System of Systems and Emergent Behavior: Leveraging Knowledge Graphs for Analysis

In today's interconnected world, it would be very challenging to find any system that operates independently of the surrounding environment.

Interoperability is a fundamental factor in system interdependencies. A systems-of-systems approach provides a framework for analyzing synergies across domains to realize ultimate value.

According to Wikipedia "System of systems is a collection of task-oriented or dedicated systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems."

This is how systems communicate within the enterprise to support various operations. The interoperability of generation plants, transmission networks, and distribution systems enables us to deliver resilient power grids.

Interoperability between systems can lead to emergent behavior, where new, often unexpected patterns or properties arise from their interactions. Emergent behavior is one of the main tenets of system of systems. In a system of systems, emergent behavior refers to unpredictable outcomes that arise from the interactions of various independent systems. These behaviors are challenging to predict based on the individual systems alone, as they emerge from the complex relationships within the broader system.

Emergent behavior can have both positive and negative consequences. In the case of power grids, a chain reaction of failures can lead to widespread blackouts. Similarly, a failure in one system within an operation can disrupt the entire process, causing delays and downtime.

To avoid negative consequences of emergent behavior, we need a well-planned approach that considers the complex interactions between systems. This involves gathering detailed information about how systems are connected, how they depend on each other, and how they share data within the specific operational setting.

Collecting that kind of information allows for the review, analysis, and visualization of the impact of any given system within any given context on overall behavior. This enables the simulation of real-time visualized impacts of emergent behavior within large systems. This also allows us to take measurable steps in planning and implementing system upgrades, as well as using alternative methods in case of emergencies.

This activity will require input from various stakeholders, including system owners, operations managers, software engineers, data owners, and other relevant parties within the enterprise. This information should be verified, integrated and converted into useful format once collected.

One of the tools to conduct such an analysis is a knowledge graph. You can get more information about knowledge graphs at the following URL:

https://neo4j.com/blog/what-is-knowledge-graph/

It is powerful tool for modeling and understanding complex systems. When applied to systems of systems, it can provide a comprehensive view of the interconnections and dependencies between different components. By using knowledge graphs and applying graph algorithms, we can:

• Visualize complex relationships: Clearly depict the interconnectedness of systems.
• Identify critical dependencies: Pinpoint crucial links that can impact overall system performance.
• Predict cascading failures: Anticipate potential failures and their ripple effects.
• Model complex interactions: Simulate how systems interact and influence each other in intricate ways.
• Analyze emergent behavior: Understand how unexpected behaviors arise from system interactions.
• Facilitate knowledge discovery: Uncover hidden patterns and insights.
• Support decision-making: Provide a holistic view for informed decision-making.
• Enhance AI and ML: Provide rich contextual information to improve AI and ML performance.
• Optimize system performance: Identify opportunities to streamline processes and reduce costs.
• Improve system resilience: Develop strategies to mitigate risks and improve system reliability.
• Identify opportunities for enhancements: Enable predictive analytics and uncover hidden relationships

In this post, we first reviewed systems of systems, communication between systems, and how communication and interaction deliver emergent behavior. After that, we briefly discussed the possible impacts of emergent behaviors and how we can benefit from knowledge graphs to prevent undesirable outcomes.