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

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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.