Bio-Inspired Design for Robust Power Networks

Panyam, Varuneswara; Huang, Hao; Pinte, Bogdan; Davis, Katherine

doi:10.1109/tpec.2019.8662130

Cited by 16 publications

(21 citation statements)

References 21 publications

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“…Matsuyama (1995) is a good introduction to this literature. [See also, e.g., Venables and Smith (1986), Driffill and van der Ploeg (1993), Weitzman (1994), Bilbiie et al (2008), Ulanowicz (2009a, b), Lietaer et al (2010), Kiss and Kiss (2018), Bilbiie et al (2019), Panyam et al (2019), and Dave and Layton (2019). ]…”

Section: Introductionmentioning

confidence: 99%

On the emergence of ecological and economic niches

et al. 2020

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The origin of economic niches, conceived as potential markets, has been mostly neglected in economic theory. Ecological niches emerge as new species evolve and fit into a web of interactions, and the more species come into existence, the more (exponentially or power-law distributed) ecological niches emerge. In parallel fashion, economic niches emerge with new goods, and niche formation in economics is also exponentially or power-law distributed. In economics and ecology alike, autocatalytic processes drive the system to greater and greater diversity. Novelty begets novelty in a positive feedback loop. An autocatalytic set of self-enabling transactions feed back upon one another in combinatoric fashion to generate progressive diversity. While these combinatorial dynamics cannot be prestated, the model explains the "hockeystick of economic growth"-a pattern of prolonged stasis followed by a sudden takeoff, such as occurred during the Industrial Revolution or the Cambrian explosion in ecology. Several implications derive from our niche emergence model, including the idea that the evolutionary process of technological change is not something we do; rather, it happens to us.

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Section: Introductionmentioning

confidence: 99%

On the emergence of ecological and economic niches

et al. 2020

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“…A corollary of the tradeoff between complexity and scale is the tradeoff between adaptability and efficiency [10][11][12][13][14][15]. Adaptability arises when there are many possible actions happening in parallel that are mostly independent from one another, i.e., when the system has high complexity.…”

Section: Tradeoffs Between Complexity and Scalementioning

confidence: 99%

An Introduction to Complex Systems Science and Its Applications

2020

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The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multiscale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. This paper assumes only a high school mathematical and scientific background so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.

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“…ENA metrics can then be calculated using T. In this section, the ENA modeling process and the DoSO metric are elaborated, focusing on the notional SoS architectures used in this study. Interested readers may refer to previous work, which has adapted ENA for applications in industrial networks, [66][67][68][69][70][71] power grids, 72 water distribution networks, 73,74 and supply chains. 75 Consider the architecture presented in Figure 3(C).…”

Section: Sos Architectures' Doso Evaluationmentioning

confidence: 99%

Ecology‐inspired resilient and affordable system of systems using degree of system order

Chatterjee

Malak

Layton

2021

Systems Engineering

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An optimal trade-off between resilience (the ability to survive and recover from disruptions) and affordability is highly desirable in System of Systems (SoS) architectures. However, it is difficult to design SoS architectures for these goals as existing resilience evaluation methods require detailed disruption models that are not readily available in the early stages of design. Biological ecosystems (nature's resilient SoS) achieve these goals through a unique balance of efficient and redundant interactions in their architectures-measured using the metric Degree of System Order (DoSO).This research tests if this ecological architecting principle and the DoSO metric are useful for designing resilient and affordable SoS. The resilience versus affordability tradespace of a large number of notional SoS architectures is investigated using the DoSO metric. Results indicate that the majority of Pareto optimal SoS architectures, under various disruptions, lie in the ecologically-identified favorable DoSO range. Further, SoS architectures within this DoSO range were found to have better resilience and affordability attributes, in general, than the architectures outside it. DoSO evaluation does not require knowledge of disruption scenarios and is the first network architecture metric to consider resilience versus affordability trade-offs, making it a valuable addition to the SoS engineering toolset.

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Bio-Inspired Design for Robust Power Networks

Cited by 16 publications

References 21 publications

On the emergence of ecological and economic niches

On the emergence of ecological and economic niches

An Introduction to Complex Systems Science and Its Applications

Ecology‐inspired resilient and affordable system of systems using degree of system order

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