Designing algorithms to manage deep uncertainty in infrastructure projects: The role of psychological artificial intelligence

Decision-makers in infrastructure projects routinely rely on informal heuristics to navigate volatile, uncertain, complex, and ambiguous conditions. These heuristics are often treated as liabilities, attributed to cognitive bias. However, emerging evidence indicates that when heuristics are deliberately designed, they can match—or even outperform—machine learning models in inference tasks under deep uncertainty. Despite this, systematic algorithms for managing uncertainty associated with delivery-stage events, such as rework, remain underdeveloped. Psychological artificial intelligence (AI) offers a promising foundation by focusing on designing efficient, transparent decision rules grounded in human cognition. Against this backdrop, our paper addresses the question: How can psychological AI be used to design and implement algorithms that support the management of rework-related uncertainty? Using an interpretive case study, the analysis examines how experiences within a water infrastructure alliance shaped behavioral processes, specifically recency (memory) and imitation (learning), among personnel involved in a transport mega-project. We reveal that these processes were associated with the enactment of informal decision rules for responding to rework as it emerged. Although psychological AI offers strong potential to generate ecologically rational and transparent decision tools, its application in infrastructure delivery remains limited, and more broadly, in project environments. This paper responds by opening a new line of inquiry into how such tools can be developed to address rework and other unforeseen events. Two contributions are advanced. First, a theoretical framing is introduced to inform the design of simple, interpretable decision algorithms suited to unexpected events in large-scale infrastructure projects. Second, the analysis suggests how psychological principles can be translated into decision rules to guide decision-making under uncertainty.