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Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World

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  • Benedikt Fuchs
  • Stefan Thurner

Abstract

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions of every player over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in western countries. In particular we find an approximate exponential for low wealth and a power-law tail. The Gini index is found to be $g=0.65$, which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degree in the trade network, relatively low nearest-neighbor degree and a low clustering coefficient. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. We find that players that are not organized within social groups with at least three members are significantly poorer on average. We observe that high `political' status and high wealth go hand in hand. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies.

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  • Benedikt Fuchs & Stefan Thurner, 2014. "Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World," Papers 1403.6342, arXiv.org.
  • Handle: RePEc:arx:papers:1403.6342
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    1. Hegyi, Géza & Néda, Zoltán & Augusta Santos, Maria, 2007. "Wealth distribution and Pareto's law in the Hungarian medieval society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 271-277.
    2. Jayadev, Arjun, 2008. "A power law tail in India's wealth distribution: Evidence from survey data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 270-276.
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    1. Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World
      by Alessandro Cerboni in Knowledge Team on 2014-04-01 01:17:27

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    Cited by:

    1. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    2. Andres M Belaza & Jan Ryckebusch & Koen Schoors & Luis E C Rocha & Benjamin Vandermarliere, 2020. "On the connection between real-world circumstances and online player behaviour: The case of EVE Online," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    3. Young Bin Kim & Kyeongpil Kang & Jaegul Choo & Shin Jin Kang & TaeHyeong Kim & JaeHo Im & Jong-Hyun Kim & Chang Hun Kim, 2017. "Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum," Complexity, Hindawi, vol. 2017, pages 1-10, December.

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