NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants
Taylor Lundy, Narun K. Raman, Scott Duke Kominers, Kevin Leyton-Brown
Abstract
Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the bandwagon effect (goods increase in value as they become more popular) and the snob effect (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d97a1ed2-a45e-44c0-b040-d6ec9ce9bf96Builds on2
- Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price predictionDavide Costa, Lucio La Cava, Andrea TagarelliWWW 2023 · 36 citations
- Pay to (Not) Play: Monetizing Impatience in Mobile GamesTaylor Lundy, Narun K. Raman, Hu Fu, Kevin Leyton-BrownAAAI 2024 · 3 citations
Related papers
- "My Painting Belongs to 810 People": Investigating the First Encounter Perception of Digital Shares of Physical CollectiblesJenny Berkholz, Dean-Robin Kern, Aniqa Rahman, Gunnar StevensCSCW 2025
- Unveiling the Paradox of NFT ProsperityJintao Huang, Pengcheng Xia, Jiefeng Li, Kai Ma et al.WWW 2024 · 19 citations
- NFTDisk: Visual Detection of Wash Trading in NFT MarketsXiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu et al.CHI 2023 · 28 citations
- DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on GraphsHanna Kim, Jian Cui, Eugene Jang, Chanhee Lee et al.NDSS 2024
- "Centralized or Decentralized?": Concerns and Value Judgments of Stakeholders in the Non-Fungible Tokens (NFTs) MarketYunpeng Xiao, Bufan Deng, Siqi Chen, Kyrie Zhixuan Zhou et al.CSCW 2024 · 22 citations
