partners - Blog - Global Risk Community2024-03-28T17:04:18Zhttps://globalriskcommunity.com/profiles/blogs/feed/tag/partnersLooking for partners with CFAR-m (risk analytic)https://globalriskcommunity.com/profiles/blogs/looking-for-partners-with-cfar-m-risk-analytic2012-07-22T01:52:34.000Z2012-07-22T01:52:34.000ZRemi Molliconehttps://globalriskcommunity.com/members/RemiMollicone<div><p>CFAR-m main features (unique algo and features) </p><p><br />Aggregation is a way to combine several single indicators representing different components (dimensions) <br />of the same concept to form a single aggregate. The result leads to a single score, called a composite <br />indicator, which has the ability to summarize a large amount of information in a comprehensible form .<br />Aggregation requires the determination of a weighting scheme of the different components. This task is <br />extremely difficult and is one of the central problems in the construction of composite indicators. <br />Weights must take into account all existing forms of interaction between the components aggregated and <br />have a significant effect on the result. However, there is no universally agreed methodology and the <br />arbitrary nature of the weighting process by which components are combined constitutes the main <br />weakness of composite indicators which CFAR-m overcomes.<br />CFAR-m OVERCOMES THIS PROBLEM:<br /> CFAR-m is an original method of aggregation based on neural networks which can summarize <br />with great objectivity the information contained in a large number of variables emanating from <br />many different fields.<br /> Its contribution lies in determining, from the database itself, a wei ghting scheme of variables <br />specific to each individual. CFAR-m solves the major problem of fixing the subjective importance <br />of each variable in the aggregation.<br /> It avoids the adoption of an equal weighting or a weighting based on exogenous criteria. Th e <br />weightings for CFAR-m emanate only from the information content of variables themselves and <br />their own internal dynamics.<br />THE RANKING PROVIDED BY CFAR-m HAS THE FOLLOWING ENABLES THE FOLLOWING <br />ADVANTAGES:<br /> Objectivity: No handling of weightings - the weighting is resolutely objective and it emanates from <br />the informational content of the variables themselves of their research and internal dynamics.<br /> Specificity: a specific equation for each individual piece of data to is used calculate the indicator<br /> Decision support: ability to run simulations and propose to the decision makers plans of action <br />and optimal sequences of reforms.<br />In addition:<br /> It can provides the contribution of the variables to the ranking<br /> It keeps all the variables during the calculus and so it is helpful for extracting what is happening <br />within the noise. This is very interesting for predicitve model</p><p></p><p>-- <br />Remi Mollicone<br />remi@cfar-m.com<br />Innovation, Alliances/Partnerships, Business Development<br />Tel: +33 6 30 72 90 13<br />Tel: +33 6 27 70 56 76<br />Fax: +33 9 59 12 01 82<br />Skype: remimollicone7<br /><a href="http://www.cfar-m.com">www.cfar-m.com</a></p></div>