From Theory to Behaviour: Towards a General Model of Engagement

Engagement is a fuzzy concept. In the present work we operationalize engagement mechanistically by linking it directly to human behaviour and show that the construct of engagement can be used for shaping and interpreting data-driven methods. First we outline a formal framework for engagement modelling. Second we expanded on our previous work on theory-inspired data-driven approaches to better model the engagement process by proposing a new modelling technique, the Melchoir Model. Third, we illustrate how, through model comparison and inspection, we can link machine-learned models and underlying theoretical frameworks. Finally we discuss our results in light of a theory-driven hypothesis and highlight potential application of our work in industry.Comment: In review for being included in the proceedings of "Conference on Games

A methodology for approximating motivation-related latent states in large scale scenarios: and its role in engagement prediction within a video game setting

Motivation is a fundamental psychological process guiding our everyday behaviour. For doing so, it heavily relies on the ability to attribute relevance to potentially rewarding objects and actions (i.e., incentives). However, despite its importance, quantifying the saliency that an individual might attribute to an object or an action is not an easy task, especially if done in naturalistic contexts. In this view, this thesis aims to outline a methodology for approximating the amount of attributed incentive salience in situations where large volumes of behavioural data are available but no experimental control is possible. Leveraging knowledge derived from theoretical and computational accounts of incentive salience attribution, we designed an Artificial Neural Network (ANN) tasked to infer a latent representation able to predict duration and intensity of future interactions between individuals and a series of video games. We found video games to be the ideal context for developing such methodology due to their reliance on reward mechanics and their ability to provide ecologically robust behavioural measures at scale. We developed and tested our methodology on a series of large-scale ($N> 10^6$) longitudinal datasets evaluating the ability of the generated latent representation to approximate some functional properties of attributed incentive salience. The present work opens with an overview of the concept of motivation and its interconnection with engagement in a video-game setting. It proceeds by formulating the theoretical and computation foundations on which our methodology is built upon. It then describes the iterative process of model building, evaluation and expansion underlying the implementation of our methodology. It continues by analysing the latent representation generated by the ANN and comparing its functional characteristics with those of attributed incentive salience. The manuscript ends with a general overview of the potential applications of our methodology with a particular focus on the area of automated engagement prediction and quantification in videogames settings

A Data-driven Statistical Approach to Customer Behaviour Analysis and Modelling in Online Freemium Games

The video games industry is one of the most attractive and lucrative segments in the entertainment and digital media, with big business of more than $150 billion worldwide. A popular approach in this industry is the online freemium model, wherein the game is downloadable free of cost, while advanced and bonus content have optional charges. Monetisation is through micro payments by customers and the focus is on maintaining average revenue per user and lifetime value of players. The overall aim of this research is to develop suitable data-driven methods to gain insight about customer behaviour in online freemium games, with a view to providing recommendations for successful business in this industry.Three important aspects of user behaviour are modelled in this research - engagement, time until defection, and number of micro transactions made. A multiple logistic regression using penalised likelihood approach is found to be most suitable for modelling and demonstrates good fit and accuracy for assigning observations to engaged and non-engaged categories. Cox’s proportional hazards model is adopted to analyse time to defection, and a negative binomial zero-inflated model results in the best fit to the data on micro payments. Cluster analysis techniques are used to classify the wide variety of customers based on their gameplay styles, and social network models are developed to identify prominent ‘actors’ based on social interactions. Some of the significant predictors of engagement and monetisation are amount of premium in-game currency, success in missions and competency in virtual fights, and quantity of virtual resources used in the game.This research offers extensive insight into what drives the reputation, virality and commercial viability of freemium games. In particular it helps to fill a gap in understanding the behaviour of online game players by demonstrating the effectiveness of applying a data analytic approach. It gives more insight into the determinants of player behaviour than relying on observational studies or those based on survey research. Additionally, it refines statistical models and demonstrates their implementation in R to new and complex data types representing online customer behaviours

Three essays on individual behavior and new technologies

The dissertation consists of three essays on individual behavior and new technologies. The first essay presents evidence that despite wide availability of potential savings, publication of contracted hospital prices does not affect the short-run demand for health care. The second essay studies indirect reciprocity in a large market of video game streaming where individuals are more likely to transfer their viewers to each other if they received similar gifts in the recent past. Finally, the third essay shows that while social networks of co-producing content can decrease individual churn from an online platform, they also speed up the exit process once the shutdown of the platform is announced to the users

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Newcomer Retention and Productivity in Online Peer-Production Communities

University of Minnesota Ph.D. dissertation. July 2018. Major: Computer Science. Advisor: Joseph Konstan. 1 computer file (PDF); x, 159 pages.Online communities are online interaction spaces for people that break the barriers of time, space, and scale and provide opportunities for companionship and social support, information exchange, retail, and entertainment. Among them are online peer production communities that have a fantastic business model where volunteers come together to produce content and drive traffic to these sites. Although as a class these communities are successful, the success of individual communities greatly varies. To become and remain successful, these communities must meet a number of challenges related to starting communities, retention of members, encouraging commitment, and contribution from their members, regulating the behavior of members and so on. This dissertation focuses on the specific challenge of newcomer retention and productivity in the context of online peer-production communities. Exploring three different communities with entirely different structures and compositions – MovieLens, GitHub, and Wikipedia and building upon prior work in this space, this dissertation offers a number of important predictors of retention and productivity of newcomers. First, this dissertation explores the value of early activity diversity in the presence of the amount of early activity as a predictor of newcomer retention. Second, this dissertation digs into more fundamental psychological traits of newcomers such as personality and presents findings on relationships between personality and newcomer retention, preferences, and productivity. Third, this dissertation explores and presents results on the relationship between community interactions (apart from norms, policies and rigid structures) and newcomer retention. Fourth, this dissertation studies and presents the effects of various kinds of prior experience of newcomers on retention and productivity in a new group they join. This dissertation concludes by offering a number of directions for future research