From Data to Integrated Risk Management and Smart Living: Mathematical Modelling, Statistical Inference, and Decision Making

智能生活是一个强大的趋势，在先进社会中迅速出现，寻求优化和改善人类的生活条件，特别是通过开发基于大量数据的综合风险管理技术，结合先进的统计建模和推理，以做出更明智，更经济的决策。 我的研究计划的主要科学目标是进一步推进尖端方法，并培养高素质的人才，建模和预测复杂系统的行为，可靠性和弹性，如经济，（例如，财富和收入在地理区域之间的重新分配）、工业（如可靠性、复原力、生存能力）、金融和保险（如风险计量和管理、趋势建模、预测、成本效益分析）。这些领域一直主导着我的研究，并将继续主导我的研究。 具体而言，我的研究一直在以下相互交织的方向取得进展： 定量和定性风险的汇总和分配技术，这在风险测量和管理以及决策中至关重要。事实上，许多风险（如运营风险、战略风险）源自复杂系统（如合作、竞争、模糊、多国等）。并且可以以经常难以有意义地量化的定性形式观察到。 有和没有足够大的数据集的风险回报和成本效益分析。而在“有”的情况下，我将致力于开发适当的参数，半和非参数的统计推断技术，在“没有”的情况下，我将主要依靠基于知识的概率（例如行为分析），校准和更新技术（例如信息流）。 通过结合随机过程（例如经验和分位数过程，非线性时间序列）以及快速发展的定量心理学领域（例如消费者行为分析）的技术进行风险回报预测和决策。后一种技术允许决策者相对快速地评估和平衡决策精度与现实生活中施加的时间限制。 系统风险建模，在静态和动态环境中开发风险回报汇总和分配方法、投资组合多样化和优化技术（例如随机优势）方面发挥关键作用。 所有这些研究方向构成了当今迅速发展的智能生活综合风险管理方法的一个组成部分，该方法一直主导着国内和国际的研究。精通尖端数学和统计建模、计算和统计推断以及决策的研究人员可以为智能生活做出很大贡献。

Smart Living is a powerful trend that is rapidly emerging in advanced societies that seek to optimize and improve human living conditions by, in particular, developing integrated risk management techniques based on massive data in combination with advanced statistical modelling and inference for making wiser and more economical decisions. The key scientific objective of my research program is to further advance the cutting-edge methodology of, and train highly qualified personnel in, modeling and forecasting the behavior, reliability and resilience of complex systems, such as economic (e.g. re-distribution of wealth and incomes among geographic regions), industrial (e.g. reliability, resilience, survivability), financial and insurance (e.g. risk measurement and management, modelling of trends, forecasting, cost-benefit analysis). These are the areas that have dominated, and will continue dominating, my research. Specifically, my research has been progressing in the following intertwined directions: Aggregation and allocation techniques for quantitative and qualitative risks, which are pivotal in risk measurement and management, as well as in decision making. Indeed, a number of risks (e.g. operational, strategic) originate from complex systems (e.g. co-operative, competing, vague, multi-state, etc.) and are observable in qualitative forms that are frequently difficult to meaningfully quantify. Risk-reward and cost-benefit analyses with and without sufficiently large data sets. Whereas in the “with” case I will work on developing appropriate parametric, semi- and non-parametric techniques of statistical inference, in the “without” case I will crucially rely on knowledge-based probabilities (e.g. behavioral analysis), calibration and updating techniques (e.g. information flows). Risk-reward forecasting and decision-making by combining techniques of stochastic processes (e.g. empirical and quantile processes, non-linear time series) as well as those of the rapidly developing area of quantitative psychological heuristics (e.g. consumer behavioral analysis). The latter techniques allow decision-makers to relatively quickly assess and balance decision-precision with real-life imposed time constraints. Systemic-risk modeling, which plays a pivotal role in developing risk-reward aggregation and allocation methods, portfolio diversification and optimization techniques (e.g. stochastic dominance) in static and dynamic environments. All of these research directions make up an integral part of the nowadays rapidly developing integrated risk management approach to Smart Living that has been dominating research nationally and internationally. Researchers who are well versed in cutting-edge mathematical and statistical modelling, computing and statistical inference, as well as in decision making have much to contribute to Smart Living.