Conversational Decision Support for Information Search Under Uncertainty: Effects of Gist and Verbatim Feedback

2026-02-16

AI Summary

This work addresses the challenge of suboptimal human decision-making under uncertainty, where individuals often struggle to determine when to stop information search due to high cognitive load. While existing AI assistance primarily focuses on optimizing final outcomes, it frequently neglects low-burden support during the search process itself. To bridge this gap, the authors propose SERA, a conversational decision-support system powered by large language models that introduces feedback granularity—summary versus verbatim—as a core design variable. SERA generates real-time, cognitively grounded information-gain feedback across three uncertainty scenarios. Experimental results demonstrate that SERA significantly improves both decision accuracy and confidence: summary feedback curbs over-sampling, while verbatim feedback encourages deeper exploration, with pronounced benefits under high uncertainty. This approach establishes a novel paradigm for adaptive, process-oriented decision support.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Metareasoning and Metaheuristics
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Many real-world decisions rely on information search, where people sample evidence and decide when to stop under uncertainty. The uncertainty in the environment, particularly how diagnostic evidence is distributed, causes complexities in information search, further leading to suboptimal decision-making outcomes. Yet AI decision support often targets outcome optimization, and less is known about how to scaffold search without increasing cognitive load. We introduce SERA, an LLM-based assistant that provides either gist or verbatim feedback during search. Across two experiments (N1=54, N2=54), we examined decision-making outcomes and information search in SERA-Gist, SERA-Verbatim, and a no-feedback baseline across three environments varying in uncertainty. The uncertainty in environment is operationalized by the perceived gain of information across the course of sampling, which individuals may experience diminishing return of information gain (decremental; low-uncertainty), or a local drop of information gain (local optimum; medium-uncertainty), or no patterns in information gain (high-uncertainty), as they search more. Individuals show more accurate decision outcomes and are more confident with SERA support, especially under higher uncertainty. Gist feedback was associated with more efficient integration and showed a descriptive pattern of reduced oversampling, while verbatim feedback promoted more extensive exploration. These findings establish feedback representation as a design lever when search matters, motivating adaptive systems that match feedback granularity to uncertainty.

Problem

• information search
• decision-making under uncertainty
• cognitive load
• feedback representation
• evidence sampling

Innovation

• conversational decision support
• gist vs. verbatim feedback
• information search under uncertainty
• LLM-based assistant
• adaptive feedback granularity