Data Sharing and Competition in Learning-by-Deploying Industries: Insights from Robotics and Beyond
2026-06-30 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors study how companies learn and improve their products based on data from using their current products. They compare two ways of organizing this learning: sharing data across companies (pooling) or keeping it separate within each company. They find that when prices are fixed, sharing data helps everyone by making future products better, but companies don't invest enough early on. However, when companies compete by setting quantities (Cournot competition), sharing data lowers prices and reduces the benefits of sharing, sometimes making it harmful. They identify a threshold related to how sensitive demand is to price, which affects whether sharing data is sustainable.
learning-by-deployinglearning curvecapacity choiceCournot competitionproduct-market competitionwelfaredata poolingdemand elasticityirreversible investment
Authors
Yunjin Tong, Luca-Andrei Manea
Abstract
Many modern technologies improve through use. Each unit deployed generates data that trains the next generation, so deployment is both production and an investment in a shared learning stock. We study how the architecture of this learning, whether pooled across firms or fragmented within them, interacts with firms' deployment decisions and with product-market competition. In a two-period model, symmetric firms make irreversible capacity choices, and capacity in use feeds a learning curve that raises future productivity. We call this learning-by-deploying, replacing the production experience of the classic learning-by-doing tradition with deployment-generated data. With exogenous prices, pooling raises welfare but firms underinvest in early deployment. Downstream Cournot competition overturns this: pooling depresses the price, so the private value of sharing falls with competition and can turn negative. We characterize a sustainability threshold governed, under general demand, by the elasticity of industry demand over the output range pooling induces, and confirm the patterns numerically.