Systemic risk and network dynamics

The financial crisis that rippled through the world’s economies in 2007?2008 highlighted the risks of cascading failures in interconnected markets.

Similar systemic risks are prevalent in food-supply chains, disease dynamics, food webs, energy grids, transportation networks, and information flows. EEP is pursuing analyses of systemic risk and of the underlying network dynamics in close collaboration with IIASA’s ASA Program, to identify indicators of systemic risk and resilience across different network-structured natural and anthropogenic systems.

Two research efforts in 2012 examined systemic risks in food webs. Veshchinskaya et al. (in preparation) developed a model of collapse dynamics in ecological networks and used it to investigate how structural features of food webs affect the risk of cascading collapses. Ayers et al. (in preparation) studied how sensitive the different species in the KwaZulu-Natal Bight ecosystem are to anthropogenic harvesting. In a complementary effort, Puchkova et al. (in preparation) developed and evaluated a novel method for forecasting critical changes in dynamical systems. Fasani and Rinaldi (2013) advanced our understanding of network dynamics by studying their propensity to synchronize, and Rinaldi (2012) illustrated the utility of the developed techniques for analyzing network dynamics and assessing systemic risk, by deriving simple and explicit conditions for pest-insect outbreaks in spatially structured forests.

Fitness elasticities enable an objective and integrative comparison of selection pressures and of the benefits of potential management interventions across traits, populations, and species. (a) The first step is to establish an elasticity path diagram for the pairwise relationships between phenotypic traits, fitness components, and the population growth rates of subpopulations. Arrows indicate how a proportional change of one variable results in a proportional change of another variable. Elasticities are shown in bold and italics for subpopulations A and B, respectively. For example, a 100% increase in the annual juvenile growth increment results in a 287% increase in fecundity (2.87). (b) The second step is to calculate a phenotypic trait’s fitness elasticity by following in (a) all left-to-right paths from the trait to the population growth rate, multiplying elasticities along the arrows and summing over all paths. In subpopulation A, the annual juvenile growth increment has a fitness elasticity of (–0.71 × 0.25) + (–0.71 × 0.75) + (2.87 × 0.25) = 0.01, whereas the date of river entry has a fitness elasticity of 0.91 × 0.25 = 0.23. The corresponding results for subpopulation B are 0.61 and 0.46. This shows that investing conservation efforts into improving juvenile growth in subpopulation B would be the most effective management intervention.

Last edited: 31 October 2013