Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data

TL;DR

This paper analyzes blockchain-based gambling data to empirically study risk attitudes and strategies, providing insights into human decision-making under risk beyond traditional lab experiments.

Contribution

It introduces a novel real-world dataset from a blockchain casino, enabling empirical validation of theoretical models of gambling and risk attitudes.

Findings

Identification of gambling strategies and systems in real-world data

Empirical evidence supporting probability-theoretic models of risk behavior

Insights into human risk preferences in financial decision-making

Abstract

The statistical concept of Gambler's Ruin suggests that gambling has a large amount of risk. Nevertheless, gambling at casinos and gambling on the Internet are both hugely popular activities. In recent years, both prospect theory and lab-controlled experiments have been used to improve our understanding of risk attitudes associated with gambling. Despite theoretical progress, collecting real-life gambling data, which is essential to validate predictions and experimental findings, remains a challenge. To address this issue, we collect publicly available betting data from a \emph{DApp} (decentralized application) on the Ethereum Blockchain, which instantly publishes the outcome of every single bet (consisting of each bet's timestamp, wager, probability of winning, userID, and profit). This online casino is a simple dice game that allows gamblers to tune their own winning probabilities. Thus the dataset is well suited for studying gambling strategies and the complex dynamic of risk attitudes involved in betting decisions. We analyze the dataset through the lens of current probability-theoretic models and discover empirical examples of gambling systems. Our results shed light on understanding the role of risk preferences in human financial behavior and decision-makings beyond gambling.