Modeling Complex Layered Systems with Functional Reasoning
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Solution Overview
Problem
Current systems are inadequate for modeling complex systems with multiple levels of structure, particularly in biological research, as they struggle to reveal hidden dynamics, manage streaming data, and understand systems composed of agents with agency, and fail to provide intuitive insights into system dynamics and causal relationships.
Innovation Solution
A computer system employing functional programming techniques, category theory, and situation theory, with a novel application of cognitive narratology, to extract features from diverse data sources, reason over unknown elements, and present intuitive displays of system dynamics and causal relationships across vast networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current systems are used to model complex systems with multiple levels of structure, then the modeling process is simple, but the systems cannot reveal hidden dynamics or provide intuitive insights into system dynamics and causal relationships
Solution Approach 1:
The patent segments complex systems into multiple hierarchical levels, where each level can be modeled and analyzed independently while maintaining connections to other levels. This allows hidden dynamics at different scales to be revealed without overwhelming the modeling system with monolithic complexity.
Solution Approach 2:
The patent implements nested modeling where systems are composed of subsystems that are themselves systems, allowing multi-level structure to be represented. Each nested level can have its own dynamics and causal relationships, enabling comprehensive analysis of complex systems while maintaining manageable complexity at each level.
2Loss of information
If current systems are used to manage streaming data from vast networks, then data collection is straightforward, but the systems cannot reason over unknown elements or provide causal insights
Solution Approach 1:
The patent introduces an intermediary reasoning layer that sits between data collection and analysis, capable of inferring causal relationships and reasoning about unknown elements. This intermediary system processes streaming data and generates causal insights without requiring the entire system to become exponentially more complex.
Solution Approach 2:
The patent creates a universal reasoning framework that can handle multiple types of data, unknown elements, and causal inference tasks through a single integrated system. This multi-functional approach allows the system to reason about diverse phenomena without requiring separate specialized systems for each task.
3Loss of information
If current systems are used to model systems composed of agents with agency, then the modeling approach is simple, but the systems cannot understand overarching system dynamics or hidden behaviors
Solution Approach 1:
The patent adds a temporal and hierarchical dimension to agent-based modeling, allowing analysis of how agent actions aggregate to produce system-level dynamics over time. This dimensional expansion enables understanding of overarching patterns without requiring excessive complexity in the base modeling framework.
Data Source
AI summary
Method and system for modeling of complex systems using a two-sorted reasoning system. Information is received by Distributed Feature Extraction Processors. A first level of reasoning is performed on the information by Distributed Regular Reasoning Processors. A second reasoning process is performed on the information by Distributed Situation Reasoning Processors, which use a Functional Fabric configured to analyze the information received and use functions to modify previous inferences. Client applications allow for viewing and manipulating both reasoning systems and their associated information.