Systems Thinking - Summary

Systems Thinking Concepts

Complexity and Uncertainty

  • Complexity: Cause-effect relationships are understood as events unfold.
  • Complicated: Cause-effect relationships are understood and used to forecast with enough time, information, and resources.
  • Chaotic: Prediction is impossible, and explanation is difficult even after events.
  • Simple: Cause-effect relationships are self-evident.
  • Puzzles (Complex): Defined structure, known answers (e.g., crosswords).
  • Problems (Uncertain): No defined structure or answers.

Making Sense of Complexity

  • Learn about yourself: objectives, biases
  • Learn about the environment: boundaries, other participants, interaction, control/knowledge, dynamic changes
  • Use frameworks (models)

Models

  • Simplifications that focus on objectives while recognizing others' objectives and environmental uncertainty.
  • Different models highlight different features; framing provides different perspectives.

System Definition

  • A system is a complex of interrelated parts connected by a common purpose.

System Components

  • Input: Information or resources entering the system.
  • Transformation Process: Actions or operations that convert inputs into outputs.
  • Output: The result or product of the system's process.
  • Feedback: Information about the output that is returned to the system to influence future inputs or processes.

Reductionism vs. Systems Approach

  • Reductionism: Breaking down a complex problem into essential components.
  • Systems Approach: Tackle problems using a broader perspective, considering interconnections.

Systems Thinking

  • Understanding the whole system rather than individual parts.

Fundamentals of Systems Thinking

  • Consider the Big Picture, both short and long term.
  • Systems thinking versus linear thinking.

Linear vs. Systems Thinking

  • Linear Thinking: Factors are independent, causality is one-way, factors are equally important, static assumptions.
  • Systems Thinking: Factors are interdependent, causality is circular, assumptions can change.

Feedback Loops

  • Positive (Reinforcing): Amplifies behavior.
  • Negative (Balancing): Dampens behavior.

Language of Systems Thinking

  • Interrelationship of components: interacting, interrelated, or interdependent elements.
  • Boundary definition: the system's scope separating the elements from environment.
  • Holism: presence of the whole system considered as the focus of study.
  • Feedback between elements: mutual influence between elements.
  • Causality of relationships: fundamentally causal relationships between elements.
  • Dynamics: behaviour-over-time is important.
  • Emergent Property: The whole is greater than the sum of its parts; unexpected properties arise.
  • Control of a system: elements may be controlled by actors.

Systems Methodology Overview

  • Developed by Professor Jay Forrester at MIT.
  • Based on system dynamics (SD) methodology.

Uses of Systems Thinking and Modeling

  • Design of new systems.
  • Re-engineering existing systems.
  • Prediction of behaviour under conditions.
  • Understanding sub-system interaction.
  • Strategy development and testing.
  • Scenario modelling.
  • Group and organizational learning.

Systems Thinking and Modelling Framework

  • Phase 1: Problem structuring
  • Phase 2: Causal loop modelling
  • Phase 3: Dynamic modelling
  • Phase 4: Scenario planning and modelling
  • Phase 5: Implementation & organisational learning

Causal Loop Modelling Steps

  1. Summarise the situation.
  2. Establish the issue.
  3. Prepare stakeholder map.
  4. Identify main variables.
  5. Prepare behavior over time chart.
  6. Develop causal loop diagram (CLD).
  7. Identify feedback loop types.
  8. Identify system archetypes.
  9. Identify key leverage points.
  10. Develop intervention strategies.