IDEAS home Printed from https://ideas.repec.org/a/eee/anture/v115y2025ics0160738325001586.html

Silicus Traveler: An agent to simulate tourist behavior

Author

Listed:
  • Serantes, Juan
  • Araña, Jorge E.

Abstract

•Silicus Traveler: a large language model–driven agent for tourist behavior•Low-cost, scalable simulations for early-stage prototyping•Episode Records provide structured, context- and affect-labeled inputs.•Responsible use: mitigate bias; document provenance; complement humans

Suggested Citation

  • Serantes, Juan & Araña, Jorge E., 2025. "Silicus Traveler: An agent to simulate tourist behavior," Annals of Tourism Research, Elsevier, vol. 115(C).
    • Handle: RePEc:eee:anture:v:115:y:2025:i:c:s0160738325001586
    DOI: 10.1016/j.annals.2025.104052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160738325001586
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.annals.2025.104052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. John J. Horton & Apostolos Filippas & Benjamin S. Manning, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    2. John Tribe & Brendan Paddison, 2025. "Tourism Economics: 20 Years After the Critical Turn," Tourism and Hospitality, MDPI, vol. 6(1), pages 1-17, February.
    3. Tussyadiah, Iis, 2020. "A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism," Annals of Tourism Research, Elsevier, vol. 81(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Ying & Liu, Xinyi & Zhang, Xinyuan & Li, Zhiyong, 2024. "Retrospective of interdisciplinary research on robot services (1954–2023): From parasitism to symbiosis," Technology in Society, Elsevier, vol. 78(C).
    2. Liu, Xing (Stella) & Li, Xiaonan & Law, Rob, 2025. "Leveraging rituals to boost unity of employee-robot team," Annals of Tourism Research, Elsevier, vol. 115(C).
    3. Liu, Xing (Stella) & Wan, Lisa C. & Yi, Xiao (Shannon), 2022. "Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic?," Annals of Tourism Research, Elsevier, vol. 94(C).
    4. Chryssoula Chatzigeorgiou & Evangelos Christou & Ioanna Simeli, 2025. "From Brochures to Bytes: Destination Branding through Social, Mobile, and AI—A Systematic Narrative Review with Meta-Analysis," Administrative Sciences, MDPI, vol. 15(9), pages 1-57, September.
    5. Polachek, Solomon & Romano, Kenneth & Tonguc, Ozlem, 2026. "Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers," IZA Discussion Papers 18545, IZA Network @ LISER.
    6. Iñaki Aldasoro & Ajit Desai, 2025. "Money Talks: AI Agents for Cash Management in Payment Systems," Staff Working Papers 25-35, Bank of Canada.
    7. Joy Buchanan & Stephen Hill & Olga Shapoval, 2024. "ChatGPT Hallucinates Non-existent Citations: Evidence from Economics," The American Economist, Sage Publications, vol. 69(1), pages 80-87, March.
    8. Felipe A. Csaszar & Harsh Ketkar & Hyunjin Kim, 2024. "Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors," Strategy Science, INFORMS, vol. 9(4), pages 322-345, December.
    9. Ivanov, Stanislav & Webster, Craig, 2024. "Automated decision-making: Hoteliers’ perceptions," Technology in Society, Elsevier, vol. 76(C).
    10. Kevin Leyton-Brown & Paul Milgrom & Neil Newman & Ilya Segal, 2024. "Artificial Intelligence and Market Design: Lessons Learned from Radio Spectrum Reallocation," NBER Chapters, in: New Directions in Market Design, pages 119-151, National Bureau of Economic Research, Inc.
    11. C. Monica Capra & Thomas J. Kniesner, 2025. "Daniel Kahneman’s underappreciated last published paper: Empirical implications for benefit-cost analysis and a chat session discussion with bots," Journal of Risk and Uncertainty, Springer, vol. 71(1), pages 29-51, August.
    12. Erica Mingotto & Federica Montaguti & Michele Tamma, 2021. "Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 493-510, September.
    13. Wu, Lunwen & Wang, Zhouyiying & Liao, Zhixue & Xiao, Di & Han, Peng & Li, Wenyong & Chen, Qin, 2024. "Multi-day tourism recommendations for urban tourists considering hotel selection: A heuristic optimization approach," Omega, Elsevier, vol. 126(C).
    14. Erdem, Orhan & Ashok, Ragavi Pobbathi, 2026. "LLMs are not weird: Comparing AI and human financial decision-making," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 120(C).
    15. van Loon, Austin & Kanopka, Klint, 2026. "Using large language models as a source of human behavioral data in social science experiments," SocArXiv y74mu_v1, Center for Open Science.
    16. Song, Mengmeng & Jiang, Wenjing & Xing, Xinyu & Mou, Jian & Duan, Yucong, 2026. "The more human-like the better? Effect of anthropomorphic level on customer intention to participate in AI service recovery," Technology in Society, Elsevier, vol. 84(C).
    17. Hongshen Sun & Juanjuan Zhang, 2025. "From Model Choice to Model Belief: Establishing a New Measure for LLM-Based Research," Papers 2512.23184, arXiv.org.
    18. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    19. Shu Wang & Zijun Yao & Shuhuai Zhang & Jianuo Gai & Tracy Xiao Liu & Songfa Zhong, 2025. "When Experimental Economics Meets Large Language Models: Evidence-based Tactics," Papers 2505.21371, arXiv.org, revised Jul 2025.
    20. Iñaki Aldasoro & Ajit Desai, 2025. "AI agents for cash management in payment systems," BIS Working Papers 1310, Bank for International Settlements.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:anture:v:115:y:2025:i:c:s0160738325001586. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/annals-of-tourism-research/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.