AI decision support for index-fund investors is under-studied: few papers design AI tools to counter cognitive biases, and attention bias among retail investors is especially neglected; a proposed AI artifact and simulation suggest potential gains but lack empirical validation.

Main Finding

In information‑intensive, high‑noise markets, investors—especially retail investors—suffer from attention bias and information overload, degrading decision quality. A progressive systematic literature review reveals that existing research has not treated cognitive‑bias mechanisms as explicit design drivers for AI‑enabled investment artifacts, and AI decision‑support research in the index‑fund context is scarce. The study proposes a conceptual AI‑enabled decision‑support artifact targeted at mitigating retail investors’ attention bias for index‑fund investing and provides preliminary directional validation via a system‑dynamics simulation; an evaluation agenda for future empirical development is outlined.

Key Points

• Market context: High information volume and noise push investors toward heuristics and selective attention, producing cognitive biases and lower decision quality.
• Opportunity for AI: AI can ingest large datasets, detect non‑linear patterns, and deliver real‑time support, potentially offsetting information overload.
• Literature gap: No prior studies systematically incorporate cognitive‑bias mechanisms (used as design drivers) into AI investment artifacts; index‑fund decision support is underexplored.
• Focal mechanism: Attention bias is identified as especially relevant—retail investors tend to over‑attend to salient signals and under‑attend to broader fundamentals.
• Contribution: The paper develops a conceptual AI decision‑support artifact for index‑fund investors explicitly designed to mitigate attention bias.
• Validation & next steps: A system‑dynamics simulation offers preliminary, directional evidence that the artifact can improve decision quality. The paper proposes an agenda for empirical evaluation and iterative artifact development.

Data & Methods

• Literature synthesis: A progressive systematic literature review was used to map research across AI‑enabled decision support, behavioral finance, and index‑fund investing and to identify gaps (no specific databases or counts reported here).
• Design development: The study uses findings from the review to derive cognitive‑bias–informed design requirements and to construct a conceptual AI‑enabled decision‑support artifact aimed at reducing attention bias among retail investors in the index‑fund context.
• Preliminary validation: A system‑dynamics simulation model captures investor attention dynamics, decision processes, and the intervention effects of the AI artifact to produce directional validation (i.e., indicative, not causal or fully empirical).
• Evaluation agenda: The study outlines future empirical approaches (e.g., controlled experiments, field trials, A/B testing with retail platforms, behavioral measurement of attention, robustness checks) to iteratively develop and validate the artifact.

Implications for AI Economics

• Improved retail outcomes: AI artifacts that explicitly mitigate attention bias can raise retail investors’ decision quality, potentially leading to better portfolio outcomes and welfare gains for households.
• Market effects: If widely adopted, bias‑reducing AI decision support for index‑fund investing could alter retail flow patterns, reduce noise trading driven by attentional frictions, and affect short‑term price dynamics and liquidity in indexed assets.
• Policy and regulation: Designing AI tools with behavioral mechanisms in mind raises regulatory considerations (consumer protection, transparency, disclosure of model behavior) and suggests regulators should evaluate not just algorithmic accuracy but behavioral impact.
• Research directions: Integrating behavioral microfoundations into AI design opens several avenues: empirical measurement of attentional frictions, field experiments to test artifact efficacy, calibration of system‑dynamics models to market data, analysis of general equilibrium effects of large‑scale adoption, and investigation of heterogeneity across investor types.
• Cautions: Overreliance on AI, model misspecification, or poor human–AI interaction design could create new risks (misunderstanding of recommendations, moral hazard). Iterative, transparent evaluation is necessary before deployment at scale.