67 research outputs found
âPrice-Quakesâ Shaking the World's Stock Exchanges
Background: Systemic risk has received much more awareness after the excessive risk taking by major financial instituations
pushed the worldâs financial system into what many considered a state of near systemic failure in 2008. The IMF for example
in its yearly 2009 Global Financial Stability Report acknowledged the lack of proper tools and research on the topic.
Understanding how disruptions can propagate across financial markets is therefore of utmost importance.
Methodology/Principal Findings: Here, we use empirical data to show that the worldâs markets have a non-linear threshold
response to events, consistent with the hypothesis that traders exhibit change blindness. Change blindness is the tendency
of humans to ignore small changes and to react disproportionately to large events. As we show, this may be responsible for
generating cascading eventsâpricequakesâin the worldâs markets. We propose a network model of the worldâs stock
exchanges that predicts how an individual stock exchange should be priced in terms of the performance of the global
market of exchanges, but with change blindness included in the pricing. The model has a direct correspondence to models
of earth tectonic plate movements developed in physics to describe the slip-stick movement of blocks linked via spring
forces.
Conclusions/Significance: We have shown how the price dynamics of the worldâs stock exchanges follows a dynamics of
build-up and release of stress, similar to earthquakes. The nonlinear response allows us to classify price movements of a
given stock index as either being generated internally, due to specific economic news for the country in question, or
externally, by the ensemble of the worldâs stock exchanges reacting together like a complex system. The model may provide
new insight into the origins and thereby also prevent systemic risks in the global financial network
Future landscapes: managing within complexity
A regional landscape is a complex socialâecological system comprising a dynamic mosaic of land uses. Management at this scale requires an understanding of the myriad interacting human and natural processes operating on the landscape over a continuum of spatial and temporal scales.Complexity science, which is not part of traditional management approaches, provides a valuable conceptual framework and quantitative tools for dealing with cross-scale interactions and non-linear dynamics in socialâecological systems. Here, we identify concepts and actions arising from complexity science that can be learned and applied by ecosystem managers and discuss how they might be implemented to achieve sustainable future landscapes.Lael Parrott and Wayne S Meye
Community-driven dispersal in an individual-based predator-prey model
We present a spatial, individual-based predator-prey model in which dispersal
is dependent on the local community. We determine species suitability to the
biotic conditions of their local environment through a time and space varying
fitness measure. Dispersal of individuals to nearby communities occurs whenever
their fitness falls below a predefined tolerance threshold. The spatiotemporal
dynamics of the model is described in terms of this threshold. We compare this
dynamics with the one obtained through density-independent dispersal and find
marked differences. In the community-driven scenario, the spatial correlations
in the population density do not vary in a linear fashion as we increase the
tolerance threshold. Instead we find the system to cross different dynamical
regimes as the threshold is raised. Spatial patterns evolve from disordered, to
scale-free complex patterns, to finally becoming well-organized domains. This
model therefore predicts that natural populations, the dispersal strategies of
which are likely to be influenced by their local environment, might be subject
to complex spatiotemporal dynamics.Comment: 43 pages, 7 figures, vocabulary modifications, discussion expanded,
references added, Ecological Complexity accepte
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Viewing forests through the lens of complex systems science
Complex systems science provides a transdisciplinary framework to study systems characterized by (1) heterogeneity, (2) hierarchy, (3) selfâorganization, (4) openness, (5) adaptation, (6) memory, (7) nonâlinearity, and (8) uncertainty. Complex systems thinking has inspired both theory and applied strategies for improving ecosystem resilience and adaptability, but applications in forest ecology and management are just beginning to emerge. We review the properties of complex systems using four wellâstudied forest biomes (temperate, boreal, tropical and Mediterranean) as examples. The lens of complex systems science yields insights into facets of forest structure and dynamics that facilitate comparisons among ecosystems. These biomes share the main properties of complex systems but differ in specific ecological properties, disturbance regimes, and human uses. We show how this approach can help forest scientists and managers to conceptualize forests as integrated socialâecological systems and provide concrete examples of how to manage forests as complex adaptive systems
Uniting statistical and individual-based approaches for animal movement modelling
<div><p>The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.</p></div
Social & Ecological Well-Being: Human Health & Connection to Regional Landscapes â Opportunities on Campuses
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