🤖 AI Summary
This study addresses the joint optimization of personnel assignment and performance evaluation rules in networked organizations where employee output depends on both individual effort and exogenous, uncontrollable advantages. The authors develop a dual-network framework: a competition network that shapes effort decisions and a spillover network that propagates advantage effects. By integrating game-theoretic equilibrium analysis, Katz–Bonacich centrality, and mechanism design theory, they demonstrate that optimal evaluation rules generally deviate from true output—employing negative matching with low weights when effort dominates, and positive matching with high weights when advantages dominate. The work also provides the first quantification of efficiency loss due to pairwise stability constraints and characterizes the intrinsic relationship between equilibrium effort, network position, and effective advantage, offering tailored optimal strategies for diverse production environments.
📝 Abstract
This paper studies how organizations should jointly design evaluation rules and assign workers when performance depends on both effort and non-discretionary advantage. Agents choose effort in positions linked by a competition network, while their effective advantage depends on own type and spillovers through a second network. The planner chooses both the assignment and the effort weight in evaluation. Equilibrium effort rises with a position's Katz-Bonacich centrality and falls with effective advantage. The optimal evaluation rule generally differs from true output. When effort is more important in production, the planner lowers the effort weight and uses negative assortative assignment to strengthen incentives. When advantage is more important, the planner raises the effort weight and uses positive assortative assignment to exploit spillovers. We also study a constraint requiring assignments to be pairwise stable, which creates an output loss depending on the intensity of competition.