Causal Layered Analysis

Definition

Causal Layered Analysis (CLA) is a futures studies method that helps to uncover and analyze the different layers of causality behind an issue or phenomenon. It breaks down complex problems into four levels: the litany, system, worldview, and myth/metaphor, enabling a deeper understanding of the underlying structures and assumptions that shape our perspectives on the future.

5 Must Know Facts For Your Next Test

1. Causal Layered Analysis was developed by Sohail Inayatullah in the 1990s as a tool for exploring the deeper meanings of social issues.
2. The four levels of CLA—litany, system, worldview, and myth/metaphor—allow analysts to examine not only what is happening but also why it is happening at different depths.
3. CLA encourages participants to challenge dominant narratives and uncover hidden assumptions that influence decision-making processes.
4. By using CLA, policymakers and strategists can better anticipate future challenges and opportunities by gaining insight into how current events are shaped by underlying structures.
5. Causal Layered Analysis has been applied in various fields, including education, environmental studies, and health care, to foster more inclusive and transformative approaches to future planning.

Review Questions

• How does Causal Layered Analysis help in understanding complex social issues?
  • Causal Layered Analysis helps in understanding complex social issues by breaking them down into four distinct layers: the litany, which captures immediate concerns; the system level, addressing underlying structures; the worldview layer, which includes cultural beliefs; and the myth/metaphor level, revealing deep-rooted narratives. This multi-layered approach allows analysts to grasp not only what is happening but also the deeper causes behind it, facilitating more informed discussions about potential futures.
• Discuss the implications of using Causal Layered Analysis for strategic foresight in policy-making.
  • Using Causal Layered Analysis for strategic foresight in policy-making has significant implications as it encourages policymakers to question existing narratives and consider diverse perspectives. By addressing multiple layers of causality, they can identify not just surface-level issues but also systemic problems and cultural assumptions that may hinder effective solutions. This comprehensive approach fosters more resilient policies that are better equipped to adapt to changing circumstances and uncertainties.
• Evaluate how Causal Layered Analysis can transform conventional methods of scenario planning in understanding future uncertainties.
  • Causal Layered Analysis can transform conventional methods of scenario planning by providing a richer framework for examining uncertainties. Instead of merely forecasting based on trends, CLA delves deeper into the underlying structures and cultural narratives that shape potential outcomes. This not only enhances the robustness of scenario planning but also promotes inclusive dialogue among stakeholders by recognizing multiple viewpoints and interpretations of future possibilities. As a result, organizations can develop more nuanced strategies that consider both visible trends and hidden dynamics influencing the future.

Related terms

Futures Studies: An interdisciplinary field that seeks to understand and anticipate possible futures by analyzing trends, patterns, and uncertainties.

Scenario Planning: A strategic planning method that organizations use to create flexible long-term plans based on different future scenarios.

Systems Thinking: An approach that focuses on understanding the interconnections and relationships between different components within a system to address complex problems.