropy

will be λ times the original entropy, S(U, V), where, σ is the Stefan-Boltzmann
constant. Whereas extensitivity involves magnifying all the extensive variables
by the same proportion, additivity in the sense of being superadditive or subadditive
deals with a subclass of extensive variables, because the condition of extensivity
of the entropy imposes that the determinant formed from the second derivatives
of the entropy vanish^{[13]}. The entropy of black-body radiation, (13),
is extensive yet it is sub-additive in either of the extensive variables. The
property of subadditivity is what Lorentz used to show how interactions lead
to a continual increase in entropy^{[13]}. This is a simple consequence
of Minkowski’s inequality,

where, u = U/V is the energy density. Hence, (sub-or super-) extensivity is something very different from (sub-or super-) additivity.

**Strong additivity of degree-q**

where, q_{ij} is the conditional probability. Strong additivity of degree-q describes the situation in which the sets of outcomes of two experiments are not independent. Additivity of degree-q, (10), follows from strong additivity by setting q_{1k} = q_{2k} =… = q_{mk} = q_{k} and taking (1) into consideration^{[2]}.

A doubly stochastic matrix (q_{ij}), where, m = n, is used in majorization to distribute things, like income, more evenly^{[14]} and this leads to an increase in entropy. For if

and

it follows from the convexity of Ψ= x ln x, or

that

since
We may say that p majorizes q, p>q if and only if (15) holds for some doubly
stochastic matrix (q_{ij})^{[15]}. A more even spread of incomes
increases the entropy. Here we are at the limits of equilibrium thermodynamics
because we are invoking a mechanism for the increase in entropy, which in the
case of incomes means taking from the rich and giving to the poor^{[16]}.
This restricts q in the ‘Tsallis’ entropy to ]0, 1[. Values of q in
]1, 2[ show an opposing tendency of balayage or sweeping out^{[17]}.
Whereas averaging tends to decrease inequality, balayage tends to increase it^{[16]}.

Yet Tsallis^{[4]} refers to processes with q<1, i.e. p^{q}_{i}>p_{i}, as rare events and to q>1, i.e. p^{q}_{i}<p_{i} as frequent events. However, only in the case where, q<1 will the Shannon entropy, (16) be a lower bound to other entropies like, the Rényi entropy

which is the negative logarithm of the weighted mean of p_{i}^{q-1}. The Rényi entropy has the attributes of reducing to the Shannon-Gibbs entropy, (16), in the limit as q→1 and to the Hartley-Boltzmann, entropy

in the case of equal apriori probabilities p_{i} = 1/n. This leads to the property of

**n-maximal**

for any given integer n≥2. The right-hand side of (19) should be a monotonic increasing function of n. As we have seen, the tendency of the entropy to increase as the distribution becomes more uniform is due to the property of concavity (2). Hence, it would appear that processes with q<1 would be compatible with the second law of thermodynamics, rather than being rare exceptions to it!

**Continuity:** The entropy is a continuous function of its n variables.
Small changes in the probability cause correspondingly small changes in the
entropy. Additive entropies of degree-q are small for small probabilities, i.e.,

**CODING THEORY AND ENTROPY FUNCTIONS**

The analogy between coding theory and entropy functions has long been known^{[18]}. If k_{1},...,k_{n} are the lengths of codewords of a uniquely decipherable code with D symbols then the average codeword length

is bounded from below by the Shannon-Gibbs entropy (16) if the logarithm is to the base D. The optimal code-word length is k_{i} = -ln p_{i}, which represents the information content in event E_{i}. When D = 2, p_{i} = 1/2 and contains exactly one bit of information.

Ordinarily, one tries to keep the average codeword length (20) small, but it cannot be made smaller than the Shannon-Gibbs entropy. An economical code has frequently occurring messages with large p_{i} and small k_{i}. Rare messages are those with small p_{i} and large k_{i}. The solution n_{i} = -ln p_{i} has the disadvantage that the codeword length is very great if the probability of the symbol is very small. A better measure of the codeword length would be:

where τ = (1-q)/q, thereby limiting q to the interval [0, 1]. As τ→∞,
the limit of (21) is the largest of the k_{i}, independent of p_{i}.
Therefore, if q is small enough, or tau large enough, the very large k_{i}’s
will contribute very strongly to the average codeword length (21), thus keeping
it from being small even for very small p_{i}. The optimal codeword
length is now:

showing that the Rényi entropy is the lower bound to the average codeword
length (21)^{[19]}. Just as the
are the optimum probabilities for the Shannon-Gibbs entropy, the optimum probabilities
for the Rényi entropy are the so-called escort probabilities,

As p_{i}→0, the optimum value of k_{i} is asymptotic to -q ln p_{i} so that the optimum length is less than ln p_{i} for q<1 and sufficiently small p_{i}. This provides additional support for keeping q within the interval [0,1]^{[17]}.

Although the Rényi entropy is additive it does not have other properties listed above; for instance, it is not recursive and does not have the branching property nor the sum property. It is precisely the ‘Tsallis’ entropy which fills the gap, while not being additive, it has many of the other properties that an entropy should have^{[20]}. Therefore, in many ways the additive entropy of degree-q (1) is closer to the Shannon entropy, (16) than the Rényi entropy is^{[21]}. The so-called additive entropies of degree-q can be written as:

where the function f is a solution to the functional equation:

subject to f(0) = f(1), which was rederived by Curado and Tsallis^{[22]}. The property of additivity of degree-q (10) was referred to them as pseudo-additivity, omitting the original references. What these authors appeared to have missed are the properties of strong additivity, (14) and recursivity of degree-q (7). These properties can be proven by direct calculation using the normalized additive entropy of degree-q, (1). Additive entropies of degree-q≥1 are also subadditive.

Moreover, additive entropies of degree-q satisfy the sum property, (5) where,

Only for q>0 will (24) and consequently (1), be concave since

where, the prime stands for differentiation with respect to p_{i}.
This is contrary to the claim that the additive entropy of degree-q is “extremized
for all values of q"^{[1]}. It can easily be shown that the concavity
property

implies the monotonic increase in the entropy (19). Setting p_{i} =
1/n and using the sum property (5) lead to

showing that S_{n,q}(1/n,... , 1/n) is maximal.

In order to obtain explicit expressions for the probabilities, Tsallis and collaborators maximized their non-normalized entropy

with respect to certain constraints. Taking their cue from Jaynes’^{[23]}
formalism of maximum entropy, (25) was to be maximized with respect to the finite
norm^{[24]}

and the so-called q average of the second moment^{[22]}

The latter condition was introduced because the variance of the distribution did not exist and the weights, (p^{q}), have been referred to as ‘escort’ probabilities [cf. (22) above]. The resulting distribution is almost identical to Student’s distribution

where, (3-q)/(q-1) is the number of degrees of freedom and μ is the Lagrange multiplier for the constraint (26)^{[25]}.

The Gaussian distribution is the only stable law with a finite variance, all the other stable laws have in finite variance. These stable laws have much larger tails than the normal law which is responsible for the infinite nature of their variances. The initial distributions are given by the intensity of small jumps^{[26]}, where the intensity of jumps having the same sign of x and greater than x in absolute value is^{[27]}.

for x>1. For β<1, the generalized random process, which is of a Poisson nature, produces only positive jumps, whose intensity (28) is always increasing. No moments exist and the fact that

where, λ is both positive and real, follows directly from Pσlya’s
theorem: If for each λ, Z(0) = 1, Z (λ)≥0, Z(λ) = Z (-λ),
Z(λ) is decreasing and continuous convex on the right half interval, then
Z(λ) is a generating function^{[28]}. Convexity is easily checked
for 0<α≤1 and it is concluded that Z(λ) is a generating function.
In other words,

exists for a positive argument of the Gamma function and that implies β<1.

This does not hold on the interval 1<β<2, where it makes sense to talk about a compensated sum of jumps, since a finite mean exists. In the limit β = 2, positive and negative jumps about the mean value become equally as probable and the Wiener-Levy process results, which is the normal limit. If one introduces a centering term in the expression, -λx, the same expression for the generating function, (29), is obtained to lowest power in λ, as λ→0 and x →∞, such that their product is finite.

These stable distributions, 0<β<1, (and quasistable ones, 1<β<2,
because the effect of partial compensation of jumps introduces an arbitrary
additive constant) are related to the process of super-diffusion, where the
asymptotic behavior of the generalized Poisson process has independent increments
with intensity^{[28]}. For strictly stable processes, the super-diffusion packet
spreads out faster than the packet of freely moving particles, while a quasi-stable
distribution describes the random walk of a particle with a finite mean velocity.
It was hoped that these tail distributions could be described by an additive
entropy of degree-q, where the degree of additivity would be related to the
exponent of the stable, or quasistable, distribution. Following the lead of
maximum entropy, where the optimal distribution results from maximizing the
entropy with all that is known about the system, the same would hold true for
maximizing the additive entropy of degree-q. However, it was immediately realized
that the variance of the distribution does not exist.

Comparing the derivative of the tail density (28) with (27) identifies β = (3-q)/(q-1), requiring the stable laws to fall in the domain 5/3<q<3^{[24]}. However, it is precisely in the case in which we are ignorant of the variance that the Student distribution is used to replace the normal since it has much fatter tails and only approaches the latter as the number of degrees of freedom increases without limit^{[27]}. Just as the ratio of the difference of the mean of a sample and the mean of the distribution to the standard deviation is distributed normally, the replacement of the standard deviation by its estimator is distributed according to the Student distribution. This distribution (27) was not to be unexpected, because it stands in the same relation to the normal law as the ‘Tsallis’ entropy, (25), is to the shannon entropy in the limit as the number of degrees of freedom is allowed to increase without limit.

Whereas weighted means of order-q

do have physical relevance for different values of q, the so-called q-expectation

has no physical significance for values of q ≠ 1. Since the connection between statistical mechanics and thermodynamics lies in the association of average values with thermodynamic variables, the q-expectations would lead to incorrect averages. This explains why for Tsallis the internal energy of a composite system is not the same as the internal energies of the subsystems and makes the question “if we are willing to consider the nonadditivity of the entropy, why it is so strange to accept the same for the energy?"^{[29]} completely meaningless. Yet, the zeroth law of thermodynamics and the derivation of the Tsallis nonintensive inverse temperature,

where, U_{q} is the q-expectation of the internal energy, rest on the fact that the total energy of the composite system is conserved^{[30]}.

It is as incorrect to speak of ‘Tsallis’ statistics^{[31]} as it would be to talk of Rényi statistics. These expressions are mere interpolation formulas leading to statistically meaningful expressions for the entropy in certain well-defined limits. Whereas for the Rényi entropy the limits q→1 and q→0 give the Shannon-Gibbs and Hartley-Boltzmann entropies, respectively, without assuming equal probabilities, the additive entropy of degree-q reduces to the Shannon entropy in the limit as q→1, but it must further be assumed that the a priori probabilities are equal in order to reduce it to the Hartley-Boltzmann entropy. Hence, only the Rényi entropies are true interpolation formulas.

Either the average of -ln p_{i} leading to the Shannon entropy, or
the negative of the weighted average of p_{i}^{q-1}, resulting
in the Rényi entropy will give the property of additivity^{[2]}.
Whereas the Shannon entropy is the negative of the logarithm of the geometric
mean of the probabilities,

where:

is the geometric mean, the Rényi entropy is the negative of the logarithm
of the weighted mean

where:

is the weighted mean of p_{i}^{q-1}. If the logarithm is to
the base 2, the additive entropies of degree-q are exponentially related to
the Rényi entropies of order-q by

which make it apparent that they cannot be additive. But nonadditivity has nothing to do with nonextensivity.

As a concluding remark it may be of interest to note that undoubtedly the oldest
expression for an additive entropy of degree-2 was introduced by Gini^{[32]}
in 1912, who used it as an index of diversity or inequality. Moreover, generalizations
of additive entropies of degree-q are well-known. It has been claimed that “Tsallis
changed the mathematical form of the definition of entropy and introduced a
new parameter q"^{[33]}. Generalizations that introduce additive entropies
of degree-q+r_{i}-1^{[34]}

with n+1 parameters, should give even better results when it comes to curve fitting.