CAREER: Optimization-Based Computational Discovery of Decision-Making Processes

Decision making is fundamental to everyday life, but many decision-making processes are poorly understood. For example, experts in the operation of chemical plants make decisions based on years of experience, but their decision strategies often are not well documented and, due to the complexity of these manufacturing processes, are difficult to explain even to fellow operators.

This means the complete transfer of expert knowledge to new operators remains an unsolved problem. Likewise in microbiology, cells can be considered autonomous agents that make decisions regarding gene expression and cell metabolic function. While we can observe the decisions cells make in experiments, we often do not understand the motivation for these choices.

Answering this question would provide fundamental insights that could advance cancer treatment, immunology research, and biomanufacturing operations. These challenges provide the motivation for this research program which aims to develop a computational framework that uses observations of decisions to uncover the underlying decision-making processes.

Our research will advance the theory and algorithmic representation of this fundamental problem. Through our integrated research and education activities, we will teach future scientists and engineers to use advanced decision-making tools and promote interdisciplinary collaborations between researchers that work in the field of decision science.

Our proposed approach is inspired by the principle of optimality, which conjectures that autonomous agents generally make decisions in some optimal fashion. Following this principle, we propose to model decision-making processes as mathematical optimization problems in which decisions are considered optimal solutions. Given a set of observations, each represented by the decisions made in a specific situation, the goal is to infer the optimization model whose solution results in the observed decisions; this is referred to as Inverse Optimization (IO).

The IO approach enjoys all the modeling flexibility provided by mathematical optimization, facilitates incorporation of domain knowledge, and allows the generation of inherently interpretable decision-making models. In this research, we will develop computationally efficient IO algorithms and apply them to a range of problems in science and engineering.

Three specific Aims are proposed: (1) learning unknown objective functions, (2) learning unknown constraints, and (3) optimization with IO-based models. Aims 1 and 2 focus on the development of computational methods addressing the challenging aspects of IO, such as nonlinearity, discrete decisions, model selection, and adaptive sampling. Mixed-integer programming, bilevel optimization, and decomposition will be applied in innovative ways to ensure computational tractability.

In Aim 3, we will demonstrate how optimization models derived from IO can not only help discover hidden decision-making processes but also serve as surrogate optimizers and embedded models in hierarchical optimization, with specific applications in bioprocess optimization and environmental policy design. Because the principle of optimality enjoys broad (albeit often approximate) validity and the IO methods developed in our research will be generalizable, our work has the potential to broadly impact artificial intelligence research, robotics, biology, healthcare, and even management and behavioral science.

We will pursue a set of activities that include teaching K-12 students the basic concepts of decision science through games, incorporating optimization into our chemical engineering curriculum, establishing a short course on decision making, and organizing cross-disciplinary workshops.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.