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

SBIR 第一阶段：预测多种经济和社会回报的先进计算方法

• 批准号: 2051851
• 负责人: Trenton Ashburn
• 金额: $ 25.6万
• 依托单位: SIMPACT CO.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051851&HistoricalAwards=false
• 关键词: SBIR; Phase; Advanced; computational; methods

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.

小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力为社会企业提供了使用机器学习、计算机模拟和数据可视化执行社会影响预测和跟踪的软件。这些数字工具为社会企业提供了更好的可见性，使其能够将资源投资到哪里，以实现最大影响。定量数据分析的采用使商业企业变得更加强大，但这些工具在进入由非营利组织、慈善基金会、国际发展机构、影响力投资者和政府机构组成的7万亿美元世界方面进展缓慢。该软件通过提供对每美元影响的定量估计，为投资策略提供信息。该软件还将跟踪每项资源分配的持续社会影响，使社会企业能够反复调整和提高其业务效率，并对其资金来源提供更多问责。这些软件工具可以帮助解决比健康、可持续发展、贫困和受教育机会更令人头疼的一些社会问题。这个小企业创新研究(SBIR)第一阶段项目利用了软件数据分析工具，包括基于代理的建模(ABM)、机器学习(如梯度增强机器和人工神经网络)和交互数据可视化。基于代理的建模包括构建目标生态系统的数字仿真，捕获关键的参与者和行为。例如，对于阿片类药物流行，这可能是成瘾者/毒品/医生/药剂，以及他们的属性、病史和相互作用。这种建模允许模拟干预情景并量化结果。机器学习技术一旦用目标生态系统的过去数据进行了训练和校准，就可以提供预测，并允许探索假设情景。为每个目标社会问题建立一个自下而上的模型，由相关的社会企业共享，并使用他们的集体数据和主题专家进行校准。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。