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

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