Welcome to the first episode of Institutional Design for the AI Age, the podcast that transforms institutions from invisible forces into measurable building blocks of behavior. In this deep-dive, we confront the defining paradox of institutional theory: the discipline that set out to explain stable collective behavior cannot measure its own central object. After three decades, scholars still rely on unobservable concepts—rules, norms, constraints—that resist every attempt at operationalization. This episode delivers a complete one-hour investigation into the measurement crisis and the radical redefinition that finally makes institutional design for the AI age an empirical discipline.

We dismantle why Douglass North's "rules of the game" and W. Richard Scott's three pillars are structurally non-operationalizable in the behavioral trace data that modern platforms produce. Then we expose the three empirical fracture points: collapsed randomization in platform A/B tests, systematic algorithmic exclusion, and the platform-as-architect problem that destroys the clean separation between institution and governed entity. The solution draws on Anatol Rapoport's subjectivism to redefine institutions as statistically stable behavioral clusters—groupings of similar time-ordered reactions to informational signals, carved from data by the analyst, not discovered as pre-existing entities. A product no longer "creates a habit"; it generates a measurable behavioral cluster in response to a specific signal.

🔥 What you'll gain from this episode:

• Why classical definitions fail. North and Scott described institutions after identifying them; they gave no procedure for finding them in behavioral records.
• The three fracture points. Facebook users are unknowingly in ~10 simultaneous experiments; algorithms exclude groups by cost optimization, not policy; platforms act simultaneously as legislator, police, court, and lab—breaking institutional theory's core assumptions.
• The new framework: behavioral clusters. A behavioral trajectory is a time-ordered sequence of reactions to signals. A behavioral cluster is a statistically stable grouping of such trajectories. A behavioral spectrum captures the full distribution of possible reactions.
• Why this matters now. For regulators demanding algorithmic accountability and product managers engineering habits, the framework delivers precise, measurable language that turns institutional design for the AI age into an engineering discipline.

Stop treating institutions as invisible. Measure them, design them, and hold platforms accountable with precision. Listen to the first episode and ground your institutional design in real behavioral data.

📚 Dive deeper into the research and the book:

• Full paper: Read on ResearchGate
• Book: Habit Machine: AI Product Management
• AI A2A HUB: itinai.com

Connect with the author:

• Telegram: t.me/vlruso
• Email: vladimiruso@gmail.com
• LinkedIn: linkedin.com/in/uxproduct