Toward a pluralistic conception of resilience

Abstract

The concept of resilience occupies an increasingly prominent position within contemporary efforts to confront many of modernity's most pressing challenges, including global environmental change, famine, infrastructure, poverty, and terrorism, to name but a few. Received views of resilience span a broad conceptual and theoretical terrain, with a diverse range of application domains and settings. In this paper, we identify several foundational tenets-dealing primarily with intent/intentionality and uncertainty-that are seen to underlie a number of recent accounts of resilience, and we explore the implications of these tenets for ongoing attempts to articulate the rudiments of an overarching resilience paradigm. Firstly, we explore the complemental nature of risk and resilience, looking, initially, at the role that linearity assumptions play in numerous resilience frameworks found in the literature. We then explore the limitations of these assumptions for efforts directed at modeling risk and resilience in complex domains. These discussions are then used to motivate a pluralistic conception of resilience, drawing inspiration and content from a broad range of sources and empirical domains, including information, network, and decision theories. Secondly, we sketch the rudiments of a framework for engineered resilience, the primary focus of which is the exploration of the fundamental challenges that system design and system performance pose for resilience managers. The conception of engineered resilience set forth here also considers how challenges concerning time and predictability should factor explicitly into the formal schemes that are used to represent and model resilience. Finally, we conclude with a summary of our findings, and we provide a brief sketch of possible future research directions.

The application of resilience theory in urban development: a literature review

“…For example, Haimes ( 2009 ) pointed out “resilience refers to the ability of a system to withstand significant damage under unacceptable degradation parameters and recover at an appropriate time and reasonable costs and risks.” The Intergovernmental Panel on Climate Change (IPCC) described the resilience as “a system’s ability to anticipate, absorb, accommodate, or recover from the impact of a hazardous event” (Rana 2020 ). Cinner and Barnes ( 2019 ) pointed out “resilience is commonly defined as the ability to cope with changing social or environmental conditions while maintaining main structural, functional, and identity elements.” Convertino and Valverde ( 2019 ) put forward “resilience can be seen as the response of a system to the observations or predictions of one or more definable risks.”…”

Emerging Priorities for Microbiome Research

“…Each node of the community can contain a detailed characterization of the microbiome interaction network or graph (see Figure 4). Systemic inter-community networks can also be inferred from information theoretic models (Convertino and Valverde, 2019;Li and Convertino, 2019) or statistical models based on interdependence of microbial patterns.…”

“…These functional and/or structural networks can be the basis of verified co-occurrence networks as defined later (see section "Harnessing Association Graphs to Discover Co-occurrence Relationships"). Figure 3A shows the typical modeling trade-off between model complexity, uncertainty and scale (see Convertino and Valverde, 2019), as well as potential plots of interest for microbiome research generated by information theoretic or other stochastic models. These plots, in order of information significance from left to right, refer to key features of the microbiome, such as microbiome functional diversity, which is known to affect health and disease in populations (Martí et al, 2017;Li and Convertino, 2019).…”

“…Finally, the last plot in Figure 3B shows the whole probability distribution function (pdf) of a microbiome in relation to stochastic changes in the stressors; this plot is the most informative since any pdf can correspond to a ''microbiome state" as shown by the middle plot, and the whole range of values (with the corresponding probability) is identified. Typically, a ''state" is associated to a microbiome functional network that is reflected by a certain pdf type (Convertino and Valverde, 2019).…”

“…Proper biological treatment (e.g., composting, aeration, anaerobic digestion) or chemical disinfection (e.g., chlorine, lime stabilization, UV, ozone) should be conducted to reduce the pathogens and ARB inside before disposal or land application (Spiehs and Goyal, 2007). However, the effectiveness of those management and ecohydrological control approaches needs to be investigated further and likely a portfolio approach of multiple environmental controls is needed (Convertino and Valverde, 2019).…”