We study damage propagation in networks, with an emphasis on production-chain models. The models are formulated as systems of Boolean delay equations. This formalism helps take into account the complexity of the interactions between firms; it turns out to be well adapted to investigating propagation of an initial damage due to a climatic or other natural disaster. We consider in detail the effects of distinct delays and forcing, which represent external intervention to prevent economic collapse. We also account for the possible presence of randomness in the links and the delays. The paper concentrates on two different network structures, periodic and random, respectively; their study allows one to understand the effects of multiple, concurrent production paths, and the role played by the network topology in damage propagation. Applications to the recent network modeling of climate variability are discussed.
Boolean Delay Equations (BDEs) are semi-discrete dynamical models with Boolean-valued variables that evolve in continuous time. Systems of BDEs can be classified into or , in a manner that parallels the classification of ordinary or partial differential equations. Solutions to certain conservative BDEs exhibit growth of complexity in time; such BDEs can be seen therefore as metaphors for biological evolution or human history. Dissipative BDEs are structurally stable and exhibit multiple equilibria and limit cycles, as well as more complex, fractal solution sets, such as Devil’s staircases and “fractal sunbursts.” All known solutions of dissipative BDEs have stationary variance. BDE systems of this type, both free and forced, have been used as highly idealized models of climate change on interannual, interdecadal and paleoclimatic time scales. BDEs are also being used as flexible, highly efficient models of colliding cascades of loading and failure in earthquake modeling and prediction, as well as in genetics. In this paper we review the theory of systems of BDEs and illustrate their applications to climatic and solid-earth problems. The former have used small systems of BDEs, while the latter have used large hierarchical networks of BDEs. We moreover introduce BDEs with an infinite number of variables distributed in space (“partial BDEs”) and discuss connections with other types of discrete dynamical systems, including cellular automata and Boolean networks. This research-and-review paper concludes with a set of open questions.
We consider here prediction of abrupt overall changes (“critical transitions”) in the behavior of hierarchical complex systems, using the model developed in the first part of this study. The model merges the physical concept of colliding cascades with the mathematical framework of Boolean delay equations. It describes critical transitions that are due to the interaction between direct cascades of loading and inverse cascades of failures in a hierarchical system. This interaction is controlled by distinct delays between switching of elements from one state to another: loaded vs. unloaded and intact vs. failed. We focus on the earthquake prediction problem; accordingly, the model's heuristic constraints are taken from the dynamics of seismicity. The model exhibits four major types of premonitory seismicity patterns (PSPs), which have been previously identified in seismic observations: (i) rise of earthquake clustering; (ii) rise of the earthquakes' intensity; (iii) rise of the earthquake correlation range; and (iv) certain changes in the size distribution of earthquakes (Gutenberg–Richter relation). The model exhibits new features of individual PSPs and their collective behavior, to be tested in turn on observations. There are indications that the premonitory phenomena considered are not seismicity-specific, but may be common to hierarchical systems of a more general nature.
Following the complete sequencing of several genomes, interest has grown in the construction of genetic regulatory networks, which attempt to describe how different genes work together in both normal and abnormal cells. This interest has led to significant research in the behavior of abstract network models, with Boolean networks emerging as one particularly popular type. An important limitation of these networks is that their time evolution is necessarily periodic, motivating our interest in alternatives that are capable of a wider range of dynamic behavior. In this paper we examine one such class, that of continuous-time Boolean networks, a special case of the class of Boolean delay equations (BDEs) proposed for climatic and seismological modeling. In particular, we incorporate a biologically motivated refractory period into the dynamic behavior of these networks, which exhibit binary values like traditional Boolean networks, but which, unlike Boolean networks, evolve in continuous time. In this way, we are able to overcome both computational and theoretical limitations of the general class of BDEs while still achieving dynamics that are either aperiodic or effectively so, with periods many orders of magnitude longer than those of even large discrete time Boolean networks.
We review results concerning dynamics in a class of hybrid ordinary differential equations which incorporates logical control to yield piecewise linear equations. These equations relate qualitative features of the structure of networks to qualitative properties of the dynamics. Because of their simple structure, they have been studied using techniques from discrete mathematics and nonlinear dynamics. Initially developed as a qualitataive description of gene regulatory networks, many generalizations of the basic approach have been developed. In particular, we show how this qualitative approach may be adapted to switching biochemical systems without degradation, illustrated by an example of a motif in which two branches of a pathway may be regulated differently when the thresholds for the two pathways are separated.
Interconnected systems are prone to propagation of disturbances, which can undermine their resilience to external perturbations. Propagation dynamics can clearly be affected by potential time delays in the underlying processes. We investigate how such delays influence the resilience of production networks facing disruption of supply. Interdependencies between economic agents are modeled using systems of Boolean delay equations (BDEs); doing so allows us to introduce heterogeneity in production delays and in inventories. Complex network topologies are considered that reproduce realistic economic features, including a network of networks. Perturbations that would otherwise vanish can, because of delay heterogeneity, amplify and lead to permanent disruptions. This phenomenon is enabled by the interactions between short cyclic structures. Difference in delays between two interacting, and otherwise resilient, structures can in turn lead to loss of synchronization in damage propagation and thus prevent recovery. Finally, this study also shows that BDEs on complex networks can lead to metastable relaxation oscillations, which are damped out in one part of a network while moving on to another part.
We consider a prominent feature of hierarchical nonlinear (“complex”) systems: persistent recurrence of abrupt overall changes, called here “critical transitions.” Motivated by the earthquake prediction problem, we formulate a model that uses heuristic constraints taken from the dynamics of seismicity. Our conclusions, though, may apply to hierarchical systems that arise in other areas.We use the Boolean delay equation (BDE) framework to model the dynamics of colliding cascades, in which a direct cascade of loading interacts with an inverse cascade of failures. The elementary interactions of elements in the system are replaced by their integral effect, represented by the delayed switching of an element's state.The present paper is the first of two on the BDE approach to modeling seismicity. Its major results are the following: (i) A model that implements the approach. (ii) Simulating three basic types of seismic regime. (iii) A study of regime switching in a parameter space of the loading and healing rates. The second paper focuses on the earthquake prediction problem.
The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited observations, gives a new perspective on data assimilation. Synchronization among different models of the same system defines a supermodel that can achieve partial consensus among models that otherwise disagree in several respects. Finally, novel methods of time series analysis permit a better description of synchronization in a system that is only observed partially and for a relatively short time. This Focus Issue discusses synchronization in extended systems or in components thereof, with particular attention to data assimilation, supermodeling, and their applications to various areas, from climate modeling to macroeconomics.
ABSTRACT ‘Tipping points’ (TPs) are thresholds of potentially disproportionate changes in the Earth's climate system associated with future global warming and are considered today as a ‘hot’ topic in environmental sciences. In this study, TP interactions are analysed from an integrated and conceptual point of view using two qualitative Boolean models built on graph grammars. They allow an accurate study of the node TP interactions previously identified by expert elicitation and take into account a range of various large‐scale climate processes potentially able to trigger, alone or jointly, instability in the global climate. Our findings show that, contrary to commonly held beliefs, far from causing runaway changes in the Earth's climate, such as self‐acceleration due to additive positive feedbacks, successive perturbations might actually lead to its stabilization. A more comprehensive model defined TPs as interactions between nine (non‐exhaustive) large‐scale subsystems of the Earth's climate, highlighting the enhanced sensitivity to the triggering of the disintegration of the west Antarctic ice sheet. We are claiming that today, it is extremely difficult to guess the fate of the global climate system as TP sensitivity depends strongly on the definition of the model. Finally, we demonstrate the stronger effect of decreasing rules (i.e. mitigating connected TPs) over other rule types, thus suggesting the critical role of possible ‘stabilizing points’ that are yet to be identified and studied.