The role of the environment in collective perception:A generic complexity measure

We propose a novel generic information-theoretic framework for characterizing the task difficulty in the Collective Perception paradigm. Our formalism builds on the notion of Empowerment - a task-independent, universal and generic utility function, which characterizes the level of perceivable control an embodied agent has over its environment. Series of simulations with an empowerment model of the collective perception scenario revealed a significant correlation between the levels of empowerment and the accuracy demonstrated by a set of standard collective decision-making strategies and a recent state-of-the-art neural network controller on nine benchmark patterns, used previously for assessing swarm performance. The results elucidate the key role of both the agent embodiment and the environmental pattern in characterising task difficulty, and justify the application of empowerment to analytically assess this role, which could help predict swarm performance and support the development of more efficient decision-making strategies

Self-organizing particle systems

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Advances in Complex Systems following peer review. The version of record, Malte Harder and Daniel Polani, ‘Self-organizing particle systems’, Advs. Complex Syst. 16, 1250089, published October 22, 2012, is available online via doi: https://doi.org/10.1142/S0219525912500890 Published by World Scientific Publishing.The self-organization of cells into a living organism is a very intricate process. Under the surface of orchestrating regulatory networks there are physical processes which make the information processing possible, that is required to organize such a multitude of individual entities. We use a quantitative information theoretic approach to assess self-organization of a collective system. In particular, we consider an interacting particle system, that roughly mimics biological cells by exhibiting differential adhesion behavior. Employing techniques related to shape analysis, we show that these systems in most cases exhibit self-organization. Moreover, we consider spatial constraints of interactions, and additionaly show that particle systems can self-organize without the emergence of pattern-like structures. However, we will see that regular pattern-like structures help to overcome limitations of self-organization that are imposed by the spatial structure of interactions.Peer reviewe

Dynamical criticality: overview and open questions

Systems that exhibit complex behaviours are often found in a particular dynamical condition, poised between order and disorder. This observation is at the core of the so-called criticality hypothesis, which states that systems in a dynamical regime between order and disorder attain the highest level of computational capabilities and achieve an optimal trade-off between robustness and flexibility. Recent results in cellular and evolutionary biology, neuroscience and computer science have revitalised the interest in the criticality hypothesis, emphasising its role as a viable candidate general law in adaptive complex systems. This paper provides an overview of the works on dynamical criticality that are - to the best of our knowledge - particularly relevant for the criticality hypothesis. The authors review the main contributions concerning dynamics and information processing at the edge of chaos, and illustrate the main achievements in the study of critical dynamics in biological systems. Finally, the authors discuss open questions and propose an agenda for future work

Modeling and Analyzing Systemic Risk in Complex Sociotechnical Systems The Role of Teleology, Feedback, and Emergence

Recent systemic failures such as the BP Deepwater Horizon Oil Spill, Global Financial Crisis, and Northeast Blackout have reminded us, once again, of the fragility of complex sociotechnical systems. Although the failures occurred in very different domains and were triggered by different events, there are, however, certain common underlying mechanisms of abnormalities driving these systemic failures. Understanding these mechanisms is essential to avoid such disasters in the future. Moreover, these disasters happened in sociotechnical systems, where both social and technical elements can interact with each other and with the environment. The nonlinear interactions among these components can lead to an “emergent” behavior – i.e., the behavior of the whole is more than the sum of its parts – that can be difficult to anticipate and control. Abnormalities can propagate through the systems to cause systemic failures. To ensure the safe operation and production of such complex systems, we need to understand and model the associated systemic risk. Traditional emphasis of chemical engineering risk modeling is on the technical components of a chemical plant, such as equipment and processes. However, a chemical plant is more than a set of equipment and processes, with the human elements playing a critical role in decision-making. Industrial statistics show that about 70% of the accidents are caused by human errors. So, new modeling techniques that go beyond the classical equipment/process-oriented approaches to include the human elements (i.e., the “socio” part of the sociotechnical systems) are needed for analyzing systemic risk of complex sociotechnical systems. This thesis presents such an approach. This thesis presents a new knowledge modeling paradigm for systemic risk analysis that goes beyond chemical plants by unifying different perspectives. First, we develop a unifying teleological, control theoretic framework to model decision-making knowledge in a complex system. The framework allows us to identify systematically the common failure mechanisms behind systemic failures in different domains. We show how cause-and-effect knowledge can be incorporated into this framework by using signed directed graphs. We also develop an ontology-driven knowledge modeling component and show how this can support decision-making by using a case study in public health emergency. This is the first such attempt to develop an ontology for public health documents. Lastly, from a control-theoretic perspective, we address the question, “how do simple individual components of a system interact to produce a system behavior that cannot be explained by the behavior of just the individual components alone?” Through this effort, we attempt to bridge the knowledge gap between control theory and complexity science

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Organic Service-Level Management in Service-Oriented Environments

Dynamic service-oriented environments (SOEs) are characterised by a large number of heterogeneous service components that are expected to support the business as a whole. The present work provides a negotiation-based approach to facilitate automated and multi-level service-level management in an SOE, where each component autonomously arranges its contribution to the whole operational goals. Evaluation experiments have shown an increased responsiveness and stability of an SOE in case of changes

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms