9.1 Theory of Change Mapping
Tests every assumption and link in your path from activities to impact to identify what's proven versus speculative. Launch on platform.
What is it?
Dragonfly’s Theory of Change Analyst provides a forensic, system-level evaluation of impact logic to distinguish credible strategic pathways from wishful thinking. This tool systematically dissects every causal link, assumption, and risk to assess whether a strategy is designed to deliver outcomes—or simply hopes to.
It uses a rigorous blend of evidence auditing, probability modelling, and complexity science to test logic integrity, expose hidden dependencies, and identify the real levers for change. The result is a sharper, more actionable Theory of Change built for adaptive execution in uncertain systems.
Why is it useful?
Employing the Theory of Change Analyst enables you to:
Validate logic integrity: Map and evaluate causal pathways from inputs to impact, identifying gaps, weak links, and alternative routes. Excavate hidden assumptions: Reveal political, behavioural, and resource dependencies that underpin (and sometimes undermine) intended change. Quantify risk and reward: Assess probability of success, risk exposure, and reward potential with explicit calculations and confidence intervals. Evaluate resilience architecture: Test how shock-absorbent, adaptive, and transformative the strategy is under different scenarios. Apply complexity science: Identify feedback loops, tipping points, and non-linear dynamics to avoid failure in complex systems. Raise evidence standards: Audit the strength of empirical backing across each link—separating proven mechanisms from untested optimism.
How does it work?
The Theory of Change Analyst applies a six-part deep analysis:
Logic Chain Integrity
Focus: Assess the completeness, evidence base, and reliability of each causal step.
Example: Identifying that the pathway from digital training to employment outcomes lacks a clear mechanism or proof of behaviour change.
Assumption Excavation
Focus: Uncover critical assumptions in areas like stakeholder behaviour, resource flows, and environmental stability.
Example: Revealing that the ToC assumes local political support for change, despite prior opposition and no mitigation strategy.
Risk-Reward-Resilience
Focus: Quantify risks by category, reward potential by depth and reach, and test system resilience to disruption.
Example: Calculating that the catalytic potential of an education reform depends on one fragile partnership without redundancy.
Complexity Dynamics
Focus: Map feedback loops, delays, and tipping points. Identify emergent properties and leverage points.
Example: Discovering that a reinforcing loop of community engagement → trust → uptake → visibility can amplify outcomes—but only after a 12-month delay.
Evidence Architecture
Focus: Audit quality and type of evidence supporting each causal link, highlight critical gaps, and propose a research agenda.
Example: Finding that 42% of links are speculative and only 12% supported by experimental evidence relevant to context.
Implementation Reality
Focus: Test the real-world feasibility of delivering the strategy—resources, capabilities, dependencies, and critical path.
Example: Stress-testing shows that a funding gap and three skill dependencies lower implementation probability to 48% ±12%.
Turning Complex Analysis into Action
To effectively utilise ToC insights:
Rebuild around causality: Prioritise proven mechanisms and redesign weak or missing links with stronger foundations.
Address high-risk assumptions: Integrate monitoring and fallback plans for critical dependencies and political risks.
Focus on leverage, not logic chains: Shift resources toward points of maximum influence in complex systems.
Upgrade evidence and adapt iteratively: Treat the ToC as a living hypothesis that matures with data, learning, and feedback from the field.
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