Evolving Complex Systems Research Articles

A Dynamic Holding Approach to Stabilizing a Bus Line Based on the Q-Learning Algorithm with Multistage Look-Ahead

The unreliable service and the unstable operation of a high-frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method that combines the classic approximate dynamic programming (ADP) with the multistage look-ahead mechanism. The holding time, the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remaining observation period. The multistage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes it easy that the algorithm gets through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into account most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Compared with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.

Fractal evolution of urban street networks in form and structure: a case study of Hong Kong

ABSTRACT Cities are spatially evolving complex systems. The order and pattern beneath the apparent chaos and diverse physical forms of cities are still unclear. How the form and structure of a city evolve to improve its functions needs further exploration. To fill thisgap, we examine the geometric fractal (GF), the topological fractal (TF), and the hierarchical fractal (HF) evolution of cities by taking Hong Kong street networks from year 1971 to 2018 as an example. We find that these networks keep to be fractals both in form and structure. The values of GF, TF, and HF dimensions increase with fluctuations, revealing a more mature and complex street network. The radius-length GF dimensions demonstrate the bi-fractal property, with values ranged 1.653–1.832 and 0.677–0.892, respectively, reflecting a core-periphery pattern. The values of TF dimensions increase steadily with a wider gap to GF dimensions, indicating progressively structural optimization of street networks. These street networks keep showing fractal properties in form and structure through spatial extension, local densification, vertical stratification, hierarchies enrichment, and shortcuts construction. Moreover, street networks are GFs and TFs at the city, county, and MSA scales. The discoveries advance our understanding of urban development.

Perspectives From Systems Biology to Improve Knowledge of Leishmania Drug Resistance.

Neglected Tropical Diseases include a broad range of pathogens, hosts, and vectors, which represent evolving complex systems. Leishmaniasis, caused by different Leishmania species and transmitted to humans by sandflies, are among such diseases. Leishmania and other Trypanosomatidae display some peculiar features, which make them a complex system to study. Leishmaniasis chemotherapy is limited due to high toxicity of available drugs, long-term treatment protocols, and occurrence of drug resistant parasite strains. Systems biology studies the interactions and behavior of complex biological processes and may improve knowledge of Leishmania drug resistance. System-level studies to understand Leishmania biology have been challenging mainly because of its unusual molecular features. Networks integrating the biochemical and biological pathways involved in drug resistance have been reported in literature. Antioxidant defense enzymes have been identified as potential drug targets against leishmaniasis. These and other biomarkers might be studied from the perspective of systems biology and systems parasitology opening new frontiers for drug development and treatment of leishmaniasis and other diseases. Our main goals include: 1) Summarize current advances in Leishmania research focused on chemotherapy and drug resistance. 2) Share our viewpoint on the application of systems biology to Leishmania studies. 3) Provide insights and directions for future investigation.

Democracy as an evolutionary process

In this chapter we argue that contemporary Democracies undergo intense political processes of competitive transformation, in such a way that they can be analyzed as evolving complex systems. We draw upon Schumpeterian evolutionary economics, to explore how local and global interactions between heterogeneous boundedly-rational citizens and platforms, innovative processes of competition to reach power and gradually transform society, and the co-evolution between the political and the economic realms, all lead to institutional transformations underlying political and economic change. The evolutionary approach to Democracy highlights new political paradoxes and dilemmas that must be explored, monitored and faced in a dynamic way. We claim that there exists a fruitful evolutionary approach to political economy that can add surprising findings to those provided by Public Choice theory and Neoclassical Welfare Economics.

On Perspectives of the Interdisciplinary Synthesis in Social Sciences at the End of the 20th Century

When in the last two decades of the 20th century new ideas on complexity had started to diffuse in social sciences, the economic thought became a typical area of interdisciplinary research in which the concepts of chaos, nonlinear movement, turbulence, and instability became best to apply. The article looks at how the approach of complexity has been implemented in the Santa Fe interdisciplinary project almost in it’s first ten years between two major workshops on the economy as an evolving complex system. At first it concentrates on general issues of complexity discussed by natural and social scientists in Santa Fe Institute accepting all of them as certain bias for the epistemological breach. Then it turns to the several topics, which problematized the Santa Fe thought in particular and made it unique among the scholars who have adopted the new concepts proposed by complexity science. It also argues that the term of so called “restricted complexity” applied by some epistemologists to characterize a limited score of research topics in Santa Fe is far from being adequate. Although all developments were not equally successful, they did turn out to have enormous resonance in terms of extensive theoretical and practical pertinence and broad areas of application in social sciences even then the complexity approach was challenged by widely spread revision inspired by Niklas Luhmann legacy. Based on a body of work of Brian Arthur and John Holland and their colleagues from Stanford and Massachusetts where complexity has been understood in ways of system behavior, the article presents a complex adaptive system not as the opposite but as a source and confederate of certain order.

Envisioning entrepreneurship and digital innovation through a design science research lens: A matrix approach

Design Science Research (DSR) in the information systems (IS) field is, at its essence, about Digital Innovation (DI). Innovative sociotechnical design artifacts involve digital information technologies (IT) being used in ways that result in profound disruptions to traditional ways of doing business and to widespread societal changes. The pervasiveness of DI means that the individuals involved in bringing it about have diverse backgrounds, including application specialists, software engineers, data scientists, business managers, economists, venture capitalists, various user groups, and entrepreneurial leaders. This range of backgrounds means that DI, much more than traditional innovation, leads to varied perspectives on the methods and tools to be used in the development of effective and evolvable complex systems incorporating digital innovations. In this paper we present a new matrix approach to DI based on DSR, entrepreneurship, and innovation theories. Clear strategic guidance allows these multiple stakeholders to make sense of the diverse landscape and to understand when and how different entrepreneurial strategies for innovation can best be applied. We define the combined DSR and DI matrix approach in terms of four strategies: invention; advancement; exaptation; and exploitation and their associated DI practices. The research contribution is a novel DSR-DI matrix process model. This model extends entrepreneurship theory as it enriches effectuation thinking with more detailed process guidance for ambidextrous entrepreneurship and it enriches DSR models for DI by showing more explicitly the different pathways corresponding to different quadrants in the knowledge-innovation matrix.

Collective sensing of evolving urban structures: From activity-based to content-aware social monitoring

Cities are constantly evolving complex systems. Detection methods of land use distribution have to keep pace with their rapidly changing landscapes. While traditional analysis relies on surveys and census data typically refreshed at most yearly, the widespread use of mobile devices allows cell phone activity measurements to be used as sensors for the functional clustering of urban districts. These activity-based proprietary measurements are recently complemented by publicly available geosocial network records that even enable content-aware analysis. As a bridge between separate methods, in this work we analyze the relation of conversation content and functional spatial clusters of cities using a double dataset approach. We look at the differentiating power of the content of local conversations in activity-driven land use detection based on mobile phone records. In addition to intra-city analysis of three metropolises, we present a comparative study of London, New York City, and Los Angeles sharing the common language of English, but having very different cultural backgrounds. We show that the share of words with a similar temporal pattern to that of local mobile activities is different across cities, as well as between functional clusters. We find that the conversational content can effectively differentiate both functional clusters of a single city, and similar locations of the same function across many cities, like business areas that otherwise have a common temporal heartbeat. Moreover, we identify words related to activity types as the most important features emerging from the content-based, data-driven classification.

A Simple Method to Estimate Entropy and Free Energy of Atmospheric Gases from Their Action.

A convenient practical model for accurately estimating the total entropy (ΣSi) of atmospheric gases based on physical action is proposed. This realistic approach is fully consistent with statistical mechanics, but reinterprets its partition functions as measures of translational, rotational, and vibrational action or quantum states, to estimate the entropy. With all kinds of molecular action expressed as logarithmic functions, the total heat required for warming a chemical system from 0 K (ΣSiT) to a given temperature and pressure can be computed, yielding results identical with published experimental third law values of entropy. All thermodynamic properties of gases including entropy, enthalpy, Gibbs energy, and Helmholtz energy are directly estimated using simple algorithms based on simple molecular and physical properties, without resource to tables of standard values; both free energies are measures of quantum field states and of minimal statistical degeneracy, decreasing with temperature and declining density. We propose that this more realistic approach has heuristic value for thermodynamic computation of atmospheric profiles, based on steady state heat flows equilibrating with gravity. Potentially, this application of an action principle can provide better understanding of emergent properties of many natural or evolving complex systems, including modelling of predictions for global warming.

Some elements for a definition of an evolutionary efficiency criterion

Together with the concepts of equilibrium, scarcity, choosing, etc., efficiency is at the core of economics. However, in an evolutionary context, efficiency raises several issues concerning to rationality, the complex evolving nature of the economy, economic change as the fundamental economic problem, and the role of expectations —that link purposeful action to actual action. The main goal of this paper is to provide some necessary elements to accommodate an efficiency criterion within an evolutionary theory of the production of action. In a nutshell, an evolving complex system could be considered as being (or “becoming”) efficient if the agents’ intentions could “materialize” in actions that would give rise to real states of affairs which, essentially, were compatible (even similar; never identical of course) with what it was expected (ex-ante) when the action “plans” were elaborated and selected. We set out this criterion as a micro-criterion and then we explore an extension of it at a systemic level using the theory of meso-level connections.

Modelling sequences and temporal networks with dynamic community structures

In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components’ dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks’ large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.

Detection of timescales in evolving complex systems.

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system’s configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.

Complexity Theory and Process Organization Studies

This chapter offers practical guidance to Process Organizational Studies (PROS) scholars and researchers who wish to use complexity theory to build conceptual and causal models of organizational processes. We address four questions: How can complexity theory add value to PROS? What markers signal to a PROS researcher that he or she should substitute complexity theory for the general linear model? What critical systemic features should the researcher specify as a prelude to constructing a model of a complex system? We conclude that the connection between PROS and complexity theory is ineluctable, since conducting a process organizational study automatically plunges the researcher into an evolving complex system.

Detection of timescales in evolving complex systems

Detection of timescales in evolving complex systems

Knowledge Maps of the UDC: Uses and Use Cases

Insight into the depth and breadth of knowledge for use in and across disciplines is of vital importance. Our knowledge maps are visualizations based on empirical evidence about both collection characteristics and knowledge clusters such as disciplines. We report in this paper on collaborative efforts over several years, combining the resources of the Knowledge Space Lab and the Research and Innovation Group at DANS. In particular, we were interested in the narrative of how knowledge and knowledge systems change over time. Knowledge organization systems are evolving complex systems. Their analysis, both concerning inner structure, evolution over time, and their implementation in information spaces is important to better understand how knowledge is produced and can be navigated through.

Understanding Policy Diffusion in the U.S.: An Information-Theoretical Approach to Unveil Connectivity Structures in Slowly Evolving Complex Systems.

Detecting and explaining the relationships among interacting components has long been a focal point of dynamical systems research. In this paper, we extend these types of data-driven analyses to the realm of public policy, whereby individual legislative entities interact to produce changes in their legal and political environments. We focus on the U.S. public health policy landscape, whose complexity determines our capacity as a society to effectively tackle pressing health issues. It has long been thought that some U.S. states innovate and enact new policies, while others mimic successful or competing states. However, the extent to which states learn from others, and the state characteristics that lead two states to influence one another, are not fully understood. Here, we propose a model-free, information-theoretical method to measure the existence and direction of influence of one state's policy or legal activity on others. Specifically, we tailor a popular notion of causality to handle the slow time-scale of policy adoption dynamics and unravel relationships among states from their recent law enactment histories. The method is validated using surrogate data generated from a new stochastic model of policy activity. Through the analysis of real data in alcohol, driving safety, and impaired driving policy, we provide evidence for the role of geography, political ideology, risk factors, and demographic and economic indicators on a state's tendency to learn from others when shaping its approach to public health regulation. Our method offers a new model-free approach to uncover interactions and establish cause-and-effect in slowly-evolving complex dynamical systems.

Enhanced Time-Expanded Decision Network: The Original TDN and More

This paper suggests several improvements over the original Time-expanded Decision Network (TDN; a powerful and generic methodology introduced by Silver and de Weck for designing evolvable complex systems) in order to leverage its ease, flexibility, and scope of use. A more accurate model of the switching process, a simpler network representation, and the ability to address systems with unknown lifetime are some of the improvements. However, the most important added feature is the ability to account for other design requirements within the process, which enables the designers to embed the TDN methodology into their general concept exploration phase. As a result, the enhanced TDN (ETDN) mitigates the need for iterations over several requirement domains and makes achieving the optimal design easier. It is also discussed in the paper that all of the improvements are attained without adding to the original method's complexity and only by a marginal increment of computational costs. As an example, the design of a sports car system is described and all of the new features are applied and the results are thoroughly discussed—although the example's focus is solely on the new improvements and not on the features that are directly inherited from the original method.

A COMPLEX NETWORKS PERSPECTIVE ON COLLABORATIVE SOFTWARE ENGINEERING

Large collaborative software engineering projects are interesting examples for evolving complex systems. The complexity of these systems unfolds both in evolving software structures, as well as in the social dynamics and organization of development teams. Due to the adoption of Open Source practices and the increasing use of online support infrastructures, large-scale data sets covering both the social and technical dimension of collaborative software engineering processes are increasingly becoming available. In the analysis of these data, a growing number of studies employ a network perspective, using methods and abstractions from network science to generate insights about software engineering processes. Featuring a collection of inspiring works in this area, with this topical issue, we intend to give an overview of state-of-the-art research. We hope that this collection of articles will stimulate downstream applications of network-based data mining techniques in empirical software engineering.

Windy trees

We present a novel method for combining developmental tree models with turbulent wind fields. The tree geometry is created from internal growth functions of the developmental model and its response to external stress is induced by a physically-plausible wind field that is simulated by Smoothed Particle Hydrodynamics (SPH). Our tree models are dynamically evolving complex systems that (1) react in real-time to high-frequent changes of the wind simulation; and (2) adapt to long-term wind stress. We extend this process by wind-related effects such as branch breaking as well as bud abrasion and drying. In our interactive system the user can adjust the parameters of the growth model, modify wind properties and resulting forces, and define the tree's long-term response to wind. By using graphics hardware, our implementation runs at interactive rates for moderately large scenes composed of up to 20 tree models.

Methods and applications for IT capacity decisions: Bringing management frameworks into practice

This paper discusses some aspects of the decisional challenges implied by the management of modern information technology (IT) systems. These rapidly evolving complex systems are crucial to the overall performance of industrial companies. Two challenges are delineated: (1) the complexity of modern systems requires a system-thinking approach; (2) the critical role of IT in business performance implies taking into account these technological aspects within any business plan. Some IT management frameworks are designed to face these challenges but seem not to provide enough comprehensible decisional support to IT managers in real time. That is why this paper proposes innovative decision-aid methods, designed for some of the activities related to capacity management. Attention is paid to modelling and monitoring activities, through methods that combine qualitative and quantitative analyses. These approaches can provide operational support to IT managers. The added value of the methods developed is demonstrated by practical examples implemented in a semiconductor manufacturing firm. These results are an encouraging first step towards more comprehensive and more effective solutions.

Evolving Symmetry for Modular System Design

Symmetry is useful as a constraint in designing complex systems such as distributed controllers for multilegged robots. However, it is often difficult to determine which symmetries are appropriate. It is therefore desirable to design such systems automatically, e.g., by utilizing evolutionary algorithms that produce symmetry through developmental mechanisms. The success of these algorithms depends on how well they explore the space of valid symmetries. This paper presents an approach called evolution of network symmetry and modularity (ENSO) that utilizes group theory to search the space of symmetries effectively. This approach was evaluated by evolving neural network controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as fast as those having hand-designed symmetry, and significantly faster than those without symmetry. On inclined ground, where the appropriate symmetries are difficult to determine manually, ENSO produced significantly faster gaits that also generalize better than those of other approaches. On robots with a more complicated structure including knee joints, ENSO resulted in more regular gaits than the other approaches. These results suggest that ENSO is a promising approach for evolving complex systems with modularity and symmetry.