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Firedoglake » FDL Book Salon Welcomes Scott Page: The Difference
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The key insight is that a single strong heuristic will do worse than a collective of individually weak but diverse heuristics. The problems are too hard for any one heuristic to solve perfectly, but the diverse heuristics can, so to speak, cover each others' weaknesses and help each other out when they get stuck; a single strong heuristic can't. A collection of diverse strong heuristics would be even better, but the strong heuristics for a problem tend to be similar to each other, so a group of them lacks diversity. In problem solving and prediction, diversity is exactly as important as individual ability.
[0907.4290] Dragon-Kings, Black Swans and the Prediction of Crises
We develop the concept of ``dragon-kings'' corresponding to meaningful outliers, which are found to coexist with power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. These dragon-kings reveal the existence of mechanisms of self-organization that are not apparent otherwise from the distribution of their smaller siblings. We present a generic phase diagram to explain the generation of dragon-kings and document their presence in six different examples (distribution of city sizes, distribution of acoustic emissions associated with material failure, distribution of velocity increments in hydrodynamic turbulence, distribution of financial drawdowns, distribution of the energies of epileptic seizures in humans and in model animals, distribution of the earthquake energies). We emphasize the importance of understanding dragon-kings as being often associated with a neighborhood of what can be called equivalently a phase transition, a bifurcation, a catastrophe (in the sense of Rene Thom), or a tipping point. The presence of a phase transition is crucial to learn how to diagnose in advance the symptoms associated with a coming dragon-king. Several examples of predictions using the derived log-periodic power law method are discussed, including material failure predictions and the forecasts of the end of financial bubbles.
Emergent
emergentTM (a major rewrite of PDP++) is a comprehensive simulation environment for creating complex, sophisticated models of the brain and cognitive processes using neural network models. These same networks can also be used for all kinds of other more pragmatic tasks, like predicting the stock market or analyzing data.
I cite: Emergence? Not again
Short critique of Emergence by Stephen Johnson (and inter alia other emergence popularizations) re: the confusions between small and large scale, face to face with computer mediated communication, and reflexivity.
Disorderly genius: How chaos drives the brain - life - 29 June 2009 - New Scientist
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These are purely physical systems, but the brain has much in common with them. Networks of brain cells alternate between periods of calm and periods of instability - "avalanches" of electrical activity that cascade through the neurons. Like real avalanches, exactly how these cascades occur and the resulting state of the brain are unpredictable.
It might seem precarious to have a brain that plunges randomly into periods of instability, but the disorder is actually essential to the brain's ability to transmit information and solve problems. "Lying at the critical point allows the brain to rapidly adapt to new circumstances," says Andreas Meyer-Lindenberg from the Central Institute of Mental Health in Mannheim, Germany.
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The neuronal avalanches that Beggs investigated, for example, are perfect for transmitting information across the brain. If the brain was in a more stable state, these avalanches would die out before the message had been transmitted. If it was chaotic, each avalanche could swamp the brain.
At the critical point, however, you get maximum transmission with minimum risk of descending into chaos. "One of the advantages of self-organised criticality is that the avalanches can propagate over many links," says Beggs. "You can have very long chains that won't blow up on you."
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[cond-mat/0403299] Discrete Hierarchical Organization of Social Group Sizes
The "social brain hypothesis" for the evolution of large brains in primates has led to evidence for the coevolution of neocortical size and social group sizes. Extrapolation of these findings to modern humans indicated that the equivalent group size for our species should be approximately 150 (essentially the number of people known personally as individuals). Here, we combine data on human grouping in a comprehensive and systematic study. Using fractal analysis, we identify with high statistical confidence a discrete hierarchy of group sizes with a preferred scaling ratio close to 3: rather than a single or a continuous spectrum of group sizes, humans spontaneously form groups of preferred sizes organized in a geometrical series approximating 3, 9, 27,... Such discrete scale invariance (DSI) could be related to that identified in signatures of herding behavior in financial markets and might reflect a hierarchical processing of social nearness by human brains.
Wolfram Blog : Minimum Inventory, Maximum Diversity
A minimum inventory/maximum diversity system is a kit of modular parts and rules of assembly that gives you maximal design bang for your design-component buck. It’s a system that achieves a wide variety of effects from a small variety of parts.
PLoS Computational Biology: Broadband Criticality of Human Brain Network Synchronization
Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.
Influenza A(H1N1) Outbreak 2009 - Research on Complex Systems, ESAM, Northwestern University
Robert Paterson's Weblog: Cynefin - Matching Math to the type of problem - Fractals for our Time
FT.com / Columnists / GillianTett - Good idea that turned bad
Far from promoting “dispersion” or “diversification”, innovation has ended up producing concentrations of risk, plagued with deadly correlations, too. Hence AIG’s inability to honour its insurance deals to the rest of the financial system, until it was bailed out by US taxpayers.
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