Backend
Incentive Mechanisms for Welfare Maximization Under Individual Rationality in Noncooperative Dynamical Systems
arXiv cs.SY0 views
arXiv:2610.30718v1 Announce Type: new Abstract: Human societies are shaped by strategic interactions among self-interested decision-makers, where conflicts between individual and social interests naturally arise. This paper investigates the design of incentive mechanism for multi-agent game systems to reconcile social welfare maximization with individual benefits under economic constraints. We develop a linear-quadratic incentive framework to reshape Nash equilibria while reconciling the constraints of Pareto efficiency, individual rationality, and budget balance. The optimal target state is identified as the solution of a constrained optimization problem associated with the social welfare and aggregated payoff functions. Our theoretical analysis characterizes the conditions of incentive functions that steer self-interested agents towards the optimal target state as a Pareto-efficient Nash equilibrium for achieving the maximum attainable social welfare. We further characterize how the choice of social welfare function constrains achievable individual outcomes, revealing a structural paradox associated with priority weighting under individual rationality. Several numerical examples are presented to demonstrate the efficacy of our results.
Source:https://arxiv.org/abs/2610.30718PreviousHalf-Slot Link Scheduling for Open GNSS with $x$-Tx $y$-Rx Multibeam Phased Array AntennasNextAnomaly Score Preserving Data Poisoning Attacks on Data-Driven Controller Design
Related posts
DeepSeek's Open-Source "Super-Agent" Codes Without Being Asked
0dev.to GitHub
All-in-one platform or a stack you assemble? Where do coaches and educators land?
0dev.to Discuss
I Wanted to Live on Coruscant. PAT Scared Me. Now My Alexa Lives on Mute.
0dev.to Discuss
Detect What You Can Name: PaliGemma 2 Object Detection with Transformers
0dev.to Machine Learning
When Should an LLM Router Stop Being Sticky? llm-d 1.0 Cache-Affinity Routing, Break-Even Math, and a Simulation
0dev.to Machine Learning

Understanding HackerTube: Karpathy’s Two-Hour Experiment in Better Recommendations
0dev.to Machine Learning