A mining company asked us to perform a Prefeasibility hazard adjusted NPV evaluation. Our action first focused on bringing clarity in their risk register which presented numerous classic mistakes. Among these the usual confusion in terminology, confusing risks and hazards, uncertainties vs risks, etc. Once the risk register was corrected it was possible to perform the requested hazard adjusted NPV and to draw valuable conclusions. Among these, we highlighted potential fatal flaws of the project which warranted in depth analyses…
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Writing lessons learned in twenty years of hazard, risk and resilience analyses has been a difficult exercise for various reasons. We compiled and explain lessons learned in our practice of risk assessment and resilience advice to industries, municipalities and regional governments around the world in our latest book, Convergent Leadership – Divergent Exposures: Climate Change, Resilience, Vulnerabilities, and Ethics. However, the fact that we systematically apply our quantitative convergent approach (ORE, Optimum Risk Estimates) to all sorts of industries and…
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Uncertainties in risk assessments derive from data, model and system uncertainties. In addition, these mix with soft issues like language and cultural barriers. There are for example languages where the term “risk” simply does not exist, like in Japanese. The closest word Japanese have in their language is 危険 (kiken). It means danger, risk, hazard, peril, jeopardy, pitfall all in one. Thus it fails to grasp the specific concept of risk and generates confusion. This is the reason they had…
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The singularity scenario is well known. AI becomes overnight smarter than us. We become a hazard (aren’t we already?) to our planet, generating consequences, hence risks. Machines get rid of us because of that very reason and because we may hinder their mission. It is only a matter of point of view, right? Before that night AI was only 50% as smart than us. The giant “last night” leap is the result of exponential growth. We have all seen it…
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Predict business startups success example How to do it ORE applied to Startups development came up when we read examples of “archival analyses” on startups. Researchers used data mining to show that: board “age”, “gender”, “capital”, “IP” and finally “race” have insignificant effects (after 5 years) on either merger/acquisition rate OR survival of operations of startups. Those results left the authors of the research (Kaufmann Foundation) with the feeling that “something was missing” in this type of analyses. Those results…
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Franco Oboni interviewed by an Italian journalist This blog post is a summary of an interview released by Riskope’s Franco Oboni in Italy. Q1 Could you please define risk management and explain its scope? Risk Management in Riskope’s day-to-day practice is a scientific, rational approach based on over twenty years of applied research. It allows our clients to optimize their resources, all of them, including the “hidden ones”, and finally “boldly go where – sorry for the Star-Trek hint here–…
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Data for this “L’Aquila earthquake verdict explained from Risk Crisis Management point of view” summary have been gathered through media and publicly available records. Details we consider “irrelevant” for this discussion on L’Aquila earthquake verdict explained from Risk Crisis Management point of view have been omitted because of space limitations. We will start this post by summarizing the tragic tale of L’Aquila, a city featuring many historic public buildings and centuries old residential structures located in Italy, a country where retrofitting of…
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At Riskope we have been writing and discussing insurance denial issues hitting miners around the world since 2009. Now there is evidence that the problem of Insurance Denial not Only for Mining and is extending to flood insurance (and most certainly to other natural hazard-linked events in the future). From a societal risk management point of view the problem is quite intriguing Seen from a societal risk management point of view the problem is quite intriguing: 1) Governments (at all…
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