This thesis investigates the use of control theory as a means to study and ultimately control the psychological dynamics of people in a crowd. Gustav LeBon's suggestibility theory, a well-known account of collective behaviour, is used to develop a discrete-time nonlinear model of psychological crowd behavior that, consistent with suggestibility theory, is open-loop unstable. As a first attempt to stabilize the dynamics, linear observer-based output-feedback techniques and a collection of simple nonlinear control strategies are pursued. The poor performance afforded by these schemes motivates an agent-oriented control strategy in which authoritative figures, termed control agents, are interspersed within the crowd and, similar to the technique of feedback linearization, use knowledge of the system dynamics to issue signals that propagate through the crowd to drive specific components of the state to zero. It is shown that if these states are chosen judiciously then it follows that a collection of other state signals are, themselves, zero. This realization is used to develop a stability result for a simple crowd structure and this result is, in turn, used as a template to develop similar results for crowds of greater complexity. Simulations are used to verify the functionality of the reported schemes and the advantages of using multiple control agents, instead of a single control agent, are emphasized. While the mathematical study of complex social phenomena, including crowds, is prefixed by an assortment of unique challenges, the main conclusion of this thesis is that control theory is a potentially powerful framework to study the underlying dynamics at play in such systems.

This thesis investigates the use of control theory as a means to study and ultimately control the psychological dynamics of people in a crowd. Gustav LeBon's suggestibility theory, a well-known account of collective behaviour, is used to develop a discrete-time nonlinear model of psychological crowd behavior that, consistent with suggestibility theory, is open-loop unstable. As a first attempt to stabilize the dynamics, linear observer-based output-feedback techniques and a collection of simple nonlinear control strategies are pursued. The poor performance afforded by these schemes motivates an agent-oriented control strategy in which authoritative figures, termed control agents, are interspersed within the crowd and, similar to the technique of feedback linearization, use knowledge of the system dynamics to issue signals that propagate through the crowd to drive specific components of the state to zero. It is shown that if these states are chosen judiciously then it follows that a collection of other state signals are, themselves, zero. This realization is used to develop a stability result for a simple crowd structure and this result is, in turn, used as a template to develop similar results for crowds of greater complexity. Simulations are used to verify the functionality of the reported schemes and the advantages of using multiple control agents, instead of a single control agent, are emphasized. While the mathematical study of complex social phenomena, including crowds, is prefixed by an assortment of unique challenges, the main conclusion of this thesis is that control theory is a potentially powerful framework to study the underlying dynamics at play in such systems.

Currently, mobile computing is mandating or influencing the direction of new developments in information technology. The high level of adoption that mobile devices have among individuals allows for multiple opportunities for new developments applicable to academic communities, governments and businesses. Data of various types can be collected in a crowd-sourced manner. As such, this thesis examines the collection and application of data collected through a purpose-designed app relying on Bluetooth and geo-location technologies on mobile devices. Through three distinct development iterations and using Bluetooth connectivity, information about connectivity to other mobile devices can be obtained, and in this way the number, type, and device names of “connecting” devices are gathered and stored. Another interesting aspect associated with this type of data collection is that the mobile device may be either moving or stationary during the data collection process. Information can be collected and mined to help map real-life events such as traffic patterns or crowd movement within mass gatherings, as well as ethereal social interactions, and these data can in turn be used as input to various models and simulators. When geo-location technologies are incorporated, a higher level of detail can be obtained on the location of devices. This technology allows for mapping movement and contacts made between people, allowing for the gathering of more detailed social patterns of individuals. As part of this study, the technology developed using Bluetooth connectivity and geo-location is then taken to an additional iteration to develop a mobile system that is able to find and establish direct connections with other individuals and initiate real-life interactions. The work demonstrates that mobile technologies can provide a broad framework of action for the generation and collection of valuable data that can be used for behavioural studies, simulations and other type of research that involves real-life social interactions.

The goal of this thesis is to investigate the key success factors in the digital marketing approach used for campaigns done on crowdfunding platforms, and how it will change influence the decisions of the crowd to invest in a project. Regarding the structure of this thesis, we will firstly explain in details what are the roots of the crowdfunding, describe the different type of platforms and in which context they are used. In the second and third part, we will see how crowdfunding represent a boost for the entrepreneurial initiative and how digital marketing is influencing the process of a raising fund campaign. Finally, thanks a research that has been conducted on 46 respondents, we will analyze the behavior of the crowd regarding the marketing approach used by crowdfunders. As findings, we can state that a crowdfunder must establish a project with a substantial quality content that will pull the crowd toward the project, and choose the right approach in selecting an adapted crowdfunding platforms and rewards.

A common practice in crowd animation is the use of human templates. A human template is a 3D character defined by its mesh, skeletal structure, materials, and textures. A crowd simulation is created by repeatedly instantiating a small set of human templates. For each instance, one texture is randomly chosen from the template’s available texture set, and color and shape variety techniques are applied so that multiple instances of the same template appear different (Thalmann & Musse, 2013). When dealing with very large crowds, it is inevitable to end up with instances that are exactly identical to other instances, as the number of different textures and shape modifications is limited. This poses a problem for crowd animation, as the viewers’ perception of identical characters could significantly decrease the believability of the crowd simulation. A variety of factors could affect viewers’ perception of identical characters, including crowd size, distance of the characters from the camera, background, movement, lighting conditions, etc. The study reported in this paper examined the extent to which the type of locomotion of the crowd characters affects the viewer’s ability to perceive identical instances within a medium size crowd (20 characters). The experiment involved 51 participants and compared the time the participants took to recognize two identical characters in three different locomotion scenarios (i.e. standing, walking, and running). Findings show that the type of locomotion did not have a statistically significant effect on the time subjects took to identify identical characters within the crowd. Hence, results suggest that audience perception of identical characters in a medium size crowd is not affected by the type of movement of the characters.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24726号 / 情博第814号 / 新制||情||137(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 西野 恒, 教授 河原 達也, 教授 神田 崇行, 准教授 延原 章平 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

<div>Digital transformation greatly affects all segments of our society. There are three powerful trends unleashed by the digital revolution: machine, platform, and crowd. The first trend emphasizes that machine learning can either complements or supplements human capabilities, which leads to data-driven decision making. The second trend shows that value creation is moving from physical products to platforms (e.g., Uber and Airbnb) where network effects can have a great impact. The third trend is about the emergence of online crowds. Several good examples are crowdfunding platforms like Indiegogo and collaborative platforms such as Wikipedia. My research work studies these three trends from different aspects. </div><div><br></div><div>In the first project, we investigated how professional reviewers influence subsequent non-editor reviewers in their writing behaviors. Restaurants that receive editorial reviews are found to have reviewers who not only post more frequently, but also give lengthier and more neutral feedback. Further investigation of the mechanism finds that in terms of the topics, sentiment, and readability, following reviews of restaurants that receive editorial reviews become increasingly similar to their editorial reviews, indicating that a herding effect is the main driver of the shift in later reviews. In this study, we not only look at quantitative review characteristics such as rating and review length, but also extract qualitative review characteristics embedded in review text using Natural Language Processing (NLP) techniques (e.g., Topic modeling and Sentiment analysis). </div><div><br></div><div>In the second project, we studied how AI-based shelf monitoring can help manufacturers with their shelf management efforts. In general, we've discovered that AI-powered shelf monitoring boosts product sales. We further reveal that the positive effect shall be attributed to independent retailers rather than chained retailers. More broadly, the finding further suggests that AI-powered monitoring is more scalable, allowing manufacturers to cope more effectively with more heterogeneous objects. In this study, we analyzed shelf photos using deep learning (e.g., image recognition). Furthermore, we conducted a qualitative study (i.e., interviews) as a supplement attempt to uncover the underlying mechanism behind the interesting phenomenon found in our field experiment. </div><div><br></div><div>In the third project, we tried to understand the dynamic contribution patterns caused by backers’ multiple roles and fundraisers’ strategic behaviors. We show that projects described by more subjective content (i.e., title and introduction) significantly repel potential donors. We further show that fundraisers’ contribution to their own projects might increase donor’ intention to donate and has no significant impact on reward pledging of subsequent backers. Above that, we find a positive interplay between donation and reward pledge, suggesting a cross-channel peer influence that will facilitate the fundraising progress. </div>

Crowd simulation is used for many applications including (but not limited to) videogames, building planning, training simulators, and various virtual environment applications. Particularly, crowd simulation is most useful for when real life practices wouldn't be practical such as repetitively evacuating a building, testing the crowd flow for various building blue prints, placing law enforcers in actual crowd suppression circumstances, etc. In our work, we approach the fidelity to scalability problem of crowd simulation from two angles, a programmability angle, and a scalability angle, by creating new methodology building off of a struct of arrays approach and transforming it into an Object Oriented Struct of Arrays approach. While the design pattern itself is applied to crowd simulation in our work, the application of crowd simulation exemplifies the variety of applications for which the design pattern can be used. / Master of Science

Crowd Simulation and Visualization are an important aspect of many applications such as Movies, Games and Virtual Reality simulations. The advantage with crowd rendering in movies is that the entire rendering process can be done off-line. And hence computational power is not much of an overhead. However, applications like Games and Virtual Reality Simulations demand real-time interactivity. The sheer processing power demanded by real time interactivity has, thus far, limited crowd simulations to specialized equipment. In this thesis we try to address the issue of rendering and visualizing a large crowd of animated figures at interactive rates. Recent trends in hardware capabilities and the availability of cheap, commodity graphics cards capable of general purpose computations have achieved immense computational speed up and have paved the way for this solution. We propose a Graphics Processing Unit(GPU) based implementation for animating virtual characters. However, simulation of a large number of human like characters is further complicated by the fact that it needs to be visually convincing to the user. We suggest a motion graph based animation-splicing approach to achieving this sense of realism. / Master of Science

Crowd simulations are attracting increasing attention from both academia and the industry field and are implemented across a vast range of applications, from scientific demonstrations to video games and films. As such, the demand for greater realism in their aesthetics and the amount of agents involved is always growing. A successful crowd simulation must simulate large numbers of pedestrians' behaviours as realistically as possible in real-time. The thesis looks at two important aspects of crowd simulation and real-time animation. First, this thesis introduces a new data structure called Extended Oriented Bounding Box (EOBB) and related methods for fast collision detection and obstacle avoidance in the simulation of crowd motion in virtual environments. The EOBB is extended to contain a region whose size is defined based on the instantaneous velocity vector, thus allowing a bounding volume representation of both geometry and motion. Such a representation is also found to be highly effective in motion planning using the location of vertices of bounding boxes in the immediate neighbourhood of the current crowd member. Second, we present a detailed analysis of the effectiveness of spatial subdivision data structures, specifically for large-scale crowd simulation. For large-scale crowd simulation, computational time for collision detection is huge, and many studies use spatial partitioning data structure to reduce the computational time, depicting their strengths and weaknesses, but few compare multiple methods in an effort to present the best solution. This thesis attempts to address this by implementing and comparing four popular spatial partitioning data structures with the EOBB.