10 research outputs found

    A complex systems approach to education in Switzerland

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    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

    On the evolution of genotype-phenotype mapping: exploring viability in the Avida articial life system

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    The seminal architecture of machine self-reproduction originally formulated by John von Neumann underpins the mechanism of self-reproduction equipped with genotype and phenotype. In this thesis, initially, a hand-designed prototype von Neumann style selfreproducer as an ancestor is described within the context of the artificial life system Avida. The behaviour of the prototype self-reproducer is studied in search of evolvable genotype-phenotype mapping that may potentially give rise to evolvable complexity. A finding of immediate degeneration of the prototype into a self-copying mode of reproduction requires further systematic analysis of mutational pathways. Through demarcating a feasible and plausible characterisation and classification of strains, the notion of viability is revisited, which ends up being defined as quantitative potential for exponential population growth. Based on this, a framework of analysis of mutants' evolutionary potential is proposed, and, subsequently, the implementation of an enhanced version of the standard Avida analysis tool for viability analysis as well as the application of it to the prototype self-reproducer strain are demonstrated. Initial results from a one-step single-point-mutation space of the prototype, and further, from a multi-step mutation space, are presented. In the particular case of the analysis of the prototype, the majority of mutants unsurprisingly turn out to be simply infertile, without viability; whereas mutants that prove to be viable are a minority. Nevertheless, by and large, it is pointed out that distinguishing reproduction modes algorithmically is still an open question, much less finer-grained distinction of von Neumann style self-reproducers. Including this issue, speciifc limitations of the enhanced analysis are discussed for future investigation in this direction

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    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

    Paradoxes of Interactivity

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    Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. »Paradoxes of Interactivity« brings together reflections on »interactivity« from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound

    Paradoxes of interactivity: perspectives for media theory, human-computer interaction, and artistic investigations

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    Current findings from anthropology, genetics, prehistory, cognitive and neuroscience indicate that human nature is grounded in a co-evolution of tool use, symbolic communication, social interaction and cultural transmission. Digital information technology has recently entered as a new tool in this co-evolution, and will probably have the strongest impact on shaping the human mind in the near future. A common effort from the humanities, the sciences, art and technology is necessary to understand this ongoing co- evolutionary process. Interactivity is a key for understanding the new relationships formed by humans with social robots as well as interactive environments and wearables underlying this process. Of special importance for understanding interactivity are human-computer and human-robot interaction, as well as media theory and New Media Art. "Paradoxes of Interactivity" brings together reflections on "interactivity" from different theoretical perspectives, the interplay of science and art, and recent technological developments for artistic applications, especially in the realm of sound

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

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    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
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