Paper: arXiv 2610.35738

Authors: Pranay Anchuri

Abstract

Demand on blockchain-based marketplaces arrives in bursts and at a high rate. On these platforms the data are completely visible, which makes it feasible to compare the bursty demand against the usual load. The congestion manifests as an increased demand for gas, the primary resource a blockchain sells. Existing studies consider how to price the gas. The demand itself, and what it is made of during bursts, has not been measured. We measure it on Robinhood Chain, a layer-2 blockchain that quickly became a venue for trading tokenized equities and memecoins. We collected the data from every block of its first three active months and more than 50 million Uniswap swaps that occurred during bursty periods. We find that a burst trades about as many pools as a quiet period, mostly different ones, and trades them harder, on top of elevated demand that accumulated for hours. With the bursts aligned on their peaks, the median peak is 2.5 times the usual load. Both the rise and the fall of the demand are described by the sum of two exponential functions with different timescales, a slow one of hours and a fast one of minutes. Against a matched quiet period, a burst has 1.05 times the pools, 1.40 times the trades, and 1.21 times the senders. We use two models to explain the arrival of bursts: externally triggered cascades and self-excitation. The former fits the data relatively better. Inside a burst, the entry of each trader into a pool is followed for about 24 seconds by more entries than the existing traders predict. Whether this is because of imitation or because of a shared signal is left open.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper demonstrates strong empirical rigor through extensive data collection and statistical analysis of blockchain demand bursts. Its novelty lies in measuring and modeling the shape and composition of these bursts, a previously unaddressed area. The mathematical models, while not overly complex, are appropriately applied to the empirical findings.

Research Flowchart

  flowchart TD
    A[Research Goal: Measure & Explain Blockchain Demand Bursts] --> B{Key Methodology: Quantify Demand Bursts};
    B --> C[Data/Inputs: Robinhood Chain Blocks (3 months), 50M Uniswap Swaps];
    C --> D{Computational Processes: Analyze Gas Demand, Uniswap Swaps, Trader Behavior};
    D --> E[Key Finding 1: Bursts trade more pools, harder, on top of accumulated demand];
    E --> F[Key Finding 2: Demand rise/fall described by two exponential functions (hours/minutes)];
    F --> G[Key Finding 3: Bursts have higher pools, trades, senders vs. quiet periods];
    G --> H[Key Finding 4: Externally triggered cascades fit better than self-excitation for burst arrival];
    H --> I[Key Finding 5: Trader entry into a pool followed by increased entries for ~24s (imitation/shared signal?)];