03957nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072001700183072002300200072001500223072001600238100007900254245013400333250001800467264006900485300006600554336002600620337002600646338003600672347002400708505091600732520144201648650002503090650003103115650003103146650004503177650004203222650002103264710003403285773002603319776003603345776003603381776003603417856004603453978-3-031-56988-3DE-He21320251030112302.0cr nn 008mamaa240606s2024    sz |    s    |||| 0|eng d  a97830315698837 a10.1007/978-3-031-56988-32doi 4aT57.6-.97 7aKJT2bicssc 7aKJMD2bicssc 7aBUS0490002bisacsh 7aKJT2thema 7aKJMD2thema1 aRaydugin, Yuri G.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aRisk-Based Project Decisions in Situations of High Complexity and Deep Uncertaintyh[electronic resource] /cby Yuri G. Raydugin.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXXVII, 382 p. 94 illus., 5 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- 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.  aThis 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. 0aOperations research. 0aTechnological innovations. 0aFinancial risk management.14aOperations Research and Decision Theory.24aInnovation and Technology Management.24aRisk Management.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303156987608iPrinted edition:z978303156989008iPrinted edition:z978303156990640uhttps://doi.org/10.1007/978-3-031-56988-3