Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design

TL;DR

This paper analyzes 955 real-world conversations to uncover human heuristics in decision-making, informing more natural and effective conversational AI design.

Contribution

It provides empirical insights into human decision heuristics and interaction strategies, guiding AI systems to better mimic natural human decision processes.

Findings

Humans prioritize satisficing over optimization in conversations.

Interactional strategies help manage cognitive load during decision-making.

Frequency-efficiency mismatch in heuristics affects conversational flow and resolution.

Abstract

Conversational AI increasingly supports everyday decision-making, yet most systems rely on data-centric reasoning rather than the heuristic and interactional strategies people use in natural conversation. To ground design in actual human practice, we analyze 955 real-world Korean conversations (15,476 utterances) involving food and travel decisions, applying a decision-making codebook through an LLM-assisted coding pipeline. Our findings reveal that people prioritize satisficing over optimization, relying heavily on internal knowledge and interactional strategies to manage cognitive load. Critically, we identify a frequency-efficiency mismatch: the most prevalent heuristics sustain conversational flow during exploration, whereas infrequent, rule-based strategies are highly effective at driving resolution during exploitation. By mapping how these patterns transfer across the spectrum of human-AI interaction, this work provides empirical grounding consistent with cognitive theories of decision-making and offers design implications that align AI systems with human heuristic processes.