Digital Twin Agent Modeling for Emergent Decision Behavior
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Solution Overview
Problem
Existing decision-making technologies for complex systems, such as modern enterprises, rely heavily on qualitative approaches and human intuition, leading to ineffective decisions due to excessive uncertainty and emergent behavior, with existing quantitative methods like inferential techniques and mathematical models being inadequate for dynamic and uncertain environments.
Innovation Solution
A learning-based modeling approach using digital twins and reinforcement learning (RL) to simulate and analyze complex systems, incorporating digitally configured dynamic agents that adapt and learn from their environment to make quantitative decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If qualitative approaches and human intuition are used for decision-making, then ease of operation is improved, but reliability deteriorates due to excessive uncertainty and emergent behavior
Solution Approach 1:
The patent introduces digital twins as an intermediary between the complex system and decision-makers. These digital twins simulate system behavior and provide quantitative predictions, mediating between intuitive qualitative approaches and reliable quantitative analysis. The digital twin acts as a mediator that translates complex emergent behaviors into understandable predictions without requiring direct human intuition about the complex interactions.
Solution Approach 2:
The patent replaces human intuition and qualitative judgment with automated computational models and simulations. Instead of relying on human cognitive processing of complex systems, the system uses digital twins with automated simulation engines that quantitatively predict system behavior, substituting mechanical human decision-making processes with computational algorithms.
2Reliability
If existing quantitative methods like inferential techniques and mathematical models are used, then reliability is improved, but adaptability deteriorates because they are suitable only for static environments or mechanistic systems
Solution Approach 1:
The patent creates dynamic digital twins that can adapt to changing environments and system states. Unlike static mathematical models, these digital twins continuously simulate system behavior under varying conditions, allowing the quantitative analysis to adapt to dynamic environments. The digital twin model evolves and adjusts its simulations based on new data and changing system parameters, maintaining both reliability and adaptability.
Solution Approach 2:
The patent utilizes parameter changes in the digital twin simulation to adapt to different scenarios and environmental conditions. By modifying simulation parameters and running multiple scenarios, the system maintains reliable quantitative analysis across diverse dynamic conditions. The digital twin can adjust its internal parameters and simulation conditions to match the actual evolving system state.
3Reliability
If digital twins with reinforcement learning are used for automated decision-making, then reliability and adaptability are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex decision-making system into modular digital twin components, each handling specific aspects of system simulation. The digital twin is divided into manageable modules that can be independently developed and validated. This segmentation reduces the overall complexity by breaking down the monolithic modeling challenge into smaller, more tractable simulation components that can be composed together.
Solution Approach 2:
The patent creates simplified digital copies (digital twins) of the complex system that replicate essential behaviors without requiring full complexity of the original system. The digital twin is a simplified model that captures key dynamics and emergent behaviors sufficient for decision-making purposes. This copying approach reduces complexity by creating a manageable simulation model that preserves only the necessary system characteristics.
Data Source
AI summary
The disclosure generally related to a learning-based modelling of an emergent behavior of a complex system. Existing decision-making at complex systems primarily relies on qualitative approaches, which often results in inaccurate outputs. The disclosed system includes a digital twin of the complex system and a digital twin of an environment of said complex system, and captures an interaction and dynamic behavior of agents of the digital twins. The agents of the digital twins are simulated and modelled using learning-based models such as RL and genetic algorithms that learns the behavior (i.e. actions and their outcomes) over a period of time. Hence, the agents (or actors) of the digital twins are dynamic in nature. The actor-based bottom up simulation approach is capable of producing sufficient insight for effective decision making prior to implementation.