Exploring Cognitive Attributes in Financial Decision-Making
Mallika Mainali, Rosina O. Weber
TL;DR
This work addresses the gap in AI alignment by focusing on cognitive attributes that shape human financial decision-making. It combines a historical survey, a formal five-criterion framework, and an empirical literature review to identify 19 finance-relevant cognitive attributes, classified into four domains (risk perception, processing style, emotional/social influences, and biases). The study demonstrates how these attributes influence financial choices and argues for incorporating them into metacognitive AI systems to better reflect human reasoning and preferences in high-stakes finance. The findings provide a concrete basis for personalized, human-aligned AI guidance and prediction in financial contexts, with future work aimed at real-world application and integration into AI decision-making processes.
Abstract
Cognitive attributes are fundamental to metacognition, shaping how individuals process information, evaluate choices, and make decisions. To develop metacognitive artificial intelligence (AI) models that reflect human reasoning, it is essential to account for the attributes that influence reasoning patterns and decision-maker behavior, often leading to different or even conflicting choices. This makes it crucial to incorporate cognitive attributes in designing AI models that align with human decision-making processes, especially in high-stakes domains such as finance, where decisions have significant real-world consequences. However, existing AI alignment research has primarily focused on value alignment, often overlooking the role of individual cognitive attributes that distinguish decision-makers. To address this issue, this paper (1) analyzes the literature on cognitive attributes, (2) establishes five criteria for defining them, and (3) categorizes 19 domain-specific cognitive attributes relevant to financial decision-making. These three components provide a strong basis for developing AI systems that accurately reflect and align with human decision-making processes in financial contexts.