Consequence Intelligence Engine For Predicting Decision Impact In Complex Systems

Abstract

Strategic Decision-making In Complex Organizational Environments Is Inherently Associated With Risk And Uncertainty. Despite Widespread Adoption Of Decision Support Systems, Most Existing Tools Focus On Structuring Decisions Rather Than Predicting Their Downstream Consequences. This Limitation Leaves Organizations Without The Intelligence Required To Anticipate Risk Trajectories, Quantify Impact Severity, Or Understand The Temporal Evolution Of A Decision's Effects. The Consequence Intelligence Engine (CIE) Addresses This Gap By Introducing A Machine Learning-driven Framework Capable Of Predicting The Risk And Organizational Impact Of Strategic Decisions Before Execution. The Proposed System Accepts Natural Language Decision Descriptions Combined With Structured Organizational Context Parameters, Including Company Size, Industry Type, Market Condition, Growth Stage, Workforce Morale, Attrition Trends, And Risk Appetite. A Random Forest Regressor Is Employed As The Core Prediction Model, Trained On A Synthetic Scenario-based Dataset Capturing Diverse Organizational Decision Profiles. Feature Engineering And Context-aware Encoding Enable The Model To Generate An Overall Risk Score, Categorized Risk Classifications, And A Temporal Impact Timeline Spanning Short-term Through Long-term Horizons. A FastAPI Backend Exposes The Prediction Engine As A RESTful Service, While A React-based Interactive Dashboard Presents Results In An Interpretable And Actionable Format. The System Contributes A Novel Approach To Decision Intelligence By Combining Predictive Modeling, Temporal Risk Analysis, And Contextual Awareness In A Unified, Accessible Platform.

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