Using Influence To Understand Complex Systems
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Using Influence to Understand Complex Systems
This thesis is concerned with understanding the behavior of complex systems, particularly in the common case where instrumentation data is noisy or incomplete. We begin with an empirical study of logs from production systems, which characterizes the content of those logs and the challenges associated with analyzing them automatically, and present an algorithm for identifying surprising messages in such logs. The principal contribution is a method, called influence, that identifies relationships among components---even when the underlying mechanism of interaction is unknown---by looking for correlated surprise. Two components are said to share an influence if they tend to exhibit surprising behavior that is correlated in time. We represent the behavior of components as surprise (deviation from typical or expected behavior) over time and use signal-processing techniques to find correlations. The method makes few assumptions about the underlying systems or the data they generate, so it is applicable to a variety of unmodified production systems, including supercomputers, clusters, and autonomous vehicles. We then extend the idea of influence by presenting a query language and online implementation, which allow the method to scale to systems with hundreds of thousands of signals. In collaboration with system administrators, we applied these tools to real systems and discovered correlated problems, failure cascades, skewed clocks, and performance bugs. According to the administrators, it also generated information useful for diagnosing and fixing these issues.
Emergent Nested Systems
This book presents a theory as well as methods to understand and to purposively influence complex systems. It suggests a theory of complex systems as nested systems, i. e. systems that enclose other systems and that are simultaneously enclosed by even other systems. According to the theory presented, each enclosing system emerges through time from the generative activities of the systems they enclose. Systems are nested and often emerge unplanned, and every system of high dynamics is enclosed by a system of slower dynamics. An understanding of systems with faster dynamics, which are always guided by systems of slower dynamics, opens up not only new ways to understanding systems, but also to effectively influence them. The aim and subject of this book is to lay out these thoughts and explain their relevance to the purposive development of complex systems, which are exemplified in case studies from an urban system. The interested reader, who is not required to be familiar with system-theoretical concepts or with theories of emergence, will be guided through the development of a theory of emergent nested systems. The reader will also learn about new ways to influence the course of events - even though the course of events is, in principle, unpredictable, due to the ever-new emergence of real novelty.
Fostering Understanding of Complex Systems in Biology Education
Author: Orit Ben Zvi Assaraf
language: en
Publisher: Springer Nature
Release Date: 2022-05-25
This book synthesizes a wealth of international research on the critical topic of ‘fostering understanding of complex systems in biology education’. Complex systems are prevalent in many scientific fields, and at all scales, from the micro scale of a single cell or molecule to complex systems at the macro scale such as ecosystems. Understanding the complexity of natural systems can be extremely challenging, though crucial for an adequate understanding of what they are and how they work. The term “systems thinking” has become synonymous with developing a coherent understanding of complex biological processes and phenomena. For researchers and educators alike, understanding how students’ systems thinking develops is an essential prerequisite to develop and maintain pedagogical scaffolding that facilitates students’ ability to fully understand the system’s complexity. To that end, this book provides researchers and teachers with key insights from the current research community on how to support learners systems thinking in secondary and higher education. Each chapter in the book elaborates on different theoretical and methodological frameworks pertaining to complexity in biology education and a variety of biological topics are included from genetics, photosynthesis, and the carbon cycle to ecology and climate change. Specific attention is paid to design elements of computer-based learning environments to understand complexity in biology education.