SBIR Phase I: Advanced computational methods for forecasting multiple types of economic and social returns

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is providing social enterprises with software that performs social-impact forecasting and tracking using machine learning, computer simulation, and data visualization. These digital tools provide social enterprises with better visibility into where their resources can be invested for maximum impact. The adoption of quantitative data analysis has made commercial enterprises more formidable, but these tools are slow in coming to the $7 trillion world of non-profits, charitable foundations, international development agencies, impact investors, and government agencies. The software informs investment strategies by providing a quantitative estimate of impact-per-dollar. The software will also track the ongoing social impact of each resource allocation, enabling social enterprises to iteratively adapt and improve the efficacy of their operations, and provide more accountability to their funding sources. These software tools can help address some of the most vexing societal concerns than span health, sustainability, poverty, and access to education. This Small Business Innovation Research (SBIR) Phase I project makes use of software data analysis tools including Agent Based Modeling (ABM), machine learning (such as Gradient Boosted Machines and Artificial Neural Networks), and Interactive Data Visualization. Agent Based Modeling involves building a digital simulation of the target ecosystem, capturing the essential actors and behaviors. For example, for the opioid epidemic, this may be the addicts/drugs/doctors/pharma agents, along with their attributes, histories, and interactions with each other. This modeling allows simulation of intervention scenarios and quantification of outcomes. Machine learning techniques, once trained and calibrated with past data on the target ecosystem, provide forecasting and allow the exploration of what-if scenarios. A bottom-up model is built for each target social issue, shared by relevant social enterprises, and calibrated using their collective data and subject matter experts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.