Effect and Uncertainty Planning System for Adversarial Strategy
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Solution Overview
Problem
In competitive environments, entities face challenges in disrupting their competitors' ability to gauge their own performance effectively, as existing strategies lack advanced computational methods to generate and deploy complex, adaptive plans that increase uncertainty and confusion.
Innovation Solution
The Effect and Uncertainty Planning and Control System (EUPCS) uses contextual data to generate an effect-web plan, which involves a network of information gatherer, network builder, and performer systems, employing machine learning and game theory to implement adversarial effects, thereby degrading a competitor's performance monitoring capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional competitive strategies are used, then simplicity of implementation is maintained, but ability to degrade competitor's performance monitoring is insufficient
Solution Approach 1:
The system segments the competitive strategy into modular components: effect-web generation module, team-system selection module, contextual data processing module, and monitoring module. Each component handles a specific aspect of the adversarial effect generation, allowing the complex system to be managed through independent, specialized subsystems that can be developed and deployed separately.
Solution Approach 2:
The patent introduces an intermediary computational layer (the EUPCS system itself) that sits between traditional competitive strategies and their execution. This intermediary processes contextual data, generates effect-web plans, and coordinates team-systems, thereby mediating between simple strategic intent and complex adversarial execution while maintaining system manageability.
2Loss of information
If complex effect-web plans are generated and deployed, then uncertainty is increased against competitor, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary actions by continuously gathering and processing contextual data about the competitor and environment before executing adversarial effects. Effect-web plans are pre-generated based on analyzed contextual patterns, allowing the system to rapidly deploy pre-prepared strategies rather than computing everything in real-time, thus reducing deployment time while maintaining uncertainty generation effectiveness.
Solution Approach 2:
The system maintains continuous operation by constantly monitoring contextual data, updating effect-web plans, and coordinating team-systems in an ongoing manner. This continuous action ensures that uncertainty is consistently imposed on the competitor without interruption, while the system efficiently manages computational resources through sustained, optimized operations rather than periodic intensive processing.
3Adaptability or versatility
If team-systems are configured to execute effect-web plans, then adversarial effects are achieved, but system configuration complexity increases
Solution Approach 1:
The team-system configuration is made dynamic rather than static. The system continuously adapts team-system assignments based on updated contextual data, competitor responses, and effect-web plan modifications. This dynamic reconfiguration allows the system to maintain high adaptability to changing competitive conditions while managing complexity through automated, rule-based adjustment mechanisms rather than manual design of all possible configurations.
Solution Approach 2:
The system manages team-system configuration complexity by changing parameters such as team composition, resource allocation, and effect-web plan details based on contextual analysis. Rather than designing a fixed complex architecture, the system dynamically adjusts configurable parameters to achieve desired adversarial effects, thereby achieving versatility through parameter optimization rather than structural complexity.
Data Source
AI summary
Contextual data is received and processed that characterizes a current state of people, processes, and technology resources of an entity. In addition, data comprising desired adversarial effects against the entity and received and processed. A computer-implemented effect and uncertainty strategy application programming interface (API) generates an effect-web plan the based on the contextual data and desired effects requirements. The effect-web plan includes a plurality of effects, actions and task plans to implement the desired adversarial effects. Thereafter, available human-machine team-systems to execute the effect-web plan are determined. The generated effect-web plan can be deployed by multiple selected team-systems. Data characterizing a multi-order impact of the deployment of the effect-web plan on the entity can be monitored. The generated effect-web plan can be modified and deployed based on the monitoring so as to increase a likelihood of an occurrence and success of the desired adversarial effects. Related apparatus, systems, techniques, and articles are also described.