Positive Risk Management/Opportunity Management Literature Reviewer, Wildfire Prevention CrowdDoing
Prevention Derivatives draws upon "positive risk" and "opportunity management". We are scaling this sector up. Reviewing the literature here could be helpful.
-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.
Data science will be utilized in the following ways:
Explore/Visualize data currently available on Wildfires
Identify trends and patterns in Historical data
Quantify historical losses in dollars based on property destruction, casualties, acres burnt, etc.
Build predictive models to identify areas of high wildfire risk based on factors such as weather, vegetation, topography, etc.
Visualization of Model outcomes
Scenario building (changing input variables and observing impact on outcome)
Tools - R, Python, MATLAB, SQL, PowerPoint
Knowledge or Interest in anyone or more:
Identify papers on Simulation of Wildfires, Catastrophe Modeling
Review and present Technical papers in a way that everyone can understand
Assist in Model development and testing by contributing in finding data and programming
Identify/Collect data relevant to wildfire Impact
Work cross-functionally

Traits:
Mathematically inclined, highly analytical, creative problem solver, can conduct analyses independently or with minimal supervision
Programming for Data Science
Mathematics
Statistics
Predictive Analytics
Prescriptive Analytics
Machine Learning - Supervised/Unsupervised learning
Artificial Intelligence
Data Mining
Computer Science
Monte Carlo Simulations
Expectations.

If you have any questions about processes for joining crowddoing as a volunteer please write to: volunteerorientation@crowddoing.world