Systems Thinking and Beyond

The AI team takes a deep dive into a text which introduces systems science as an interdisciplinary framework designed to bridge the gap between specialized academic "silos" such as biology, physics, and engineering. Written by Avner Engel, the book Systems Science for Engineers and Scholars outlines ten fundamental principles—including hierarchy, complexity, and emergence—that govern all systems regardless of their specific domain. It encourages professionals to adopt holistic thinking to solve modern global dilemmas, such as the climate and energy crises, by applying lessons learned from one field to another through isomorphic mapping. The material also provides a detailed roadmap of the book’s structure, which covers practical applications in risk management, decision-making, and accident analysis. Ultimately, the text serves as a guide for using systemic methodologies to design more resilient technologies and understand the interconnected nature of the universe.

The AI team takes a deep dive into the technical architecture and operational logic of Large Language Models (LLMs). They explain that these systems are trained through a multi-stage process; pre-training, fine-tuning, and human feedback, to predict text sequence. A central focus is the Transformer architecture, which uses an attention mechanism to understand relationships between words and manage linguistic nuances such as spelling errors. The team clarify that AI "memory" is actually a process where the entire conversation history is re-read during every interaction to maintain coherence. Finally, the team define LLMs as probabilistic state machines that, despite their sophisticated conversational abilities, face limitations such as factual hallucinations and fixed knowledge cutoffs.

The AI team take a deep dive into a book, Introduction to System Science with MATLAB by Gary Marlin Sandquist Zakary and Robert Wilde. The book introduces system science as a multidisciplinary framework for analyzing and modeling rational systems through the use of MATLAB. It emphasizes that effective practitioners must combine mathematical proficiency with computer competence to evaluate complex phenomena ranging from physical sciences to human history and sociology. By applying the principle of causality, the material demonstrates how to quantify diverse topics such as economic growth, medical diagnoses, and even religious impacts or personal stress. The provided excerpts offer various system equations and modeling exercises that explore the relationship between inputs, outputs, and feedback mechanisms. Ultimately, the book seeks to provide students with the computational tools necessary to simulate and understand the interconnected nature of the modern world.

The AI team takes a deep dive into a provided text, The Collapse of MBSE and the Collateral Damage to Systems Engineering, by Art Villanueva, DEng, ESEP which argues that Model-Based Systems Engineering (MBSE) has mistakenly become a substitute for the broader discipline of systems engineering, leading to a decline in professional authority and decision-making quality. While MBSE is a valuable tool for organizing and documenting system information, it often lacks the analytical power required to drive critical engineering choices, which are instead handled by external simulations and expert judgment. This misalignment results in models that serve as post-hoc documentation rather than load-bearing assets, causing stakeholders to view the entire field as administrative overhead. The author suggests that organizations must re-establish systems engineering as a cognitive, decision-oriented discipline while positioning MBSE strictly as supporting infrastructure for coordination. To resolve this, the text advocates for clearer role definitions that distinguish the representative work of modelers from the analytical responsibilities of engineers. Ultimately, the source concludes that even advanced tools like SysML v2 and AI cannot replace human reasoning and the necessity for rigorous, tool-agnostic engineering leadership.

You can find the paper and information about his upcoming book (to be released March 24), The Garden and the Machine: Designing Systems that Thrive on Disruption at https://phronos.com.

The AI takes a deep dive into a Case Study which introduces temporal analysis as a superior method for evaluating nonprofit effectiveness compared to traditional single-year snapshots. Using the INCOSE Foundation as a detailed case study, the text illustrates how longitudinal data can expose governance red flags, such as inconsistent state registrations and systematic bylaw violations. While the organization maintains high ratings from automated evaluators like Charity Navigator, the author reveals a paradox where efficiency metrics mask stagnant grantmaking and excessive asset accumulation.

The analysis highlights significant reporting contradictions between public activity reports and IRS filings, specifically regarding international programs and management fees. Ultimately, the source serves as a call to action for donors and regulators to demand greater transparency through multi-year pattern recognition. It concludes by providing a methodological checklist for stakeholders to conduct their own independent assessments of charitable integrity.

Disclaimer the AI Team confused the 2024 INCOSE And INCOSE Foundation mailing addresses. INCOSE changed its address from California to Indiana, the INCOSE Foundation address remained in California.

The Case Study can be seen on YouTube at https://youtu.be/0zcYCseg4ZE

The AI team takes a deep dive into how individuals can navigate the modern information war by using critical thinking and artificial intelligence. It highlights that social media is often filled with biased narratives and emotional manipulation regarding global conflicts and political figures. To combat this, the AI team suggest using AI tools like ChatGPT or Claude to analyze claims for accuracy, missing context, and intent. By focusing on critiquing information rather than attacking people, users can contribute more balanced perspectives to online discourse. Ultimately, the source encourages a disciplined approach to consuming and sharing content to avoid becoming a casualty of digital misinformation.

The AI team takes a deep dive into Prof Joseph Kasser's draft manuscript which proposes a scientific foundation for systems engineering to resolve the discipline's long-standing identity crisis and its conflation with management. The framework moves away from defining the field by observed workplace roles (Systems Engineering The Role (SETR) , instead focusing on Systems Engineering The Activity (SETA) as an enabling discipline grounded in objective system science axioms. This structure is organized into a four-layer hierarchy that translates universal truths about systems into action-oriented systems engineering principles. These proposed principles require systems engineers to produce verifiable outputs, such as interaction architectures and unintended consequence registers, ensuring designs are rooted in system science rather than heuristics. Ultimately, the proposal seeks to begin to provide a rigorous conceptual scaffold that justifies the value of systems engineering through measurable outcomes and ethical accountability.

The AI team takes a deep dive into the 15 INCOSE Systems Engineering Principles and an iterative AI analysis of those principles.

The AI team critique INCOSE for not defining principles, but stating 'so-called' principles as "transcendent truths" that explicitly avoid "how-to" methods, effectively turning engineering into philosophy. True engineering principles, such as Ohm’s Law, must be mathematical, predictive, and falsifiable.

An analysis of the language in the 15 principles found that 89% of the INCOSE document is management-focused, dealing with organizational structures and stakeholder consensus rather than physics.

The AI team also describe Principle 6 (Progressive Understanding) as a tautology and Principle 13 (Discipline Integration) as mere "stamp collecting", namely observing disciplines without providing the mathematical "glue" to integrate them.

The AI team highlight the irony that the only mathematically proven sections in the INCOSE text are labelled as hypotheses, while vague management advice is presented as transcendent truth.

The AI team also critique the INCOSE principles for employing circular logic and tautologies that describe goals as the methods for reaching them, effectively offering "vague life advice" rather than engineering rigor.

The AI critique contrasts INCOSE with the seven Kasser and Hitchins principles (2011) which provide a prescriptive "recipe" for success based on a singular objective and rigorous partitioning of subsystems.

The fact that hard engineering bodies like the IEEE and AIAA signed off on these principles is seen by the AI as a worrying sign that the industry is confusing "meeting agendas with blueprints". Ultimately, the AI team warn that drifting from hard, verifiable principles to "soft, vibes-based management" is actively dangerous for safety-critical systems such as autonomous cars or nuclear plants. It suggests that if the guardians of engineering continue to prioritize consensus over physics, real-world-changing engineering might eventually move away from legacy institutions toward small, focused teams that "care a whole lot more about the math than the meeting minutes.

Why not download the INCOSE principles document and decide for yourself?

Systems Engineering Principles, https://www.incose.org/wp-content/uploads/legacy/professional-development-portal/pdp-pdf-non-webinar-documents/systems_engineering_principles_book_v12_watson.pdf?utm_source=chatgpt.com, accessed 20 February 2026

Kasser, J. E. and Hitchins, D. K., Unifying systems engineering: Seven principles for systems engineered solution systems, proceedings of the 21st International Symposium of the INCOSE, Denver, 2011.

The AI team takes a deep dive into Agentic AI for Dummies. The book provides an introduction to a transformative technology that moves beyond simple content generation to proactive decision-making and independent action. Unlike traditional software, these systems utilize multi-agent coordination and adaptive behavior to complete complex, multi-step goals with minimal human oversight. The material details the technical architecture required for these agents, emphasizing the importance of memory modules, reasoning engines, and tool integration through APIs. It also provides a practical framework for planning and deployment, highlighting the shift from static applications to dynamic, personalized workflows across various industries. Furthermore, the source addresses critical ethical and safety considerations, such as maintaining human control and implementing robust guardrails to prevent unintended consequences. Ultimately, it explores how this shift toward autonomous agency will redefine professional roles and the global digital economy.

The AI team takes a deep dive into the book Applying AI in Learning & Development. The author, Josh Cavalier explores the transformative role of generative artificial intelligence within the modern workplace. The text provides a comprehensive roadmap for education professionals to transition from traditional content creation to AI-enhanced performance consulting. Key concepts include the Human-AI Task Scale, the mechanics of multimodal systems, and the strategic use of structured prompting frameworks like TRACI. Beyond technical implementation, the author emphasizes the importance of data privacy, ethical guardrails, and human-centric design to ensure technology amplifies rather than replaces human expertise. Ultimately, the source serves as a practical guide for building future-ready capabilities through automated workflows and personalized learning ecosystems.