Bayes Theorem - Web3 Games

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This diagram models the behavior and decisions of users in a Web3 game system, focusing on two primary user personas: Fun Seekers and Crypto Enthusiasts, with the additional distinction of whether they sell NFTs. It starts with a source of new users that feeds into a pool representing the total number of new players. These players are then distributed between Fun Seekers and Crypto Enthusiasts through different gates, indicating decision points within the game environment. A portion of each persona then further decides to sell NFTs, demonstrating a deeper engagement or investment in the game's economy. This system is designed to model and track user progression from entry into the game, through engagement levels, to specific economic activities (selling NFTs), segmenting the users into more precise categories based on their in-game behavior.

Moreover, the diagram employs Bayes' theorem to calculate the probabilities related to these user personas and their decisions to sell NFTs, facilitating a deeper understanding of the user base by calculating the likelihood of a user being a Fun Seeker given that they have sold an NFT. Key variables for Bayesian calculation include prior probability (the likelihood of a hypothesis before any observation), likelihood (the probability an observation occurs if the hypothesis is true), and probability of observation. Registers calculate these probabilities based on user flows between different states (e.g., Fun Seeker, Crypto Enthusiast, selling NFTs), aiming to quantify the impact of observed behaviors (selling NFTs) on the likelihood of a player being classified within a specific user persona.