Multi-agent simulations for the evaluation of Looting Systems design in MMOG and MOBA games

Massively Multiplayer Online Games (MMOGs) are persistent worlds where a huge number of players interact with each other in order to improve their avatar's characteristics. Multiplayer Online Battle Arenas (MOBAs) - also known as Action Real-Time Strategy (ARTS) games - are video games in which each player controls a single character in one of two competing teams; goal of the game is to destroy the antagonist team. In both genres, players’ characters typically exploit their special abilities, which contribute to the overall strategy of their faction or team. Social interactions among players are at the core of both these game types, and a careful design of the game social architecture is a key factor in determining the success of a specific product. The attention of researchers and practitioners has, till now, focused mainly on several game features, while others have been considered secondary, possibly underestimating their importance in terms of the game overall quality. For instance, in MMOGs, loot items (a type of in-game reward) are not distributed evenly, and the competition for getting the best prize, often, is left in the hands of the players. To handle this issue, players have created resource allocation algorithms called Looting Systems (LS). Generally, the adoption of a specific LS is based on a gentlemen's agreement among the players, and the respect of its outcomes largely depends on mutual trust. Quite recently, ad hoc forms of LS have been introduced also into MOBAs. This topic has received moderate attention by the scientific community, anyway, we sustain that a LS could influence the players' behaviour and, if mismanaged, possibly the survival of the whole community of players in a game. Hence, detecting and tracking the hidden social effects of apparently minor features could become a critical factor in the development of games genres which heavily depend on the quality of social interactions among players. To tackle this issue, we present a simulative study - based on Agent-Based Model (ABM) techniques - of the effects of the adoption of different LSs on heterogeneous player bases. The final goal of our study is to provide several guidelines and hints about the design of LSs to game designers working on MMOs or MOBAs.