Evolution, Emergence, and Learning in Complex Systems

This is the first year of the new Track on Complex Systems. No doubt the idea of what a complex system is will be different to different people. For the purpose of this Track, a complex system may be large or small in scale. An important characteristic, however, is that such a system exhibiting a behavior under stress that is difficult to predict. This may be because models are not well understood (i.e. load models in electric power systems, behavioral models in social and economic systems). It may be because the number of variables is so large that it is beyond simulation capabilities of current computers, or because the relation between a large number of variables is so complex that current mathematics or simulation methods are inadequate. This track seeks to explore methods at the frontier of understanding complex system phenomena and the electric power system is a worthy example of such a system.There are five mini-tracks in this Track. The mini-track on Information Management seeks to explore techniques for managing and visualizing large-scale models that may be distributed across multiple operating authorities. Papers that cover both distribution and transmission network applications are scheduled for presentation.Another Mini-track focuses on topics related to the ability of complex systems such as power systems to survive disturbances with minimal impact on performance. Specific topics to be presented are steady state and dynamic security assessment where the impacts of pre-specified contingencies are analyzed and Available Transfer Capability (ATC), which quantifies the ability of the interconnected system to accept increases in power, transfers.Many large complex systems exhibit evidence of self-organized criticality. Issues such as the role of network size and topology along with the influence of network loading and operation on self-organized criticality are of interest. Evidence that large network disturbances are of a self-organized type and mechanisms of self-organized behavior in large networks are to be presented.Hybrid systems can be viewed as systems that allow interactions between discrete events and continuous dynamics. As such, they are natural models for complex interactive networks and systems such as manufacturing, power, communications, and transportation systems. A satisfactory theory for such systems, which draws from several disciplines including control theory, computer science, and applied mathematics, will have an enormous impact on the design, synthesis, and operations of many practical systems. Computational and algorithmic approaches to such problems encounter considerable difficulties. In addition to modeling and analysis of such systems, this mini-track explores novel computational paradigms that are able to accommodate uncertainties in the system at various levels.Finally, there are three sessions in the mini-track on Markets and Economics. The aim of this mini-track is to explore the ability of commercial trading models to effectively represent the complex physical behavior of an electricity industry, an issue that is critical to the success of electricity industry restructuring. Important aspects of this issue include the design of efficient spot markets and ancillary service markets, and mechanisms to incorporate network effects in electricity trading models. Papers will be presented that address these and other aspects of this important problem.

Genomics, proteomics, and metabolomics have all vastly advanced our understanding of human biology and disease. But the functioning of even a simple system such as a single yeast cell or bacterium is much more complicated than the sum of its genes or proteins or metabolites; it’s the activity of all those components and their relationships to one another that add up to a living organism. Recognizing that complexity, the emerging field of systems biology attempts to harness the power of mathematics, engineering, and computer science to analyze and integrate data from all the “omics” and ultimately create working models of entire biological systems. “Traditionally, scientists—toxicologists included—have relied on a reductionist approach to biology,” says William Suk, director of the NIEHS Center for Risk and Integrated Sciences. Even now, many studies examine complex systems by looking at cellular components in isolation. For instance, a common experiment involves using DNA microarrays to observe the effect of a chemical exposure on thousands of genes at once. This technique can quickly tell a scientist which genes may be vulnerable to that exposure. But a systems biology approach would attempt to model not only the chemical’s effect on gene expression but also how that expression will affect protein function, and in turn how the exposure will affect cell signaling. “There’s nothing wrong with what we’ve been doing,” Suk says. “But systems biology is going to take it to another level.”

Background.The relevance of the article is determined by the complexity and dynamism of resource allocation systems (RR systems), which include numerous time-varying elements, both within the system and in the external environment, and require organization into hierarchical subsystems. The uncertainty of the informational component and influencing factors necessitates the use of effective analytical tools based on decision theory under uncertainty for the objective assessment and management of such systems. Purpose. To develop a set of mathematical models for finding optimal solutions in complex resource management systems under uncertainty, required for designing resource allocation structures. Materials and methods. The study employs a combination of mathematical modeling, decision theory, and optimization techniques to address multicriteria problems in complex systems, particularly in resource allocation systems (RR systems) within transport complexes. The classical decision-making approach involves selecting the optimal solution from a set of alternatives formalized through mathematical models representing the problem situation. For deterministic optimization tasks, the model includes a set of feasible solutionsXXand a vector criterionf(x)f(x)to evaluate alternatives. In multicriteria optimization under uncertainty, the lack of a unified mathematical framework requires the use of diverse methods such as combinatorics, graph theory, heuristics, linear and dynamic programming, and search algorithms. The research highlights the challenges of applying these methods in complex systems, where external and internal uncertainties complicate the formulation of constraints and the integration of qualitative and quantitative criteria. The transformation of multicriteria problems into single-criterion formulations with constraints is also explored, emphasizing limitations imposed by unpredictable external factors and the need for experimental validation in complex transport systems. Results. The main challenge in building effective resource allocation systems (RR systems) lies in the large number of qualitative criteria, which are difficult to formalize and integrate into mathematical models. Qualitative criteria, such as scoring or expert judgments, require the use of ordinal scales, where only monotonic transformations are permissible, limiting quantitative comparison. To address multicriteria problems, methods based on binary relations and value functions are proposed, enabling the formalization of preferences and ranking of alternatives. These approaches, including linear aggregations and utility functions, enhance the objectivity of decisions in complex systems, such as transport complexes, where both quantitative and qualitative criteria are present. However, their application requires careful analysis and adaptation to the specifics of the tasks.

Data based identification and prediction of nonlinear and complex dynamical systems

The European Conference on Complex Systems 2010 (ECCS’10) took place in September 2010 and was located at Lisbon in an excellent conference environment. The conference attracted more than 500 participants from a whole range of scientists having a joint interest in complex systems science. Two major tracks of the conference were devoted to biological systems on different scales, ranging from molecular to ecological interactions. Biological systems created an important motivation to study complex systems right from the beginning of the movement. But it becomes increasingly clear that both fields—complex and biological systems—become even more entangled as complex systems deliver new ways to understand biological complexity, and the more the biological systems are investigated it becomes apparent that evolution has already invented numerous ways to tackle ‘complexity’ in the wider sense. In the spirit of design, it is always worth first looking at the answer of the question: ‘how has nature solved the problem’? The papers collected in this special issue of Theory in Biosciences represent a part of the research presented during ECCS’10. Complex systems research is surely at the innovation boundary of modern interand multi-disciplinary science. The journal ‘Theory in Biosciences’ has supported this process for quite some time, and we are pleased to add another ECCS contribution with this special issue. Network theory is one of the current major branches of complex systems science. The contribution of Susan Khor on structural characteristics of protein residue networks takes the subject of linking proteins much further: now the aspect of protein folding is included. The first protein networks available in the literature have been constructed on experimental binding evidence only. Unfortunately, the assays used proved unreliable in certain cases for certain proteins, making the results and their implications to some degree doubtful. Finding the structural reasons why proteins can connect or bind to each other provides much needed additional information on the reliability of such protein binding data. Moreover, the analysis gives some insight in the true reasons why certain proteins can bind to each other, and others do not. The special issue contains two contributions on molecular kinetics. Reaction systems are generally important for complex systems, as they can be compared with agentbased models which are equally based on rules, i.e. events like reactions. Moreover, they are generic models to study the action of feedback loops and stochastic noise. In their contribution A. Lindo, B. Faria and F. de Abreu study a model of molecular tunable kinetic proofreading, which is based on the actions of feedback loops. The second contribution by A. Filisetti, A. Graudenzi, R. Serra, M. Villani, R.M. Fuchslin, N. Packard, S.A. Kauffman and I. Poli on stochastic autocatalytic reaction systems extends the originally deterministic theory of autocatalytic cycles by Stuart Kauffman to a new stochastic setting. The paper is at the end of some extensions based on early criticisms of Kauffman’s model, making the whole framework more robust. M. Kirkilionis (&) Mathematics Institute, University of Warwick, Zeeman Building, CV4 7AL Coventry, UK e-mail: mak@maths.warwick.ac.uk URL: http://www.maths.warwick.ac.uk/*mak/

The turbulent events of the global financial crises have highlighted the importance of audit quality. Auditing in today's business environment involves navigating through organizational information technology (IT) landscape dominated by ERP systems. Organizations depend on ERP systems for financial reporting which involve dealing with statutory and regulatory provisions. ERP systems thus, have become an integral part of compliance strategy due to their support for internal controls. ERP systems are associated with inherent system and business process complexities capable of carving new auditing landscape for auditors. However, the implications of such ERP-induced process changes and system complexities on audit quality have not been well understood and investigated. This study attempts to bridge this gap. The primary goal of this research is to frame business process complexity, system complexity and audit process as key predictors of audit quality as perceived by external auditors. Using empirical evidence gathered from auditors experienced in post-ERP audit, the research found that auditors' post-ERP perception in an audit due to ERP implementation influenced the perceived audit quality. Specifically, system complexity, audit process changes and control risk were significant determinants of perceived audit quality. In addition, the findings reveal business process complexity and system complexity as key antecedents of control risk in an ERP audit.

Organoids are three-dimensional structures derived from stem cells that mimic the organization and function of specific organs, making them valuable tools for studying complex systems in biology. This paper explores the application of complex systems theory to understand and characterize organoids as exemplars of intricate biological systems. By identifying and analyzing common design principles observed across diverse natural, technological, and social complex systems, we can gain insights into the underlying mechanisms governing organoid behavior and function. This review outlines general design principles found in complex systems and demonstrates how these principles manifest within organoids. By acknowledging organoids as representations of complex systems, we can illuminate our understanding of their normal physiological behavior and gain valuable insights into the alterations that can lead to disease. Therefore, incorporating complex systems theory into the study of organoids may foster novel perspectives in biology and pave the way for new avenues of research and therapeutic interventions to improve human health and wellbeing.

The stability of a complex system generally decreases with increasing system size and interconnectivity, a counterintuitive result of widespread importance across the physical, life, and social sciences. Despite recent interest in the relationship between system properties and stability, the effect of variation in response rate across system components remains unconsidered. Here I vary the component response rates (γ) of randomly generated complex systems. I use numerical simulations to show that when component response rates vary, the potential for system stability increases. These results are robust to common network structures, including small-world and scale-free networks, and cascade food webs. Variation in γ is especially important for stability in highly complex systems, in which the probability of stability would otherwise be negligible. At such extremes of simulated system complexity, the largest stable complex systems would be unstable if not for variation in γ. My results therefore reveal a previously unconsidered aspect of system stability that is likely to be pervasive across all realistic complex systems.

Accidents in complex systems seldom arise from a single source, and are most often the result of multiple factors occurring at different levels of the system. Understanding the “systems” aspects of human performance (and performance error) in complex systems is a necessary part of any effort to avoid serious mishaps due to human error. This panel is intended to coincide with the development of a major research effort at the University of Wisconsin to address these issues. The Center for Human Performance in Complex Systems will apply the disciplines of systems engineering and ergonomics design to improve complex systems processes from the perspective of human performance. The purpose of this panel is to foster and demonstrate the Center's interest in bringing together a variety of perspectives and expertise bases to improve the overall quality and breadth of its activities. Each of the participants has a longstanding interest in improving the quality of human performance in complex and critical systems environments. Although they cannot represent the entire spectrum of relevant disciplines and perspectives of ergonomics and systems analysis, they provide a balance of insights, experience, and enthusiasm. This balance is essential to improving our understanding of factors affecting complex socio-technical systems, and implementing strategies to prevent and ameliorate the effects of system degradation and breakdown.

Risk assessment is the key and core technologies ensuring IT system security. Based on the comprehensive analysis to complex information systems, this paper first summarizes the typical characters of complex information systems and then gives new risk factors that complex system need to face. Furthermore, a new risk assessment method is proposed to evaluate the complex information systems. The method takes full account of the effect of complexity of complex information systems in each process of risk assessment, and utilizes multi-level risk views to carry out in-depth analysis to the risk of complex system.

Change in a complex system—for example, to its technology, procedures, or information flows— no matter how small, has the potential to create large effects and ripples of disruption. A complex system’s dynamics cannot be fully known, and the effects and disruptions produced by change are difficult to predict. Nonetheless, complex systems can be at least partly understood in terms of patterns in their dynamics, generalizable principles, and mechanisms of control, balance, and adaptation. This panel will focus on complex systems research and what they suggest about how to introduce change into a complex system such that the work system resilience and health are disrupted minimally. Case studies may be discussed, as well; examples of changes to established complex work systems. These changes include introducing remotely piloted aircraft systems (RPAS) into the National Airspace System (NAS), additional automation into air traffic control, and new technology into military air combat training.

Systems engineering is the branch of engineering concerned with the development of large and complex systems, where a system is understood to be an assembly or combination of interrelated elements or parts working together toward a common objective. Past experience has shown that formal systems engineering methodologies have not always been successfully applied to large and complex cybersecurity systems. These complex systems have become commonplace when applying cyberstrategies in cybersecurity operations. The ability to build, operate and maintain such systems is crucial to the effectiveness of cybersecurity operations. Most importantly, a cyberstrategy program must surround these systems on a global scale across multiple inter-related platforms. In this chapter, the authors demonstrate why a systems engineering approach is best suited for large and complex information systems used in cybersecurity, as well as the overall cyberstrategies that must also reside over these systems.

Information plays a critical role in complex biological systems. Complex systems like immune systems and ant colonies co-ordinate heterogeneous components in a decentralized fashion. How do these distributed decentralized systems function? One key component is how these complex systems efficiently process information. These complex systems have an architecture for integrating and processing information coming in from various sources and points to the value of information in the functioning of different complex biological systems. This article proposes a role for information processing in questions around the origin of life and suggests how computational simulations may yield insights into questions related to the origin of life. Such a computational model of the origin of life would unify thermodynamics with information processing and we would gain an appreciation of why proteins and nucleotides evolved as the substrate of computation and information processing in living systems that we see on Earth. Answers to questions like these may give us insights into non-carbon based forms of life that we could search for outside Earth. We hypothesize that carbon-based life forms are only one amongst a continuum of systems in the universe. Investigations into the role of computational substrates that allow information processing is important and could yield insights into: 1) novel non-carbon based computational substrates that may have life-like properties, and 2) how life may have actually originated from non-life on Earth. Life may exist as a continuum between non-life and life and we may have to revise our notion of life and how common it is in the universe. Looking at life or phenomenon through the lens of information theory may yield a broader view of life.

The article's materials analyze complex military-technical systems' main properties and characteristic features. It was found that the most critical task in researching the functioning of an unmanned aircraft complex is the search for rational ways of its application. Since such a complex belongs to complex technical systems, the principles of the system approach are used to solve this problem. It has been studied that the system approach provides a set of methodological techniques for finding and justifying decisions during the analysis of the functioning of complex systems. One of the main procedures when applying a system approach to the study of the effectiveness of an unmanned aircraft complex is the construction of a model of their functioning, which reflects the main patterns and relationships in the actual situation. Modeling is necessary for making informed decisions for combat use. In addition, it was investigated that the prerequisite for combat use is detecting and recognizing objects that need to be affected. In the materials of the article, a functional model is proposed, which allows not only the forecast of the capabilities of the on-board recognition system but also to estimate the optimal values of the parameters and characteristics of the complex systems and the conditions of use according to the criterion of the maximum value of the probability of object recognition from the images of the optical-electronic complex systems. We have proposed a functional model that allows not only forecasting the capabilities of the onboard recognition system but also estimating the optimal values of the parameters and characteristics of the complex systems and the conditions of use according to the criterion of the maximum value of the probability of object recognition from the images of the optical-electronic complex systems.

Despite significant effort on understanding complex biological systems, we lack a unified theory for modeling, inference, analysis, and efficient control of their dynamics in uncertain environments. These problems are made even more challenging when considering that only limited and noisy information is accessible for modeling, which can prove insufficient for explaining, and predicting the behavior of complex systems. For instance, missing information hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the model structure and parameters of complex biological systems. Toward this end, in this paper, we discuss several important mathematical challenges that could open new theoretical avenues in studying complex systems: (1) By understanding the universal laws characterizing the asymmetric statistics of magnitude increments and the complex space-time interdependency within one process and across many processes, we can develop a class of compact yet accurate mathematical models capable to potentially providing higher degree of predictability, and more efficient control strategies. (2) In order to better predict the onset of disease and their root cause, as well as potentially discover more efficient quality-of-life (QoL)-control strategies, we need to develop mathematical strategies that not only are capable to discover causal interactions and their corresponding mathematical expressions for space and time operators acting on biological processes, but also mathematical and algorithmic techniques to identify the number of unknown unknowns (UUs) and their interdependency with the observed variables. (3) Lastly, to improve the QoL of control strategies when facing intra- and inter-patient variability, the focus should not only be on specific values and ranges for biological processes, but also on optimizing/controlling knob variables that enforce a specific spatiotemporal multifractal behavior that corresponds to an initial healthy (patient specific) behavior. All in all, the modeling, analysis and control of complex biological collective systems requires a deeper understanding of the multifractal properties of high dimensional heterogeneous and noisy data streams and new algorithmic tools that exploit geometric, statistical physics, and information theoretic concepts to deal with these data challenges.