Raydugin, Yuri G. creator author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XXVII, 382 p. 94 illus., 5 illus. in color. online resource. This book integrates for readers three areas of knowledge, pertaining to risk-based project decision making: project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU). Readers will appreciate that in practice, too often relevant complexity and uncertainty factors are either ignored or overlooked resulting in epic project failures. The author discusses a variety of methodologies and a decision-tree-type framework to determine why, when and how particular methodologies should be applied to ensure project success. These include nonlinear Monte Carlo techniques, a dynamic adaptive methodology to adapt to external environment changes, game theory for devising robust decision-making criteria, systems dynamics and cost escalation modelling, as well as risk-based & economic-based alternatives selection methodologies. This book will be an eye-opener for many PRM practitioners, helping to increase their chances of project success by properly handlinginescapable project-complexity and deep-uncertainty implications in specific contexts. Integrates project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU); Provides conceptual overview of PRM, project complexity and DMDU methodologies, their interdependencies and integration; Enables robust, risk-based decision-making for contingency development and alternatives selection, rooted in game theory. Introduction -- PRM and types of project uncertainties -- Overview of DMDU methodologies -- Project complexity concept -- Decision-making framework -- Selection of project options in situations of deep uncertainty -- Development of project schedule and cost contingencies in complex projects -- Cost escalation and exchange-rate volatility risk assessment methods -- High-level overview of simplistic Monte Carlo and parametric risk assessment methods -- Case study 1: applications of a tradition PRM (scoring method) -- Case study 2: applications of “linear Monte Carlo” methodology -- Case study 3: applications of “non-linear Monte Carlo” methodology -- Case study 4: selection of project options (a few “futures”) -- Case study 5: selection of project options (multiple “futures”) -- Case study 6: applications of cost escalation and exchange-rate volatility modelling methods -- Conclusion. by Yuri G. Raydugin. Operations research Technological innovations Financial risk management Operations Research and Decision Theory Innovation and Technology Management Risk Management T57.6-.97 9783031569883 https://doi.org/10.1007/978-3-031-56988-3 https://doi.org/10.1007/978-3-031-56988-3 240606 20251030112302.0 978-3-031-56988-3