Systematic x Systemic theory

Elementary Structure of a System

• Problem (Contradiction) & Purpose (Goal) & Values
  • Stakeholders
  • Vision
  • ...
• Functionality (I/O)
  • Environment
  • Product & Service
  • Inputs / Outputs /Throughput
  • ...
• Architecture (Elements & Relations) Structure
  • Elements
  • Relations
  • Flows (Information, Energy, Matter, Business...)
  • Boundaries
  • ...
• Genes
  • KESW (Knowledge, Experience, Skills, Wisdom)
  • Principles, Patterns, Theories, Standards, Strategies, Rules, Laws, ...
• Dynamics (Change)
  • Evolutions
  • Elaborations
  • ...

Features of a System

Systematic View

• Methodical
• Structured
• Linear
• Analytical Process (SOP), Workflows

Systemic View

• Holism and Emergence (NoneLinear & Chaotic): The Whole Beyond the Parts
• Interconnectedness and Interdependence
• Feedback Loops: The Engines of System Dynamics
• Dynamics and Change: The Evolution of Systems
• Boundaries and Environments and Interfaces: Defining the System
• Hierarchy and Organization: The Structure of Systems
• Self-Organization and autopoiesis
• Homeostasis/Equilibrium: Homeostasis
• Equifinality & Multifinality

Compare Systematic & Systemic

System Analysis Methodologies & Techniques

By combining the systematic and systemic views, we can develop a systematic analytic process in a more comprehensive and integrated way.

PSMs and SSMs

In the early stages of system analysis, especially when facing complex, ambiguous, or ill-defined "wicked problems," problem structuring plays a crucial role 34. It doesn't directly seek solutions but rather focuses on clearly defining "what the problem actually is," laying a solid foundation for subsequent in-depth analysis. Problem Structuring Methods (PSMs) are a series of "soft operations research" methods designed to help groups with different backgrounds reach consensus on problem focus and make commitments to subsequent actions 35. These methods are particularly suitable for handling complex situations with multiple interactive issues, involving numerous stakeholders, full of uncertainty, and without a single clear formulation 34. System theory itself, with its emphasis on interconnections, feedback, adaptability, and resilience, provides a natural framework for constructing complex problems and developing holistic solutions 37. Effective problem structuring is essentially participatory and fully considers the perspectives of multiple stakeholders 34. This highly aligns with the core principle of "multiple perspectives" in systems thinking 8. For example, a problem structuring method proposed for complex social decision-making (such as sustainable development of large infrastructure) explicitly draws from systems theory and cognitive psychology, and aims to accommodate different stakeholder perspectives. One of its core tools is the use of "trilemmas" to characterize and critique a set of competing forces 38. PSMs such as Soft Systems Methodology (SSM), Strategic Choice Approach, and Strategic Options Development and Analysis (SODA) provide structured approaches for handling "soft," human-centered system problems where objectives are unclear or conflicting 34. SSM is particularly suitable for analyzing unstructured complex problem situations involving human-technology interactions 39.

Systematic Analytic Process

• Clearly defining the problem or goal: This involves establishing specific and unambiguous objectives at the outset.8
• Gathering and organizing relevant information: A thorough collection and structuring of data pertinent to the problem is a prerequisite for analysis.8
• Breaking down the problem: Complex issues are decomposed into smaller, more manageable parts to allow for focused examination.8
• Analyzing data objectively and thoroughly: Each component or piece of information is subjected to careful, objective scrutiny.8
• Generating and evaluating potential solutions: Based on the analysis, various solutions are proposed and assessed against predefined criteria.8
• Implementing the chosen solution and monitoring its effectiveness: The selected solution is put into action, and its performance is tracked to ensure desired outcomes and allow for adjustments.8
• Documenting and following standard work: Adherence to established procedures and the use of tools like process check sheets are characteristic of maintaining and improving systematic processes.9

Systemic Analytic Process

[System Structure Analysis: Identifying Components and Static Relationships]

• Mathematical Models (Models for System Structure)

[System Function Analysis: Understanding System Behavior and Functional Flows]

For visualizing and documenting functional design, diagramming tools are often used, such as activity diagrams in SysML, SV-3 or SV-10 diagrams in the UAF framework, and Functional Flow Block Diagrams (FFBDs) 44. FFBDs are particularly common, using rectangles to represent functions and arrows to show flows, sequences, or any type of input-output relationships between functions.

[System Dynamic Analysis: Exploring System Behavior Patterns]

• System Dynamics Modeling Fundamentals
• Behavior Patterns: Stability, Oscillation, Growth, Collapse

[System Thinking Visualization Tools]

• Causal Loop Diagrams (CLDs)
• Stock and Flow Diagrams
• The Iceberg Model
• Behavior-Over-Time Graphs (BOTGs)
• Connection Circles and Rich Pictures

Building Systematic Thinking Paths and Enhancing Analytical Capabilities

• Defining System Boundaries and Identifying Key Elements: Before analyzing any system, the first and crucial step is to clearly define the system boundaries to be studied and identify the key elements that are essential for understanding and solving the problem.

Building a Personalized Systems Thinking Methodology

Building a path map from theory to practice means organically integrating the content from the previous sections—the foundations of systems thinking (Part 1), systematic methods of system analysis (Part 2), core methods and tools (Part 3), and strategies for building systematic thinking paths (Part 4)—into a coherent, operational analytical framework.

Problem Perception & Initial Definition: Core Activities: Identify problem symptoms, sense complexity. Use the "Iceberg Model" for initial exploration, thinking about patterns behind events. System Principles Application: Initial perception of wholeness, interconnectedness. Tool Support: Behavior Over Time Graphs (BOTGs) to record initially observed dynamics.

Problem Structuring & Boundary Setting: Core Activities: Interact with stakeholders, collect multiple perspectives. Use rich pictures, connection circles, and other tools for divergent exploration. Clarify analysis goals and scope, define initial system boundaries. System Principles Application: Multiple perspectives, boundaries, wholeness. Tool Support: Rich pictures, connection circles, stakeholder analysis, goal hierarchy analysis. Soft Systems Methodology (SSM) concepts can be introduced at this stage.

System Structure & Function Analysis: Core Activities: Within defined boundaries, identify key system components and their static connection relationships (structural analysis). Analyze the functions that these elements achieve individually and collaboratively (functional analysis). System Principles Application: Interconnectedness, hierarchy, wholeness. Tool Support: Component diagrams, flowcharts, Functional Flow Block Diagrams (FFBDs).

System Dynamic Mechanism Exploration: Core Activities: Deeply analyze feedback loops, time delays, and nonlinear effects that drive system behavior. Use Causal Loop Diagrams (CLDs) to depict causal relationships and feedback structures. Identify potential system archetypes. System Principles Application: Feedback, dynamics, emergence, nonlinearity. Tool Support: Causal Loop Diagrams (CLDs), system archetype analysis.

(Optional) Quantitative Modeling & Simulation: Core Activities: For problems requiring more precise prediction or policy testing, transform qualitative CLDs into quantitative stock and flow diagrams and conduct computer simulations. System Principles Application: Feedback, dynamics, cumulative effects. Tool Support: Stock and flow diagrams, system dynamics simulation software.

Leverage Point Identification & Intervention Strategy Design: Core Activities: Based on understanding of system structure and dynamics (especially feedback loops and archetypes), find "leverage points" that can produce significant, lasting positive changes at relatively small cost. Design and evaluate alternative intervention strategies, considering their potential short and long-term impacts and possible unintended consequences. System Principles Application: Feedback, equifinality, wholeness. Tool Support: Leverage point analysis (referencing Meadows et al.), scenario analysis.

Action, Monitoring & Learning: Core Activities: Implement selected intervention strategies. Continuously monitor changes in system behavior, evaluate intervention effectiveness. Learn from practice, reflect on analysis process and intervention results, adjust mental models and subsequent actions. System Principles Application: Dynamics, feedback, adaptability. Tool Support: Behavior Over Time Graphs (BOTGs) to track changes, regular review and reflection meetings.

AI integration space & exploration

Chances and Opportunities behind the AI Revolution

Beyond augmenting systemic and systematic thinking in isolation, AI holds the potential to facilitate a more fluid and integrated interplay between these two crucial cognitive modes. AI can act as a bridge, translating insights from one mode into actionable inputs for the other.

• The inherent nature of many AI systems, particularly in machine learning, involves iterative refinement through feedback loops (e.g., backpropagation in neural networks, reward signals in reinforcement learning).1
• Human-AI collaboration is emerging as a key paradigm for tackling complex problems.

Work in a cooperation way with AI.

Systematic x AI

AI Fortifying Systematic Processes: From "System 0 Thinking" to Automated Structured Analysis and Logical Progression

• Information Gathering and Organization: AI tools, including Large Language Models (LLMs) like ChatGPT, can assist in formulating research questions, developing comprehensive search strategies for literature reviews, and automating the screening and extraction of relevant information from vast document repositories.25
• Data Extraction and Processing: Technologies like Intelligent Document Processing (IDP), powered by machine learning, can automatically extract and validate data from both structured and semi-structured documents such as invoices or research papers.28 AI can also analyze large volumes of unstructured data, like emails, to perform tasks such as document validation and information routing.28
• Problem Decomposition and Solution Generation: AI problem-solving methodologies often involve exploring potential solutions through sophisticated reasoning techniques and computational modeling.29 Fundamental to this are AI search algorithms (both uninformed, like Breadth-First Search, and informed, like A* search) that systematically explore complex problem spaces to identify optimal or near-optimal solutions.29

Systemic x AI

AI Amplifying Systemic Insight: Modeling Complexity, Unveiling Interdependencies, and Identifying Feedback Loops

• Tools and platforms are emerging that leverage AI to make the creation and interpretation of systems thinking models, such as system dynamics models or causal loop diagrams, more accessible and intuitive.
• AI can play a crucial role in identifying feedback loops and leverage points within these complex models. By analyzing system dynamics, AI algorithms can highlight critical feedback mechanisms that drive system behavior and pinpoint leverage points.
• Network analysis, a field significantly enhanced by AI and machine learning, directly supports the systemic focus on relationships and interconnections.
• Causal reasoning. Moving beyond mere correlation, AI techniques are being developed to infer cause-and-effect relationships within systems.

Thinking Techniques Applied to S&S research

Models

• The ICEberg Model
• Feedback Loops
• Stock and Flow Thinking
• Time Delays
• Shifting the Burden Archetype
• Boundaries and Perspectives
• Ladder of Inference
• Second-order Thinking
• Bottleneck Model

Frameworks

• Senge's Five Disciplines: Developed by Peter Senge, this framework outlines five essential disciplines for building "learning organizations" – organizations capable of continuous adaptation, innovation, and achieving desired futures.
• SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats)
• Failure Mode and Effects Analysis (FMEA)
• PESTEL Analysis (Political, Economic, Sociocultural, Technological, Environmental, Legal factors)
• Porter's Five Forces (analyzing industry competition)
• McKinsey 7S Framework (analyzing internal organizational alignment)
• Business Model Canvas (visualizing business models)
• BCG Matrix (portfolio analysis)

Tools&Patterns

• Causal Loop Diagrams (CLDs)
• Stock and Flow Diagrams
• Behavior-Over-Time Graphs (BOTGs)
• Connection Circles
• Concept Mapping
• Process Mapping (Flowcharts, etc.)
• UML standard modeling language

• Root Cause Analysis (RCA)
  • 5 Whys
  • Fishbone (Ishikawa) Diagrams
• Decision Trees
• Algorithmic Reasoning
• Checklists
• Decomposition / Work Breakdown Structure (WBS)
• MECE Principle (Mutually Exclusive, Collectively Exhaustive)

Reference

• An Examination of Systems Theory and Systems Thinking: Foundations, Principles, and Applications
• Systemic and Systematic Thinking in the AI Era: Augmentation, Theory Development, and Intelligent Research Systems