This dissertation studies economic issues in the digital economy with a specific focus on the economic aspects of how firms acquire and use consumer data. Chapter 1 empirically studies the drivers of digital attention in the space of social media applications. In order to do so I conduct an experiment where I comprehensively monitor how participants spend their time on digital services and use parental control software to shut off access to either their Instagram or YouTube. I characterize how participants substitute their time during and after the restrictions. I provide an interpretation of the substitution during the restriction period that allows me to conclude that relevant market definitions may be broader than those currently considered by regulatory authorities, but that the substantial diversion towards non-digital activities indicates significant market power from the perspective of consumers for Instagram and YouTube. I then use the results on substitution after the restriction period to motivate a discrete choice model of time usage with inertia and, using the estimates from this model, conduct merger assessments between social media applications. I find that the inertia channel is important for justifying blocking mergers, which I use to argue that currently debated policies aimed at curbing digital addiction are important not only just in their own right but also from an antitrust perspective and, in particular, as a potential policy tool for promoting competition in these markets. More broadly, my paper highlights the utility of product unavailability experiments for demand and merger analysis of digital goods. I thank Maayan Malter for working together with me on collecting the data for this paper. Chapter 2 then studies the next step in consumer data collection process – the extent to which a firm can collect a consumer’s data depends on privacy preferences and the set of available privacy tools. This chapter studies the impact of the General Data Protection Regulation on the ability of a data-intensive intermediary to collect and use consumer data. We find that the opt-in requirement of GDPR resulted in 12.5% drop in the intermediary-observed consumers, but the remaining consumers are trackable for a longer period of time. These findings are consistent with privacy-conscious consumers substituting away from less efficient privacy protection (e.g, cookie deletion) to explicit opt out—a process that would make opt-in consumers more predictable. Consistent with this hypothesis, the average value of the remaining consumers to advertisers has increased, offsetting some of the losses from consumer opt-outs. This chapter is jointly authored with Yeon-Koo Che and Tobias Salz. Chapter 3 and Chapter 4 make up the third portion of the dissertation that studies one of the most prominent uses of consumer data in the digital economy – recommendation systems. This chapter is a combination of several papers studying the economic impact of these systems. The first paper is a joint paper with Duarte Gonçalves which studies a model of strategic interaction between producers and a monopolist platform that employs a recommendation system. We characterize the consumer welfare implications of the platform’s entry into the production market. The platform’s entry induces the platform to bias recommendations to steer consumers towards its own goods, which leads to equilibrium investment adjustments by the producers and lower consumer welfare. Further, we find that a policy separating recommendation and production is not always welfare improving. Our results highlight the ability of integrated recommender systems to foreclose competition on online platforms. The second paper turns towards understanding how such systems impact consumer choices and is joint with Duarte Gonçalves and Shan Sikdar. In this paper we study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.

This thesis investigates different aspects of children's computer game world. In the opening chapters are described available computer games for kids, which are split into groups. After that follows theoretical introduction to the problems of sociological research and advantages of using computer and Internet for it. On the basis of observation are introduced three types of applications for investigation of children's interest and abilities in computer games and parents opinions. Two of them were already used for investigation within this thesis. Thesis also presents results of realized survey.

The use of bicycle as a transportation mode has increased in popularity during the last four decades. The reasons that could explain why the use of bicycles have increased in popularity are many. Nevertheless, three possible reasons for the increasing in popularity are because of the benefit in terms of health, reduced motorised traffic congestion, and air pollution. As bicycle traffic flows increase, the evaluating of the bicycle traffic infrastructure will become more important for bicyclist safety, and comfort. One possible evaluating tool for bicycle traffic is microscopic traffic simulation and one key component is the free-riding. The free-riding is a bicyclist that is not interacting with other bicyclist, and other road users during a ride. Furthermore, to develop a suitable simulation model of the free-riding needs data and a possible methodology for data collection on the free-riding is using instrumented bicycles. Therefore, this thesis will investigate a methodology that can be used to collect, process, and analyse data for two bicyclist and their interactions with the infrastructure. The methodology for data collection using instrumented bicycle includes a pre-defined travel route, two types of bicycles as a conventional, and an electric bicycle. Additionally, the equipment is used in the methodology should be easy to switch between bicycles to keep the behaviour as natural as possible for bicyclists. Nevertheless, the equipment is easy to switch between bicycles, if only a few tools is needed to switch between bicycles in the methodology. Moreover, data collection using instrument bicycle includes an interview survey on each participating bicyclist, and investigate weather conditions, and effort experience during the data collection on each participating bicyclist. Results indicate that negative acceleration i.e., deceleration, at intersections, curves, uphill when a conventional bicycle is used. Meanwhile, it is also negative acceleration i.e., deceleration at downhills when an electric bicycle is used. Furthermore, the use of electric bicycle leads to higher travel speed and lower power output usage on average than when a conventional bicycle is used as expected. Moreover, at downhills the speed can still increase even though the power output usage is zero, according to the analysis of free-riding behaviour. In addition, data collection using instrumented bicycle collects other measurement of the effort for the bicyclist such as the heart rate, and cadence. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

A$ Virtual Reality (VR) becomes increasingly popular and affordable, and is applied in other fields than entertainment, such as education and industrial use, there is also a grow­ing risk related to its integrity and security. VR equipment tracks user biometric data as a means to interact with the VR environment, which creates sets of biometric data that could be used to identify u ers. Such biometric tempJates are potentially harmful if stolen by a malicious third party. This thesis investigates if user identification is possible within a set of participants ( =10) through a study using their movement and eye biometric data gathered within VR sessions, where they perform a teleoperation task designed to sim­ulate a real-world use case. By performing 3 data collection sessions for each participant and using the gathered data to train 4 classification models, we show that a high level of accuracy can be attained while using simple machine learning approaches, achieving a peak accuracy of 89.26% with a data5et designed to challenge our models. We further ana­lyze the accuracy results from the trained models, and di5cuss the identification power of different data types, which highlights how the characteristics of the task performed affects the usefulness of data types.

Denna studie undersöker svenska internetanvändares kunskaper och inställningar till datainsamling genom cookies. Syftet med studien är att ta reda på vilken nivå deras kunskap ligger på och vad de har för inställning till cookies samt om det finns något samband mellan dessa. Studien baseras på en kvantitativ undersökning genom en webbenkät och kompletteras med en kvalitativ undersökning i form av semi-strukturerade intervjuer. Webbenkäten genererade åttiotre svar och av dessa var det sex respondenter som blev intervjuade. Resultatet visade på en låg kunskapsnivå om datainsamling genom cookies och en neutral eller negativ inställning till dessa. Det fanns en oro bland respondenterna att deras personliga information hamnar i fel händer eller att deras integritet kränks. Trots oron fanns det även en nonchalans och likgiltighet inför internetanvändarnas egna ansvar och agerande till datainsamlingen. Slutsatserna som kunde dras var att inställningen till cookies var mestadels negativ och kunskapsnivån kring dem var generellt väldigt låga. Ett behov av relevant utbildning inom ämnet skulle behövas då kunskapsnivån hos internetanvändare måste bli högre och de behöver utveckla en digital kompetens för att få rätt förutsättningar i dagens digitaliserade samhälle. / This study examines Swedish internet users' knowledge and attitude towards data collection through cookies. The purpose of the study is to find out the level of their knowledge and what attitude they have towards cookies as well as examining if there is a relation between the two. The study is based on quantitative research through an online survey and was strengthened by qualitative research in the shape of semi-structured interviews. The survey generated eighty-three responses and from them six respondents were interviewed. The result showed a low level of knowledge about data collection through cookies and a neutralt or negative attitude towards these. There was a general concern among the respondents of their personal information falling into the wrong hands or having their privacy violated. Despite the concerns there was also a casualness and indifference towards the internet users own responsibility and behavior with data collection. In conclusion the attitude towards cookies was mostly negative and the knowledge was in general very low. There’s a need for relevant education of the subject to raise the level of knowledge by the internet users so that they can develop a digital literacy and get the right conditions in today's digitalized society.

A great deal of research has been conducted on Central Florida toll roads to better understand the characteristics of the tolling operation. In this thesis, the development and calibration of a toll plaza simulation models will be analyzed using two simulation programs varying mostly in their modeling theory. The two models utilized are, SHAKER, a deterministic queuing model for vehicles utilizing toll collection facilities, and VISSIM, a globally popular stochastic simulation software. The benefits of simulation models leads to the purpose of this thesis, which is to examine the effectiveness of two toll modeling programs that are similar in purpose but vary in approach and methodology. Both SHAKER and VISSIM toll plaza models have the potential to work as a tool that can estimate the maximum throughput and capacity of toll plazas. Major operational benefits resulting from developing these models are to simulate and evaluate how traffic conditions will change when demand increases, when and if queues increase when a lane is closed due to maintenance or construction, the impact of constructing additional lanes, or determining whether or not the best lane type configuration is currently implemented. To effectively calibrate any model available site data must be used to compare simulation results to for model validity. In an effort to correctly calibrate the SHAKER toll plaza tool and VISSIM model, an extensive field collection procedure was conducted at four Florida Turnpike operated toll facilities located in Central Florida. Each site differed from the others in terms of number of lanes, lane configuration, toll base fee, highway location, traffic demand, and vehicle percentage. The sites chosen for data collection were: the Lake Jesup Mainline Plaza along the Seminole Expressway (SR-417), the Beachline West Expressway Toll Plaza along the SR-528, the Daniel Webster Western Beltway Plaza along SR-429, and the Leesburg Toll Plaza along the Florida Turnpike Mainline SR-91. Upon completion of calibration of the two simulation models it is determined that each of the two software are successful in modeling toll plaza capacity and queuing. As expected, each simulation model does possess benefits over the other in terms of set up time, analysis reporting time, and practicality of results. The SHAKER model setup takes mere seconds in order to create a network and input vehicle, another few seconds to calibrate driving parameters, and roughly 10 additional seconds to report analysis. Conversely, setting up the VISSIM model, even for the most experienced user, can take several hours and the report analysis time can take several more hours as it is dependant on the number of required simulation runs and complexity of the network. VISSIM is most beneficial by the fact that its modeling allows for driver variability while SHAKER assumes equilibrium amongst lane choice and queuing. This creates a more realistic condition to observed traffic patterns. Even though differences are prevalent, it is important that in each simulation model the capacity is accurately simulated and each can be used to benefit operational situations related to toll plaza traffic conditions.

Syftet med arbetet var att utforska möjligheten att hämta data automatiskt från ett ett escape room. Vilken data som är väsentlig att samla in för att stödja utvecklingen av ett escape room? Även hur ska datan samlas in från det fysiska rummet samt vilka komponenter från digitala spel kan användas i insamlings- och analysprocessen? En intervjubaserad metod användes för att granska dom tre olika behoven en projektledare, fullskaligt system och prototyputveckling kräver av ett utvärderingssystem. Resultatet blev att information som tiden för att lösa ett pussel, hur många ledtrådar och vad spelarna interagerar är information som behövs. Användningen av digitala komponenter som Unity Analytics och Xbox Adaptiv Controller har bevisats användas för mer än bara datorspel. Systemet är inte komplett utan enbart en prototyp. Prototypen visar dock på ett gott resultat och implementationen mot ett fullskaligt system är mer än möjligt vid ett framtida arbete. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

During the broadcast of a golf tournament, a way to show the audience what a player's swing looks like would be to draw a trace following the movement of the club head. A computer vision model can be trained to identify the position of the club head in an image, but due to the high speed at which professional players swing their clubs coupled with the low frame rate of a typical broadcast camera, the club head is not discernible whatsoever in most frames. This means that the computer vision model is only able to deliver a few sparse detections of the club head. This thesis project aims to develop a machine learning model that can predict the complete motion of the club head, in the form of a swing trace, based on the sparse club head detections. Slow motion videos of golf swings are collected, and the club head's position is annotated manually in each frame. From these annotations, relevant data to describe the club head's motion, such as position and time parameters, is extracted and used to train the machine learning models. The dataset contains 256 annotated swings of professional and competent amateur golfers. The two models that are implemented in this project are XGBoost and a feed forward neural network. The input given to the models only contains information in specific parts of the swing to mimic the pattern of the sparse detections. Both models learned the underlying physics of the golf swing, and the quality of the predicted traces depends heavily on the amount of information provided in the input. In order to produce good predictions with only the amount of input information that can be expected from the computer vision model, a lot more training data is required. The traces predicted by the neural network are significantly smoother and thus look more realistic than the predictions made by the XGBoost model.

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