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Typicality First

Wilhelm, Isaac

Published Jan 1, 2025• 0 citations
OpenAlex

Abstract

Instances of the law of large numbers are used to model many different physical systems. In this paper, I argue for a particular interpretation, of those instances of that law, which appeals to typicality. As I argue, the content of that law, when used to model physical systems, is that the probability of an event typically---rather than probably---approximates the frequency with which that event occurs.

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11. Measurement devices

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De Gruyter eBooks•Oct 24, 2019•0 citations
Instances of the law of large numbers are used to model many different physical systems. In this paper, I argue for a particular interpretation, of those instances of that law, which appeals to typicality. As I argue, the content of that law, when used to model physical systems, is that the probability of an event typically---rather than probably---approximates the frequency with which that event occurs.
Abstract This article gives an account of a theoretical research subdivided into three parts. The first stage recalls the theorem of large numbers and the theorem a single number which demonstrate how the frequentist and the subjective models of probability can be used according to distinct assumptions. Based on these achievements, the second stage describes the temporal evolution of the random outcome (this article distinguishes the outcome from the random event or phenomenon that produces it). It is proved that the outcome of a single unpredictable event switches from the indeterministic to the deterministic state when the event winds up. The third stage means to show how the theoretical results apply to physical situations.
Appeals to typicality are common in physics, but it is often unclear what it means for a physical state to be typical relative to a probability measure, and correspondingly unclear what a law that appeals to typicality asserts. Here we consider how one might characterize physical typicality using ideas from the theory of algorithmic randomness. As a concrete example, we show how taking a physical state to be typical relative to a computable measure when it is Martin-Löf random allows one to formulate the distribution postulate in Bohmian mechanics as a statistical constraining law of the theory. Using a toy model, we show how this constraint guarantees the standard Born statistics for computable experimental protocols. Algorithmic Bohmian mechanics (aBM) thus illustrates how algorithmic randomness may be used to provide precise content to a statistical law.
A popular strategy for understanding the probabilities that arise in physics is to interpret them via reductionist accounts of chance—indeed, it is sometimes claimed that such accounts are uniquely well-suited to make sense of the probabilities in classical statistical mechanics. Here it is argued that reductionist accounts of chance carry a steep but unappreciated cost: when applied to physical theories of the relevant type, they inevitably distort the relations of probability that they take as input.
An event occurs and passes. What remains is physical: the surrounding domain evolves differently from how it would have evolved had the event not occurred. That difference—not a symbol or a measurement outcome—is taken here as the primitive object and called an event-dependent physical record. Our central result is that a record can persist exactly while ceasing to be detectable. We exhibit a unitary evolution in which the record's norm is conserved and its spectral modulus remains unchanged at every frequency, yet its projection onto any fixed finite-rank detector falls below every prescribed detection threshold in finite time, with an explicit upper bound on the operational lifetime. Physical persistence and operational accessibility are therefore logically independent properties. Three further results follow. First, persistence is shown to be a consequence of reversibility rather than a postulate. Second, physical distinctness, detectability, and reconstructibility form a strictly nested hierarchy, with counterexamples to both converse implications. Third, for a Gaussian coordinate, the predictive content recoverable from a given observation channel is bounded by the channel's mutual information, independently of the reconstruction algorithm and the control law.
This chapter presents the measurement problem from the point of view of the thermal interpretation of quantum physics. Everything physicists measure is measured in a thermal environment, for which statistical thermodynamics is relevant. The thermal interpretation agrees with how one interprets measurements in thermodynamics, the macroscopic part of quantum physics, derived via statistical mechanics. By its very construction, the thermal interpretation naturally matches the classical properties of our quantum world: The thermal interpretation assigns states-and a realistic interpretation for them-to individual quantum systems, in a way that large quantum systems are naturally described by classical observables. Since measurement devices are large quantum systems, this is the key to solving the measurement problem. We discuss the role played by macroscopic systems and the weak law of large numbers in getting readings with small uncertainty. Since quantum physics makes many deterministic predictions, for example regarding observed spectra, but also assertions about probabilities, we distinguish deterministic and statistical measurements. To clarify the meaning of the concept of measurement, we postulate (in Section 11.1) a measurement principle that defines what it means in the thermal interpretation to measure a quantity with a specified uncertainty. This turns the measurement problem from a philosophical riddle into a scientific problem in the domain of quantum statistical mechanics, namely how the quantum dynamics correlates macroscopic readings from an instrument with properties of the state of a measured microscopic system. The essence of scientific practice is the reproducibility of measurements, discussed in Section 11.2. Unlike in traditional interpretations, single, nonreproducible observations do not count as measurements, since this would violate the reproducibility of measurements-the essence of scientific practice. As a consequence, the measurement of a Hermitian quantity A is regarded as giving an uncertain value approximating the q-expectation {A} rather than (as tradition wanted to have it) as an exact revelation of an eigenvalue of A. This difference is most conspicuous in the interpretation of single discrete microscopic events. Except in very special circumstances, these are not reproducible. Thus, they have no scientific value in themselves and do not constitute measurement results. Scientific value is, however, in ensembles of such observations, which result in approximate measurements of q-probabilities and q-expectations. Sections 11.3 and 11.4 distinguish deterministic and statistical measurements, depending on whether a single observation is reproducible, and discuss the role played by macroscopic systems and the weak law of large numbers in getting readings with small uncertainty. How to measure the q-probabilities of events, described in terms of POVMs, is discussed in Section 11.5. Section 11.6 poses the measurement problem as a problem of the statistical mechanics of measurement devices, and discuss recent work by Allahverdyan, Balian, and Nieuwenhuizen on its resolution. Section 11.7 discusses the problem how, in the thermal interpretation, discrete events can arise, although qexpectations are by their very nature continuous. This is resolved by pointing to work by Breuer and Petruccione on piecewise deterministic processes (PDP). Section 11.8 sheds additional light on the origin of discrete events by an analysis of the role dissipative bistability plays in obtaining discreteness in classical physics.