000 04318nam a22005415i 4500
001 978-3-031-56988-3
003 DE-He213
005 20251030112302.0
007 cr nn 008mamaa
008 240606s2024 sz | s |||| 0|eng d
020 _a9783031569883
_9978-3-031-56988-3
024 7 _a10.1007/978-3-031-56988-3
_2doi
050 4 _aT57.6-.97
072 7 _aKJT
_2bicssc
072 7 _aKJMD
_2bicssc
072 7 _aBUS049000
_2bisacsh
072 7 _aKJT
_2thema
072 7 _aKJMD
_2thema
100 1 _aRaydugin, Yuri G.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978384
245 1 0 _aRisk-Based Project Decisions in Situations of High Complexity and Deep Uncertainty
_h[electronic resource] /
_cby Yuri G. Raydugin.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXVII, 382 p. 94 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aOperations research.
_957943
650 0 _aTechnological innovations.
_959244
650 0 _aFinancial risk management.
_959046
650 1 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aInnovation and Technology Management.
_966187
650 2 4 _aRisk Management.
_959047
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031569876
776 0 8 _iPrinted edition:
_z9783031569890
776 0 8 _iPrinted edition:
_z9783031569906
856 4 0 _uhttps://doi.org/10.1007/978-3-031-56988-3
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
999 _c60577
_d60577