A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

X-Machines for Agent-Based Modeling

This book discusses various aspects of agent-based modeling and simulation using FLAME (Flexible Large-scale Agent-Based Modeling Environment) which is a popular agent-based modeling environment that enables automatic parallelization of models. Along with a focus on the software engineering principles in building agent-based models, the book comprehensively discusses how models can be written for various domains including biology, economics and social networks. The book also includes examples to guide readers on how to write their own models

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

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

A Bio-inspired Load Balancing Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of multiple distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging trade-offs. This thesis is concerned with the load balancing of Wireless Sensor Networks (WSNs). We present an approach, inspired by bees’ pheromone propagation mechanism, that allows individual nodes to decide on the execution process locally to solve the trade-off between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using a system-level simulator. The effectiveness of the algorithm is evaluated on case studies based on sound sensors with different scenarios of existing approaches on variety of different network topologies. The performance of our approach is dependant on the values chosen for its parameters. As such, we utilise the Simulated Annealing to discover optimal parameter configurations for pheromone-based load balancing technique for any given network schema. Once the parameter values are optimised for the given network topology automatically, we inspect improving the pheromone-based load balancing approach using robotic agents. As cyber-physical systems benefit from the heterogeneity of the hardware components, we introduce the use of pheromone signalling-based robotic guidance that integrates the robotic agents to the existing load balancing approach by guiding the robots into the uncovered area of the sensor field. As such, we maximise the service availability using the robotic agents as well as the sensor nodes

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u