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.
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)
CrowdDoing [dot] world is a joint initiative of Match4Action Foundation and Reframe It. CrowdDoing is focused on addressing the social, economic, and environmental challenges our world faces by collaborating with professionals and volunteers from many different industries. We offer a platform for individuals to connect and collaborate toward creating systemic change. We would love for you to join the team! What makes us different? CrowdDoing aims to support social innovations with transformative impact potential through global multi-disciplinary volunteering, micro-leadership and service learning. We work through operating leverage for systems change to achieve collective agency. We orient to ikigai and self-determination theory in order to help each person have the perfect role. #systemschange #systemsthinking #servicelearning #socialinnovation
Match4Action connects those wanting to volunteer their skills with those in need for remote or local project support, advice, collaboration and mentoring.
Our smart technology platform uses the latest machine learning, artificial intelligence and virtual assistants to match the demand for resources for social impact with the skills and resources available, anywhere in the world. We use blockchain to create and manage a social impact currency.This unique, self sustainable open platform has one key purpose: accelerating social impact through collaboration and linking local projects with our global presence.
CrowdDoing aims to leverage micro-leadership, service learning and massively multi-disciplinary collaboration supported with project managers and human resource business partners. Collaborate with people whom you can learn from while changing the world virtually together.