An Agent-Based Model of Mortality Shocks, Intergenerational Effects, and Urban Crime

Rational criminals choose crime over lawfulness because it pays better; hence poverty correlates to criminal behavior. This correlation is an insufficient historical explanation. An agent-based model of urban crime, mortality, and exogenous population shocks supplements the standard economic story, closing the gap with an empirical reality that often breaks from trend. Agent decision making within the model is built around a career maximization function, with life expectancy as the key independent variable. Rational choice takes the form of a local information heuristic, resulting in subjectively rational suboptimal decision making. The effects of population shocks are explored using the Crime and Mortality Simulation (CAMSIM), with effects demonstrated to persist across generations. Past social trauma are found to lead to higher crime rates which subsequently decline as the effect degrades, though \'aftershocks\' are often experienced.Agent-Based Model, Crime, Bounded Rationality, Life Expectancy, Rational Choice

Social Simulation and Analysis of the Dynamics of Criminal Hot Spots

Within the field of Criminology, the spatio-temporal dynamics of crime are an important subject of study. In this area, typical questions are how the behaviour of offenders, targets, and guardians can be explained and predicted, as well as the emergence and displacement of criminal hot spots. In this article we present a combination of software tools that can be used as an experimental environment to address such questions. In particular, these tools comprise an agent-based simulation model, a verification tool, and a visualisation tool. The agent-based simulation model specifically focuses on the interplay between hot spots and reputation. Using this environment, a large number of simulation runs have been performed, of which results have been formally analysed. Based on these results, we argue that the presented environment offers a valuable approach to analyse the dynamics of criminal hot spots.Agent-Based Modelling, Criminal Hot Spots, Displacement, Reputation, Social Simulation, Analysis

An open-data, agent-based model of alcohol related crime

The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically accumulated at all. We present a graph-constrained agent based simulation model of alcohol-related violent crime that is capable of predicting areas of likely violent crime without requiring any historical data. The only inputs to our simulation are publicly available geographical data, which makes our method immediately applicable to a wide range of tasks, such as optimal city planning, police patrol optimisation, devising alcohol licensing policies. In experiments, we evaluate our model and demonstrate agreement of our model's predictions on where and when violence will occur with real-world violent crime data. Analyses indicate that our agent based model may be able to make a significant contribution to attempts to prevent violence through deterrence or by design

Social Simulation and Analysis of the Dynamics of Criminal Hot Spots

Contains fulltext : 194199.pdf (publisher's version ) (Open Access)Within the field of Criminology, the spatio-temporal dynamics of crime are an important subject of study. In this area, typical questions are how the behaviour of offenders, targets, and guardians can be explained and predicted, as well as the emergence and displacement of criminal hot spots. In this article we present a combination of software tools that can be used as an experimental environment to address such questions. In particular, these tools comprise an agent-based simulation model, a verification tool, and a visualisation tool. The agent-based simulation model specifically focuses on the interplay between hot spots and reputation. Using this environment, a large number of simulation runs have been performed, of which results have been formally analysed. Based on these results, we argue that the presented environment offers a valuable approach to analyse the dynamics of criminal hot spots.19 p

Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios

Scenario-based Bayesian networks (BNs) have been proposed as a tool for the rational handling of evidence. The proper evaluation of existing methods requires access to a ground truth that can be used to test the quality and usefulness of a BN model of a crime. However, that would require a full probability distribution over all relevant variables used in the model, which is in practice not available. In this paper, we use an agent-based simulation as a proxy for the ground truth for the evaluation of BN models as tools for the rational handling of evidence. We use fictional crime scenarios as a background. First, we design manually constructed BNs using existing design methods in order to model example crime scenarios. Second, we build an agent-based simulation covering the scenarios of criminal and non-criminal behavior. Third, we algorithmically determine BNs using statistics collected experimentally from the agent-based simulation that represents the ground truth. Finally, we compare the manual, scenario-based BNs to the algorithmic BNs by comparing the posterior probability distribution over outcomes of the network to the ground-truth frequency distribution over those outcomes in the simulation, across all evidence valuations. We find that both manual BNs and algorithmic BNs perform similarly well: they are good reflections of the ground truth in most of the evidence valuations. Using ABMs as a ground truth can be a tool to investigate Bayesian Networks and their design methods, especially under circumstances that are implausible in real-life criminal cases, such as full probabilistic information

Freshman Class Analysis using AgentSheets

The 2005 CMST Challenge Project is to survey the ninth grade class at East High School to see how many of their lives are affected by: pregnancy, drug use, crime arrests, family structure, student employment, and value of education. It was very hard to find the percentages of these factors for the United States. Therefore only certain questions will be used in the simulation. The results of the survey will be run through an agent sheet simulation to estimate the number of students that will successfully graduate using the data collected from the 2004/2005 freshman class. In order to successfully simulate the passing rate of the 2004/2005 freshman class we researched teen mothers, drug abusers, criminal offenders, students with broken homes, and student employees and found out the percentage rate in the United States is for dropping out of high school based on these risk factors. Once we have researched and found the percentage rate for all of these factors we can then go ahead with collecting and organize the data the East High School freshman class. The students that are working with us on the challenge project will enter the data in excel. They can then use excel to graph the results and tabulate quantities for the risk factors. Once percentages have been researched for each risk factor, an agent sheet will be set up setting each risk factor as a disease decreasing chance of survival or in this case graduating. The CMST challenge project that we have chosen is to predict how many of the 2004/2005 freshman class will end up graduating based on pregnancy, drug use, crime arrests, family structure, and student employment. Once we have found the percentages for all risk factors our students will use excel and agent sheets to predict the graduating rate of the 2004/2005 freshman class. We choose to use agent sheets because agents sheets gave us the best representation of students as characters. We could also enter each different combination of risk factors as a separate agent. This allowed each survey response to be represented accurately on the floor as an agent. We were also able to program in the percentages for drop out as a characteristic for each agent