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PGM risk Sensitivity Analysis

PGM risk Sensitivity Analysis. 20 July 2008 Henry Neimeier. OWS Threads. National Assets. Reachback. CSG C2 Node. Tactical C2 Node. Ground Station. Ground Station. Fiber Node. Theatre C2 Node. Fiber Node. Fiber. GPS/GBS. SATCOM. Radio. Air Layer.

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PGM risk Sensitivity Analysis

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  1. PGM risk Sensitivity Analysis 20 July 2008 Henry Neimeier

  2. OWS Threads National Assets Reachback CSG C2 Node Tactical C2 Node Ground Station Ground Station Fiber Node Theatre C2 Node Fiber Node Fiber GPS/GBS SATCOM Radio Air Layer

  3. PGM risk Player Interface: green/gray buttons inputs, red buttons output results

  4. Model Details (Module InfluenceDiagram & Module Summary) Portfolio: Selects scenario options, calculates TLE, and sets uncertain input variable distributions. Target Movement: Calculates target escape probability from target movement statistics Weapons: Assigns weapons to target classes to maximize effective kills per sortie corrected for target location error TLE, survivable throughput, escape probability Comm Net: Calculate path delay, path survival probability, and survivable throughput for specified network parameters and threat level Kill Thread Delay: Set thread task, sense, fuse, plan, execute times. Compare total time to enemy response time to determine targets at risk. ISR: Assigns ISR platforms to scenario, calculates revisit time, & probability of target classification Terrain Weather: Calculates ISR coverage multiplier to correct for scenario weather and terrain masking.

  5. Portfolio Module: Specifies uncertain input variable distribution and selects scenario options Input Chance Variable Triangular Distributions Light Blue Ellipses TLE multiplier of kill probability

  6. Kill Thread Delay

  7. Weapons • Kill probability is product of following factors: • TLE multiplier (Portfolio) • Effective kill probability per sortie (Weapons) • Probability of classification (Weapons) • Proportion of time SA fixed (TacTom) • Probability of escape (Target motion) • Survivable throughput (Comm) • Targets at risk (Kill Thread)

  8. Expected Kills Per Sortie Weapons Input (nominal Unclassified)

  9. Y X Sensitivity Analysis • Model non linear in both functions and interactions (products) of factors • Sensitivity results vary by input variables not selected • Operating point effects sensitivity • Global versus local techniques • Analytica supports both deterministic (analytic) and simulation modes in same model (output display option) • Deterministic (mid) mode-fast same answer each time (analytic) • Chance probability distribution – slower slightly different answer with different random number seeds (simulation) • Selected techniques: Tornado Diagrams, Elasticity, Rank Correlation, Stepwise Regression, Orthogonal Factorial Designs, Correlation & Partial correlation • Note the PGM risk model was developed in Analytica that supports the above sensitivity analysis techniques directly Reference: Sensitivity Analysis by A. Saltelli…

  10. Sensitivity Analysis Modules Elasticity Tornado Sensitivity Analysis Regression Rank Correlation

  11. Tornado Diagrams • Select output variable (MOE) • Select Input variables and low, baseline, and high values for them • Calculate output variable values for low and high values of the selected input variable with all other input values at their baseline values • Repeat for each successive input variable • Sort the input variables in decreasing order of the high-low output MOE values • Note changes in non selected input values can change order (non monotonic tornado values) • Many real world processes are not controllable • Weather, enemy actions, time delays.. • Tornado process assumes total parameter control of all input variables • Handles both fixed and chance variable distributions • Distribution parameter values (min, mode, max…) controllable

  12. Example Tornado Results(MOE: kill probability, average kill probability, priority 1 path delay) MCO2, All Options, Low Intensity, Cell Overfly, Link Upgrade

  13. Explanation Of Example Tornado Plot • Input variables in order of significance to medium ship kill probability included link survival probability, red response time, network utilization, baulking utilization • Input variables were changed by +10% (high) and -10% (low) from the baseline case • Operating point: • Threat level: low intensity • All options selected • The target class is medium ship • The target is in the 40-150 kilometer range band • Baseline kill probability for the operating point is 37% • Raising the link survival probability by 10% improves the kill probability to 51% • Lowering the link survival probability by 10% reduces the kill probability to 23% • Increasing red response time by 10% increases kill probability to 41 • Decreasing red response time by 10% reduces the kill probability to 33% • Increasing utilization by 10% reduces kill probability to 33% • Decreasing utilization by 10% increases kill probability to 41% • Other parameters have less impact • Non linear system: high and low bars different lengths • Other MOEs shown are: average kill probability, communication path delay in minutes, and survivable throughput.

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