A Novel Incentive-Compatible Neural Network Optimization Model (ICNNOM) with Optimal Contract Structure

In this paper, we propose a novel neural network optimization framework called the Incentive Compatible Neural Network Optimization Model (ICNNOM). This model combines the incentive compatibility idea in game theory with the optimal contract theory to simulate the "incentive and effort" mechanism between the internal layers of a deep neural network, aiming to improve the learning effect of the network. This paper uses two sets of codes with different architectures to conduct experiments on the CIFAR-10 and CIFAR-100 datasets and compares them with traditional neural network models. The results show that ICNNOM outperforms traditional models in multiple evaluation indicators such as accuracy, precision, recall, and F1 value, proving the effectiveness of introducing incentive mechanisms for model optimization. Incentive compatibility (IC) refers to designing mechanisms so that each participant's best interest aligns with truthful or cooperative behavior, while optimal contract theory studies designing agreements to maximize benefits under informational asymmetry. By integrating these concepts, ICNNOM explicitly coordinates the effort of each neural network layer to improve overall training consistency and efficiency.