Enhancing Reward Distribution Fairness in Collaborative Teams: A Quadratic Optimization Framework

In team collaboration environments, ensuring fair reward distribution is crucial for maintaining motivation and productivity. However, existing reward allocation methods often suffer from biases in self-assessment, leading to inequitable outcomes. In this study, we introduce a ranking mechanism that converts self-assessed contribution ratios into task orders based on the values of these ratios. Then we propose two methods using this mechanism: Method 1 uses quadratic optimization to adjust the contribution ratios, aligning them more closely with actual values, while Method 2 incorporates task reward differences to ensure fairer reward allotment. Experimental results show that the reward allotment method in the latest research reduces the loss by 25.31% compared to the conventional method, while our methods achieve a loss reduction of 53.28% for Method 1 and 64.4% for Method 2. Sensitivity analysis confirms the effectiveness of both methods under varying self-assessment errors, reward amounts, and task sizes, maintaining an average loss reduction of over 30%. These findings provide valuable insights for optimizing reward distribution, such as enhancing self-assessment accuracy and adjusting employee task assignments for improved fairness.