Agent-Based Modeling for Understanding Patterns of Crime

A field of study that constructs, analyzes, and tests models made of interacting entities in a given environment.

A simplified depiction of a real-world process that is implemented as a computer program, adopting a bottom-up strategy in which agents are imbued with unique traits and broad behavioral guidelines.

The use of ABMs to simulate social processes as the results of individual decisions and produce unexpected outcomes, improving our understanding of processes that are difficult to quantify directly.

The use of ABMs to forecast outcomes based on various inputs or behavioral norms, of significant interest to scientists due to their discovery and formalization value.

The process of formalizing crucial features of the theory to program an ABM.

The use of ABMs to examine theoretical mechanisms and reject plausible explanations that cannot be derived from individual behavior.

Individuals or groups with a collective identity that can be represented in an ABM, possessing properties such as autonomy, heterogeneity, and proactivity.

Groups of agents that share similar action capabilities in an ABM and are instantiated into objects with distinct properties during model execution.

The surroundings in which agents exist in an ABM, allowing for interaction with other agents and the ability to perceive and react to their surroundings.

The characteristics of agents in an ABM, such as age, inclination to commit crimes, and money.

Software packages that merge GIS and ABM to create a platform for the spatial and temporal modeling of individuals, allowing for the consideration of how real environmental characteristics influence the behaviors of spatially aware agents.

A tool constructed with Repast for Python Scripting and intended to be integrated as a toolbox to ArcGIS, utilizing the temporal and spatial capabilities of ABMs and GIS to collect data on the characteristics of each individual revealed during an interaction.

A model that encapsulates both the main constructs and their interrelationships, and is codified so that they may be written into the computer program.

Random numbers used in ABMs that provide numbers that conform to a statistical distribution, compensating for the less certain aspects of how a phenomenon "works."

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

Numbers generated by the RNG with an equal chance of being a certain value within a given range, used in ABMs to simulate specific situations an agent meets using mathematical formulae.

ABMs lack comprehensive top-down control methods. Each agent in the simulation independently perceives, argues, and acts. There is no centralized controller that regulates behavior.

ABMs imitate many agents, both within and between groups. Agents may use probabilistic or deterministic thinking. Capturing unit heterogeneity is crucial for studying real-world phenomena when unit homogeneity is rare.

ABMs depict creatures embedded in an abstract or realistic space, facilitating the formation of the concept of local interaction.

Agents' rational decision-making can be confined to localized, limited information. Rationality is restricted by decision-making knowledge. Agent behaviors can use bounded computation to avoid endlessly searching all possible actions to find the best solution.

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

______ 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.

It has been developed for communicating models to facilitate replication and evaluation. The first section of the it 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.