Complexity thinking, complexity science and complexity theory are often referenced in discussions of cultural change and learning in the Anthropocene. But for many readers the relationship between systems theory, chaos theory and complexity are uncertain and sometimes confusing. Giorgio Bertini has developed an interactive map showing how the concept of complexity has evolved in relation to other trends in contemporary science since the mid twentieth century.
Giorgio is the polymath behind Learning Change, a project of direct relevant to our inquiry here. He lists his research interests as: society, culture, neuroscience, cognition, critical thinking, intelligence, creativity, autopoiesis, self-organization, rhizomes, complexity, systems, networks, leadership, sustainability, thinkers, futures ++
One of Giorgio’s posts from Learning Change can be reviewed below the Complexity Map.
Use the horizontal scroll to reveal the full map. Click on specific items for more information.
The Leadership of Emergence – A Complex Systems Leadership Theory of Emergence
Complexity science reframes leadership by focusing on the dynamic interactions between all individuals, explaining how those interactions can, under certain conditions, produce emergent outcomes. We develop a Leadership of Emergence using this approach, through an analysis of three empirical studies which document emergence in distinct contexts. Each of these studies identifies the same four “conditions” for emergence:
- the presence of a dis-equilibrium state,
- amplifying actions,
- recombination/“self-organization”, and
- stabilizing feedback.
From these studies we also show how these conditions can be generated through nine specific behaviors which leaders can enact, including:
- Disrupt existing patterns through embracing uncertainty and creating controversy,
- Encourage novelty by allowing experiments and supporting collective action,
- Provide sense-making and sense-giving through the artful use of language and symbols, and
- Stabilize the system by Integrating local constraints.
Finally, we suggest ways for advancing a meso-model of leadership, and show how our findings can improve complexity science applications in management.
