Approximate Proportionality in Online Fair Division

1. 1Analyze the limitations of greedy allocation rules for approximating proportionality up to one good (PROP1)
2. 2Design and evaluate the performance of the uniform random allocation under a non-adaptive adversary
3. 3Develop and test online algorithms that incorporate coarse predictions for robust PROP1 approximation
4. 4Compare the guarantees and limitations of different approaches for approximating PROP1
5. 5Apply the findings to real-world scenarios, such as resource allocation and decision-making

Researchers and practitioners in AI, game theory, and multiagent systems can benefit from this study, as it provides new insights and algorithms for fair division in online settings

💡 The uniform random allocation can achieve a meaningful PROP1 approximation with high probability under a non-adaptive adversary, but incorporating coarse predictions can provide more robust guarantees

🤖 New study on approximate proportionality in online fair division! 📊 Greedy allocation rules and learning-augmented algorithms can help, but with limitations 🚨