Purdue University

Sbe-ukri: a Novel Theory of Ordered Judgment Processes -difficult Decisions Are Commonplace, Yet Often Require Decision Makers to Gather Information From Disparate Places and Combine It to Make Conclusions. for Example, When Choosing Whether to Have a Risky, But Potentially Needed, Medical Procedure, a Patient May Need to Consult Several Doctors That Have Different Specializations, Knowledge, and Backgrounds, Leaving the Patient to Aggregate This Information and Determine a Course of Action. This Interdisciplinary Research Explores How to Support Individuals, Organizations and Governments in Making Better Decisions Through a Novel Approach That Explores the Way Decision-making and Information Are Interrelated. Decisions Are Shaped Not Only by How Individuals Weigh Information, But Also by How They Weigh the Information Sources, Such as Who Exactly Is Providing This Information. Through Blending Advances in Psychology and Computer Science, the Project Seeks to Explain Previously Unexplainable Behavior and Better Predict Judgments in New Situations. Such Knowledge Can Facilitate Decision Support Systems Across Many Domains. Additionally, This Research Holds the Potential to Reveal New Pathways Towards Translating Complex Artificial Intelligence (AI) Systems Into a Form That Is More Compatible With Human Cognitive Processes. the Project Proposes a New Theory of Judgment That Fuses Psychological Theory With Operational and Theoretical Advances From Computer Science in the Areas of Data Aggregation and Artificial Intelligence. the Theory of Ordered Judgment Provides the First Operational Framework for Empirical Tests of the Role of Ordering in Judgment and Beyond, and Impacts Both Psychological and Computer Science Research. for Psychology, the Theory of Ordered Judgment Has Many Implications for How to Display Data to Facilitate Accurate Processing That Can Be Useful for Decision-support and Human Factors Work in Contexts Ranging From Graphical User Interfaces to Operating Machinery. for Computer Science, This Work Contributes to the Crucial Area of Explainable Artificial Intelligence by Articulating Direct Links Between Complex Computer Algorithms and Human Reasoning. This Can Afford Mechanisms to Understand, Evaluate and Validate Ai Driven Decision-making Approaches in Critical Applications Such as Security and Defense, Energy, and Healthcare. 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.- Subawards Are Not Planned for This Award.