Complexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation.
Here are a few things you should know about complex systems,
result of a worldwide collaborative effort from leading experts, practitioners and students in the field.
Lee Smolin has a radical idea for how to understand an object with no exterior: Imagine it built bit-by-bit from relationships between events.
• The dynamics of frequency-rank relations in six languages is analyzed.
• Acceptable agreement is found for the quenched rank frequency data of these languages.
• This approach might find applications in other ranked systems, such as sports.
Rank-frequency distribution of natural languages: A difference of probabilities approach
Germinal Cocho, Rosalío F. Rodríguez, Sergio Sánchez, Jorge Flores, Carlos Pineda, Carlos Gershenson
Physica A: Statistical Mechanics and its Applications
Volume 532, 15 October 2019, 121795
We introduce five measures describing the system-wide behaviour of complex ecological systems. Within an information-theoretic framework, these measures account for changes in both species diversity and total biomass to describe (i) overall system change, (ii) sustainability to external pressure, (iii) shift from a baseline state and two types of resilience: (iv) ability to recover from local pressures and (v) overall potential to return to a baseline state. We apply these measures to study the behaviour of three computer models: a large 59-functional groups complex ecological model (Ecopath with Ecosim) of north Western Australia undergoing internal dynamics, a smaller 6-group coral reef model subjected to various combinations of single and multiple stressors and a prey–predator model displaying limit cycles. We demonstrate the state-dependency of properties like resilience and sustainability by showing how these measures change in time as a function of internal dynamics and external forcing. Furthermore, we show how our proposed measures can simplify system analysis and monitoring by providing indicators of changes in system behaviour, sustainability, and resilience.
Information-theoretic measures of ecosystem change, sustainability, and resilience
Fabio Boschetti Karine Prunera Mathew A Vanderklift Damian P Thomson Russell C Babcock Christopher Doropoulos Anna Cresswell Hector Lozano-Montes
ICES Journal of Marine Science, fsz105
We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures not only withstand stress but also benefit from it.
Equihua Zamora M, Espinosa M, Gershenson C, López-Corona O, Munguia M, Pérez-Maqueo O, Ramírez-Carrillo E. 2019. Ecosystem antifragility: Beyond integrity and resilience. PeerJ Preprints 7:e27813v1