The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability

In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making

Hybrid Societies : Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area

On robustness in biology: from sensing to functioning

Living systems are subject to various types of spatial and temporal noise at all scales and stages. Nevertheless, evolving under the pressure of natural selection, biology has mastered the ability of dealing with stochasticity. This is particularly crucial because these systems encounter numerous situations which require taking robust and proper actions in the presence of noise. Due to the complexity and variability of these situations, it is impossible to have a prescribed plan for an organism that keeps it alive and fully functional. Therefore, they have to be active, rather than passive, by following three essential steps: I) gathering information about their fluctuating environment, II) processing the information and making decisions via circuits that are inevitably noisy, and finally, III) taking the appropriate action robustly with organizations crossing multiple scales. Although various aspects of this general scheme have been subject of many studies, there are still many questions that remain unanswered: How can a dynamic environmental signal be sensed collectively by cell populations? and how does the topology of interactions affect the quality of this sensing? When processing information via the regulatory network, what are the drawbacks of multifunctional circuits? and how does the reliability of the decisions decrease as the multifunctionality increases? Finally, when the right decision is made and a tissue is growing with feedbacks crossing different scales, what are the crucial features that remain preserved from one subject to another? How can one use these features to understand the mechanisms behind these processes? This thesis addresses the main challenges for answering these questions and many more using methods from dynamical systems, network science, and stochastic processes. Using stochastic models, we investigate the fundamental limits arising from temporal noise on collective signal sensing and context-dependent information processing. Furthermore, by combining stochastic models and cross-scale data analyses, we study pattern formation during complex tissue growth.Lebende Systeme sind in allen Größenordnungen und Stadien verschiedenen Arten von räumlichem und zeitlichem Rauschen ausgesetzt. Dennoch hat die Biologie, die sich unter dem Druck der natürlichen Selektion entwickelt hat, die Fähigkeit gemeistert, mit stochastischen Fluktuationen umzugehen. Dies ist besonders wichtig, da Organismen auf zahlreiche Situationen stoßen, die es erfordern, in Gegenwart von Rauschen robuste und angemessene Maßnahmen zu ergreifen. Aufgrund der Komplexität und Variabilität dieser Situationen ist es unmöglich, einen vorgeschriebenen Plan für einen Organismus zu haben, der ihn überlebens- und funktionsfähig hält. Daher können Organismen sich nicht passiv verhalten, sondern befolgen aktiv drei wesentliche Schritte: I) Das Sammeln von Informationen über ihre dynamische Umgebung, II) Das Verarbeiten von Informationen und das Treffen von Entscheidungen über Regelnetzwerke, die unvermeidlich mit Rauschen behaftet sind, und schließlich, III) das robuste Funktionieren durch organisierte Maßnahmen, welche mehrere Größenordnungen überbrücken. Obwohl verschiedene Aspekte dieses allgemeinen Schemas Gegenstand vieler Studien waren, bleiben noch viele Fragen unbeantwortet: Wie kann ein dynamisches externes Signal kollektiv von Zellpopulationen wahrgenommen werden? Wie beeinflusst die Topologie der Interaktionen die Qualität dieser Wahrnehmung? Was sind die Nachteile multifunktionaler Schaltkreise bei der Verarbeitung von Informationen über das Regelnetzwerk? Wie nimmt die Zuverlässigkeit der Entscheidungen mit zunehmender Multifunktionalität ab? Und abschließend, wenn die richtige Entscheidung getroffen wurde und ein Gewebe wächst und dabei Rückkopplungen auf verschiedenen Größenordnungen erfährt, was sind die entscheidenden Merkmale, die von einem Versuchsobjekt zum anderen erhalten bleiben? Wie kann man diese Merkmale nutzen, um die Prozesse zu verstehen? Diese Arbeit befasst sich mit den wichtigsten Herausforderungen zur Beantwortung dieser und vieler weiterer Fragen mit Methoden aus dynamischen Systemen, Netzwerkforschung und stochastischen Prozessen

An experience of elicited inquiry elucidating the electron transport in semiconductor crystals

In this study we report the results of an inquiry-driven learning path experienced by a sample of 10 electronic engineering students, engaged to investigate the electron transport in semiconductors. The undergraduates were first instructed by following a lecture-based class on condensed matter physics and then involved into an inquiry based path of simulative explorations. The students were invited by two instructors to explore the electron dynamics in a semiconductor bulk by means of Monte Carlo simulations. The students, working in group, had to design their own procedure of exploration, as expected in a traditional guided inquiry. But they experienced several difficulties on planning and carrying out a meaningful sequence of simulative experiments, many times coming to a standstill. At this stage, the two instructors actively participated to the students’ debate on the physics governing the observed phenomena, never providing exhaustive explanations to the students, but giving comments and hints, sometimes expressly incorrect, but effective to stimulate students’ reasoning and activating a proficient scientific inquiry. The relation between this teaching intervention and student cognitive and affective development has been investigated by methods of discourse and behaviour analysis, as well as by the analysis of a student motivation/satisfaction inventory. The elicited inquiry stimulated the students to follow a question-driven path of exploration, starting from the validation of the model of electron dynamics within the semiconductor, up to performing reasoned inquiries about the observed characteristic of charge transport. Our results show that the stimulated activation of the inquiry process constitutes an efficient teaching/learning approach both to effectively engage students into an active learning and, at the same time, to clarify important experimental and technological aspects of semiconductor science, representing a viable example of integration of a traditional lecture-based teaching approach with effective learning strategies

Key Topics in Deep Geological Disposal : Conference Report (KIT Scientific Reports ; 7696)

The current state of knowledge of central aspects of radioactive waste repository research was presented in the course of the DAEF conference "Key topics in deep geological disposal". For the first time socio-economic and socio-technical issues played an important role within a conference focusing on the disposal of radioactive waste. Scientists from about 16 different countries presented their scientific work in 8 sessions and during a poster session

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844