Data-driven diffusion modeling to examine deterrence

APSA 2010 Annual Meeting …, 2010

Which actions by governments stoke or pacify an insurgency? Scholarly research on the topic has often been relegated to the study of this question at the country level, comparing across large units and rarely looking inside the state. Our research focuses on the primary actors in a contest for authority within a state: the government, dissidents, and the population. In contrast to most previous work, we tackle the difficult question of how population dynamics affect the rise and fall of insurgency. We investigate the question in the context of India and its states and territories. India is particularly well suited to this research as it presently experiences terrorism, insurgency, ethnic conflict, riots and other actions that threaten the stability of the state. Using an agent-based model (ABM), geographic information systems (GIS), data on public sentiment, and events data, we address this question from a multidisciplinary approach. The agent-based model formalizes the interactions of states, dissidents, and the population, the GIS framework allows for actual demographic and geographic information to influence this interaction, and the events data and sentiment data allow us to test empirical implications from the model directly. As the NSF-Minerva grant is midstream, this paper reports initial work to couple the ABM and empirical analysis. We expect the results to have important implications for the study of political violence, order, and state-building. The approach is policy relevant, furthermore, and can be adapted to other regions and countries.

2011

Participants developed position papers in response to one or more of these themes, and we presented and discussed these papers at the workshop.

Purpose-The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm. Design/methodology/approach-To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP. Findings-The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises. Originality/value-The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.

Researchers and policy makers are increasingly turning to multi-agent and dynamic-network multi-agent models to study real-world systems. The models hold particular appeal because of their intuitive representation of complex real-world systems that can be thought of as "complex systems." Both policy makers and those affected by the policies influenced by these models often question whether a model is valid. We explore the intended use of these models, the extent to which they can be validated, and the consequent implications for their use in setting policy. We ground the analysis using a dynamic-network multi-agent model we are helping to develop called the Regional Threat Evaluator (RTE), applied to data from Indonesia and Thailand. We find that there are three core difficulties in validating these models: defining the appropriate operating domain, data availability, and validating a model that integrates multiple theories.

Social systems are complex structures that consist of different sub-systems. Therefore, understanding social systems are more difficult than comprehending electronic or mechanical systems. What makes social systems more complex than other systems is that society is not simply the sum of each individual in the society. In the current global system, the countries, which have become small villages, try to meet national security needs by converting the unknown to known and identifying the correlation among political, military, social and economic events. The current crisis management concepts are conducted through systematic approaches. Besides, the management of social, economic and political crises need to be conducted in a holistic approach covering all sub-systems. At this point, the function of Social Network Analysis (SNA) emerges. SNA, which forms the main subject of this paper, is a tool for examining the structure of a crisis through correlating the sub-elements. The aim of this study is to show how SNA can be used in crisis management. First, SNA is performed on a generic crisis situation and the results are presented. Then, the additional critical data requirements are put forward to manage the crisis effectively.

Journal of Genocide Research

Lecture Notes in Computer Science, 2018

Social media tools have changed our world due to the way they convey information between individuals; this has led to many social movements either starting on social media or being organised and managed through this medium. At times however, certain human-induced events can trigger Human Security Threats such as Personal Security, Health Security, Economic Security or Political Security. The aim of this paper is to propose a holistic Data Analysis Framework for examining Social Movements and detecting pernicious threats to National Security interests. As a result of this, the proposed framework focuses on three main stages of an event (Detonating Event, Warning Period and Crisis Interpretation) to provide timely additional insights, enabling policy makers, first responders, and authorities to determine the best course of action. The paper also outlines the possible computational techniques utilised to achieve in depth analysis at each stage. The robustness and effectiveness of the framework are demonstrated by dissecting Warning Period scenarios, from real-world events, where the increase of Human Security aspects were key to identifying likely threats to National Security.

2010

Social conflict entails a variety of social phenomena, including international conflict, civil war, genocide, organized violence, insurgencies and rebellions, terrorism, riots, etc. Given the heterogeneity of social phenomena encompassed by this notion, it is not surprising that a variety of methodological and theoretical approaches have been applied to study it, ranging from formal game theoretic models to the hermeneutics of narratives. Social conflict has also been studied by means of complex systems research methods, such as agent-based social simulation. We conduct a review of the main formal-theoretical approaches to social conflict including agent-based modeling. We promote the usage of agentbased social simulation for it affords shedding light onto the nature of generative processes related to social conflict. We discuss the implications of such an approach to the study of social conflict against orthodox research designs and point toward its advantages which may facilitate development of more adequate conflict prevention and conflict management procedures.

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.

2003

The reality of life is embedded in social networks. Understanding how social networks affect disease propagation and how the consequences of disease change social networks is critical for modeling early manifestation of diseases on diverse human activities. Moreover, disease outbreaks do not happen in vacuum, they are constrained by physical, economical, technological, media, health, and governmental infrastructures. It is critical to be able to systematically reason about the nature of outbreaks, early manifestation of diseases on diverse human activities, the potential of media and inoculation campaigns, and the relative value of various early warning devices. There is simply not enough actual data on bioattacks. What is needed is a cost-effective, ethical system for reasoning about such events. Focusing on sample-based statistical methods, conventional epidemiology ignores the reality of social networks and their dynamics, thus is illsuited for this. BioWar-a scalable city-wide multi-agent network modeladdresses the above needs. BioWar uses cognitively realistic agents embedded in social, health, and professional networks to describe how people acquire diseases, manifest symptoms, seek information, and seek care. We describe BioWar and present results showing the efficacy of the approach.