Eleni Nisioti, Colby Clark, Kaushik Kunal Das, Ekkehard Ernst, Nicholas A. Friedenberg, Emily Gates, Maryl Lambros, Anita Lazurko, Nataša Puzović, Ilvanna Salas

Front. Complex Syst., 25 September 2023

The term “resilience” has risen in popularity following a series of natural disasters, the impacts of climate change, and the Covid-19 pandemic. However, different disciplines use the term in widely different ways, resulting in confusion regarding how the term is used and difficulties operationalising the underlying concept. Drawing on an overview of eleven disciplines, our paper offers a guiding framework to navigate this ambiguity by suggesting a novel typology of resilience using an information-theoretic approach. Specifically, we define resilience by borrowing an existing definition of individuals as sub-systems within multi-scale systems that exhibit temporal integrity amidst interactions with the environment. We quantify resilience as the ability of individuals to maintain fitness in the face of endogenous and exogenous disturbances. In particular, we distinguish between four different types of resilience: (i) preservation of structure and function, which we call “strong robustness”; (ii) preservation of function but change in structure (“weak robustness”); (iii) change in both structure and function (“strong adaptability”); and (iv) change in function but preservation in structure (“weak adaptability”). Our typology offers an approach for navigating these different types and demonstrates how resilience can be operationalised across disciplines.

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Michael Batty

Environment and Planning B: Urban Analytics and City Science Volume 50, Issue 7

A basic canon of the systems approach applicable to any field is the notion that a system is separable and distinct from its wider environment. In short, to formally study such a system, it must have a well-defined boundary beyond which it has no substantial impact on its wider context, while its wider context is usually composed of similar systems which have minimal impact on the system in question. The implication is that the environment defined by its boundary ‘excludes’ any significant actions or interactions essential for the functioning of the system itself. This is, in some respects, equivalent to the notion that we are defining a closed system which we can study in isolation from any extraneous or exogenous factors that might affect its operation. It is the definition used by Karl Popper (1959) to justify the use of the classical scientific method as fashioned in experimental science where the laboratory must be closed from the outside environment for robust theories to be tested and validated. In the case of cities, historically or at least from the middle of the last century, such boundaries are typically defined to minimise the overall interactions between the system and its environment. The implication is that insofar as there are many distinct systems, to minimise the interactions between one another, they are often arranged as a hierarchy. To minimise the exchange of energies between the system and all the systems within its environment, a good working definition of a system is that it contains the most significant interactions within the system itself (Simon, 1969). This question of course turns on what is regarded as ‘significant’.

Read the full article at: journals.sagepub.com

Oliver M. Cliff, Annie G. Bryant, Joseph T. Lizier, Naotsugu Tsuchiya & Ben D. Fulcher 
Nature Computational Science (2023)

Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems, but these computational methods—from contemporaneous correlation coefficients to causal inference methods—define and formulate interactions differently, using distinct quantitative theories that remain largely disconnected. Here we introduce a large assembled library of 237 statistics of pairwise interactions, and assess their behavior on 1,053 multivariate time series from a wide range of real-world and model-generated systems. Our analysis highlights commonalities between disparate mathematical formulations of interactions, providing a unified picture of a rich interdisciplinary literature. Using three real-world case studies, we then show that simultaneously leveraging diverse methods can uncover those most suitable for addressing a given problem, facilitating interpretable understanding of the quantitative formulation of pairwise dependencies that drive successful performance. Our results and accompanying software enable comprehensive analysis of time-series interactions by drawing on decades of diverse methodological contributions.

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Jean-François Bonnefon Iyad Rahwan Azim Shariff

Moral psychology was shaped around three categories of agents and patients: humans, other animals, and supernatural beings. Rapid progress in Artificial Intelligence has introduced a fourth category for our moral psychology to deal with: intelligent machines. Machines can perform as moral agents, making decisions that affect the outcomes of human patients, or solving moral dilemmas without human supervi- sion. Machines can be as perceived moral patients, whose outcomes can be affected by human decisions, with important consequences for human-machine cooperation. Machines can be moral proxies, that hu- man agents and patients send as their delegates to a moral interaction, or use as a disguise in these interactions. Here we review the exper- imental literature on machines as moral agents, moral patients, and moral proxies, with a focus on recent findings and the open questions that they suggest.

Read the full article at: psyarxiv.com