Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars

Malleson, Nicolas; See, Linda; Evans, Andrew; Heppenstall, Alison

doi:10.1007/978-3-642-39149-1_12

Cited by 9 publications

(10 citation statements)

References 62 publications

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“…This parameter adjustment process is often referred to as parameter calibration. Popular optimization techniques include simulated annealing [31], genetic algorithms, [32,33] and approximate Bayesian computation [34]. Parameter calibration, especially with ABMs, is often only implemented once, and therefore cannot account for any changes that may take place within the system during run time.…”

Section: Research Problem and Related Workmentioning

confidence: 99%

Dealing with uncertainty in agent-based models for short-term predictions

2020

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Agent-based models (ABM) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimised. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimised by a combination of parameter calibration and DA. The proposed model and framework can also be used in an passenger information system, or in an Intelligent Transport Systems to provide forecasts of bus locations and arrival times.A PREPRINT -AUGUST 23, 2019 varies over the route and with at off-peak to peak periods. There are methods to incorporate streaming data into models, such as data assimilation (DA) routines [26,45]. Broadly, DA refers to a suite of techniques that allow observational data to be incorporated into models [45] to provide an optimal estimate of the evolving state of the system. Performing DA increases the probability of having an accurate representation of the current state of the system, thereby reducing the uncertainty of future predictions. This is a technique that has been widely applied in fields such as meteorology, hydrology and oceanography [21].There are, however, two methodological challenges that must be overcome to apply DA in ABM. First, DA methods are often intrinsic to their underlying models which are typically systems of partial differential equations with functions linearised mathematically. Hence DA methods typically rely on linearising the underlying model [16]. One of the most appealing aspects of agent-based models is that they are inherently non-linear, so it is not clear whether the assumptions of traditional DA methods will hold. Second, it is still unknown how much uncertainty DA can effectively deal with when implemented within ABM. Assimilation of real-time data into ABMs has only been attempted a few times and these examples are limited by their simplicity [28,46,47]. This paper is part of a wider programme of work 2 that is focused on developing DA methods to be readily used in ABM. This paper focuses on one particular model that aims to make predictions of bus locations in real time. Bus route operation has been chosen due to its inherent uncertainties -for example a model will need to account for uncertain factors affecting how buses travel on the roads [22] -but also for its tractability -there are many fewer interactions than present in, say, a model of a crowd. We also focus on one particular DA algorithm -the Particle Filter (PF). This method is chosen due to its ability to incorporate data into non-linear models such as ABM...

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Section: Research Problem and Related Workmentioning

confidence: 99%

Dealing with uncertainty in agent-based models for short-term predictions

2020

Self Cite

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“…The most recent and complete work, [34][35][36][37][38][39] attempts to simulate burglary in the city of Leeds by means of ABM, accurately representing the environment and offender behavior. The agents can navigate the virtual environment and decide whether to burglar a property or not, depending on inner drives and believes, as well as on the characteristics of the property.…”

Section: Previous Abm To Predict Crimementioning

confidence: 99%

“…To summarize, the most promising ABM built to this date is the one from [34][35][36][37][38][39], being the only one with a rather realistic representation of the environment and with detailed offender architecture. Still, this work lacks a realistic implementation of agents in general, and does not use automated calibration of the model.…”

Section: Previous Abm To Predict Crimementioning

confidence: 99%

Design of an Agent-Based Model to Predict Crime (WIP)

Brüngger

Kadar

Cvijikj

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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In modern societies where fighting crime has a long history, establishing effective methods for crime prevention is of high significance. For this purpose, police departments worldwide undertake efforts to analyze past crime data. They aim to detect the most prolific crime areas and predict their development, in order to direct their prevention efforts. In parallel, scholars investigate possibilities to build crime prediction models, by applying various techniques, from simple regression to data mining. Acknowledging the latest advances in this field, which suggest that Agent-Based Modeling (ABM) is a promising method, in this paper we present the design of an ABM capable of predicting where and when future crimes will most probably happen. We extend the previous work by accounting for offender behavior and integrating a realistic representation of the environment. In contrast to existing models, past crime data will be included to achieve automatic calibration. Furthermore, we will assess how crime data can be used to model agent's behavior and we will include environmental data stepwise, in order to achieve the optimal balance between prediction accuracy and complexity. The resulting ABM will be developed as a crime prediction tool, and as an experimental environment to test prevention strategies.

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“…), but also in the social domain (i.e., people, economy, etc.) [ 25 , 26 ]. The properties of this geometry arise from the dynamical nature of cities.…”

Section: Introductionmentioning

confidence: 99%

Information Dynamics in Urban Crime

Melgarejo

Neira²

2018

Entropy

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Information production in both space and time has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems. However, information production in urban crime has barely been studied. This work copes with this problem by using multifractal analysis to characterize the spatial information scaling in urban crime reports and nonlinear processing tools to study the temporal behavior of this scaling. Our results suggest that information scaling in urban crime exhibits dynamics that evolve in low-dimensional chaotic attractors, and this can be observed in several spatio-temporal scales, although some of them are more favorable than others. This evidence has practical implications in terms of defining the characteristic scales to approach urban crime from available data and supporting theoretical perspectives about the complexity of urban crime.

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Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars

Cited by 9 publications

References 62 publications

Dealing with uncertainty in agent-based models for short-term predictions

Dealing with uncertainty in agent-based models for short-term predictions

Design of an Agent-Based Model to Predict Crime (WIP)

Information Dynamics in Urban Crime

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