Casual Layered Analysis Masterclass

Causal Layered Analysis (abbreviated as CLA) is one of the most innovative approaches in futures studies and foresight since the end of the 1990s. It is used to create alternative futures. The masterclass will consist of presentations, case studies and workshops allowing participants to experiment the approach.

CLA assumes four levels of reality, each equally important: the litany or the day-to-day unquestioned reality — headlines and data; the systemic or social causation level in which reality is accounted for and solutions offered; the worldview or discourse level wherein multiple perspectives and stakeholder positions are included; and the deepest level of myth and metaphor linked to culture that creates worldviews and where long-term transformation often occurs. CLA goes up and down these layers of analysis searching for solutions and strategies, finding new stories with accompanying new litanies.

CLA has been developed over the past thirty years and now is used throughout the world, and in particular in Asia, in a number of settings: (1) as a way to deepen analysis so that policy solutions and innovative strategies are more inclusive, broader and successful, (2) as a strategy tool to deepen and broaden traditional business and executive strategic thinking, (3) as a tool to create structured conversations around gaps in policymaking, (4) as a tool for  fleshing out backcasts, to ensure that they are robust, (5) as part of incasting scenarios, (6) as a way for practitioners to gain self-reflection on their core narratives, and (7) as a research tool for doctoral students.

CLA has been used, for example, in projects with international police, city councils, professional associations, large multinational corporations, small and medium-sized enterprises, and national governments. Topics have included terrorism, the knowledge revolution, libraries, quality and safety in health, bio-security, university futures and education policy.

The masterclass will be conducted in English.