Engagement Effects of Player Rating System-Based Matchmaking for Level Ordering in Human Computation Games

Human computation games lack established ways of balancing the difficulty of tasks or levels served to players, potentially contributing to their low engagement rates. Traditional player rating systems have been suggested as a potential solution: using them to rate both players and tasks could estimate player skill and task difficulty and fuel player-task matchmaking. However, neither the effect of difficulty balancing on engagement in human computation games nor the use of player rating systems for this purpose has been empirically tested. We therefore examined the engagement effects of using the Glicko-2 player rating system to order tasks in the human computation game Paradox. An online experiment (n=294) found that both matchmaking-based and pure difficulty-based ordering of tasks led to significantly more attempted and completed levels than random ordering. Additionally, both matchmaking and random ordering led to significantly more di cult tasks being completed than pure difficulty-based ordering. We conclude that poor balancing contributes to poor engagement in human computation games, and that player rating system-based difficulty rating may be a viable and efficient way of improving both

Player Rating Systems for Balancing Human Computation Games : Testing the Effect of Bipartiteness

Human Computation Games (HCGs) aim to engage volunteers to solve information tasks, yet suffer from low sustained engagement themselves. One potential reason for this is limited difficulty balance, as tasks difficulty is unknown and they cannot be freely changed. In this paper, we introduce the use of player rating systems for selecting and sequencing tasks as an approach to difficulty balancing in HCGs and game genres facing similar challenges. We identify the bipartite structure of user-task graphs as a potential issue of our approach: users never directly match users, tasks never match tasks. We therefore test how well common rating systems predict outcomes in bipartite versus non-bipartite chess data sets and log data of the HCG Paradox. Results indicate that bipartiteness does not negatively impact prediction accuracy: common rating systems outperform baseline predictions in HCG data, supporting our approach’s viability. We outline limitations of our approach and future work

Analysis of Matchmaking Optimization Systems Potential in Mobile eSports

Matchmaking systems are one of the core features of experience in online gaming. They influence player satisfaction, engagement, and churn risk. The paper looks into the current state of the theoretical and practical implementation of such systems in the mobile gaming industry. We propose a basic classification of matchmaking systems into random and quasi-random, skill-based, role-based, technical factor-based, and engagement based. We also offer an analysis of matchmaking systems in 16 leading mobile Esport games. The dominant industry solution is skill and rank based systems with a different level of skill depth measurement. In the further part of the paper, we present a theoretical model of engagement and a time-optimized model

Motivated Agents : Toward the Computational Modeling of Motivational Affordances

Video games routinely use procedural content generation, player modelling, and other forms of computational interaction that provide a good starting point for engaging computational interfaces. However, across these practices, games model environment (game content) and actor (player type) separately, which is out of tune with both basic and applied research. The ecological construct of motivational affordances, formalized as actor-environment system ratios, provides a promising alternative that could also prove fruitful for computational interaction in general

Matchmakers or tastemakers? Platformization of cultural intermediation & social media’s engines for ‘making up taste’

There are long-standing practices and processes that have traditionally mediated between the processes of production and consumption of cultural content. The prominent instances of these are: curating content by identifying and selecting cultural content in order to promote to a particular set of audiences; measuring audience behaviours to construct knowledge about their tastes; and guiding audiences through recommendations from cultural experts. These cultural intermediation processes are currently being transformed, and social media platforms play important roles in this transformation. However, their role is often attributed to the work of users and/or recommendation algorithms. Thus, the processes through which data about users’ taste are aggregated and made ready for algorithmic processing are largely neglected. This study takes this problematic as an important gap in our understanding of social media platforms’ role in the transformation of cultural intermediation. To address this gap, the notion of platformization is used as a theoretical lens to examine the role of users and algorithms as part of social media’s distinct data-based sociotechnical configuration, which is built on the so-called ‘platform-logic’. Based on a set of conceptual ideas and the findings derived through a single case study on a music discovery platform, this thesis developed a framework to explain ‘platformization of cultural intermediation’. This framework outlines how curation, guidance, and measurement processes are ‘plat-formed’ in the course of development and optimisation of a social media platform. This is the main contribution of the thesis. The study also contributes to the literature by developing the concept of social media’s engines for ‘making up taste’. This concept illuminates how social media operate as sociotechnical cultural intermediaries and participates in tastemaking in ways that acquire legitimacy from the long-standing trust in the objectivity of classification, quantification, and measurement processes

Accurate Player Modeling and Cheat-Proof Gameplay in Peer-to-Peer Based Multiplayer Online Games

We present the first detailed measurement study and models of the virtual populations in popular Massively Multiplayer Online Role-Playing Games (MMORPGs). Our results show that, amongst several MMORPGs with very different play styles, the patterns of behaviors are consistent and can be described using a common set of models. In addition, we break down actions common to Trading Card Games (TCGs) and explain how they can be executed between players without the need for a third party referee. In each action, the player is either prevented from cheating, or if they do cheat, the opponent will be able to prove they have done so. We show these methods are secure and may be used in many various styles of TCGs. We measure moves in a real TCG to compare to our implementation of Match+Guardian (M+G), our secure Peer-to-Peer (P2P) protocol for implementing online TCGs. Our results, based on an evaluation of M+G\u27s performance on the Android (TM) platform, show that M+G can be used in a P2P fashion on mobile devices. Finally, we introduce and outline a HYbrid P2P ARchitecture for Trading Card Games, HYPAR-TCG. The system utilizes Distributed Hash Tables (DHTs) and other P2P overlays to store cached game data and to perform game matchmaking. This helps reduce the network and computational load to the central servers. We describe how a centralized server authority can work in concert with a P2P gameplay protocol, while still allowing for reputation and authoritative account management

Validity Threats in Quantitative Data Collection with Games : A Narrative Survey

Background. Games are increasingly used to collect scientific data. Some suggest that game features like high cognitive load may limit the inferences we can draw from such data, yet no systematic overview exists of potential validity threats of game-based methods. Aim. We present a narrative survey of documented and potential threats to validity in using games for quantitative data collection. Method. We combined an unsystematic bottom-up literature review with a systematic top-down application of standard validity threat typologies to games to arrive at a systematisation of game-characteristic validity threats. Results. We identify three game characteristics that potentially impact validity: Games are complex systems, impeding the predictable control and isolation of treatments. They are rich in unwanted variance and diversity. And their social framing can differ from and interact with the framing of research studies or non-game situations they are supposed to represent. The diversity of gamers and their differences to general populations bring further complications. Discussion and Conclusions. The wealth of potential validity threats in game-based research is met by a dearth of systematic methodological studies, leading us to outline several future research directions