Challenges in data science: a complex systems perspective

The ability to process and manage large data volumes has been proven to be not enough to tackle the current challenges presented by “Big Data”. Deep insight is required for understanding interactions among connected systems, space- and time- dependent heterogeneous data structures. Emergence of global properties from locally interacting data entities and clustering phenomena demand suitable approaches and methodologies recently developed in the foundational area of Data Science by taking a Complex Systems standpoint. Here, we deal with challenges that can be summarized by the question: “What can Complex Systems Science contribute to Big Data? ”. Such question can be reversed and brought to a superior level of abstraction by asking “What Knowledge can be drawn from Big Data?” These aspects constitute the main motivation behind this article to introduce a volume containing a collection of papers presenting interdisciplinary advances in the Big Data area by methodologies and approaches typical of the Complex Systems Science, Nonlinear Systems Science and Statistical Physics.     

Quality management systems from the perspective of organization of complex systems

The aim of this model is to analyse quality management systems from the standpoint of the organization of complex systems introduced by Robert Rosen. The concept that an organized system, under certain conditions, can be modelled as a living system introduces unsuspected tools to analyse its behaviour. The model is oriented towards the understanding of the implications of modifications introduced in laboratories working with a quality organization following the ISO/IEC 17025 standard.This standard establishes management and technical requirements concerning the documental organization, control of the management and technical records, personnel, equipments, test or calibration methods, etc. The objective of the quality system implementation is to assure the quality of the product, the special consideration being the customer’s satisfaction with all aspects of the service the laboratory offers.The improper use of the quality system tools has an impact on the laboratory activity. The perception of this fact can be negligible at the moment it happens. The model allows us to realize that this alteration always has a negative impact on the customer and it specially affects the continuous improvement that the laboratory must implement.     

Packing problems and project scheduling models: an integrating perspective

This paper considers two problem classes, namely packing and project scheduling problems that are important to researchers as well as practitioners. The two problem categories are described, and a classification is given for the different kinds of packing problems and project scheduling concepts. While both problem classes are different with respect to their fields of application, similarities of their mathematical structures are examined. It is shown that all packing problems considered here are special cases of models for project scheduling. The aim is to indicate which project scheduling models can be used to capture the different types of packing problems. Finally, some implications for research on optimisation algorithms for these two problem classes are discussed, and the applicability of the results of this work in practice are pointed out.     

A perspective on artificial intelligence: Learning to learn

The classical approach to the acquisition of knowledge in artificial intelligence has been to program the intelligence into the machine in the form of specific rules for the application of the knowledge: expert systems. Unfortunately, the amount of time and resources required to program an expert system with sufficient knowledge for non-trivial problem-solving is prohibitively large. An alternative approach is to allow the machine tolearn the rules based upon trial-and-error interaction with the environment, much as humans do. This will require endowing the machine with a sophisticated set of sensors for the perception of the external world, the ability to generate trial actions based upon this perceived information, and a dynamic evaluation policy to allow it to measure the effectiveness of its trial actions and modify its repertoire accordingly. The principles underlying this paradigm, known ascollective learning systems theory, have already been applied to sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity.The fundamental building block of a collective learning system is thelearning cell, which may be embedded in a massively parallel, hierarchical data communications network. Such a network comprising 100 million learning cells will approach the intelligence capacity of the human cortex. In the not-too-distant future, it may be possible to build a race of robotic slaves to perform a wide variety of tasks in our culture. This goal, while irresistibly attractive, is most certainly fraught with severe social, political, moral, and economic difficulties.This paper was given as an invited talk on the 12th Symposium on Operations Research, University of Passau, September 1987.     

On the variational integrating matrix for hyperbolic systems

We obtain a necessary and sufficient condition for a hyperbolic system to be an Euler-Lagrange system with a first-order Lagrangian up to multiplication by some matrix. If this condition is satisfied and an integral of the system is known to us, then we can construct a family of higher symmetries that depend on an arbitrary function. Also, we consider the systems that satisfy the above criterion and that possess a sequence of the generalized Laplace invariants with respect to one of the characteristics; then we prove that the generalized Laplace invariants with respect to the other characteristic are uniquely defined. __________ Translated from Fundamentalnaya i Prikladnaya Matematika (Fundamental and Applied Mathematics), Vol. 12, No. 7, pp. 251–262, 2006.     

Weak Dirichlet processes with a stochastic control perspective

The motivation of this paper is to prove verification theorems for stochastic optimal control of finite dimensional diffusion processes without control in the diffusion term, in the case where the value function is assumed to be continuous in time and once differentiable in the space variable (C^0,1$C^{0,1}$) instead of once differentiable in time and twice in space (C^1,2$C^{1,2}$), like in the classical results. For this purpose, the replacement tool of the Itô formula will be the Fukushima–Dirichlet decomposition for weak Dirichlet processes. Given a fixed filtration, a weak Dirichlet process is the sum of a local martingale M$M$ plus an adapted process A$A$ which is orthogonal, in the sense of covariation, to any continuous local martingale. The decomposition mentioned states that a C^0,1$C^{0,1}$ function of a weak Dirichlet process with finite quadratic variation is again a weak Dirichlet process. That result is established in this paper and it is applied to the strong solution of a Cauchy problem with final condition.     

Dynamic systems identification with Gaussian processes

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.     

A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory

In this paper we integrate the rich yet fragmented insights from the extensive literature on the diffusion of innovation into an elegant, coherent model. Using complex adaptive systems theory as the overarching framework, we integrate prior literature around three constructs: agents, interactions, and an environment. The integrated model is presented in both natural language and as an agent-based simulation model. A series of validation experiments instill confidence that our agent-based model (and similar others) can be used as a virtual research laboratory. We provide theoretical and methodological directions for future research.     

Multi-echelon systems: A service measure perspective

This paper reviews the most important results on divergent multi-echelon systems. In particular, we concentrate on the interactions between the elements that constitute such a multi-echelon system, in order to determine several service measures (e.g. external customer service level and inventory holding costs). We distinguish between two types of policies: installation stock and echelon stock policies. A comparison between these two types of policies revealed that the complexity of the analysis is concentrated at different aspects, which are discussed by reveiwing the most important papers on both types. Special attention is given to the applicability of the models. Extensions to divergent multi-echelon systems with more than two stages are also treated.     

Integration of the human operator into responsive discrete production management systems

The need for production systems that can react or respond to dynamic changes is continuously increasing because of the reduction of product life cycle time and the rise of competition. To improve responsiveness, we show that integrating the intelligence of the human operator into the system helps to face complexity. However, little work has been done about the optimization of such integration considering production system constraints (real time decision making, observability, etc.) and human operator constraints (mental workload, trust in management system, self-confidence, etc.). This paper aims at discussing ways to take account of the cognitive abilities of the human operator and offers some advice on how to take accurately into account the integration of the human operator by the proposal of a set of global specifications. To illustrate how it is possible to contribute to the optimized design of a system based upon such specifications, we propose the concept of “distributed production management system”. We first address the specific interface issue. A classical example of “advanced display” designed for continuous systems, that is, the Ecological Interface Design (EID) approach, is applied to discrete production systems. We show that such an approach is coherent with parts of the introduced specifications but can be adapted to large complex and discrete systems with difficulty. To solve this issue, we propose the development of a distributed DSS where each local DSS integrates an advanced display and manages a set of production resources in cooperation with an operator, which reduces the global complexity.     

Integrability conditions for complex systems with homogeneous

In this paper we obtain conditions for the existence  of a local analytic first integral for four eight-parameter families of quintic complex system. We also discuss computational difficulties arising in the study of the problem of integrability for these systems.     

Multiple stochastic integrals constructed by special expansions of products of the integrating stochastic processes

The paper deals with problems of constructing multiple stochastic integrals in the case when the product of increments of the integrating stochastic process admits an expansion as a finite sum of series with random coefficients. This expansion was obtained for a sufficiently wide class including centered Gaussian processes. In the paper, some necessary and sufficient conditions are obtained for the existence of multiple stochastic integrals defined by an expansion of the product of Wiener processes. It was obtained a recurrent representation for the Wiener stochastic integral as an analog of the Hu–Meyer formula.     

Empirical processes of multidimensional systems with multiple mixing properties

We establish a multivariate empirical process central limit theorem for stationary R^d-valued stochastic processes (X_i)_i≥1 under very weak conditions concerning the dependence structure of the process. As an application, we can prove the empirical process CLT for ergodic torus automorphisms. Our results also apply to Markov chains and dynamical systems having a spectral gap on some Banach space of functions. Our proof uses a multivariate extension of the techniques introduced by Dehling et al. (2009) [9] in the univariate case. As an important technical ingredient, we prove a 2pth moment bound for partial sums in multiple mixing systems.     

Crisis in complex social systems: A social theory view illustrated with the chilean case

The article argues that crises are a distinctive feature of complex social systems. A quest for connectivity of communication leads to increase systems' own robustness by constantly producing further connections. When some of these connections have been successful in recent operations, the system tends to reproduce the emergent pattern, thereby engaging in a non‐reflexive, repetitive escalation of more of the same communication. This compulsive growth of systemic communication in crisis processes, or logic of excess, resembles the dynamic of self‐organized criticality. Accordingly, we first construct the conceptual foundations of our approach. Second, we present three core assumptions related to the generative mechanism of social crises, their temporal transitions (incubation, contagion, restructuring), and the suitable modeling techniques to represent them. Third, we illustrate the conceptual approach with a percolation model of the crisis in Chilean education system. © 2016 Wiley Periodicals, Inc. Complexity 21: 13–23, 2016     

Finite horizon semi-Markov decision processes with application to maintenance systems

This paper investigates finite horizon semi-Markov decision processes with denumerable states. The optimality is over the class of all randomized history-dependent policies which include states and also planning horizons, and the cost rate function is assumed to be bounded below. Under suitable conditions, we show that the value function is a minimum nonnegative solution to the optimality equation and there exists an optimal policy. Moreover, we develop an effective algorithm for computing optimal policies, derive some properties of optimal policies, and in addition, illustrate our main results with a maintenance system.