An Integrated, Probabilistic Modeling Approach to Assess the Evolution of Barrier-Island Systems Over the Twenty-First Century

Barrier-island systems, spanning ∼7% of the world’s coastlines, are of great importance to society because not only they provide attractive, liveable space for coastal communities but also act as the first line of defense from coastal storms. As many of these unique coastal systems are affected by both oceanic and terrestrial processes, it is necessary to consider the holistic behavior of applicable terrestrial and coastal processes when investigating their evolution under plausible future scenarios for climate change, population growth and human activities. Such holistic assessments, also accounting for uncertainties, can readily be achieved via reduced-complexity modeling techniques, owing to their much faster simulation times compared to sophisticated process-based models. Here, we develop and demonstrate a novel probabilistic modeling framework to obtain stochastic projections of barrier-island evolution over the twenty-first century while accounting for relevant oceanic and terrestrial processes under climate change impacts and anthropogenic activities. The model is here demonstrated at the Chandeleur islands (Louisiana, United States) under the four Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission scenarios (i.e., Representative Concentration Pathways 2.6, 4.5, 6.0, and 8.5) with results indicating that there are significant uncertainties in projected end-century barrier-island migration distance and available barrier freeboard under the high emission scenario RCP 8.5. The range of uncertainties in these projections underscores the value of stochastic projections in supporting the development of effective adaptation strategies for these fragile coastal systems.

FINANCIAL MODELING: PROBABILITY THEORETIC APPROACHES

Background. A large number of significant socio-economic events occur under the influence of unique factors. Formal application of probabilistic and statistical methods in such cases leads to analytical conclusions without sufficient scientific justification. Financial modeling reflects modern approaches to the probability interpretation, provides introduction and systematization of risk indicators, and the necessity of improving theoretical and probabilistic disciplines of economic orientation. Analysis of recent research and publications has shown that despite significant investigations, financial modeling is not theoretically complete scientific direction in terms of economic risk indicators and derivative characteristics, important scientific and practical problems remain unresolved in the analysis of socio-economic phenomena in unce­rtainty and implementation of modern achievements of scientists to the process. The aim of the article is to study theoretical and probabilistic concepts of socio-economic processes in conditions of uncertainty and uniqueness based on the financial modeling methods. Materials and methods. Analytical and statistical methods, methods of mathematical statistics and probability theory are used in the research process. Information database is data from trading sessions of world stock markets. Results. Theoretical and probabilistic concepts, including interpretations of probability and risk are considered through formalization of the analysis process by the subject of the socio-economic phenomenon in conditions of uncertainty. Models of typical stationary, dynamic, parity and dominant lotteries with introduced risk indicators are built. Risk is interpreted as the ratio of negative and favorable factors of the phenomenon information background. Relevant indicators are illustrated and calculated using various socio-economic and financial cases. Subjective-probabilistic modeling (SPM) in relation to decision-making in the financial market is studied as the development of Bayesian subjectivism. It has been shown that group consensus SPM-assessments of risk generate specific derivative financial instruments such as binary options, index derivatives, crypto-assets, etc. Conclusion. The results of the study showed the application effectiveness of financial modeling methods of risks assessment in financial markets, the prospects of relevant development in the field of financial engineering. Teaching economic disciplines, which are based on theoretical and probabilistic postulates, statistical and analytical-statistical procedures for calculating probabilistic indicators (probability, risk, prevention regulations, etc.), requires significant addition using the introduction of new methods of information analysis of social background, financial sphere to determine the optimal direction of development and investment activities. Keywords: risk ratio, probability interpretation, binary options, financial modeling, high-risk financial markets, subjective-probabilistic modeling.