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Ermolieva, T., Ermoliev, Y., Komendantova, N. 
ORCID: https://orcid.org/0000-0003-2568-6179, Norkin, V., Knopov, P., & Gorbachuk, V.
(2023).
Linking Catastrophe Modeling and Stochastic Optimization Techniques for Integrated Catastrophe Risk Analysis and Management.
In:
Modern Optimization Methods for Decision Making Under Risk and Uncertainty.
Eds. Gaivoronski, A., Knopov, P., & Zaslavskyi, V.,
pp. 15-50 Taylor & Francis.
ISBN 9781003260196 10.1201/9781003260196-2.
Abstract
Planning regional economic developments and social welfare without addressing issues related to mitigation and adaptation to low probability-high consequences catastrophe risks may lead to dangerous clustering of people, production facilities, and infrastructure in hazard-prone areas thereby critically increasing regional vulnerability. The endogeneity of the risks on production allocation and land use decisions represents new challenges for regional sustainable development planning. This chapter argues that catastrophe risk analysis and management have to be addressed with an Integrated Assessment and Management Model (IAMM) linking catastrophe risk modeling (CRM) with stochastic optimization (STO) techniques for the design of optimal and robust mitigation and adaptation strategies for dealing with catastrophe risks of all kinds. IAMM enables us to address the challenging characteristics on policies, mutually dependent losses, the lack of information, the need for long-term perspectives and geographically explicit models, the involvement of various agents (such as individuals, farmers, producers, consumers, governments, insurers, investors), safety and security standards, and the need for robust decisions. Safety and security criteria relate to Value-at-Risk and Conditional Value at Risk measures generalizing the well-known risk criteria and indicators used for regulating engineering, critical infrastructure, energy, water, agricultural safety and security requirements. These are key indicators for dealing with low probability-high consequences risks. The linkage between CRM and STO is established through an iterative stochastic quasigradient procedure (SQG) defining a sequential “searching” process, which resembles an adaptive learning environment and improvement of decisions from data and simulations, i.e., the so-called Adaptive Monte Carlo optimization. The SQG methods are applicable in cases when traditional stochastic approximation, gradient or stochastic gradient methods do not work, in particular, to general two-stage problems with implicitly defined goals and constraints functions, nonsmooth and possibly discontinuous performance indicators, risk and uncertainties shaped by decision of various agents.
| Item Type: | Book Section |
|---|---|
| Research Programs: | Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT) Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM) Biodiversity and Natural Resources (BNR) Biodiversity and Natural Resources (BNR) > Integrated Biosphere Futures (IBF) |
| Depositing User: | Luke Kirwan |
| Date Deposited: | 29 Aug 2023 06:56 |
| Last Modified: | 29 Aug 2023 06:56 |
| URI: | https://pure.iiasa.ac.at/19028 |
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