Optimal design, robustness, and risk aversion
arXiv:cond-mat/0202330 · doi:10.1103/PhysRevLett.89.028301
Abstract
Highly optimized tolerance is a model of optimization in engineered systems, which gives rise to power-law distributions of failure events in such systems. The archetypal example is the highly optimized forest fire model. Here we give an analytic solution for this model which explains the origin of the power laws. We also generalize the model to incorporate risk aversion, which results in truncation of the tails of the power law so that the probability of disastrously large events is dramatically lowered, giving the system more robustness.
11 pages, 2 figures
Cited by in corpus (14)
- Scale-free networks are rare
- Assessing Interaction Networks with Applications to Catastrophe Dynamics and Disaster Management
- Controlling Self-Organizing Dynamics on Networks Using Models that Self-Organize
- Not Normal: the uncertainties of scientific measurements
- Highly optimized tolerance and power laws in dense and sparse resource regimes
- Percolation and Loop Statistics in Complex Networks
- Error threshold in optimal coding, numerical criteria and classes of universalities for complexity
- Quantifying structure in networks
- Noncooperatively Optimized Tolerance: Decentralized Strategic Optimization in Complex Systems
- Connectedness matters: Construction and exact random sampling of connected graphs
- Resiliency and Robustness of Complex, Multi-Genre Networks
- Fingerprint for Network Topologies
- Controlling Cost in Sandpile Models Through Local Adjustment of Drive
- Entropy Maximization as a Holistic Design Principle for Complex Optimal Networks and the Emergence of Power Laws