In many complex scenarios, strategic decision-making must occur before the full picture of the future is known. However, contrary to traditional models, modern practitioners recognize that investing in additional information is often a viable option before committing to a final action. The key question is no longer just what decision to make, but what and how much information to obtain, when to do so, and at what cost.
Consider, for example, a resource management decision: before finalizing a mining plan, is it advisable to conduct intensive geological surveys to reduce uncertainty about ore grade, despite the high cost? Similarly, in public safety and policy, is it worth investing in data-driven assessments, crime analysis, or targeted pilot programs before scaling a security strategy to a national level? In both cases, acquiring actionable insights can significantly improve outcomes, but it also introduces costs and delays that require a rigorous cost-benefit analysis.
"The core challenge is that most traditional models assume uncertainty remains independent of the choices made or the data collected," explains Dr. Quezada. “My research is based on a transformative premise: specific strategic decisions provide access to information that fundamentally improves our understanding of future events. By explicitly modeling information acquisition, we can design more informed, robust, and effective decision-making frameworks that adapt to an evolving landscape.
In this context, the primary objective is to develop innovative theoretical frameworks and computational tools that integrate data collection into the decision-making process under uncertainty. This project sits at the intersection of industrial engineering and operations research, contributing to a highly competitive global field known as decision-dependent uncertainty (or endogenous uncertainty). This research line aims to surpass the limitations of classical approaches by rigorously modeling the complex interaction between proactive decisions, information flow, and risk mitigation.
Unlike purely applied approaches, the project aims to advance the state of the art from a fundamental perspective by developing innovative mathematical models, conceptual frameworks, and algorithms that enable the quantification of the true value of information and the design of more robust decisions facing adverse scenarios. These theoretical advances hold significant academic value while laying the essential groundwork for future applications in strategic sectors, including mining, renewable energy, manufacturing, logistics, and public policy.
Expected Results
Among the main expected outcomes is the development of new computational models and optimization methods designed to address critical gaps in decision theory. These tools seek to answer key questions that currently lack formal solutions: What data is truly relevant to a strategic decision? When is the optimal time for information acquisition? How does a decision change under partial information? What is the quantifiable value of information in reducing uncertainty?
The project includes publishing findings in high-impact international scientific journals and developing advanced human capital. By actively integrating undergraduate and graduate students into cutting-edge research, the project fosters the next generation of experts in the field.
From an institutional perspective, the project strengthens research within the Department of Industrial Engineering and the School of Engineering by establishing a theoretically sound research line aligned with global scientific challenges in decision-making under uncertainty. Through collaboration with international experts in stochastic optimization and risk analysis, the project significantly enhances the institution's global networks and academic visibility.
The project also aims to establish and strengthen research infrastructure focused on developing advanced computational tools for decision-making. By building these capabilities, the initiative creates a reusable framework for future academic research and community engagement projects.
“A distinctive feature is that it seeks not only better decisions on average, but also more robust and risk-responsive decisions,” notes Dr. Quezada. “This is especially relevant when strategic choices have significant economic, social, or high-public-impact consequences,” he states.
For the Faculty’s Vice Dean of R&D and Graduate Studies, Dr. Andrea Mahn, this achievement consolidates the Faculty of Engineering's (FING) leadership in research. “This milestone surpasses the results from the previous year, when the Faculty was awarded 8 projects in this same call for proposals.” She also highlights that nearly 30% of FING’s grants were awarded to women, a figure significantly higher than their current 13% representation in the academic staff, demonstrating concrete progress in gender equity and academic excellence.