Incentivizing Knowledge Transfers

2025-07-15

AI Summary

In knowledge transfer, experts withhold sharing due to concerns over personal welfare loss, novices struggle to acquire knowledge effectively, and principals allocate resources inefficiently. Method: We develop an intertemporal incentive model grounded in dynamic contract theory and game theory, characterizing the unique profit-maximizing relational contract. Contribution/Results: We identify that incomplete knowledge transfer is endogenously optimal: experts provide full, costless training initially, with gradual—potentially incomplete—knowledge transmission. The model extends to intergenerational overlap scenarios involving expert replacement and novice career progression. Crucially, we introduce an information verification mechanism that endogenously balances expert protection against knowledge leakage and transfer efficiency. Our findings yield actionable contractual design principles and theoretical foundations for organizational knowledge management.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

We study the optimal design of relational contracts that incentivize an expert to share specialized knowledge with a novice. While the expert fears that a more knowledgeable novice may later erode his future rents, a third-party principal is willing to allocate her resources to facilitate knowledge transfer. In the unique profit-maximizing contract between the principal and the expert, the expert is asked to train the novice as much as possible, for free, in the initial period; knowledge transfers then proceed gradually and perpetually, with the principal always compensating the expert for his future losses immediately upon verifying the training he provided; even in the long run, a complete knowledge transfer might not be attainable. We further extend our analysis to an overlapping-generation model, accounting for the retirement of experts and the career progression of novices.

Problem

• Design contracts to incentivize expert-novice knowledge transfer
• Balance expert's fear of rent erosion with principal's resources
• Analyze gradual perpetual transfers with compensation mechanisms

Innovation

• Relational contracts optimize knowledge transfer incentives
• Principal compensates expert for future losses immediately
• Gradual perpetual knowledge transfer with partial completion