From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

TL;DR

This study demonstrates that machine learning models can accurately predict the lifespan of virtual goods based on early adopter data, highlighting the importance of social activity in virtual economies.

Contribution

It introduces a predictive framework for virtual goods' lifespan using early adopter data and decision trees, advancing understanding of virtual economy dynamics.

Findings

Early adopter social activity predicts virtual goods' lifespan

Machine learning models achieve accurate lifespan predictions

Communication drives virtual goods propagation

Abstract

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.