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ProtestLab: A Computational Laboratory for Studying Street Protests
Abstract
We present an Agent-Based model called ProtestLab for the simulation of street protests, with multiple types of agents (protesters, police and ‘media’) and scenario features (attraction points, obstacles and entrances/exits). In ProtestLab agents can have multiple “personalities” (implemented via agent subtypes), goals and possible states, including violent confrontation. The model includes quantitative measures of emergent crowd patterns, protest intensity, police effectiveness and potential ‘news impact’, which can be used to compare simulation outputs with estimates from videos of real protests for parametrization and validation. ProtestLab was applied to a scenario of policemen defending a government building from protesters (typical of anti-austerity protests in front of the Parliament in Lisbon, Portugal) and reproduced many features observed in real events, such as clustering of ‘active’ and ‘violent’ protesters, formation of moving confrontation lines, occasional fights and arrests, ‘media’ agents wiggling around ‘hot spots’ and policemen with defensive or offensive behaviour.
... Also, studies based on socio-physical models have proposed that physical distance to the protest location acts as an impedance to attendance (Traag et al., 2017). Other studies have proposed that the relationship between environmental characteristics and the distribution of individuals across space is more complex and dynamic, prompting researchers to investigate the relationship using simulation models (Davies et al., 2013;Lemos et al., 2016;Pires and Crooks, 2017;Bacaksizlar, 2019), whose results are, therefore, of unknown ecological validity. ...
Although much has been published regarding street protests on social media, few works have attempted to characterize social media users' spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users' spatial detectability and density during a protest in Mexico City. The best-obtained model, together with explaining the spatial density of users, shows that there is high variability in the detectability of social media user protest supporters and that the collective posting rhythm and the day of observation are significant explanatory factors. The implication is that studies of collective spatial behavior would benefit by focussing on users' activity centres and their urban environment, rather than their physical proximity to the protest location, the latter being unable to adequately explain spatial variations in users' detectability and density during the protest event.
... Facsimile models attempt to reproduce a specific phenomenon as precisely as possible. These models are useful when predicting the future state of a phenomenon or when forecasting the effects of a policy [48]. Middle-range models fall between the abstract and facsimile ones with describing characteristics of social phenomenon. ...
This dissertation investigates emotional contagion in social movements within socialmedia platforms such as Twitter. The main research question is: How does a protestbehavior spread in social networks? The following sub-questions are: (a) What is thedynamic behind the anger contagion in online social networks? (b) What are the keyvariables for ensuring emotional spread? We gained access to Twitter data sets onprotests in Charlotte, NC (2016) and Charlottesville, VA (2017). Although these twoprotests differ in their triggering points, they have similarities in their macro behaviorsduring the peak protest times. To understand the influence of anger spread amongusers, we extracted user mention networks from the data sets. Most of the mentionedusers are influential ones, who have a significant number of followers. This shows thatinfluential users occur as the highest in-degree nodes in the core of the networks, anda change in these nodes affects all connected public users/nodes. Then, we examinedmodularity measures quite high within users own communities. After implementingthe networks, we ran experiments on the anger spread according to various theorieswith two main assumptions: (1) Anger is the triggering emotion for protests and (2)Twitter mentions affect distribution of influence in social networks. We found thatuser connections with directed links are essential for the spread of influence and anger;i.e., the angriest users are the most isolated ones with less number of followers, whichsignifies their low impact level in the network.
This work explores network science to understand and visualize the intricate interconnectivity within organizations. The age of big data emphasizes the importance of deriving new insights by transforming data into networks to study their connections. The document introduces a three-step maturity framework for navigating network science, starting with the basics of network construction, moving on to standard metrics, and culminating in an examination of network topology and dynamics. The author aims to clarify the subject and encourage further exploration, suggesting that while network science may not have all the answers, it offers a critical analytical framework.
Interdisciplinarity (Pombo, 2004a, b, 2005) refers to a method or mindset that merges concepts and methods in order to arrive at new approaches and solutions in scientific research and education. This convergence, along with problem-solving strategy, can be attained by mixing several scientific disciplines, namely when we face hard and complex problems.
Although much has been published regarding street protests on social media, few works have attempted to characterize social media users’ spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users’ spatial detectability and density during a protest in Mexico City. The best-obtained model, together with explaining the spatial density of users, shows that there is high variability in the probability of detection of social media user protest supporters and that the collective posting rhythm and the day of observation are significant explanatory factors. The implication is that studies of collective spatial behavior would benefit by focussing on users’ activity centres and their urban environment, rather than their physical proximity to the protest location, the latter being unable to adequately explain spatial variations in users’ probability of detection and density during the protest event.
After the involvement with a huge collection of case studies, where the experimentation may distinguish between luck and skill, our motivation was directed to see how agent-based modeling and model thinking were applied to general problem solving and case studies on complexity. Also, the development of models was directed to show the efficacy of diversity in attacking new scenarios and landscapes. And, even we avoided often Nassim Taleb’s mantra, “we tend to learn the overall precise and not the general”, the desire was to get realism (avoid embellished depiction of nature and behavior). This direction of research forced our attention upon the calibration of parameters, the validation, the use of mechanisms, the use of big data, and the activity of scaling up to check the plausibility of the outcomes.
The references on the principles and methodology for developing agent-based models of social phenomena usually describe general principles and illustrate the process using worked examples, but seldom focus on the pitfalls and errors that make practical model building a tortuous and difficult task. This chapter contains a discussion of the positive and negative aspects of my personal experience in a PhD work on simulation of large scale social conflict. The purpose will be to describe the process from the initial plan to the final dissertation, analyze the pitfalls and their overcoming in terms of principles of model development, and summarize the ideas that I found useful for practical development of agent-based models of social phenomena. The most serious pitfalls usually occur at the conception and design stages, when seemingly trivial points can be easily overlooked. These include starting with excessive ambition but unclear ideas on whether the purpose is understanding or prediction (i.e. what is the level of abstraction), poor knowledge of the relevant theories, and failure to identify which entities, variables and mechanisms must be considered. Several practical hints for avoiding these issues are presented, such as writing a reduced version of the “Overview, Design Concepts and Details” template that includes the bare minimum of items for a first working version, and devising efficient strategies for exploring the parameter space. This chapter will be of interest to MSc and PhD students working on social simulation, and to researchers developing projects on agent-based modeling of social phenomena, either individually or in teamwork.
We propose an innovative Agent-Based model of street protests with multiple actors: police agents, three types of protesters (" hardcore " , " hangers-on " and " passers-by "), and " media " agents that seek to witness and publish episodes and situations of violence. Agents have multiple goals and action selection is performed using a " personality " vector together with context rules that provide adaptation. Protesters turn active or violent according to the threshold rule proposed by Epstein, and police agents arrest violent protesters within their move range if they have sufficient backup. The model was applied to a scenario where policemen defend a government building from protesters and described several emergent crowd patterns in real protests, such as clustering of violent and active protesters and formation of a confrontation line moving back and forth with localized fights. Violent behavior was restricted to the initially more aggressive protesters and did not propagate to the bulk of the crowd.
This introductory article provides a short account of the theoretical framework and the methodological set-up of a comparative study of street demonstrations. The following articles in this issue report results from this study. The data on over 90 street demonstrations and more than 17,000 participants were collected between November 2009 and Summer 2012 in nine different countries: Belgium, Czech Republic, Italy, Mexico, the Netherlands, Spain, Sweden, Switzerland and the UK. This introductory article presents the focal theme of the project – the impact of contextual variation on mobilization dynamics, the composition of the crowd and the demonstrators’ motives. The various contextual layers we are distinguishing are discussed. Following the theoretical framework the methodological set-up is presented. Identical measures and procedures are employed in all individual protest surveys. This introduction discusses the sampling procedures; but as each article concerns a different set of measures, the selection employed is discussed in the individual articles. The introduction concludes with an overview of the articles included.
This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.
In August 2011, several areas of London experienced episodes of large-scale disorder, comprising looting, rioting and violence. Much subsequent discourse has questioned the adequacy of the police response, in terms of the resources available and strategies used. In this article, we present a mathematical model of the spatial development of the disorder, which can be used to examine the effect of varying policing arrangements. The model is capable of simulating the general emergent patterns of the events and focusses on three fundamental aspects: the apparently-contagious nature of participation; the distances travelled to riot locations; and the deterrent effect of policing. We demonstrate that the spatial configuration of London places some areas at naturally higher risk than others, highlighting the importance of spatial considerations when planning for such events. We also investigate the consequences of varying police numbers and reaction time, which has the potential to guide policy in this area.
The recent protests and uprisings in Tunisia and Egypt have both been called “Twitter revolutions” and “Facebook revolutions” due to the widespread use of user-generated content (UGC) disseminated over social networks like Facebook and Twitter by protesters, activists and supporters of the protests, as well as by those following the events around the globe. This report investigates the usage and role of UGC and social networking websites in the recent protests and uprisings in the Middle East and North Africa (MENA), as well as other cases outside of the region.
In addition to being effective tools for communication and coordination by protesters, UGC and social networking have also been used by governments in response to these protests, often to crack down on protesters. Content and social networking platforms are areas of contestation between protesters and governments not necessarily balanced in favour of protesters.
We perform analysis of data on crime and violence for 5,660 U.S. cities over the period of 2005-2009 and uncover the following trends: 1) The proportion of law enforcement officers required to maintain a steady low level of criminal activity increases with the size of the population of the city; 2) The number of criminal/violent events per 1,000 inhabitants of a city shows non-monotonic behavior with size of the population. We construct a dynamical model allowing for system-level, mechanistic understanding of these trends. In our model the level of rational behavior of individuals in the population is encoded into each citizen's perceived risk function. We find strong dependence on size of the population, which leads to partially irrational behavior on the part of citizens. The nature of violence changes from global outbursts of criminal/violent activity in small cities to spatio-temporally distributed, decentralized outbursts of activity in large cities, indicating that in order to maintain peace, bigger cities need larger ratio of law enforcement officers than smaller cities. We also observe existence of tipping points for communities of all sizes in the model: reducing the number of law enforcement officers below a critical level can rapidly increase the incidence of criminal/violent activity. Though surprising, these trends are in agreement with the data.
This paper focuses on the design and development of a spatial evolutionary multi-agent social network (EMAS) to investigate the underlying emergent macroscopic behavioral dynamics of civil violence, as a result of the microscopic local movement and game-theoretic interactions between multiple goal-oriented agents. Agents are modeled from multi-disciplinary perspectives and their behavioral strategies are evolved over time via collective co-evolution and independent learning. Experimental results reveal the onset of fascinating global emergent phenomenon as well as interesting patterns of group movement and behavioral development. Analysis of the results provides new insights into the intricate behavioral dynamics that arises in civil upheavals. Collectively, EMAS serves as a vehicle to facilitate the behavioral development of autonomous agents as well as a platform to verify the effectiveness of various violence management policies which is paramount to the mitigation of casualties.
Although pedestrians have individual preferences, aims, and destinations, the dynamics of pedestrian crowds is surprisingly predictable. Pedestrians can move freely only at small pedestrian densities. Otherwise their motion is affected by repulsive interactions with other pedestrians, giving rise to self-organization phenomena. Examples of the resulting patterns of motion are separate lanes of uniform walking direction in crowds of oppositely moving pedestrians or oscillations of the passing direction at bottlenecks. If pedestrians leave footprints on deformable ground (for example, in green spaces such as public parks) this additionally causes attractive interactions which are mediated by modifications of their environment. In such cases, systems of pedestrian trails will evolve over time. The corresponding computer simulations are a valuable tool for developing optimized pedestrian facilities and way systems.
Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren't allowed to 'push' each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density.
When two large groups of people meet in the same space, many outcomes are possible, depending on the types of groups and the occasion. These outcomes range from a peaceful mingling of the two groups to the occurrence of fights and riots. Because the group processes leading to these outcomes are difficult to study experimentally, we developed a multi-agent simulation model in which the approach-avoidance conflict has been formalised in simulated actors. We worked with three different kinds of agents: hardcore, hangers-on and bystanders, the difference between them consisting in the frequency with which they scan their surroundings. Data on clustering, and 'fights' are presented for 80 simulation runs, in which group size, size symmetry and group composition were varied. The conclusions are that fights especially happen in asymmetrical large groups with relatively large proportions of hardcore members. Moreover, it appears that it are especially the hardcore group and the hangers-on that attack other agents, whereas bystanders are The paper ends with a discussion on the validity of these findings, leading us to conclude that basic aspects of rioting behaviour are quite realistically represented in our simulation model.
The movement of crowds is a field of research that attracts increasing interest. This is due to three major reasons: pattern formation and selforganization processes that occur in crowd dynamics, the advancement of simulation techniques, and its applications (planning of pedestrian facilities, crowd management, or evacuation analysis).
In this thesis, a model for simulating crowd movement is developed and its characteristics investigated and compared to alternative approaches. Additionally, simulations of the evacuation of aircraft, buildings, and ships is presented.
Violence comes in many forms and occurs in many different circumstances for many different reasons. Is it really possible to develop a single theory that can explain all these disparate acts? In this paper, we argue it is. We will make the case that acts of violence are essentially moral actions and therefore can, and should, be analysed and explained as such. We will maintain that all acts of violence can be explained within the general framework of a theory of moral action. We will present just such a theory – Situational Action Theory – and demonstrate how it can be applied to the explanation and study of violence.
The dominant emotion in violence-threatening situations is confrontational tension/fear (ct/f), which causes most violence to abort, or to be carried out inaccurately and incompetently. For violence to be successful, there must be a pathway around the barrier of ct/f. These pathways include: attacking the weak; audience-oriented staged and controlled fair fights; confrontation-avoiding remote violence; confrontation-avoiding by deception; confrontation-avoiding by absorption in technique. Successfully violent persons, on both sides of the law, are those who have developed these skilled interactional techniques. Since successful violence involves dominating the emotional attention space, only a small proportion of persons can belong to the elite which does most of each type of violence. Macro-violence, including victory and defeat in war, and in struggles of paramilitaries and social movements, is shaped by both material resources and social/emotional resources for maintaining violent organizations and forcing their opponents into organizational breakdown. Social and emotional destruction generally precedes physical destruction.
It is suggested that the motion of pedestrians can be described as if they would be subject to `social forces'. These `forces' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically. Comment: For related work see http://www.theo2.physik.uni-stuttgart.de/helbing.html
Computational Social Science (CSS) models are most commonly used to articulate theories and explore their implications. As they become more mature, they are also valuable in monitoring real-world situations. Such applications require models to be linked to dynamic real-world data in real time. This paper explores this distinction in a specific application that tracks crowd violence in an urban setting.
In this work we describe the introduction of quantitative measures of emergent crowd patterns in an existing Agent-Based model (ABM) of street protests with multiple actors (police, protester and ‘media’ agents). The model was applied to a scenario of a police force defending a government building which protesters seek to invade. The improved model provided a coherent ‘narrative’ of the simulations and highlighted the realistic and unrealistic aspects of the agents' interactions. Two new types of police agents — ‘defensive’ and ‘offensive’ — were introduced, leading to a realistic model representation of police cordons defending a site or charging to disperse clusters of violent protesters. The new quantitative measures provided information on cluster size and orientation of clusters of violent protesters, as well as police coverage and protester breaching of the defensive perimeter, together with the time history of the bursts of localized fights and arrests. It was shown how the quantitative measures of the emergent properties can be used for both parameterization and validation of the model.
In this paper, we present a state-of-the-art review of Agent-based models (ABM) for simulation of social conflict phenomena, such as peaceful or violent street protests, civil violence and revolution. First, a simplified characterization of social conflict phenomena as emergent properties of a complex system is presented, together with a description of their macro and micro levels and the scales of the emergent properties. Then, existing ABM for simulation of crowd dynamics, civil violence and revolution are analyzed and compared, using a framework that considers their purpose/scope, environment representation, agent types and their architecture, the scales of the emergent properties, the qualitative and quantitative understanding of the phenomena provided by the results obtained from the models. We discuss the strengths and limitations of the existing models, as well as the promising lines of research for filling the gaps between the state-of-the-art models and real phenomena. This review is part of a work in progress on the assembling and dynamics of protests and civil violence, involving both simulation of the assembling process and the protest dynamics, as well as data collection in real protest events, and provides hints and guidelines for future developments.
In the social psychology literature, crowds are classified as audiences and mobs. Audiences are passive crowds, whereas mobs are active crowds with emotional, irrational and seemingly homogeneous behavior. In this study, we aim to create a system that enables the specification of different crowd types ranging from audiences to mobs. In order to achieve this goal we parametrize the common properties of mobs to create collective misbehavior. Because mobs are characterized by emotionality, we describe a framework that associates psychological components with individual agents comprising a crowd and yields emergent behaviors in the crowd as a whole. To explore the effectiveness of our framework we demonstrate two scenarios simulating the behavior of distinct mob types.
This paper presents a review of current research in the area of real-time crowd simulation. Crowd simulation has many diverse uses, for example in safety modelling, entertainment software, architecture and urban mod-elling applications. We describe three main approaches to the problem -fluid-based, cellular automata and particle-based, concentrating on the latter. Finally, we describe CrowdSim, a simple but effective implemen-tation of some of the techniques.
Simulation of human behavior in various situations is nowadays heavily used in miscellaneous research fields (e.g. emergency exits design, psychology, riot or humanitarian help simulation). The Holy Grail is to identify key elements of human beings that drive our behavior and be able to sufficiently simulate them and replicate in artificial computer environment. This work is focused on riot simulation in city conditions with multiple defense components. Behavior of agents/protesters is driven by PECS (Physical Conditions, Emotional State, Cognitive Capabilities and Social Status) psychological model and movement is adjusted by sophisticated collision avoidance system RVO2 (Reciprocal Collision Avoidance for RealTime Multi-Agent Simulation). Created simulation proposes several scenarios that resemble real demonstrations and can be parameterized for further studies.
Until recently many political scientists tended to regard violent civil conflict as a disfigurement of the body politic, neither a significant nor a proper topic for their empirical inquiries. The attitude was in part our legacy from Thomas Hobbes's contention that violence is the negation of political order, a subject fit less for study than for admonition. Moreover, neither the legalistic nor the institutional approaches that dominated traditional political science could provide much insight into group action that was regarded by definition as illegal and the antithesis of institutionalized political life.
Under some conditions, tensions among crowd members, harbored a priori or developed on site, might catalyze a crowd to riot, with dramatic consequences. We know perhaps less than we would like to about the processes that drive rioting in crowds because they are difficult to study. In particular, we know relatively little about the influence of geographic behavior on rioting, although there exists a general sense that it is important. In lieu of pragmatic avenues for studying riots, we could use simulation as a synthetic laboratory for exploration. To be useful, simulations must be based on realistic behavioral models, but the extant science for riot modeling has not traditionally provided that support. In this article, we introduce a new approach to modeling riot-prone and riotous crowds using behavior-driven computational agents. We demonstrate a simulation architecture based on socioemotional agents, modeled at atomic levels and characteristic times for riot activity, but extended with the use of geographical functionality that endows agents with spatial perception, cognition, and action that helps to determine where, when, how, and in what contexts and company their agency should be deployed and interpreted. In essence, our agents are polyspatial, with the ability to adapt their behavioral geography under shifting circumstances and to differentially process geographic information from diverse sources. We illustrate the usefulness of this scheme through simulation of a varying set of scenarios for riot formation, evolution, and dissolution, as well as in exploring the interplay among different characteristics, traits, and goals of riot participants.
PECS is a multi-purpose reference model for the simulati- on of human behaviour in a social environment. Particular emphasis is placed on emergent behaviour which is typical of the formation of groups and societies in social systems. Human behaviour is highly complex in its structure. It is influenced by physical, emotional, cognitive and social factors. The human being is consequently perceived as a psychosomatic unit with cognitive capacities who is em- bedded in a social environment. 1 THE PECS REFERENCE MODEL PECS is a reference model which makes it possible to spe- cify and to model these factors and their interactions. A detailed description of the PECS reference model and its underlying methodology including some basic examples can be found in (Schmidt 2000 & 2002).
Recent uprisings in the Arab world consist of individuals revealing vastly different preferences than were expressed prior to the uprisings. This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference revelation and falsification, social protest, and institutional change. We find that the amount of social and institutional change is decreasing with authority centralization in simulations with low network range but is increasing with authority centralization in simulations with greater network range. The relationship between institutional change and social shocks is not linear, but rather is characterized by sharp discontinuities. The threshold at which a shock can “tip” a system towards institutional change is decreasing with the geographic reach of citizen social networks. Farther reaching social networks reduce the robustness and resilience of central authorities to change. This helps explain why highly centralized regimes frequently attempt to restrict information flows via the media and Internet. More generally, our results highlight the role that information and communication technology can play in triggering cascades of preference revelation and revolutionary activity in varying institutional regimes.
This paper presents an agent-based model of worker protest. Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If workers are surrounded by more in-group members, they are more risk-taking; if surrounded by more out-group members, more risk-averse. Individual interest and group membership jointly affect patterns of workers protest: rhythm, frequency, strength, and duration of protest outbreaks. Results indicate that when wages are more unequally distributed, the previous outburst tends to suppress the next one, protests occur more frequently, and they become more intensive and persistent. Group identification does not seriously influence the frequency of local uprisings. Both their strength and duration, however, are negatively affected by the ingroup-outgroup assessment. The overall findings are valid when workers distinguish \'us\' from \'them\' through simple binary categorization, as well as when they perceive degrees of similarity and difference from their neighbors.
A feature shared by certain major revolutions is that they were not anticipated. Here is an explanation, which hinges on the observation that people who come to dislike their government are apt to hide their desire for change as long as the opposition seems weak. Because of this preference falsification, a government that appears unshakeable might see its support crumble following a slight surge in the opposition's apparent size, caused by events insignificant in and of themselves. Unlikely though the revolution may have appeared in foresight, it will in hindsight appear inevitable because its occurrence exposes a panoply of previously hidden conflicts.
This paper focuses on the development of a spatial evolutionary multiagent social network for studying the macroscopic-behavioral dynamics of civil violence, as a result of microscopic game-theoretic interactions between goal-oriented agents. Agents are modeled from multidisciplinary perspectives and their strategies are evolved over time via collective coevolution and independent learning. Spatial and temporal simulation results reveal fascinating global emergence phenomena and interesting patterns of group movement and autonomous behavioral development. Extensions of differing complexity are also used to investigate the impact of various decision parameters on the outcome of unrest. Analysis of the results provides new insights into the intricate dynamics of civil upheavals and serves as an avenue to gain a more holistic understanding of the fundamental nature of civil violence.
Description: Agent-based modeling (ABM) is a technique increasingly used in a broad range of social sciences. It involves building a computational model consisting of “agents,” each of which represents an actor in the social world, and an "environment" in which the agents act. Agents are able to interact with each other and are programmed to be pro-active, autonomous and able to perceive their virtual world. The techniques of ABM are derived from artificial intelligence and computer science, but are now being developed independently in research centers throughout the world. In Agent-Based Models, Nigel Gilbert reviews a range of examples of agent-based modeling, describes how to design and build your own models, and considers practical issues such as verification, validation, planning a modeling project, and how to structure a scholarly article reporting the results of agent-based modeling. It includes a glossary, an annotated list of resources, advice on which programming environment to use when creating agent-based models, and a worked, step-by-step example of the development of an ABM. This latest volume in the SAGE Quantitative Applications in the Social Sciences series will have wide appeal in the social sciences, including the disciplines of sociology, economics, social psychology, geography, economic history, science studies, and environmental studies. It is appropriate for graduate students, researchers and academics in these fields, for both those wanting to keep up with new developments in their fields and those who are considering using ABM for their research. Key Features - Aimed at readers who are new to ABM Offers a brief, but thorough, treatment of a cutting-edge technique - Offers practical advice about how to design and create ABM - Includes carefully chosen examples from different disciplines
This article presents an agent-based computational model of civil violence. Two variants of the civil violence model are presented. In the first a central authority seeks to suppress decentralized rebellion. In the second a central authority seeks to suppress communal violence between two warring ethnic groups.
Agent-based models (quantitative applications in the social sciences)
- N Gilbert
- N. Gilbert
Crowd Dynamics) Modeling geographic behavior in riotous crowds
- Gk Still
Still GK (2000) Crowd Dynamics. PhD thesis, University of Warwick
[31] Torrens PM, McDaniel AW (2013) Modeling geographic behavior in riotous
crowds. Annals of the Association of American Geographers 103(1):20-46
Understanding Crowd Behaviours, Volume 2-Supporting Theory and Evidence. The Stationery Office
TSO (2010) Understanding Crowd Behaviours, Volume 2-Supporting Theory and Evidence. The Stationery Office, London, Cabinet Office edn
Supporting Theory and Evidence. The Stationery Office
- Tso
TSO (2010) Understanding Crowd Behaviours, Volume 2 -Supporting Theory and Evidence. The Stationery Office, London, Cabinet Office edn
User-generated content and social networking in the Arab spring and beyond
- A Comninos
- A. Comninos
Self-organizing pedestrian movement. Environment and Planning B: Planning and Design
- D Helbing
- P Molnár
- I Farkas
- K Bolay
- D. Helbing