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Understanding the Ergodic Hypothesis Via Analogies I. INTRODUCTION II

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Understanding the Ergodic Hypothesis Via Analogies I. INTRODUCTION II APGFAPGF Understanding the ergodic hypothesis via analogies Andre R. Cunha∗ Laboratório de Meios Porosos e Propriedades Termofísicas Universidade Federal de Santa Catarina, Campus Universitário - Trindade Florianópolis, Santa Catarina, Brasil - CEP 88040-900 - P.O. Box 476 The Ergodic Hypothesis is a hypothesis in Statistical Mechanics that relates the microscopic motion of parti- cles with the macroscopic average, i. e., the observed property. Despite its importance, didactically its unders- tanding is not easy due to technical issues. Therefore, in this article we propose analogies in order to clarify some important features of the referred hypothesis. Our starting point is the perception that the same macrosco- pic property, i. e., the average of the movement, can be calculated by different procedures. After that, we build Associação de Pós-Graduandos em Física the same average in a more convenient way. We do not have as objectives to contemplate advanced implications of the referred hypothesis. On such cases, some papers will be referenced. I. INTRODUCTION implications. However, no one emphasizes its intuitive mea- ning. Other texts about Kinetic Theory of Gases [7, 8] make In many theoretical contexts, technical sophistication often use of that meaning without justifying or even commenting blurs the meaning and intuition behind a theory. Besides, the about it. Ref. [9] is an exception that introduces EH to con- difficulties that enclosure fundamental questions usually have nect micro- and macroscopic scales. In short, even on those an inhibitor effect on curious eyes. Accepting such truth, text- texts that recognizes EH’s fundamental role, its hard to com- books’ authors follow the tendencies of technical tutorials, prehend of the reasons that take us to adopt it. banning from their pages crucial explanatory steps, which We mention refs. [10–12] as good sources of information, may possibly impair the proper discernment of a hypothe- despite the fact they are high technical level readings. We sis. With that concern in mind, we propose some analogies to may still mention ref. [13] as an example that shows the ac- help explaining an important topic in Statistical Mechanics: tual relevance of this subject, which deals with the Quantum the Ergodic Hypothesis (EH). Ergodic Theorem according to John von Neumann. A. Statistical Mechanics In the last decades of 19th century, great advances were made, concerning the comprehension of matter structure. Des- II. DESCRIPTIONS OF THE MICROSCOPIC STATE pite the resistance of the energetics’ group 1, the works by Maxwell, Boltzmann, Gibbs and others were successful on The description of a physical system, such as an ideal gas interpreting matter as composed by the minors entities called with N particles, is usually carried out in terms of 3N spatial atoms. Under this new perspective, many obstacles needed coordinates q1;:::;q3N ≡ q; and the respective 3N conjuga- to be overcome, and EH appears with singular voice to solve ted momenta p1;:::; p3N ≡ p. We are then considering the some problems. Hamiltonian formalism. We still define a differential volume Briefly, the EH states that the temporal average of the mo- of phase space by dw ≡ dq1 :::dq3Ndp1 :::dp3N. vement of molecules and atoms is equal to the spatial average (in phase space, see eqs. (1) and (9)). In other words, this Imagine that we intend to calculate an (intensive) macros- means that a macroscopic property can be interpreted as an copic property Gobs, where obs means observable, taking the average taken over different data. atomics hypothesis as true. An usual way of trying to extract Despite the fundamental nature of the hypothesis, its pre- a simple and unique value from an infinite number of irregu- sentation in many textbooks is underestimated, giving an im- larly moving particles is to properly conceive an average of pression of being either a mere step in mathematical calcu- the motion (position or velocity) of the particles. lations or even a self-evident conclusion. We mention some With the aim of testing the validity of the previous proce- examples: ref. [3] just mentions it. Refs. [4, 5] make some dure, we will perform an experiment. Due to the fact that comments, specially ref. [6] whose dense book about funda- any experimental measurement occurs in a finite time t, the mentals of Statistical Mechanics has discussions about EH’s required average must be evaluated during that interval2 [9]. ∗[email protected] 2 Implicitly, we are considering that the system reaches equilibrium during 1 The energeticists were those who disagreed that the matter would be sub- the finite time interval t, otherwise there would be no use in computing divided into smaller entities, the atoms. As representatives of this current this average. So we focus our attention on studying systems in equilibrium of thought include Helm and Ostwald [1, 2]. [14]. Physicæ 10, 2011 9 Understanding the ergodic hypothesis via analogies Mathematically, this is translated as, IV. EXPANDING THE ANALOGY 1 Z t Gobs = G¯(t) = G(q;p;t)dt : (1) The idea above shows our goal: exchanging the average based t 0 on the time evolution of a single agent, for another based on Even if the function G is known, it is impracticable to com- a set of similar agents. pute all 6N equations of movement together with their precise With this motivation we choose not to hold ourselves in the initial conditions. In this way, the temporal average is useless class L. Instead of sending the consumers of L to shopping at getting some information. in just one day, we can – in an abstract way – give life to each How could we obtain Gobs? Is eq. (1) adequate? Is that possible expenditures instantaneously, i.e., in a time interval the only possible way to calculate the macroscopic measure- Dt ! 0. This means we are changing the focus from the class ment? of consumers L to the expenditures space M and eliminating the time dependence of the average. III. THE CONSUMER AND ITS EXPENDITURES We know that there are an infinity of possibilities of ex- penditures (from one simple candy from the neighborhood 3 Imagine that we wish to know the mean daily consumption grocery store to a luxurious car) , then the sum must be re- (macroscopic property Qobs) of one consumer; we call him placed by an integral. Besides, it is natural to believe that `∗. In this case, we propose to track him during a period of buying a simple candy is much more common than buying a a month (30 days), after which, we calculate the average. In sport car. So we need to assign a probability density f (m) to mathematical notation, the occurrence of each expenditure m of the set M. In mathematical terms, we now express the mean daily 30 ¯ 1 consumption, Qobs = Q(d) = ∑ Q(d) : (2) 30 d=1 Z hQ(m)i = f (m)Q(m)dm ; (5) We can now suppose that we do not have an entire month M to observe `∗; in fact, we have just one day. How could we and, know that average? Would it be possible to know Qobs so far? If we believe – here is the thesis – that there are many Z ∗ f (m)dm = 1 ; (6) consumers L with similar profiles of ` , i.e., a socioecono- M mic group L to which `∗ belongs, we could observe many of them for just one day, taking notes of their expenditures and since f is a probability density. calculating the desired average: This procedure allows us to know the same property Qobs in three different ways, 1 L hQ(`)i = Q(`) : (3) ¯ ∑ Qobs = Q(d) t = hQ(`)iL = hQ(m)iM : (7) L `=1 The family of L consumers sent to shopping is commonly called “ensemble”. If we take `∗ as one of the many possi- V. THE ERGODIC HYPOTHESIS bilities of the ensemble L, we say that `∗ is a realization of The EH consists in changing the temporal average of eq. (1) ensemble [10, 15, 16]. for a spatial average. More precisely, for an average on the It is obvious that in a certain day the consumer `∗ can per- phase space of Hamiltonian formalism. form and probably he will, different expenditures from that In this formalism, we express the microscopic state of a performed by the consumer i, which will be different from system with just one point in a space of 6N dimensions. Then those of j, and so on. instead of tracking a long path of this single point during the In fact, what we are supposing is that the unique consumer time interval and computing the integral in eq. (1), we will `∗ could perform, in any of the 30 days of the month, the t imagine an ensemble of similar points4 and assign to each several expenditures of his pairs, in such way that the average portion of that ensemble a probability density f (q;p;t) that of the socioeconomic class L taken during one simple day N quantifies the movements in all directions of phase space. Af- equals the average of the unique consumer `∗ taken during a terwards, we integrate the possibilities in all directions to ob- whole month. tain the macroscopic tendency. Hypothesis : Z L consumers an unique consumer `∗ Gobs = hG(t)i = fN(q;p;Dt ! 0)G(q;p;Dt ! 0)dw ; ≡ : W in just one day during a whole month (8) In this way, we change a temporal average Q¯(d) for another one built on socioeconomic class L, hQ(`)i. The important fact is that we consider ourselves successful in satisfying our curiosity about the mean daily consumption of the specific 3 We should have in mind that the only possible expenditures are those that ∗ consumer ` .
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