Doctoral Dissertation Research in DRMS: Building a comprehensive understanding of enterprise risks and their interdependencies for improved risk-intelligence

DRMS 博士论文研究：全面了解企业风险及其相互依赖性，以提高风险情报

• 批准号: 2049782
• 负责人: Joseph Sinfield
• 金额: $ 3.8万
• 依托单位: Purdue University
• 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: 2021-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049782&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; DRMS; Building

Businesses are ubiquitous and inseparably merged with human lives. Thriving corporations provide the fundamental activity necessary for an equitable society with benefits ranging from the ready availability of goods and services to regional employment in support of economic prosperity. Simultaneously, high-risk events such as COVID‐19 are bleak reminders that enterprises are continuously threatened, and that building enterprise resilience is crucial. Yet, one of the cornerstones of enterprise resilience - risk intelligence – or the adequate awareness of the various risk factors and their complex interdependence, remains grossly underdeveloped. This project addresses this gap in knowledge by developing complex network views of enterprise risks. Employing big data and tools of artificial intelligence the project identifies and develops an enterprise-agnostic risk inventory that is considerably more comprehensive than any such publicly available resource. In addition, expert input allows for converting seemingly disconnected risk factors into interconnected complex risk networks, which enable the search for risk chains that may compound and lead to more significant adverse effects. This work builds a knowledge resource base useful to explain mechanisms of cascading risks and to predict the varying impacts of risk events on enterprises. Thus, the work serves national interest and is in alignment with NSF’s mission to promote the progress of science, and via that, advance national prosperity and welfare. This work is grounded in the complexity systems view of enterprise risk management and seeks to build a comprehensive, data‐informed view of the dynamic risk network influencing enterprises to systematically enhance risk awareness and contribute toward the evolution of truly risk‐intelligent organizations. This perspective is achieved through: a) the development of a comprehensive enterprise agnostic risk factor inventory, b) the generation of risk networks that map risk factor interrelations, and c) the exploration of the complex dynamics of these risk networks. The work entails a mixed-methods approach utilizing information extraction (IE) on a large, curated dataset of company risks, and Fuzzy Cognitive Mapping (FCM) for complex risk-network development and analyses. Public information (SEC filings) are augmented with private risk data (analyst reports) for enterprises in the S&P 500, providing robust coverage of true risk factors. The corpus is analyzed using IE principles, which include part‐of‐speech tagging, dependency-parsing, n‐gram extraction, and topic modeling. Surveyed and/or interviewed experts from industry and academia inform qualitative measures of risk interaction (dependencies, direction, and degree of influence) during the FCM process leading to complex risk network development. The resulting risk networks are analyzed quantitatively to reveal insights such as centrality of risks, the distances between risks, and sub‐group structures within the risk networks that could inform an order of critical risks. The research contributes to the field of Enterprise Risk Management (ERM) by increasing scholarly awareness on the breadth of risks affecting enterprises. Further, via FCM, the work converts expert understanding into a quantifiable network, bringing focus on risk interdependencies. In addition, via network analysis, the effort illuminates critical risks and propagation mechanisms that may be overlooked in traditional views. Overall, this effort provides an expansive, data‐informed view of risk factors affecting enterprises, their (non-intuitive) interactions, and related dynamics thereby advancing the complexity-view of ERM research as well as sharpening an enterprise’s ability to predict cascading effects caused by seemingly unrelated events.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.

企业无处不在，与人类生活密不可分。 蓬勃发展的公司提供了一个公平社会所必需的基本活动，其好处包括从商品和服务的随时可用性到支持经济繁荣的区域就业。 与此同时，COVID-19等高风险事件令人沮丧，提醒企业不断受到威胁，建立企业复原力至关重要。 然而，企业复原力的基石之一-风险情报-或对各种风险因素及其复杂的相互依存关系的充分认识仍然严重不足。 该项目通过开发企业风险的复杂网络视图来解决这一知识差距。利用大数据和人工智能工具，该项目确定并开发了一个与企业无关的风险清单，该清单比任何此类公开资源都要全面得多。 此外，专家的意见有助于将看似互不关联的风险因素转化为相互关联的复杂风险网络，从而能够寻找可能复合并导致更严重不利影响的风险链。 这项工作建立了一个知识资源库，有助于解释级联风险的机制，并预测风险事件对企业的不同影响。 因此，这项工作符合国家利益，并符合NSF的使命，以促进科学的进步，并通过这一点，促进国家的繁荣和福利。这项工作基于企业风险管理的复杂性系统观，旨在建立一个全面的、数据化的动态风险网络视图，影响企业系统地提高风险意识，并为真正的风险智能组织的发展做出贡献。 这一观点是通过以下方式实现的：a）开发一个全面的企业不可知风险因素清单，B）生成映射风险因素相互关系的风险网络，以及c）探索这些风险网络的复杂动态。 这项工作需要一个混合的方法，利用信息提取（IE）在一个大型的，策划的公司风险数据集，和模糊认知映射（FCM）复杂的风险网络的发展和分析。 公共信息（SEC文件）增加了标准普尔500指数中企业的私人风险数据（分析师报告），提供了对真实风险因素的强大覆盖。 使用IE原则分析该语料库，其中包括词性标记、依赖性解析、n元语法提取和主题建模。 来自行业和学术界的调查和/或访谈专家提供了FCM过程中风险相互作用（依赖性，方向和影响程度）的定性测量，导致复杂的风险网络发展。 对由此产生的风险网络进行定量分析，以揭示风险的中心性、风险之间的距离以及风险网络内的子组结构等信息，这些信息可以告知关键风险的顺序。 该研究通过提高学术界对影响企业的风险广度的认识，为企业风险管理（ERM）领域做出了贡献。 此外，通过FCM，这项工作将专家的理解转化为一个可量化的网络，重点关注风险的相互依赖性。 此外，通过网络分析，这项工作阐明了在传统观点中可能被忽视的关键风险和传播机制。 总的来说，这项工作提供了一个广泛的，数据知情的风险因素影响企业，（非直观）交互，和相关的动力学，从而提高了复杂性-该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。