Decision-Making with Information Discovery

Methods

We devise intelligent systems for robust adaptive decision-making under uncertainty, when part of the decisions decide on the information/data to be gathered over time (as in active learning). Our general solution methods apply to the Best Box problem, to Weitzman's Pandora's box problem, to R&D project portfolio optimization, and many more.

Social Good Applications

Robust Active Preference Elicitation to Mitigate Homelessness

Motivated from the problem of designing policies for allocating scarce housing resources that meet the needs of policy-makers at the Los Angeles Homeless Services Authority (LAHSA), we have devised novel models and algorithms for actively eliciting the preferences of policy- and decision-makers in an optimal fashion, when only a limited number of questions can be asked.

Preference elicitation at LAHSA: we propose to learn the policy-maker preferences over policy characteristics (e.g., fairness/efficiency trade-off) by asking them pairwise comparisons over policy outcomes or asking us to tell us how much they like a particular policy.

We have devised algorithms that strategically select, out of a potentially huge number of questions to ask the policy-makers, questions to ask to be able to offer them their preferred policy.

Design of Decision-Support Systems that Align with Human Value Judgements

Motivated by the problem of addressing ethical dilemmas faced by decision- and policy-makers during the COVID-19, we have devised a near-optimal and tractable solution scheme to ellicit and aggregate the (potentially conflicting and imprecisely known) preferences of stakeholders in a fair way.

Model of the healthcare system during the COVID-19 pandemic as a queuing system. Individuals with different characteristics and conditions arrive over time and are matched to resources that are available (e.g., CCU beds, ventilators). The resource allocation policy determines which patients get matched to what resource and in turn impacts the fairness-efficiency characteristics of the policy.

Preference elicitation for allocation of COVID-19 resources: we propose to learn the preferences of stakeholders over policy characteristics (e.g., fairness/efficiency trade-off) by asking them pairwise comparisons over policy outcomes.