Date of Award

Fall 2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Modeling & Simulation Engineering

Program/Concentration

Modeling and Simulation

Committee Director

John Sokolowski

Committee Member

Roland Mielke

Committee Member

Bryan Paine

Abstract

Learning is the process of acquiring or modifying knowledge, behavior, or skills. The ability to learn is inherent to humans, animals, and plants, and even machines are provided with algorithms that could mimic in a restricted way the processes of learning. Humans learn from the time they are born until they die because of a continuous process of interaction between them and their environment. Behavioral Psychology Theories and Social Learning Theories study behavior learned from the environment and social interactions through stimulus-response. Some computer approaches to modeling human behavior attempted to represent the learning and decision-making processes using agent-based models.

This dissertation develops a computer model for social learning that allows agents to exhibit behavior learned through social interactions and their environment. The use of an agent-based model allows representing a complex human system in a computer environment. Behavioral Psychology Theories and Social Learning Theories provide the explanatory theoretical framework. The learning processes are implemented using the Rescorla-Wagner Model. The learning structure is implemented using an adaptation of Agent Zero. The decision-making process is implemented using a threshold equation. A use case in youth gang homicides is developed, calibrated, validated, and used for policing and decision making through simulation of multiple case scenarios. The simulation results show the model accuracy in representing learning and decision-making processes similar to those exhibited in the complex human system represented.

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DOI

10.25776/ppbs-8751

ISBN

9781392734704

ORCID

0000-0003-3092-4696

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