Scaffolds, Not Skyscrapers: Information Architecture in Complex Systems

Information architecture is often framed as a way of bringing order to the mess: we sort, label, and structure so people can find, understand, and act on information. But the environments we design for today are increasingly ambiguous and unstable. They’re complex adaptive systems: living, evolving networks where meaning emerges from countless unpredictable interactions.

In this context, static information architecture quickly becomes obsolete. What’s needed instead are scaffolds: flexible, enabling structures that support ongoing adaptation, emergence, and collective sense-making. IA becomes less about building the structure and more about creating the conditions for one to emerge and evolve.

This talk explores how complexity science can help us reimagine IA for a dynamic (and yes, AI-shaped) world:

• From static structure to dynamic ecology: Information environments shift as people (and now machines) change how they create and interpret content.
• Emergence and self-organization: Meaning arises bottom-up through tagging, linking, searching, and algorithmic feedback, not just from top-down design.
• Constraints as catalysts: Metadata, governance, and schema design influence behavior without dictating it.
• IA as a learning interface: Great IA reveals drift, shapes feedback loops, and supports shared understanding over time.
• From clarity to coherence: The goal isn’t perfect order; it’s enough shared structure for humans and AI systems to orient and act together amid uncertainty.

It’s the difference between building a library and tending a forest. Both require care, but one assumes order can be imposed, while the other recognizes that order must emerge on its own terms, and our job is increasingly to shape the conditions for that emergence. This session will offer practical implications from complexity science to help you do just that.