The Costs of Information Asymmetry in Human Networks
When two people cannot efficiently assess their compatibility, both pay a cost. Most of that cost is invisible.
Information asymmetry is the condition in which two parties to a potential exchange have different information relevant to that exchange. In markets, it produces adverse selection, moral hazard, and the collapse of otherwise mutually beneficial trades.
Human networks — collaborators, partners, intellectual communities — suffer the same failure modes. You cannot tell from a LinkedIn profile whether someone thinks carefully. You cannot tell from a Twitter bio whether someone's values are compatible with yours. You cannot tell from a conference introduction whether a potential collaborator has the working style that would make a project succeed.
The cost of this asymmetry is paid in time spent on mismatched interactions, relationships that fail slowly rather than quickly, and connections that never form because the information required to initiate them was never available.
PairGeek Schema is a partial solution to this problem. By making a person's attributes, constraints, and preferences legible in a structured format, it reduces the cost of compatibility assessment for both parties. The person publishing the profile does work once; every potential counterpart benefits from that work without requiring a separate conversation.
This is not a complete solution. Verifiability, interpretation, and the limits of self-knowledge all constrain how far structured data can go. But reducing the cost of initial assessment is a large enough win to justify the protocol on its own.