Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse formulations. We focus on human choice settings that can be formulated as (integer) linear programs. In such settings, existing inverse optimization and choice modelling methods infer preferences from observed choices but typically produce point estimates or fail to capture contextual shifts, making them unsuitable for risk-averse decision-making. Using a bounded-variance score function gradient estimator, we train a predictive model mapping contextual features to a rich class of parameterizable distributions. This approach yields a maximum likelihood estimate. The model generates scenarios for unseen contexts in the subsequent optimization
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse formulations. We focus on human choice settings that can be formulated as (integer) linear programs. In such settings, existing inverse optimization and choice modelling methods infer preferences from observed choices but typically produce point estimates or fail to capture contextual shifts, making them unsuitable for risk-averse decision-making. Using a bounded-variance score function gradient estimator, we train a predictive model mapping contextual features to a rich class of parameterizable distributions. This approach yields a maximum likelihood estimate. The model generates scenarios for unseen contexts in the subsequent optimization phase. In a synthetic ridesharing environment, our approach reduces average post-decision surprise by up to 114$\times$ compared to a risk-neutral approach with perfect predictions and up to 25$\times$ compared to leading risk-averse baselines.
Executive Summary
This article presents an innovative approach to contextual preference distribution learning, addressing the limitations of existing inverse optimization and choice modeling methods in risk-averse decision-making. The proposed sequential learning-and-optimization pipeline leverages a bounded-variance score function gradient estimator to train a predictive model mapping contextual features to rich parameterizable distributions. The approach yields a maximum likelihood estimate, generating scenarios for unseen contexts in the subsequent optimization phase. The authors demonstrate the effectiveness of their method in a synthetic ridesharing environment, achieving significant reductions in average post-decision surprise compared to risk-neutral and risk-averse baselines. The study has far-reaching implications for decision-making under uncertainty, particularly in complex systems with heterogeneous and context-dependent human preferences.
Key Points
- ▸ The proposed method addresses the limitations of existing inverse optimization and choice modeling methods in risk-averse decision-making.
- ▸ The approach uses a bounded-variance score function gradient estimator to train a predictive model mapping contextual features to rich parameterizable distributions.
- ▸ The method yields a maximum likelihood estimate and generates scenarios for unseen contexts in the subsequent optimization phase.
Merits
Strength in Addressing Uncertainty
The proposed method effectively addresses the uncertainty stemming from heterogeneous and context-dependent human preferences, providing a robust approach to decision-making under uncertainty.
Flexibility in Modeling Distributions
The use of rich parameterizable distributions allows for flexibility in modeling complex preferences, making the approach suitable for a wide range of applications.
Demerits
Limitation in Generalizability
The method's performance in real-world scenarios may be limited by the availability of high-quality contextual data and the complexity of the decision-making environment.
Computational Complexity
The optimization phase of the method may be computationally intensive, particularly for large-scale decision-making problems.
Expert Commentary
The proposed contextual preference distribution learning approach offers a significant advancement in addressing the limitations of existing inverse optimization and choice modeling methods. By leveraging a bounded-variance score function gradient estimator, the method effectively captures contextual shifts in human preferences, generating scenarios for unseen contexts in the subsequent optimization phase. The study's findings are particularly relevant to complex systems with heterogeneous and context-dependent human preferences, such as risk-averse decision-making in finance and resource allocation in logistics. While the method's generalizability and computational complexity may pose limitations, the proposed approach has the potential to revolutionize decision-making under uncertainty. The study's implications for policy-making and practical applications are substantial, and further research is warranted to explore the method's potential in real-world scenarios.
Recommendations
- ✓ Future research should focus on exploring the method's generalizability in real-world scenarios and addressing the computational complexity associated with the optimization phase.
- ✓ Applications of the proposed method in various domains, such as finance, logistics, and healthcare, should be explored to demonstrate the method's practical utility.
