In this paper, we propose an approach that uses in-game\udreputation as a solution to the problem of cheating in massively\udmultiplayer online games. What constitutes cheating\udis however quite context-specific and subjective, and there is\udno universal view. Thus our approach aims to adjust to the\udparticular forms of cheating to which players object rather\udthan deciding a priori which forms of cheating should be\udcontrolled.\udThe main feature of our approach is an architecture and\udmodel for maintaining player-based and context-appropriate\udtrust and reputation measures, with the integration of these\udinto the game’s ranking system. When an avatar loses reputation,\udour approach intervenes to reduce its ranking. It\udis envisaged that players will come to attach value to reputation\udin its own right. We also present the results of relatively\udlarge-scale simulations of various scenarios involving\udsequences of encounters between players, with an initial implementation\udof our reputation and ranking model in place,\udto observe the impact on cheaters (and non-cheaters).
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