IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2506.21809.html
   My bibliography  Save this paper

OpenAlpha: A Community-Led Adversarial Strategy Validation Mechanism for Decentralised Capital Management

Author

Listed:
  • Arman Abgaryan
  • Utkarsh Sharma

Abstract

We propose \textit{OpenAlpha}, a community-led strategy validation framework for decentralised capital management on a host blockchain network, which integrates game-theoretic validation, adversarial auditing, and market-based belief aggregation. This work formulates treasury deployment as a capital optimisation problem under verification costs and strategic misreporting, and operationalises it through a decision waterfall that sequences intention declaration, strategy proposal, prediction-market validation, dispute resolution, and capital allocation. Each phase of this framework's validation process embeds economic incentives to align proposer, verifier, and auditor behaviour, producing confidence scores that may feed into a capital allocation rule. While OpenAlpha is designed for capital strategy assessment, its validation mechanisms are composable and extend naturally to evaluating external decentralised applications (DApps), enabling on-chain scrutiny of DApp performance, reliability, and integration risk. This architecture allows for adaptive, trust-minimised capital deployment without reliance on centralised governance or static audits.

Suggested Citation

  • Arman Abgaryan & Utkarsh Sharma, 2025. "OpenAlpha: A Community-Led Adversarial Strategy Validation Mechanism for Decentralised Capital Management," Papers 2506.21809, arXiv.org.
  • Handle: RePEc:arx:papers:2506.21809
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2506.21809
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koessler, Frédéric & Noussair, Charles & Ziegelmeyer, Anthony, 2008. "Parimutuel betting under asymmetric information," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 733-744, July.
    2. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arman Abgaryan & Utkarsh Sharma & Joshua Tobkin, 2025. "Decentralised Multi-Manager Fund Framework," Papers 2507.00978, arXiv.org, revised Sep 2025.

    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. Nahid Rahman & Joseph Al-Chami & Jeremy Clark, 2025. "SoK: Market Microstructure for Decentralized Prediction Markets (DePMs)," Papers 2510.15612, arXiv.org.
    2. Lambert, Nicolas S. & Langford, John & Wortman Vaughan, Jennifer & Chen, Yiling & Reeves, Daniel M. & Shoham, Yoav & Pennock, David M., 2015. "An axiomatic characterization of wagering mechanisms," Journal of Economic Theory, Elsevier, vol. 156(C), pages 389-416.
    3. Schnitzlein, Charles & Chelley-Steeley, Patricia & Steeley, James M, 2024. "Conflicting versus reinforcing private information, information aggregation, and the time series properties of asset prices," Journal of Banking & Finance, Elsevier, vol. 169(C).
    4. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    5. Marko Corn & Nejc Rov{z}man, 2021. "Unihedge -- A decentralized market prediction platform," Papers 2108.11631, arXiv.org, revised Dec 2021.
    6. Rafael Frongillo, 2022. "Quantum Information Elicitation," Papers 2203.07469, arXiv.org.
    7. Karimi, Majid & Zaerpour, Nima, 2022. "Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1035-1049.
    8. Eyster, Erik & Galeotti, Andrea & Kartik, Navin & Rabin, Matthew, 2014. "Congested observational learning," Games and Economic Behavior, Elsevier, vol. 87(C), pages 519-538.
    9. Frederic Koessler & Ch. Noussair & A. Ziegelmeyer, 2005. "Individual Behavior and Beliefs in Experimental Parimutuel Betting Markets," THEMA Working Papers 2005-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    10. Koessler, Frédéric & Noussair, Charles & Ziegelmeyer, Anthony, 2008. "Parimutuel betting under asymmetric information," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 733-744, July.
    11. Nicolas Carayol & Pascale Roux, 2006. "A strategic model of complex networks formation," Working Papers of BETA 2006-02, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    12. Michael Ostrovsky, 2012. "Information Aggregation in Dynamic Markets With Strategic Traders," Econometrica, Econometric Society, vol. 80(6), pages 2595-2647, November.
    13. Spyros Galanis & Christos A Ioannou & Stelios Kotronis, 2024. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3423-3467.
    14. Wolfers, Justin & Zitzewitz, Eric, 2006. "Prediction Markets in Theory and Practice," CEPR Discussion Papers 5578, C.E.P.R. Discussion Papers.
    15. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    16. Bradly Alicea, 2014. "Contextual and Structural Representations of Market-mediated Economic Value," Papers 1403.7021, arXiv.org.
    17. Galanis Spyros & Kotronis Stelios, 2021. "Updating Awareness and Information Aggregation," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 21(2), pages 613-635, June.
    18. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    19. Przemys{l}aw Rola, 2025. "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets," Papers 2507.09734, arXiv.org.
    20. Pavel Atanasov & Phillip Rescober & Eric Stone & Samuel A. Swift & Emile Servan-Schreiber & Philip Tetlock & Lyle Ungar & Barbara Mellers, 2017. "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls," Management Science, INFORMS, vol. 63(3), pages 691-706, March.

    More about this item

    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:2506.21809. 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.