Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911662687125504 |
|---|---|
| author | Kang, Sora Jeon, Soyun Eun, Jinsu Lee, Kwangwon Song, Chaerin Joo, Minyoung Lee, Joonhwan |
| author_facet | Kang, Sora Jeon, Soyun Eun, Jinsu Lee, Kwangwon Song, Chaerin Joo, Minyoung Lee, Joonhwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07789 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design Kang, Sora Jeon, Soyun Eun, Jinsu Lee, Kwangwon Song, Chaerin Joo, Minyoung Lee, Joonhwan Human-Computer Interaction 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. |
| title | Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.07789 |