Under a variety of names, and in a more or less explicit form, the concept that we now call ‘probability’ must have taken shape in the mind of human beings since the dawn of thought, as a nuance added to the idea of chance (randomness) or unpredictability, though chance may not be exactly the right word. Some time later, the concepts of what we now describe as ‘statistics’ and ‘statistically stable’, moved away from the idea of ‘chance’ and came closer to something else, which was called ‘probability’ and has been fuzzily conceived as being, in some sense, abstract and ‘ideal’. Throughout history it has been felt that unpredictability can have degrees, and that it can be measured using probabilities.

In this paper, I discuss the extent to which Kolmogorov drew upon von Mises' work in addressing the problem of why probability is applicable to events in the real world, which I refer to as the problem of the applicability of probability, or the applicability problem for short. In particular, I highlight the role of randomness in Kolmogorov's account, and I argue that this role differs significantly from the role that randomness plays in von Mises' account.

In this paper we propose a quantum random number generator (QRNG) that uses an entangled photon pair in a Bell singlet state and is certified explicitly by value indefiniteness. While ‘true randomness’ is a mathematical impossibility, the certification by value indefiniteness ensures that the quantum random bits are incomputable in the strongest sense. This is the first QRNG setup in which a physical principle (Kochen–Specker value indefiniteness) guarantees that no single quantum bit that is produced can be classically computed (reproduced and validated), which is the mathematical form of bitwise physical unpredictability.

We discuss the effects of various experimental imperfections in detail: in particular, those related to detector efficiencies, context alignment and temporal correlations between bits. The analysis is very relevant for the construction of any QRNG based on beam-splitters. By measuring the two entangled photons in maximally misaligned contexts and using the fact that two bitstrings, rather than just one, are obtained, more efficient and robust unbiasing techniques can be applied. We propose a robust and efficient procedure based on XORing the bitstrings together – essentially using one as a one-time-pad for the other – to extract random bits in the presence of experimental imperfections, as well as a more efficient modification of the von Neumann procedure for the same task. We also discuss some open problems.

We discuss some recent results related to the deduction of a suitable probabilistic model for the description of the statistical features of a given deterministic dynamics. More precisely, we motivate and investigate the computability of invariant measures and some related concepts. We also present some experiments investigating the limits of naive simulations in dynamics.

We examine the construction of joint probabilities for non-commuting observables. We show that there are indications in standard quantum mechanics that imply the existence of conditional expectation values, which in turn implies the existence of a joint distribution. We also argue that the uncertainty principle has no bearing on the existence of joint distributions but only constrains the marginal distributions. In addition, we show that within classical probability theory there are mathematical quantities that are similar to quantum mechanical wave functions. This is shown by generalising a theorem of Khinchin on the necessary and sufficient conditions for a function to be a characteristic function.

Quantum computation and quantum computational logics give rise to some non-standard probability spaces that are interesting from a formal point of view. In this framework, events represent quantum pieces of information (qubits, quregisters, mixtures of quregisters), while operations on events are identified with quantum logic gates (which correspond to dynamic reversible quantum processes). We investigate the notion of Shi–Aharonov quantum computational algebra. This structure plays the role for quantum computation that is played by σ-complete Boolean algebras in classical probability theory.

Stochastic gene expression (SGE) is now considered to be an established fact and has become an important subject of research (Viñuelas et al. 2012). During the last decade, the availability of new techniques has made possible the production of more precise and more spectacular data showing the extensive variability in gene expression occurring between individual cells. However, evidence supporting probabilistic models of cell behaviour and gene expression has been available for quite a long time – for example, see the reviews in Laforge et al. (2005) and Golubev (2010). It should be noted that the first model of stochastic cell differentiation was proposed in 1964 (Till et al. 1964), which is almost at the same time as the genetic programming theory (Jacob and Monod 1961), which is deterministic in nature. One can thus wonder why the determinist view of biology has remained dominant for such a long time, and what makes a probabilistic view more acceptable nowadays? In this short paper, I will briefly argue that there is a strong epistemological obstacle to the acceptance of probabilism in biology, and that even today SGE is not integrated into a fully probabilistic approach of cellular processes, but rather into a concept that I call ‘determinism with noise’. I will argue that a new fully probabilistic theoretical framework is needed to truly integrate the stochastic aspects of cell physiology.

Biology has contradictory relationships with randomness. First, it is a complex issue for an empirical science to ensure that apparently random events are truly random, this being further complicated by the loose definitions of unpredictability used in the discipline. Second, biology is made up of many different fields, which have different traditions and procedures for considering random events. Randomness is in many ways an inherent feature of evolutionary biology and genetics. Indeed, chance/Darwinian selection principles, as well as the combinatorial genetic lottery leading to gametes and fertilisation, rely, at least partially, on probabilistic laws that refer to random events. On the other hand, molecular biology has long been based on deterministic premises that have led to a focus on the precision of molecular interactions to explain phenotypes, and, consequently, to the relegation of randomness to the marginal status of ‘noise’. However, recent experimental results, as well as new theoretical frameworks, have challenged this view and may provide unifying explanations by acknowledging the intrinsic stochastic dimension of intracellular pathways as a biological parameter, rather than just as background noise. This should lead to a significant reappraisal of the status of randomness in the life sciences, and have important consequences on research strategies for theoretical and applied biology.

In this paper, we discuss the crucial but little-known fact that, as Kolmogorov himself claimed, the mathematical theory of probabilities cannot be applied to factual probabilistic situations. This is because it is nowhere specified how, for any given particular random phenomenon, we should construct, effectively and without circularity, the specific and stable distribution law that gives the individual numerical probabilities for the set of possible outcomes. Furthermore, we do not even know what significance we should attach to the simple assertion that such a distribution law “exists”. We call this problem Kolmogorov's aporia†.

We provide a solution to this aporia in this paper. To do this, we first propose a general interpretation of the concept of probability on the basis of an example, and then develop it into a non-circular and effective general algorithm of semantic integration for the factual probability law involved in a specific factual probabilistic situation. The development of the algorithm starts from the fact that the concept of probability, unlike a statistic, does not apply to naturally pre-existing situations but is a conceptual artefact that ensures, locally in space and time, a predictability that is more stable and definite than that permitted by primary statistical data.

The algorithm, which is constructed within a method of relativised conceptualisation, leads to a probability distribution expressed in rational numbers and involving a sort of quantification of the factual concept of probability. Furthermore, it also provides a definite meaning to the simple assertion that a factual probability law exists. We also show that the semantic integration algorithm is compatible with the weak law of large numbers.

The results we give provide a complete solution to Kolmogorov's aporia. They also define a concept of probability that is explicitly organised into a semantic, epistemological and syntactic whole. In a broader context, our results can be regarded as a strong, pragmatic and operational specification of Karl Popper's propensity interpretation of probabilities.

The main promotor of this special issue, Mioara Mugur-Schächter, organised a final debate for the one day conference dedicated to the theme of this special issue of Mathematical Structures in Computer Science.

Statistical entropy was introduced by Shannon as a basic concept in information theory measuring the average missing information in a random source. Extended into an entropy rate, it gives bounds in coding and compression theorems. In this paper, I describe how statistical entropy and entropy rate relate to other notions of entropy that are relevant to probability theory (entropy of a discrete probability distribution measuring its unevenness), computer sciences (algorithmic complexity), the ergodic theory of dynamical systems (Kolmogorov–Sinai or metric entropy) and statistical physics (Boltzmann entropy). Their mathematical foundations and correlates (the entropy concentration, Sanov, Shannon–McMillan–Breiman, Lempel–Ziv and Pesin theorems) clarify their interpretation and offer a rigorous basis for maximum entropy principles. Although often ignored, these mathematical perspectives give a central position to entropy and relative entropy in statistical laws describing generic collective behaviours, and provide insights into the notions of randomness, typicality and disorder. The relevance of entropy beyond the realm of physics, in particular for living systems and ecosystems, is yet to be demonstrated.