IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2404.08991.html
   My bibliography  Save this paper

Business models for the simulation hypothesis

Author

Listed:
  • Evangelos Katsamakas

Abstract

The simulation hypothesis suggests that we live in a computer simulation. That notion has attracted significant scholarly and popular interest. This article explores the simulation hypothesis from a business perspective. Due to the lack of a name for a universe consistent with the simulation hypothesis, we propose the term simuverse. We argue that if we live in a simulation, there must be a business justification. Therefore, we ask: If we live in a simuverse, what is its business model? We identify and explore business model scenarios, such as simuverse as a project, service, or platform. We also explore business model pathways and risk management issues. The article contributes to the simulation hypothesis literature and is the first to provide a business model perspective on the simulation hypothesis. The article discusses theoretical and practical implications and identifies opportunities for future research related to sustainability, digital transformation, and Artificial Intelligence (AI).

Suggested Citation

  • Evangelos Katsamakas, 2024. "Business models for the simulation hypothesis," Papers 2404.08991, arXiv.org.
    • Handle: RePEc:arx:papers:2404.08991
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2404.08991
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicholas Economides & Evangelos Katsamakas, 2006. "Two-Sided Competition of Proprietary vs. Open Source Technology Platforms and the Implications for the Software Industry," Management Science, INFORMS, vol. 52(7), pages 1057-1071, July.
    2. Nicholas C. Georgantzas & Evangelos G. Katsamakas, 2008. "Information systems research with system dynamics," System Dynamics Review, System Dynamics Society, vol. 24(3), pages 247-264, September.
    3. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    4. Pavlov, Oleg V. & Katsamakas, Evangelos, 2023. "Tuition too high? Blame competition," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 409-431.
    5. Oleg V Pavlov & Evangelos Katsamakas, 2020. "Will colleges survive the storm of declining enrollments? A computational model," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-29, August.
    6. Olivier Morin & Alberto Acerbi & Oleg Sobchuk, 2019. "Why people die in novels: testing the ordeal simulation hypothesis," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-10, December.
    7. Alex Davies & Petar Veličković & Lars Buesing & Sam Blackwell & Daniel Zheng & Nenad Tomašev & Richard Tanburn & Peter Battaglia & Charles Blundell & András Juhász & Marc Lackenby & Geordie Williamson, 2021. "Advancing mathematics by guiding human intuition with AI," Nature, Nature, vol. 600(7887), pages 70-74, December.
    8. Evangelos Katsamakas & Kostapanos Miliaresis & Oleg V. Pavlov, 2022. "Digital Platforms for the Common Good: Social Innovation for Active Citizenship and ESG," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
    9. John J. Horton, 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.
    10. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    11. Parmar, Rashik & Leiponen, Aija & Thomas, Llewellyn D.W., 2020. "Building an organizational digital twin," Business Horizons, Elsevier, vol. 63(6), pages 725-736.
    12. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    13. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    14. Nicholas C. Georgantzas & Evangelos Katsamakas, 2007. "Disruptive Innovation Strategy Effects on Hard-Disk Maker Population: A System Dynamics Study," Information Resources Management Journal (IRMJ), IGI Global, vol. 20(2), pages 90-107, April.
    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. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    2. Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
    3. Yiting Chen & Tracy Xiao Liu & You Shan & Songfa Zhong, 2023. "The emergence of economic rationality of GPT," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(51), pages 2316205120-, December.
    4. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    5. 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, National Bureau of Economic Research, Inc.
    6. 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).
    7. Zengqing Wu & Run Peng & Xu Han & Shuyuan Zheng & Yixin Zhang & Chuan Xiao, 2023. "Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations," Papers 2311.06330, arXiv.org, revised Dec 2023.
    8. Joshua C. Yang & Damian Dailisan & Marcin Korecki & Carina I. Hausladen & Dirk Helbing, 2024. "LLM Voting: Human Choices and AI Collective Decision Making," Papers 2402.01766, arXiv.org, revised Aug 2024.
    9. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    10. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
    11. Ali Goli & Amandeep Singh, 2023. "Exploring the Influence of Language on Time-Reward Perceptions in Large Language Models: A Study Using GPT-3.5," Papers 2305.02531, arXiv.org, revised Jun 2023.
    12. Yuan Gao & Dokyun Lee & Gordon Burtch & Sina Fazelpour, 2024. "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina," Papers 2410.19599, arXiv.org, revised Nov 2024.
    13. Christoph Engel & Max R. P. Grossmann & Axel Ockenfels, 2023. "Integrating machine behavior into human subject experiments: A user-friendly toolkit and illustrations," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2024_01, Max Planck Institute for Research on Collective Goods.
    14. Samuel Chang & Andrew Kennedy & Aaron Leonard & John A. List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," NBER Working Papers 33025, National Bureau of Economic Research, Inc.
    15. Jiafu An & Difang Huang & Chen Lin & Mingzhu Tai, 2024. "Measuring Gender and Racial Biases in Large Language Models," Papers 2403.15281, arXiv.org.
    16. Daniel Albert & Stephan Billinger, 2024. "Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models," Papers 2410.06932, arXiv.org.
    17. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    18. Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024. "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers 2406.05972, arXiv.org, revised Oct 2024.
    19. Fabio Motoki & Valdemar Pinho Neto & Victor Rodrigues, 2024. "More human than human: measuring ChatGPT political bias," Public Choice, Springer, vol. 198(1), pages 3-23, January.
    20. George Gui & Olivier Toubia, 2023. "The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective," Papers 2312.15524, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2404.08991. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.