Developing novel optimization and machine learning frameworks to improve and assess the safety of workplaces

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model\u27s robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace\u27s safety under an active shooter incident

Uncertainty and transparency:augmenting modelling and prediction for crisis response

Emergencies are characterised by uncertainty. This motivates the design of information systems that model and predict complex natural, material or human processes to support understanding and reduce uncertainty through prediction. The correspondence between system models and reality, however, is also governed by uncertainties, and designers have developed methods to render ‘the world’ transparent in ways that can inform, fine-tune and validate models. Additionally, people experience uncertainties in their use of simulation and prediction systems. This is a major obstacle to effective utilisation. We discuss ethically and socially motivated demands for transparency

Assessing, Managing, and Financing Extreme Events: Dealing with Terrorism

This paper discusses new challenges we face with terrorism as a catastrophic risk by focusing on risk assessment, risk management as well as risk financing issues. The special characteristics of terrorism compared with major natural hazards call for the development of public-private partnerships, as recognized in November 2002 when the Terrorism Risk Insurance Act of 2002 (TRIA) was passed. This paper shows, however, that the temporary insurance system established by TRIA is neither a complete answer nor a definitive one. It raises fundamental questions for U.S. insurers as to how they will estimate the risk in order to set premiums for terrorist coverage that they now must offer to their clients. We discuss some of the most recent developments of terrorism models for helping insurers and reinsurers assess the premiums they should charge and how much coverage they can assume as well as for firms to better understand their exposure. Since the passage of TRIA, the current level of demand for insurance coverage has remained low and we discuss some factors that may contribute to it. After presenting alternative foreign public-private partnerships and discussing the potential role for terrorist catastrophe bonds, we provide some features of a more sustainable program for terrorism insurance in the U.S. after December 31, 2005.

Expanding the Gordon-Loeb Model to Cyber-Insurance

We present an economic model for decisions on competing cyber-security and cyber-insurance investment based on the Gordon-Loeb model for investment in information security. We consider a one-period scenario in which a firm may invest in information security measures to reduce the probability of a breach, in cyber-insurance or in a combination of both. The optimal combination of investment and insurance under the assumptions of the Gordon-Loeb model is investigated via consideration of the costs and benefits of investment in security alongside purchasing insurance at an independent premium rate. Under both exponential (constant absolute risk aversion) and logarithmic (constant relative risk aversion) utility functions it is found that when the insurance premium is below a certain value, utility is maximised with insurance and security investment. These results suggest that cyber-insurance is a worthwhile undertaking provided it is not overly costly. We believe this model to be the first attempt to integrate the Gordon-Loeb model into a classical microeconomic analysis of insurance, particularly using the Gordon-Loeb security breach functions to determine the probability of an insurance claim. The model follows the tradition of the Gordon-Loeb model in being accessible to practitioners and decision makers in information security

Asymmetrical deterrence for NBC terrorism

Cataloged from PDF version of article.The aim of this thesis is to analyze the framework of deterrence theory whether it may be suited to the Nuclear, Biological and Chemical (NBC) terrorism as an asymmetrical threat. As a methodology, mainly qualitative means were applied. This thesis will argue that though the classical deterrence theory was primarily created for inter-state relations, its main premises and newly transformed features –due to the post-cold war era- can be applied on the asymmetrical relations between states and terrorist organizations which would initiate to use NBC material in particular. In the analysis of the problem of managing asymmetrical deterrence through revisiting orthodox ground of deterrence; the nature of the new threat and critics of classical theory of deterrence were discussed together to shape a unique asymmetrical deterrence. In conclusion, this thesis was finalized with the argument that to overcome the deficiencies of prevention models against asymmetrical threats as well as to remove obstacles for conducting a feasible deterrence theory against asymmetrical threats; benefiting from the deconstruction of classical deterrence theory is necessary in terms of recalling the concepts of rationality, capability and credibility.Ece, BerkM.S

Three essays on political economy and economic development

This thesis consists of three independent chapters. The first chapter examines the strategic choices of the targets and the intensity of violence by rebel groups. The chapter presents a theoretical framework that links a rebel group’s targeting decisions to income shocks. It highlights that this relationship depends on the structure of the rebels’ tax base. The hypotheses from the model are tested in the context of India’s Naxalite conflict. The second chapter estimates the impact of military recruitment on human capital accumulation in colonial Punjab. In this context, I find that higher military recruitment was associated with increased literacy at the district-religion level. The final chapter presents a model that describes the optimal design of civil-military institutions in a setting where some control of the military over domestic politics is deemed desirable

Dead Letter Law Arising from Strategic Choices: The Difficulty of Achieving Accountability for the Jus in Bello Rules on Proportionality and Precautions in Attack

The jus in bello proportionality rule establishes an upper boundary on how much collateral damage combatants can cause whilst striking a lawful target and its associated rule on precautions in attack compels them to take all feasible measures to properly understand the situation on the ground and to mitigate civilian harm. Proportionality and precautions in attack have been codified in API for over forty years, but in that time, it has been difficult to hold troops and their leaders accountable for breaches of these rules. In this study, I examine several reasons for why these rules have been difficult to apply ex post by considering the strategic motivations of state officials and prosecutors. Specifically, I propose a game-theoretic model which describes the decisions that state officials and prosecutors have historically made, and I explore what changes to this model would prompt these actors to behave differently. The model was developed using insights gained from legal case studies, archival research and a series of interviews with relevant actors. It suggests, inter alia, that to induce state officials to support a stricter liability standard for unlawful attacks, they must either ascribe much more value to legitimacy than to the success of future military operations, or they must perceive the success of future military operations to be unaffected by the possibility of losing criminal or civil adjudication. State officials may perceive losing a civil case based on state liability as being less likely to affect the success of future military operations compared with criminal liability against individual troops. Therefore, state officials may be inclined to support a stricter civil liability standard, if they believed it would help the state to secure greater legitimacy

Dead Letter Law Arising from Strategic Choices: The Difficulty of Achieving Accountability for the Jus in Bello Rules on Proportionality and Precautions in Attack

The jus in bello proportionality rule establishes an upper boundary on how much collateral damage combatants can cause whilst striking a lawful target and its associated rule on precautions in attack compels them to take all feasible measures to properly understand the situation on the ground and to mitigate civilian harm. Proportionality and precautions in attack have been codified in API for over forty years, but in that time, it has been difficult to hold troops and their leaders accountable for breaches of these rules. In this study, I examine several reasons for why these rules have been difficult to apply ex post by considering the strategic motivations of state officials and prosecutors. Specifically, I propose a game-theoretic model which describes the decisions that state officials and prosecutors have historically made, and I explore what changes to this model would prompt these actors to behave differently. The model was developed using insights gained from legal case studies, archival research and a series of interviews with relevant actors. It suggests, inter alia, that to induce state officials to support a stricter liability standard for unlawful attacks, they must either ascribe much more value to legitimacy than to the success of future military operations, or they must perceive the success of future military operations to be unaffected by the possibility of losing criminal or civil adjudication. State officials may perceive losing a civil case based on state liability as being less likely to affect the success of future military operations compared with criminal liability against individual troops. Therefore, state officials may be inclined to support a stricter civil liability standard, if they believed it would help the state to secure greater legitimacy

Assessing Terrorist Motivations for Attacking Critical Infrastructure

Certain types of infrastructure--critical infrastructure (CI)--play vital roles in underpinning our economy, security and way of life. These complex and often interconnected systems have become so ubiquitous and essential to day-to-day life that they are easily taken for granted. Often it is only when the important services provided by such infrastructure are interrupted--when we lose easy access to electricity, health care, telecommunications, transportation or water, for example--that we are conscious of our great dependence on these networks and of the vulnerabilities that stem from such dependence. Unfortunately, it must be assumed that many terrorists are all too aware that CI facilities pose high-value targets that, if successfully attacked, have the potential to dramatically disrupt the normal rhythm of society, cause public fear and intimidation, and generate significant publicity. Indeed, revelations emerging at the time of this writing about Al Qaida's efforts to prepare for possible attacks on major financial facilities in New York, New Jersey, and the District of Columbia remind us just how real and immediate such threats to CI may be. Simply being aware that our nation's critical infrastructure presents terrorists with a plethora of targets, however, does little to mitigate the dangers of CI attacks. In order to prevent and preempt such terrorist acts, better understanding of the threats and vulnerabilities relating to critical infrastructure is required. The Center for Nonproliferation Studies (CNS) presents this document as both a contribution to the understanding of such threats and an initial effort at ''operationalizing'' its findings for use by analysts who work on issues of critical infrastructure protection. Specifically, this study focuses on a subsidiary aspect of CI threat assessment that has thus far remained largely unaddressed by contemporary terrorism research: the motivations and related factors that determine whether a terrorist organization will attack critical infrastructure. In other words, this research investigates: (1) why terrorists choose to attack critical infrastructure rather than other targets; (2) how groups make such decisions; (3) what, if any, types of groups are most inclined to attack critical infrastructure targets; and (4) which types of critical infrastructure terrorists prefer to attack and why. In an effort to address the above questions as comprehensively as possible, the project team employed four discrete investigative approaches in its research design. These include: (1) a review of existing terrorism and threat assessment literature to glean expert consensus regarding terrorist target selection, as well as to identify theoretical approaches that might be valuable to analysts and decision-makers who are seeking to understand such terrorist group decision-making processes; (2) the preparation of several concise case studies to help identify internal group factors and contextual influences that have played significant roles in leading some terrorist groups to attack critical infrastructure; (3) the creation of a new database--the Critical Infrastructure Terrorist Incident Catalog (CrITC)--to capture a large sample of empirical CI attack data that might be used to illuminate the nature of such attacks to date; and (4) the development of a new analytical framework--the Determinants Effecting Critical Infrastructure Decisions (DECIDe) Framework--designed to make the factors and dynamics identified by the study more ''usable'' in any future efforts to assess terrorist intentions to target critical infrastructure. Although each is addressed separately in the following chapters, none of the four aspects of this study were developed in isolation. Rather, all the constituent elements of the project informed--and were informed by--the others. For example, the review of the available literature on terrorist target selection made possible the identification of several target selection factors that were both important in the development of the analytical framework and subsequently validated by the case studies. Similarly, statistical analysis of the CrITIC data yielded measurable evidence that supported hypotheses derived from the framework, the case studies, and the writings of various experts. Besides providing an important mechanism of self-reinforcement and validation, the project's multifaceted nature made it possible to discern aspects of CI attack motivations that would likely have been missed if any single approach had been adopted