Seeding an Uncertain Technology

TL;DR

This paper analyzes how startups optimally select initial adopters in uncertain environments, revealing that seed size grows logarithmically with network size and that targeting specific agent types maximizes viral spread efficiency.

Contribution

It provides a theoretical framework for seed selection under uncertainty, showing optimal strategies depend on network size and agent types, aligning with observed startup behaviors.

Findings

Optimal seed size grows logarithmically with network size.

Seeding agents of a single type maximizes viral potential.

Results explain practical startup seeding strategies.

Abstract

I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes the marginal cost per probability of making the product go viral. These results rationalize startup behavior in practice.