Many types of emergence exist. This chapter will discuss some of the most prominent, and broadly occurring, examples of emergent structure in space and time.

We discuss high and low-dimensional, as well as stationary and non-stationary, mathematical formalisms leading to intermittent dynamics.

The subject matter of complexity science will be identified and placed in a historical perspective.

The process of entities successively splitting into two or more is of great relevance: biological reproduction, infection spreading, rumour spreading, nuclear reactions and much more.

Emergent phenomena require some interdependence between components. Networks of nodes and connecting links are therefore a very natural and powerful language for the analysis and characterisation of complex systems.

At the microscale, the motion of atoms and molecules composing matter is governed by Hamiltonian dynamics. For classical systems, this motion is described as trajectories in the phase space of the positions and momenta of the particles. Different equilibrium and nonequilibrium statistical ensembles can be introduced, each associated with some probability distribution, which is a solution of Liouville’s equation. The BBGKY hierarchy of equations is obtained for the multiparticle distribution functions. The presentation includes the properties of ergodicity and dynamical mixing, the Pollicott–Ruelle resonances, microreversibility, and the nonequilibrium breaking of time-reversal symmetry at the statistical level of description. The concept of entropy is introduced by coarse graining. Linear response theory is developed within the classical framework, leading to the Onsager–Casimir reciprocal relations and the fluctuation–dissipation theorem. The projection-operator methods are summarized.

At the mesoscale, reaction networks are described in terms of stochastic processes. In well-stirred solutions, the time evolution is ruled by the chemical master equation for the probability distribution of the random numbers of molecules. The entropy production is obtained for these reactive processes in the framework of stochastic thermodynamics. The entropy production can be decomposed using the Hill–Schnakenberg cycle decomposition in terms of the affinities and the reaction rates of the stoichiometric cycles of the reaction network. The multivariate fluctuation relation is established for the reactive currents. The results are applied to several examples of reaction networks, in particular, describing autocatalytic bistability, noisy chemical clocks, enzymatic kinetics, and copolymerization processes.

The overview of the principles of quantum statistical mechanics are given, emphasizing the fundamental differences with respect to classical statistical mechanics, as well as the analogies prevailing for the formulation of the properties. A functional time-reversal symmetry relation is presented, allowing the deduction of response theory. The Kubo formula is obtained for the linear response properties and the fluctuation–dissipation theorem is established. For weakly coupled systems, the quantum master equation and the corresponding stochastic Schrödinger equation are deduced. The slippage of initial conditions is discussed in relation to the positivity of the reduced statistical operator. The results are illustrated with the spin-boson model.

The multivariate fluctuation relation is established for the full counting statistics of the energy and particle fluxes across an open quantum system in contact with several reservoirs on the basis of microreversibility The quantum version of the nonequilibrium work fluctuation relation is recovered in the presence of a single reservoir. In the long-time limit, the time-reversal symmetry relation is expressed in terms of the cumulative generating function for the full counting statistics. In systems with independent particles, the symmetry relation can be obtained in the scattering approach for the transport of bosons and fermions. The temporal disorder and its time asymmetry can be characterized by the quantum version of the entropy and coentropy per unit time. Their difference gives the thermodynamic entropy production rate. Furthermore, the stochastic approach is also considered for electron transport in quantum dots, quantum point contacts, and single-electron transistors.

At the mesoscale, the fluctuating phenomena are described using the theory of stochastic processes. Depending on the random variables, different stochastic processes can be defined. The properties of stationarity, reversibility, and Markovianity are defined and discussed. The classes of discrete- and continuous-state Markov processes are presented including their master equation, their spectral theory, and their reversibility condition. For discrete-state Markov processes, the entropy production is deduced and the network theory is developed, allowing us to obtain the affinities on the basis of the Hill–Schnakenberg cycle decomposition. Continuous-state Markov processes are described by their master equation, as well as stochastic differential equations. The spectral theory is also considered in the weak-noise limit. Furthermore, Langevin stochastic processes are presented in particular for Brownian motion and their deduction is carried out from the underlying microscopic dynamics.

Introduction, synopsis, and acknowledgments.

The fluctuations of energy and particle fluxes obey remarkable symmetries called fluctuation relations, which are valid arbitrarily far from equilibrium and find their origin in microreversibility. Yet they imply the nonnegativity of entropy production in accord with the second law of thermodynamics. They express the directionality of nonequilibrium processes, reducing at equilibrium to the conditions of detailed balance. The nonequilibrium work fluctuation relation and Jarzynski’s equality are presented in the absence and the presence of a magnetizing field and also for joint angular momentum transfer. Moreover, the multivariate fluctuation relation for all the fluxes across an open system in contact with several reservoirs is deduced from both the classical Hamiltonian microdynamics and the theory of stochastic processes. The multivariate fluctuation relation implies not only the fluctuation–dissipation theorem and the Onsager–Casimir reciprocal relations close to equilibrium but also their generalizations to the nonlinear response properties of relevance farther away from equilibrium.