Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication

We describe a new architecture to integrate a psychological model into a crowd simulation system in order to obtain believable emergent behaviors. Our existing crowd simulation system (MACES) performs high level wayfinding to explore unknown environments and obtain a cognitive map for navigation purposes, in addition to dealing with low level motion within each room based on social forces. Communication and roles are added to achieve individualistic behaviors and a realistic way to spread information about the environment. To expand the range of realistic human behaviors, we use a system (PMFserv) that implements human behavior models from a range of ability, stress, emotion, decision theoretic and motivation sources. An architecture is proposed that combines and integrates MACES and PMFserv to add validated agent behaviors to crowd simulations

Authoring Multi-Actor Behaviors in Crowds With Diverse Personalities

Multi-actor simulation is critical to cinematic content creation, disaster and security simulation, and interactive entertainment. A key challenge is providing an appropriate interface for authoring high-fidelity virtual actors with featurerich control mechanisms capable of complex interactions with the environment and other actors. In this chapter, we present work that addresses the problem of behavior authoring at three levels: Individual and group interactions are conducted in an event-centric manner using parameterized behavior trees, social crowd dynamics are captured using the OCEAN personality model, and a centralized automated planner is used to enforce global narrative constraints on the scale of the entire simulation. We demonstrate the benefits and limitations of each of these approaches and propose the need for a single unifying construct capable of authoring functional, purposeful, autonomous actors which conform to a global narrative in an interactive simulation

Stops and Stares: Street Stops, Surveillance, and Race in the New Policing

The use of proactive tactics to disrupt criminal activities, such as Terry street stops and concentrated misdemeanor arrests, are essential to the “new policing.” This model applies complex metrics, strong management, and aggressive enforcement and surveillance to focus policing on high crime risk persons and places. The tactics endemic to the “new policing” gave rise in the 1990s to popular, legal, political and social science concerns about disparate treatment of minority groups in their everyday encounters with law enforcement. Empirical evidence showed that minorities were indeed stopped and arrested more frequently than similarly situated whites, even when controlling for local social and crime conditions. In this article, we examine racial disparities under a unique configuration of the street stop prong of the “new policing” – the inclusion of non-contact observations (or surveillances) in the field interrogation (or investigative stop) activity of Boston Police Department officers. We show that Boston Police officers focus significant portions of their field investigation activity in two areas: suspected and actual gang members, and the city’s high crime areas. Minority neighborhoods experience higher levels of field interrogation and surveillance activity net of crime and other social factors. Relative to white suspects, Black suspects are more likely to be observed, interrogated, and frisked or searched controlling for gang membership and prior arrest history. Moreover, relative to their black counterparts, white police officers conduct high numbers of field investigations and are more likely to frisk/search subjects of all races. We distinguish between preference-based and statistical discrimination by comparing stops by officer-suspect racial pairs. If officer activity is independent of officer race, we would infer that disproportionate stops of minorities reflect statistical discrimination. We show instead that officers seem more likely to investigate and frisk or search a minority suspect if officer and suspect race differ. We locate these results in the broader tensions of racial profiling that pose recurring social and constitutional concerns in the “new policing.”

Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition—mainly family groups composed of members with differing mobility levels—plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Geometric Collision Avoidance for Heterogeneous Crowd Simulation

Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestrians, and has application in areas such as games, movies, safety planning, and virtual environments. This dissertation presents new crowd simulation methods based on geometric techniques. I will show how geometric optimization techniques can be used to efficiently compute collision-avoidance constraints, and use these constraints to generate human-like trajectories in simulated environments. This process of reacting to the nearby environment is known as local navigation and it forms the basis for many crowd simulation techniques, including those described in this dissertation. Given the importance of local navigation computations, I devote much of this dissertation to the derivation, analysis, and implementation of new local navigation techniques. I discuss how to efficiently exploit parallelization features available on modern processors, and show how an efficient parallel implementation allows simulations of hundreds of thousands of agents in real time on many-core processors and tens of thousands of agents on multi-core CPUs. I analyze the macroscopic flows which arise from these geometric collision avoidance techniques and compare them to flows seen in real human crowds, both qualitatively (in terms of flow patterns) and quantitatively (in terms of flow rates). Building on the basis of these strong local navigation models, I further develop many important extensions to the simulation framework. Firstly, I develop a model for global navigation which allows for more complex scenarios by accounting for long-term planning around large obstacles or emergent congestion. Secondly, I demonstrate methods for using data-driven approaches to improve crowd simulations. These include using real-world data to automatically tune parameters, and using perceptual user study data to introduce behavioral variation. Finally, looking beyond geometric avoidance based crowd simulation methods, I discuss methods for objectively evaluating different crowd simulation strategies using statistical measures. Specifically, I focus on the problem of quantifying how closely a simulation approach matches real-world data. I propose a similarity metric that can be applied to a wide variety of simulation approaches and datasets. Taken together, the methods presented in this dissertation enable simulations of large, complex humans crowds with a level of realism and efficiency not previously possible.Doctor of Philosoph

Designing Agent-based Modeling in Dynamic Crowd Simulation for Stressful Environment

In recent years, modeling and simulation technologies have been gaining tremendous momentum in investigating crowd dynamics. Various simulation architectures have been developed and virtual environment representations have also been constructed for crowd simulations. To represent the behavior of a crowd, a number of behavior models have been proposed with different types of modeling approaches, such as flow-based models and agent-based models. Crowd models may also concern different aspects of a crowd. In modeling stress response, a method based on well-established theory of Generalized Adaptation Syndrome (GAS) has been developed to simulate the dynamic behavior of the crowd. However, there is still lacking of method to address the way virtual agent interacts with the instant changing behavior of the crowd during stressful events. This study were review current work on modelling stress and stress behavior models and extends it into the area of crowd simulation to simulate the behavior of the stress response of virtual agent during stressful events. It attempts to look into the solution of the problem and utilized a method based on the psychological theory of GAS to develop an algorithm for responsive virtual agent under stressful events by determining the dynamic behavior

Designing agent-based modeling in dynamic crowd simulation for stressful environment

In recent years, modeling and simulation technologies have been gaining tremendous momentum in investigating crowd dynamics.Various simulation architectures have been developed and virtual environment representations have also been constructed for crowd simulations.To represent the behavior of a crowd, a number of behavior models have been proposed with different types of modeling approaches, such as flow-based models and agent-based models.Crowd models may also concern different aspects of a crowd. In modeling stress response, a method based on well-established theory of Generalized Adaptation Syndrome (GAS) has been developed to simulate the dynamic behavior of the crowd.However, there is still lacking of method to address the way virtual agent interacts with the instant changing behavior of the crowd during stressful events.This study were review current work on modelling stress and stress behavior models and extends it into the area of crowd simulation to simulate the behavior of the stress response of virtual agent during stressful events.It attempts to look into the solution of the problem and utilized a method based on the psychological theory of GAS to develop an algorithm for responsive virtual agent under stressful events by determining the dynamic behavior