Wildfire Risk Prevention Methodology & Epistemology Volunteer

Wildfire Risk Prevention Methodology & Epistemology Volunteer
Prevention Derivatives draws upon other interventions (outcome based financing in social impact bonds, renewable energy based purchasing by performance, contractual taxation zones in business improvement districts, impact incentives, Advance Market Commitments, Proportionate Reinsurance, Insurance Linked Securities. We make the case for each component of prevention derivatives being secure/safe through literature review of these and other tools.
-Prevention derivatives is driven by the thesis that there is an under-valuation of passive risk (or the cost of inaction) and an under-prioritization of positive risk. Correspondingly for wildfires as an example, there is an under-recognition of the potential shared value upside of preventative action through social innovation and social interventions (such as goats & sheep that prevent wildfires). CrowdDoing.world's aim is to guarantee positive risk through leveraging existing liabilities to allow for the implications of prescriptive analytics to be financed. The under-pricing of passive risk means that liabilities are treated as either costs of doing business or un-predictable risks even for entirely preventable risks. Risk management offices have been too biased towards avoiding taking the wrong risks rather than ensuring that institutions make their own luck by seizing the abundant positive risk opportunities in social innovation. Meanwhile, the bias against positive risk leaves social innovations not to get adopted even if there would be remarkable benefits to all stakeholders if they were adopted

In the framework of Prevention Derivatives, we want to create a predictive machine learning (ML) model that for a given geographical region will estimate likely savings (losses) due-to protection (damages) of stakeholders’ properties, business profits, common health, and regional ecology resulting in applying risk prevention solutions (or doing nothing instead). Goal of these notes is to analyze ML model’s design, offer a potential improvement and to discuss existing approaches for data collection, and training and testing the model. It is important to notice that the model is applied to the entire selected or target region. Therefore, a geographical region R is the smallest unit we apply modeling to.