Exploring Cognitive Attributes in Financial Decision-Making

2025-04-11

AI Summary

Existing AI alignment research prioritizes value alignment while neglecting the critical role of individual cognitive attributes in high-stakes financial decision-making, resulting in models that fail to authentically replicate human decision processes. This paper systematically defines financial-domain cognitive attributes for the first time: drawing on systematic literature review and conceptual analysis, it establishes five definitional criteria and constructs a domain-specific taxonomy comprising 19 cognitive attribute classes spanning perception, reasoning, risk preference, and related dimensions. The framework addresses a fundamental theoretical gap in AI alignment—namely, the absence of an individual-level cognitive dimension—and advances the alignment paradigm from “value alignment” toward “cognitive alignment.” It provides foundational theoretical grounding and engineering guidance for developing interpretable, personalized, and metacognitively faithful financial AI systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Incorporating cognitive attributes in AI for human-like financial decision-making
• Addressing lack of cognitive attribute focus in current AI alignment research
• Defining and categorizing domain-specific cognitive attributes for finance

Innovation

• Incorporates cognitive attributes in AI models
• Defines five criteria for cognitive attributes
• Categorizes 19 financial decision-making attributes