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Title: A computational game-theoretic study of reputation
Author: Yan, Chang
ISNI:       0000 0004 5353 8672
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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As societies become increasingly connected thanks to advancing technologies and the Internet in particular, individuals and organizations (i.e. agents hereafter) engage in innumerable interaction and face constantly the possibilities thereof. Such unprecedented connectivity offers opportunities through which social and economic benefits are realised and disseminated. Nonetheless, risky and damaging interaction abound. To promote beneficial relationships and to deter adverse outcomes, agents adopt different means and resources. This thesis focuses on reputation as a crucial mechanism for promoting positive interaction, and examines the topic from game-theoretic perspective using computational methods. First, we investigate the design of reputation systems by incorporating economic incentives into algorithm design. Focusing on ubiquitous user-generated ratings on the Internet, we propose a truthful reputation mechanism that not only enforces honest reporting from individual raters but also takes into account their personal preferences. The mechanism is constructed using a blend of Bayesian Truth Serum and SimRank algorithms, both specifically adapted for our use case of online ratings. We show that the resulting mechanism is Bayesian incentive compatible and is computable in polynomial time. In addition, the mechanism is shown to be resistant to common manipulations on the Internet such as uniform fake ratings and targeted collusions. Lastly, we discuss detailed considerations for implementing the mechanism in practice. Second, we investigate experimentally the relative importance of reputational and social knowledge in sustaining cooperation in dynamic networks. In our experiments, U.S-based subjects play a repeated game where, in each round, an endogenous network is formed among a group of 13 players and each player chooses a cooperative or non-cooperative action that applies to all her connections. We vary the availability of reputational and social knowledge to subjects in 4 treatments. At the aggregate level, we find that reputational knowledge is of first-order importance for supporting cooperation, while social knowledge plays a complementary role only when reputational knowledge is available. Further community-level analysis reveals that reputational knowledge leads to the emergence of highly cooperative hubs, and a dense and cluster network, while social knowledge enhances cooperation by forming a large, dense and clustered community of cooperators who exclude outsiders through link removals and link refusals. At the individual level, reputational knowledge proves essential for the emergence of network structural characteristics that are associated with cooperative actions. In contrast, in treatments without reputational information, none of the network metrics is predicative of subjects' choices of action. Furthermore, we present UbiquityLab, a pioneering online platform for conducting real-time interactive experiments for game-theoretic studies. UbiquityLab supports both synchronous and asynchronous game models, and allows for complex and customisable interaction between subjects. It offers both back-end and front-end infrastructure with a modularised design to enable rapid development and streamlined operation. For in- stance, in synchronous mode, all per-stage and inter-stage logic are fully encapsulated by a thin server-side module, while a suite of client-side components eases the creation of game interface. The platform features a robust messaging protocol, such that player connection and game states are restored automatically upon networking errors and dropped out subjects are seamlessly substituted by customisable program players. Online experiments enjoy clear advantages over lab equivalents as they benefit from low operation cost, efficient execution, large and diverse subject pools, etc. UbiquityLab aims to promote online experiments as an emerging research methodology in experimental economics by bringing its benefits to other researchers.
Supervisor: Ong, Luke; Quah, John Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Game theory,economics,social and behavioral sciences (mathematics) ; Applications and algorithms ; game theory ; algorithm ; experiments ; methodology ; platform ; reputation ; cooperation ; networks