Whether you need to tackle a complex project, communicate more effectively, rethink your organization or your job, solve world hunger, or figure out your teenager, systems thinking can help you. All of these are complex and challenging real-world problems, sometimes called wicked problems. We all confront problems, big and small, in our personal and professional lives, and most of us are searching for better ways to solve them. In this course, Drs. Derek and Laura Cabrera will demonstrate how we can use systems thinking to solve everyday and wicked problems, to transform our organizations, and to increase our personal effectiveness.

At its core, systems thinking attempts to better align the way we think with how the real world works. Our thinking is based on our mental models, but these models, created from our unique perspective with its inherent biases, are usually inadequate representations of reality. The Cabreras illustrate how we can use feedback to recognize and adapt our mental models so that they better align with reality, enhancing our problem-solving capabilities.

For systems thinking to be successful, it must be adaptive. In this course, you will explore the concept of complex adaptive systems, and while these systems seem unnecessarily complicated, the Cabreras will reveal a surprising discovery. Underlying all complex adaptive systems are simple rules, and applying these rules is the key to transforming the way we frame and solve everyday problems.

 

How It Works

Course Length
2 weeks

Effort
3 to 5 hours of study per week

Format
100% online, instructor-led
  • Managers, leaders, decision makers, consultants, and anyone responsible for projects, complex processes, and the budgets and people involved with them. Learners will come from every continent and from a diverse range of organizations, including private sector companies large and small, nonprofits, governments, and NGOs.
  • For people already interested in systems dynamics or soft systems methodologies, the core principles from this program can be applied to any systems-based models.
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