No Arabic abstract
We address the link between the controllability or observability of a stochastic complex system and concepts of information theory. We show that the most influential degrees of freedom can be detected without acting on the system, by measuring the time-delayed multi-information. Numerical and analytical results support this claim, which is developed in the case of a simple stochastic model on a graph, the so-called voter model. The importance of the noise when controlling the system is demonstrated, leading to the concept of control length. The link with classical control theory is given, as well as the interpretation of controllability in terms of the capacity of a communication canal.
Multi-scale structures are prevalent in both natural and artificial systems, as they can handle increasing complexity. Several terms are employed almost interchangeably across various application domains to refer to the multi-scale concept - e.g., hierarchy, holarchy, multi-level, multi-layer, nested, embedded, micro-macro or coarse graining. While the concrete meanings behind these terms may differ slightly, several core commonalities persist across all cases. In this position paper we aim to highlight these common features of the multi-scale concept, as a preliminary basis for a generic theory of multi-scale systems. We discuss the concepts of scale and multi-scale systems in general, and then of multi-scale feedback systems in particular, focusing on the role played by information in such systems. Our long-term objective is to develop a general theory of multi-scale feedback systems, applicable across all domains dealing with complex systems.
Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLAs learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.
In linear control theory, a structured system is a system whose entries of its system matrices are either fixed zero or indeterminate. This system is structurally controllable, if there exists a realization of it that is controllable, and is strongly structurally controllable (SSC), if for any nonzero values of the indeterminate entries, the corresponding system is controllable. This paper introduces a new controllability notion, termed partial strong structural controllability (PSSC), which naturally extends SSC and bridges the gap between structural controllability and SSC. Dividing the indeterminate entries into two categories, generic entries and unspecified entries, a system is PSSC, if for almost all values of the generic entries in the parameter space except for a set of measure zero, and any nonzero (complex) values of the unspecified entries, the corresponding system is controllable. We highlight that this notion generalizes the generic property embedded in the conventional structural controllability for single-input systems. We then give algebraic and (bipartite) graph-theoretic necessary and sufficient conditions for single-input systems to be PSSC. Conditions for multi-input systems are subsequently given for a particular case. We also extend our results to the case where the unspecified entries can take either nonzero values or zero/nonzero values. Finally, we show the established results can induce a new graph-theoretic criterion for SSC in maximum matchings over the system bipartite graph representations.
Safely deploying machine learning models to the real world is often a challenging process. Models trained with data obtained from a specific geographic location tend to fail when queried with data obtained elsewhere, agents trained in a simulation can struggle to adapt when deployed in the real world or novel environments, and neural networks that are fit to a subset of the population might carry some selection bias into their decision process. In this work, we describe the problem of data shift from a novel information-theoretic perspective by (i) identifying and describing the different sources of error, (ii) comparing some of the most promising objectives explored in the recent domain generalization, and fair classification literature. From our theoretical analysis and empirical evaluation, we conclude that the model selection procedure needs to be guided by careful considerations regarding the observed data, the factors used for correction, and the structure of the data-generating process.
Exploiting recent developments in information theory, we propose, illustrate, and validate a principled information-theoretic algorithm for module discovery and resulting measure of network modularity. This measure is an order parameter (a dimensionless number between 0 and 1). Comparison is made to other approaches to module-discovery and to quantifying network modularity using Monte Carlo generated Erdos-like modular networks. Finally, the Network Information Bottleneck (NIB) algorithm is applied to a number of real world networks, including the social network of coauthors at the APS March Meeting 2004.