LEADER 04575nam 2200553 450 001 9910220139103321 005 20220831223928.0 010 $a0-8330-9305-3 035 $a(CKB)3710000000601372 035 $a(EBL)4427115 035 $a(MiAaPQ)EBC4427115 035 $a(Au-PeEL)EBL4427115 035 $a(CaPaEBR)ebr11163860 035 $a(OCoLC)939550826 035 $a(EXLCZ)993710000000601372 100 $a20160318h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aDefense resource planning under uncertainty $ean application of robust decision making to munitions mix planning /$fRobert J. Lempert [et al.] 210 1$aSanta Monica, CA :$cRAND Corporation,$d2016. 210 4$dİ2016 215 $a1 online resource (xxii, 86 pages) $ccolor illustrations, color charts 300 $a"RAND National Security Research Division." 311 1 $a0-8330-9167-0 320 $aIncludes bibliographical references. 327 $aCover ; Title Page ; Copyright ; Preface; Contents; Figures; Tables; Summary; Acknowledgments; Abbreviations; CHAPTER ONE: Introduction ; Planning with Predictive Failure; Munitions Mix Challenge; Organization of This Report; CHAPTER TWO: The RDM Approach to Munitions Mix Planning ; Comparison of RDM and Traditional Analysis; RDM Enables Decision Makers to Discover Robust Strategies Through Iteration; XLRM Factors Shape the Design of the Experiment; CHAPTER THREE: RDM Munitions Mix Analysis ; Initial Analysis of a Broad Range of Munitions Mix Strategies 327 $aAnalysis of Potentially More Robust Munitions Mix Strategies Stress-Testing of Strategies over Many Futures ; Scenarios That Illuminate the Vulnerabilities of Strategies ; Performance of Big+Deter-Mixed Strategy in the Moderate Scenario ; Performance of the Big+Deter-Mixed Strategy in the Extreme Scenario ; Future Focus on Purchase Rules in Addition to Portfolio Goals ; CHAPTER FOUR: Conclusions ; A Robust Munitions Mix Strategy ; The Future of RDM in Defense Planning ; APPENDIXES ; A. The Weapons on Target Model ; B. EXPERIMENTAL DESIGN ; C. Data ; References 330 $a"Today's defense resource planners face unprecedented uncertainty. The planning processes currently used to determine what forces and capabilities will be needed to address future threats to our national security and interests may be vulnerable to predictive failure. To manage these risks, a new approach to planning is needed to identify strategies that perform well over a wide range of threat and funding futures and thus are better able to manage surprise. This report describes how robust decision making (RDM) may help address this need. RDM, a quantitative decision support methodology for informing decisions under conditions of deep uncertainty and complexity, has been applied to many policy areas in the last decade. This document provides a proof of concept application of RDM to defense planning, focusing on the air-launched munitions mix challenge. The study embeds a fast-running "weapons on targets" allocation model within a "scenario generator" that explores many thousands of plausible, future 20-year series of military campaigns. The RDM analysis uses these simulation models to stress-test alternative munitions mix strategies against many plausible futures. The analysis then identifies a robust munitions mix strategy, which interestingly depends not only on the desired portfolio of alternative weapons types but also on the rules used to replenish depleted weapons stocks after each campaign. The study also suggests how RDM might best be integrated into current DoD planning processes and some of the challenges that might be involved." --Back cover. 606 $aDecision making 607 $aUnited States$xArmed Forces$xProcurement 607 $aUnited States$xArmed Forces$xWeapons systems 607 $aUnited States$xArmed Forces$xEquipment 607 $aUnited States$xArmed Forces$xOrdnance and ordnance stores 615 0$aDecision making. 676 $a353.6 700 $aLempert$b Robert J.$0924033 702 $aLempert$b Robert J. 712 02$aRand Corporation.$bNational Security Research Division, 712 02$aUnited States.$bDepartment of Defense.$bOffice of the Secretary of Defense, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910220139103321 996 $aDefense resource planning under uncertainty$92894604 997 $aUNINA