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SPE Sensitivity and Configuration

Use initial condition sets, pattern sets, base modifiers, and sensitivity runs to test whether conclusions are robust.

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SPE Sensitivity and Configuration

Configuration controls how SPE interprets variable states and relationship patterns. Sensitivity analysis tests whether the comparison still holds when important assumptions move.

Configuration elements

The current Albarena SPE configuration includes:

  • Initial condition sets: probability vectors over NN, N, O, P, PP
  • Pattern sets: matrices for Sig+, Mod+, Sli+, No, Sli-, Mod-, Sig-
  • Base modifier sets: coefficients used by simulation modes that adjust base-state influence
  • Model configuration: the selected combination of initial condition set, pattern set, and base modifier set
Initial probability over five states
NN N O P PP 0 0.2 0.4 0.6 0.8 1 Probability Impact scale

The default prior starts each variable equally likely across NN, N, O, P, and PP.

Sensitivity analysis

Sensitivity analysis asks how much outcome distributions change when a Driver or Process is forced through NN, N, O, P, and PP. This helps the team identify the variables that most influence the outcome comparison.

Use sensitivity to answer:

  • Which Driver has the largest effect on the selected Outcome?
  • Which Process explains most of the variation?
  • Does Scenario 1 still beat Scenario 2 when a key variable is unfavorable?
  • Which relationships need better evidence before a recommendation is made?

Configuration discipline

Do not change pattern sets only to obtain a desired result. Pattern and base modifier adjustments should reflect evidence, expert review, or a deliberate calibration decision.

PLACEHOLDER: calibration guidance

Future documentation should describe how Albarena teams calibrate pattern matrices and base modifiers using historical data, expert workshops, and model back-testing.

References

Strategic Decisions Method Comparing Risks, Performance Outcomes, and Scenarios

Internal SPE technical source describing the Strategic Performance Engine framework, variable structure, cross-impact logic, strategies, scenarios, Monte Carlo simulation, sensitivity analysis, and outcome comparison.

SPE Core Variable Definitions

Albarena internal canonical note defining External Agents, Drivers, Processes, and Outcomes for SPE model construction.

Project Performance Modeling: A Methodology for Evaluating Project Execution Strategies

Alarcon-Cardenas, L. F., and Ashley, D. B. Construction Industry Institute Source Document 80, 1992.

Modeling Project Performance for Decision Making

Alarcon, L. F., and Ashley, D. B. Journal of Construction Engineering and Management, ASCE, 1996.

Computer Aided Strategic Planning in Construction Firms

Alarcon, L. F., and Bastias, A. Paper describing the computer-supported strategic planning implementation of the performance modeling methodology.

Un ambiente integrado para la modelacion de decisiones estrategicas

Bastias Largo, A. G. Master thesis, Pontificia Universidad Catolica de Chile, 1998.

Performance Modeling for Contractor Selection

Alarcon, L. F., and Mourgues, C. Journal of Management in Engineering, ASCE, 2002.