Operations Research Meets Strategic Foresight

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This paper traces how the emergence of large language models removed the data constraint that had kept operational research and strategic foresight apart, and documents how PreEmpt.Life has subsequently integrated OR techniques borrowed from physics and mathematics into its AI-augmented foresight platform. The author identifies twelve specific OR-derived techniques now operationalised within the platform, including quantum-inspired entanglement witnesses, geometric decision-space modelling, multiplicative power formulae, and stochastic simulation. The author argues that this convergence represents a paradigm shift: the emergence of quantitative foresight as a discipline that combines the rigour of operational research with the anticipatory imagination of futures studies, enabled by AI's capacity to generate the dynamic data points that strategic contexts inherently lack.

Why it matters

For decades, operational research and strategic foresight occupied separate intellectual territories. Operational research required many dynamic data points and well-specified parameters; strategic foresight dealt with deep uncertainty where such data did not exist. The emergence of large language models removed this data constraint, enabling the integration of OR techniques into strategic foresight and the development of a new discipline of 'quantitative foresight' that combines the rigour of OR with the anticipatory imagination of futures studies.

The details

The paper documents twelve specific OR-derived techniques now operationalised within PreEmpt.Life's ALEXIS Navigator platform, including: entanglement witness checks from quantum physics to detect hidden stakeholder correlations; phase transition analysis from statistical mechanics to identify critical thresholds in the decision landscape; Heisenberg uncertainty relationships from quantum mechanics to make measurement trade-offs explicit; cascading failure propagation analysis from network physics; decision-space geometry modelling using angular thresholds and phase transitions; multiplicative power formulae from algebraic modelling; Pareto-efficient frontier analysis from multi-objective optimisation; composite scoring systems from multi-criteria decision analysis; Monte Carlo simulation from stochastic modelling; robust decision-making from decision analysis; agent-based modelling from computational OR; and network analysis techniques for dependency mapping.

• In 2005, futurist John Smart asked whether operational research had any use in strategic foresight.
• In November 2022, OpenAI launched ChatGPT 3.5, enabling large language models to generate the dynamic data points that had previously constrained the application of OR techniques to strategic foresight contexts.
• Over the past three years, PreEmpt.Life has integrated these OR-derived techniques into its AI-augmented foresight platform.

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What’s next

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