The Wholistic Ethical Litmus of Leadership: A Practical Non-Linear Dynamics and Chaos Based Leadership Model

Problem statement: Management is generally easy to define and measure. And, good managers tend to have many of the same characteristics and skill sets. Great leaders, on the other hand, have fewer shared characteristics. Some great leaders are great orators, for example, and yet many other great leaders are terrible public speakers. Great leaders tend to be very intuitive, but other characteristics consistent with great leadership are few indeed. So, the authors of this study had a conversation over several years that led to the reduction of variables to two variables that immediately showed a pattern in individual leadership. Approach: This study presents a practical leadership matrix model based on non-linear dynamics and chaos theory. Specifically, the authors searched for two or more leadership variables (characteristics) that would create a definite pattern. The researchers intuitively believed that some combination of variables would set up a pattern just as attractors (strange or otherwise) create patterns in data and show some of the characteristics of the system being studied. Over time a set of two main variables, loosely labeled as ethics and energy at first, were identified that created a leadership pattern for individuals. This study describes the process that led to the identification of the two main variables and then to the matrix herein presented. Results: This model, called "The WELL" by the authors, was created at first to explain political leadership, yet is showing applicability to all kinds of leadership. The WELL as presented is a theoretical construct, with only experiential and qualitative evidence at present to support the patterns inferred from the model. Conclusion: In addition to the extensive political experience of the authors the experience of public safety, mental health, military and academic professionals has been sought to validate the main conclusions shown in this study and to improve the model.