Overview

• Agent-based modeling: It is a sort of computer simulation modeling that permits the investigation of dynamic processes and the results that result from individual decisions.

• It is an abbreviated version of "real life" that includes only the most crucial parts of the behavior being replicated.

• ABMs enable researchers to perform "what if" experiments by allowing group-level results to emerge from individual decision-making.

• The bottom-up technique, as opposed to the top-down approach of conventional statistical models, enables researchers to investigate and strengthen theory as well as discover fresh insights.

Fundamentals of Agent-Based Modeling

Background and Basic Definitions

• Computer simulation: It is a comprehensive field of study that enables researchers to construct, analyze, and test models made of interacting entities in a given environment.

• Agent-based modeling (ABM): It is a simplified depiction of a real-world process that is implemented as a computer program.

  • ABMs adopt a bottom-up strategy in which agents are imbued with unique traits and broad behavioral guidelines.

  • ABMs are useful for simulating social processes as the results of individual decisions and are capable of producing unexpected outcomes.

  • These can improve our understanding of processes that are difficult to quantify directly, such as the decision-making of offenders.

• Another application of ABMs is prediction.

  • ABMs are utilized to forecast outcomes on the basis of various inputs or behavioral norms. T

  • hey are of significant interest to scientists due to their discovery and formalization value.

• In order to program an ABM, the scientist must formalize crucial features of the theory.

  • Some have claimed that ABMs can be used to examine theoretical mechanisms and reject plausible explanations that cannot be derived from individual behavior.

Components of an ABM

• Agents and Their Characteristics

  • Individuals, such as potential offenders or police officers, as well as groups with a collective identity can be represented by agents.

  • A number of properties are shared by agents, including autonomy, heterogeneity, and proactivity.

  • They have a distinct collection of features, including ethnicity, gender, age, and years of experience on the job.

  • They are capable of interacting with other agents, perceiving and reacting to their surroundings, and their actions frequently involve movement. For instance, a police officer agent may patrol her designated beat utilizing a random pattern, but a possible offender may engage in a variety of typical activities.

  • A set of rules governs their behavior and interactions with other agents and their surroundings.

  • Classes identify groups of agents that share similar action capabilities.

  • Attributes are the characteristics of agents, such as age, inclination to commit crimes, and money.

  • Methods are the actions that class members can perform (e.g., movement, making an arrest, committing a crime).

  • During model execution, classes are instantiated into objects that represent individual agents with distinct properties.

• Environments in ABM

  • Agents exist in an environment that permits interaction with one another and their surroundings.

  • There are a variety of habitats that can be utilized, and the sort of environment chosen depends on the phenomenon being mimicked.

  • Agents can be connected via a social network and geometric spaces can be represented as grids or networks.

  • Placing agents in "real" contexts improves models that utilize geographic space.

  • Repast and ArcGIS are software packages that merge GIS and ABM to create a platform for the spatial and temporal modeling of individuals.

  • Agent Analyst is constructed with Repast for Python Scripting (RepastPy) and is intended to be integrated as a toolbox to ArcGIS.

    • It utilizes the temporal capabilities of ABMs and the spatial functionality of GIS to collect data on the characteristics of each individual reveal during an interaction

    • It randomly assign qualities to agents

    • It make agents create independent decisions within control the behavior, and systematically vary one attribute while keeping the others constant in order to conduct controlled, repeatable experiments.

    • GIS allows for the consideration of how the characteristics of the real environment (such as transportation networks and land use) influence the behaviors of spatially aware agents.

ABM as Methodology

• ABM is a research methodology that, along with deduction and induction, represents a third sort of scientific inquiry.

• It can incorporate both natural language and quantitative representations of behavior and relationships.

• It permits the researcher to investigate "bottom-up" processes including the interactions of heterogeneous individuals with one another and their environment.

• A key tenet of ABM is that "simpler is better."

• Modelers seek to create a model that is as simple as feasible while retaining the most essential characteristics of the phenomenon being modeled.

Building an ABM

• ABMs are utilized to explore regularities in observable societal or macro-level patterns of behavior.

• The primary duty is to identify an issue, hypothesis, or topic that requires resolution, testing, or resolution.

• The second step is to investigate relevant current theories that explain the phenomena of interest.

  • The modeler then develops a conceptual model that encapsulates both the main constructs and their interrelationships.

  • At this step, the theory's constructs are codified so that they may be written into the computer program.

• The model is then programmed.

  • ABMs are formalized as spoken rules that govern agent behavior, agent interactions, and agent interactions with the environment.

• During a simulation, specific situations an agent meets are evaluated using mathematical formulae.

• A key component of ABMs, random numbers provide numbers that conform to a statistical distribution.

  • The seed, or initial number for the RNG, generates a collection of random numbers whenever the same seed number is utilized, enabling experiments to be conducted in ABMs.

• The A 36 Agent-Based Modeling for Understanding Crime Patterns model implies that the same seed or sequence of seeds are utilized for each experiment.

  • Seeds can be systematically modified throughout multiple runs, with the outcomes of those runs then being averaged before being presented as model results.

  • RNGs are used to compensate for the less certain aspects of how a phenomenon "works."

  • When investigating guardianship, for instance, we can use a uniform random number generator with a range of 1 to 5.

  • Each new number generated by the RNG has an equal chance of being a 1, 2, 3, 4, or 5.

Verification, Validation, and Sensitivity Analysis

• Model verification involves debugging and logic testing to ensure that the interactions generated by the code conform to the theory.

• Model validation is analogous to external validity requirements utilized in conventional modeling.

• The objective of sensitivity analysis is to determine whether the parameter values used to reflect the model's assumptions affect the model's output.

• Adjusting parameter values and seeing if the overall results change allows for sensitivity testing.

• Validation experiments reveal that the model's conclusions share stylistic characteristics with empirical findings.

• Establishing model credibility is an iterative process that requires multiple comparisons; it is not an exact science.

Communication for Replication and Evaluation

• One of the most challenging yet important stages of ABM is sharing the model with other researchers.

• This requires in-depth and complete description of the model which is difficult to accomplish in the space available in the typical journal.

• A template called the Overview, Design concepts, and Details (ODD) protocol has been developed for communicating models to facilitate replication and evaluation (Grimm and Railsback 2012).

  • The first section of the ODD describes the focus of the model.

  • The next section describes how the model implements ten core design elements including emergence, adaptation, objectives, learning, prediction, sensing, interaction, stochasticity, collectives, and observation.

  • The final section contains all the information necessary to replicate the model.

• This protocol is becoming more widely used which should facilitate replication and evaluation of ABMs (Grimm and Railsback 2012).

When to Use an ABM

• ABMs can be used as exploratory tools to conduct field experiments, such as when ethical considerations restrict random assignment of individuals to treatment and control conditions or when it is impractical to alter the attribute of interest.

• ABMs can also be used to evaluate potentially costly or intrusive crime-prevention measures prior to adoption, and their mix of heterogeneous agents and complete control enables testing of a variety of crime-prevention programs and evaluation of results at minimal cost.

• ABMs are often complementary to conventional empirical research methods.

Modeling the Process and Structure of Crime

• ABMs have been applied to the study of various sorts of crime, including residential burglary, business robbery, street robbery, fraud, heroin use, drug markets, and crime in general.

• ABMs may represent both the process and structure of events, taking into consideration the interactions between actors and the decision-making procedure.

• These can also include sociological structures, such as unemployment, poverty, and education, as well as physical structures, such as transportation and land usage.

Future Directions and Challenges

• ABM is a viable alternative method for investigating how individual/micro-level activities cause group/macro-level phenomena over time.

• However, there are considerable obstacles to employing the methodology, including a high learning curve and the necessity for programming skills to implement models in existing software.

• Validation of ABMs is typically difficult, and the use of ABMs to analyze crime must rely on outcome data with widely acknowledged flaws.

• ABM can be used to assess the potential benefit of situational crime prevention approaches for crime prevention.

• It is impossible to determine whether the ABM's crime pattern is inaccurate or reflects the "real" proportion of crimes committed.

• The decisions made by non-offenders, such as potential victims, intimate handlers, and place managers, are crucial to comprehending why crime occurs in certain situations but not others.

• Given sufficiently robust theories of why a specific crime happens in a particular location, the options for testing crime-prevention techniques in a relatively low-cost setting such as an ABM are limited only by the researchers' imagination.