From mindless masses to small groups: Conceptualizing collective behavior in crowd modeling.

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory

The effects of user assistance systems on user perception and behavior

The rapid development of information technology (IT) is changing how people approach and interact with IT systems (Maedche et al. 2016). IT systems can increasingly support people in performing ever more complex tasks (Vtyurina and Fourney 2018). However, people's cognitive abilities have not evolved as quickly as technology (Maedche et al. 2016). Thus, different external factors (e.g., complexity or uncertainty) and internal conditions (e.g., cognitive load or stress) reduce decision quality (Acciarini et al. 2021; Caputo 2013; Hilbert 2012). User-assistance systems (UASs) can help to compensate for human weaknesses and cope with new challenges. UASs aim to improve the user's cognition and capabilities, benefiting individuals, organizations, and society. To achieve this goal, UASs collect, prepare, aggregate, analyze information, and communicate results according to user preferences (Maedche et al. 2019). This support can relieve users and improve the quality of decision-making. Using UASs offers many benefits but requires successful interaction between the user and the UAS. However, this interaction introduces social and technical challenges, such as loss of control or reduced explainability, which can affect user trust and willingness to use the UAS (Maedche et al. 2019). To realize the benefits, UASs must be developed based on an understanding and incorporation of users' needs. Users and UASs are part of a socio-technical system to complete a specific task (Maedche et al. 2019). To create a benefit from the interaction, it is necessary to understand the interaction within the socio-technical system, i.e., the interaction between the user, UAS, and task, and to align the different components. For this reason, this dissertation aims to extend the existing knowledge on UAS design by better understanding the effects and mechanisms during the interaction between UASs and users in different application contexts. Therefore, theory and findings from different disciplines are combined and new theoretical knowledge is derived. In addition, data is collected and analyzed to validate the new theoretical knowledge empirically. The findings can be used to reduce adaptation barriers and realize a positive outcome. Overall this dissertation addresses the four classes of UASs presented by Maedche et al. (2016): basic UASs, interactive UASs, intelligent UASs, and anticipating UASs. First, this dissertation contributes to understanding how users interact with basic UASs. Basic UASs do not process contextual information and interact little with the user (Maedche et al. 2016). This behavior makes basic UASs suitable for application contexts, such as social media, where little interaction is desired. Social media is primarily used for entertainment and focuses on content consumption (Moravec et al. 2018). As a result, social media has become an essential source of news but also a target for fake news, with negative consequences for individuals and society (Clarke et al. 2021; Laato et al. 2020). Thus, this thesis presents two approaches to how basic UASs can be used to reduce the negative influence of fake news. Firstly, basic UASs can provide interventions by warning users of questionable content and providing verified information but the order in which the intervention elements are displayed influences the fake news perception. The intervention elements should be displayed after the fake news story to achieve an efficient intervention. Secondly, basic UASs can provide social norms to motivate users to report fake news and thereby stop the spread of fake news. However, social norms should be used carefully, as they can backfire and reduce the willingness to report fake news. Second, this dissertation contributes to understanding how users interact with interactive UASs. Interactive UASs incorporate limited information from the application context but focus on close interaction with the user to achieve a specific goal or behavior (Maedche et al. 2016). Typical goals include more physical activity, a healthier diet, and less tobacco and alcohol consumption to prevent disease and premature death (World Health Organization 2020). To increase goal achievement, previous researchers often utilize digital human representations (DHRs) such as avatars and embodied agents to form a socio-technical relationship between the user and the interactive UAS (Kim and Sundar 2012a; Pfeuffer et al. 2019). However, understanding how the design features of an interactive UAS affect the interaction with the user is crucial, as each design feature has a distinct impact on the user's perception. Based on existing knowledge, this thesis highlights the most widely used design features and analyzes their effects on behavior. The findings reveal important implications for future interactive UAS design. Third, this dissertation contributes to understanding how users interact with intelligent UASs. Intelligent UASs prioritize processing user and contextual information to adapt to the user's needs rather than focusing on an intensive interaction with the user (Maedche et al. 2016). Thus, intelligent UASs with emotional intelligence can provide people with task-oriented and emotional support, making them ideal for situations where interpersonal relationships are neglected, such as crowd working. Crowd workers frequently work independently without any significant interactions with other people (Jäger et al. 2019). In crowd work environments, traditional leader-employee relationships are usually not established, which can have a negative impact on employee motivation and performance (Cavazotte et al. 2012). Thus, this thesis examines the impact of an intelligent UAS with leadership and emotional capabilities on employee performance and enjoyment. The leadership capabilities of the intelligent UAS lead to an increase in enjoyment but a decrease in performance. The emotional capabilities of the intelligent UAS reduce the stimulating effect of leadership characteristics. Fourth, this dissertation contributes to understanding how users interact with anticipating UASs. Anticipating UASs are intelligent and interactive, providing users with task-related and emotional stimuli (Maedche et al. 2016). They also have advanced communication interfaces and can adapt to current situations and predict future events (Knote et al. 2018). Because of these advanced capabilities anticipating UASs enable collaborative work settings and often use anthropomorphic design cues to make the interaction more intuitive and comfortable (André et al. 2019). However, these anthropomorphic design cues can also raise expectations too high, leading to disappointment and rejection if they are not met (Bartneck et al. 2009; Mori 1970). To create a successful collaborative relationship between anticipating UASs and users, it is important to understand the impact of anthropomorphic design cues on the interaction and decision-making processes. This dissertation presents a theoretical model that explains the interaction between anthropomorphic anticipating UASs and users and an experimental procedure for empirical evaluation. The experiment design lays the groundwork for empirically testing the theoretical model in future research. To sum up, this dissertation contributes to information systems knowledge by improving understanding of the interaction between UASs and users in different application contexts. It develops new theoretical knowledge based on previous research and empirically evaluates user behavior to explain and predict it. In addition, this dissertation generates new knowledge by prototypically developing UASs and provides new insights for different classes of UASs. These insights can be used by researchers and practitioners to design more user-centric UASs and realize their potential benefits

Common Crowd Dynamics: Shaping Behavioral Intention Models

As the human population grows, so too does the need to understand human behavior. One particularly important aspect of human behavior is how it changes within conglomerations of people, i.e. crowds. In this thesis, a method for modeling crowd behavior is proposed. This method draws inspiration from the concept of behavioral intention and the related forces of attitudes, influences, and social norms. These topics are first defined and detailed, followed by a survey of related research. Next, the model is presented and adapted to three common crowd dynamics, each stressing a different component of behavioral intention. Observations are made about these models, and extensions to the models and directions for future research are considered

Mass evacuation - human behavior and crowd dynamics - What do we know?

The field of mass evacuation has existed for a long time. Already during the Roman Empire era evacuation problematic was considered. In modern times the field has gained more attention during the last couple of decades, especially for sports grounds and stadiums. Through analysis of some well-known historical crowd disasters and through a literature survey the aim has been to compile the most important findings. The aim has also been to analyze problem areas, knowledge and development opportunities. Regarding the problems of mass evacuation, preventive measures like design and contingency plan is of high importance. In addition there is a need for good communication and to take proper actions when an accident occurs. Some phenomenon that may arise during crowded situations have been found. These phenomenon are an indication that a catastrophic situation might emerge. With knowledge and understanding of those the expectation is, with the help of live video recordings and simulation softwares, to get a warning about the elevated risk for the crowd

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees? safety and overall experience. Single-Input-SingleOutput (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input?output MISO models, linear and neural, are identified using a datadriven approach in which input?output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models? structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenariosUAH-Catedra MANED

Virtual and Augmented Reality: New Frontiers for Clinical Psychology

In the last decades, the applied approach for the use of virtual reality (VR) and augmented reality (AR) on clinical and health psychology has grown exponentially. These technologies have been used to treat several mental disorders, for example, phobias, stress-related disorders, depression, eating disorders, and chronic pain. The importance of VR/AR for the mental health field comes from three main concepts: (1) VR/AR as an imaginal technology, people can feel “as if they are” in a reality that does not exist in external world; (2) VR/AR as an embodied technology, the experience to feel user’s body inside the virtual environment; and (3) VR/AR as connectivity technology, the “end of geography’. In this chapter, we explore the opportunities provided by VR/AR as technologies to improve people’s quality of life and to discuss new frontiers for their application in mental health and psychological well-being promotion

The Effectiveness Of Virtual Humans Vs. Pre-recorded Humans In A Standardized Patient Performance Assessment

A Standardized Patient (SP) is a trained actor who portrays a particular illness to provide training to medical students and professionals. SPs primarily use written scripts and additional paper-based training for preparation of practical and board exams. Many institutions use various methods for training such as hiring preceptors for reenactment of scenarios, viewing archived videos, and computer based training. Currently, the training that is available can be enhanced to improve the level of quality of standardized patients. The following research is examining current processes in standardized patient training and investigating new methods for clinical skills education in SPs. The modality that is selected for training can possibly affect the performance of the actual SP case. This paper explains the results of a study that investigates if there is a difference in the results of an SP performance assessment. This difference can be seen when comparing a virtual human modality to that of a pre-recorded human modality for standardized patient training. The sample population navigates through an interactive computer based training module which provides informational content on what the roles of an SP are, training objectives, a practice session, and an interactive performance assessment with a simulated Virtual Human medical student. Half of the subjects interact with an animated virtual human medical student while the other half interacts with a pre-recorded human. The interactions from this assessment are audio-recorded, transcribed, and then graded to see how the two modalities compare. If the performance when using virtual humans for standardized patients is equal to or superior to pre-recorded humans, this can be utilized as a part task trainer that brings standardized patients to a higher level of effectiveness and standardization. In addition, if executed properly, this tool could potentially be used as a part task trainer which could provide savings in training time, resources, budget, and staff to military and civilian healthcare facilities

Multi-scale Models for Transportation Systems Under Emergency Conditions

The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

Applying the lessons of the attack on the World Trade Center, 11th September 2001, to the design and use of interactive evacuation simulations

The collapse of buildings, such as terminal 2E at Paris' Charles de Gaule Airport, and of fires, such as the Rhode Island, Station Night Club tragedy, has focused public attention on the safety of large public buildings. Initiatives in the United States and in Europe have led to the development of interactive simulators that model evacuation from these buildings. The tools avoid some of the ethical and legal problems from simulating evacuations; many people were injured during the 1993 evacuation of the World Trade Center (WTC) complex. They also use many concepts that originate within the CHI communities. For instance, some simulators use simple task models to represent the occupants' goal structures as they search for an available exit. However, the recent release of the report from the National Commission on Terrorist Attacks upon the United States (the '9/11 commission') has posed serious questions about the design and use of this particular class of interactive systems. This paper argues that simulation research needs to draw on insights from the CHI communities in order to meet some the challenges identified by the 9/11 commission

Understanding User Engagement in the Open Collaboration Model of Crowdsourcing

Crowdsourcing refers to the use of technologies to gather the collective effort and wisdom from an undefined group of online users for organizational innovation and/or problem solving. Further, open collaboration model refers to the crowdsourcing type wherein the crowd members discuss the submitted contributions among themselves to provide the final outcomes to problem owners. Regardless of crowdsourcing forms, a critical challenge for crowdsourcing service providers is to engage online participants in making sustained contributions. Inspired by Flow Theory (Csikszentmihalyi & Csikszentmihayi, 1988), the purpose of this dissertation is to examine whether the conditions of challenge-skill balance and clear and immediate feedback invoke the flow state, specifically an absorbed and enjoyable experience, and consequently make Internet users more engaged in the open collaboration events. The proposed relationships were tested through lab experiment, with the flow state being measured through both self-report survey and eye-tracking. As for the results, I found that perceived challenge-skill balance and perceived feedback were associated with the invocation of fun, but not the holistic flow experience in the brainstorming task. Moreover, fun was also found to positively associate with the indicators of the intensity and sustainability of user engagement. I also identified some exploratory ocular patterns of participants when they enjoyed the task at hand