Computational trust in Multiplayer Online Games.

Abstract

Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this field has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments affect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network. Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we define and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person's social capital as a proxy.