Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money

The performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examinedEvolutionary Strategies, Risk Adjusted Divisia, Inflation, Neural Networks

Display luminance and responses to transient light stimuli

The subject of enquiry is the effect of high intensities of ambient luminance on task performance by human subjects, and the task employed, the detection of transient light signals in an illuminated display encompassing the whole of the subject's visual field. Experimental conditions were designed to test one particular prediction from previous work, that high intensities of environmental stimulation tend to reduce the range of cues utilised from the environment in performance of a task. ln the case of high intensities of luminance the prediction would be of a reduction in the size of the visual field, or 'tunnel vision'. The data produced does not bear out this prediction. Subjects show a decreased consistency of response when observed under the higher intensities of luminance so that their detection rate for peripheral signals is comparatively lower than for other intensities, but not in a sufficiently clear-cut fashion to be 'tunnel vision' as predicted. [Continues.

Synchronization of clocks

Peer reviewedPostprin