SoK: Market Microstructure for Decentralized Prediction Markets (DePMs)
Authors:
Nahid Rahman,
Joseph Al-Chami,
Jeremy Clark
Abstract:
Decentralized prediction markets (DePMs) allow open participation in event-based wagering without fully relying on centralized intermediaries. We review the history of DePMs which date back to 2011 and includes hundreds of proposals. Perhaps surprising, modern DePMs like Polymarket deviate materially from earlier designs like Truthcoin and Augur v1. We use our review to present a modular workflow…
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Decentralized prediction markets (DePMs) allow open participation in event-based wagering without fully relying on centralized intermediaries. We review the history of DePMs which date back to 2011 and includes hundreds of proposals. Perhaps surprising, modern DePMs like Polymarket deviate materially from earlier designs like Truthcoin and Augur v1. We use our review to present a modular workflow comprising eight stages: underlying infrastructure, market topic, share structure and pricing, market initialization, trading, market resolution, settlement, and archiving. For each module, we enumerate the design variants, analyzing trade-offs around decentralization, expressiveness, and manipulation resistance. We also identify open problems for researchers interested in this ecosystem.
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Submitted 8 July, 2026; v1 submitted 17 October, 2025;
originally announced October 2025.
Quest Love: A First Look at Blockchain Loyalty Programs
Authors:
Joseph Al-Chami,
Jeremy Clark
Abstract:
Blockchain ecosystems -- such as those built around chains, layers, and services -- try to engage users for a variety of reasons: user education, growing and protecting their market share, climbing metric-measuring leaderboards with competing systems, demonstrating usage to investors, and identifying worthy recipients for newly created tokens (airdrops). A popular approach is offering user quests:…
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Blockchain ecosystems -- such as those built around chains, layers, and services -- try to engage users for a variety of reasons: user education, growing and protecting their market share, climbing metric-measuring leaderboards with competing systems, demonstrating usage to investors, and identifying worthy recipients for newly created tokens (airdrops). A popular approach is offering user quests: small tasks that can be completed by a user, exposing them to a common task they might want to do in the future, and rewarding them for completion. In this paper, we analyze a proprietary dataset from one deployed quest system that offered 43 unique quests over 10 months with 80M completions. We offer insights about the factors that correlate with task completion: amount of reward, monetary value of reward, difficulty, and cost. We also discuss the role of farming and bots, and the factors that complicate distinguishing real users from automated scripts.
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Submitted 19 March, 2025; v1 submitted 30 January, 2025;
originally announced January 2025.