39 research outputs found
Self-Organized Critical Noise Amplification in Human Closed Loop Control
When humans perform closed loop control tasks like in upright standing or while balancing a stick, their behavior exhibits non-Gaussian fluctuations with long-tailed distributions. The origin of these fluctuations is not known. Here, we investigate if they are caused by self-organized critical noise amplification which emerges in control systems when an unstable dynamics becomes stabilized by an adaptive controller that has finite memory. Starting from this theory, we formulate a realistic model of adaptive closed loop control by including constraints on memory and delays. To test this model, we performed psychophysical experiments where humans balanced an unstable target on a screen. It turned out that the model reproduces the long tails of the distributions together with other characteristic features of the human control dynamics. Fine-tuning the model to match the experimental dynamics identifies parameters characterizing a subject's control system which can be independently tested. Our results suggest that the nervous system involved in closed loop motor control nearly optimally estimates system parameters on-line from very short epochs of past observations
Why have asset price properties changed so little in 200 years
We first review empirical evidence that asset prices have had episodes of
large fluctuations and been inefficient for at least 200 years. We briefly
review recent theoretical results as well as the neurological basis of trend
following and finally argue that these asset price properties can be attributed
to two fundamental mechanisms that have not changed for many centuries: an
innate preference for trend following and the collective tendency to exploit as
much as possible detectable price arbitrage, which leads to destabilizing
feedback loops.Comment: 16 pages, 4 figure