Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making

Shaheen A. Abdulkareem

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

67 Downloads (Pure)

Abstract

Complexity in human behaviour can play a crucial role in socio-environmental processes like disease diffusion. An example of such complex behaviour is risk perception, and behavioural change due to perceived risk. Computational models, and in particular Agent-based models (ABMs), have evolved as tools for simulating complex real-world processes.
ABMs for describing and simulating a system composed of behavioural entities, ABMs provide the most natural environment. ABMs often use naive deterministic algorithms, which are rule-based, to simulate behavioural change in agents. While agents in ABMs are sometimes endowed with memory, the actual learning in machine learning style is rarely implemented. The endogenous switching of expectations formation strategies using learning algorithm is underdeveloped in ABMs.
The goal of my PhD research is to systematically test the effects of implementing social and environmental intelligence on the dynamics and emergent outcomes of spatial ABM. Spatial ABMs often use spatial data (GIS data) to construct real geographic environments in which agents are situated. Agents need to take changes in the spatial environment into account and adjust their behaviour accordingly. In this PhD research, intelligence, rational, and risk perception are playing an important role in the decision making of agents. Understanding the learning processes of agents in the spatial ABM can assist developing better strategies in problem-solving and coordination mechanisms.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Department of Governance and Technology for Sustainability
Supervisors/Advisors
  • Filatova, Tatiana , Supervisor
  • Mustafa, Yaseen Taha, Co-Supervisor
  • Augustijn, Ellen-Wien, Co-Supervisor
Award date10 Apr 2019
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4748-2
DOIs
Publication statusPublished - 10 Apr 2019

Fingerprint

artificial intelligence
decision making
risk perception
learning
human behavior
spatial data
GIS

Keywords

  • Risk perception
  • Machine learning
  • Bayesian networks
  • Infectious diseases
  • Adaptation
  • Human behaviour

Cite this

Abdulkareem, Shaheen A.. / Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making. Enschede : University of Twente, 2019. 195 p.
@phdthesis{7c9c61ea6f16439b889e945d4c227632,
title = "Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making",
abstract = "Complexity in human behaviour can play a crucial role in socio-environmental processes like disease diffusion. An example of such complex behaviour is risk perception, and behavioural change due to perceived risk. Computational models, and in particular Agent-based models (ABMs), have evolved as tools for simulating complex real-world processes. ABMs for describing and simulating a system composed of behavioural entities, ABMs provide the most natural environment. ABMs often use naive deterministic algorithms, which are rule-based, to simulate behavioural change in agents. While agents in ABMs are sometimes endowed with memory, the actual learning in machine learning style is rarely implemented. The endogenous switching of expectations formation strategies using learning algorithm is underdeveloped in ABMs.The goal of my PhD research is to systematically test the effects of implementing social and environmental intelligence on the dynamics and emergent outcomes of spatial ABM. Spatial ABMs often use spatial data (GIS data) to construct real geographic environments in which agents are situated. Agents need to take changes in the spatial environment into account and adjust their behaviour accordingly. In this PhD research, intelligence, rational, and risk perception are playing an important role in the decision making of agents. Understanding the learning processes of agents in the spatial ABM can assist developing better strategies in problem-solving and coordination mechanisms.",
keywords = "Risk perception, Machine learning, Bayesian networks, Infectious diseases, Adaptation, Human behaviour",
author = "Abdulkareem, {Shaheen A.}",
year = "2019",
month = "4",
day = "10",
doi = "10.3990/1.9789036547482",
language = "English",
isbn = "978-90-365-4748-2",
publisher = "University of Twente",
address = "Netherlands",
school = "Department of Governance and Technology for Sustainability",

}

Abdulkareem, SA 2019, 'Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making', Doctor of Philosophy, Department of Governance and Technology for Sustainability, Enschede. https://doi.org/10.3990/1.9789036547482

Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making. / Abdulkareem, Shaheen A.

Enschede : University of Twente, 2019. 195 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

TY - THES

T1 - Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making

AU - Abdulkareem, Shaheen A.

PY - 2019/4/10

Y1 - 2019/4/10

N2 - Complexity in human behaviour can play a crucial role in socio-environmental processes like disease diffusion. An example of such complex behaviour is risk perception, and behavioural change due to perceived risk. Computational models, and in particular Agent-based models (ABMs), have evolved as tools for simulating complex real-world processes. ABMs for describing and simulating a system composed of behavioural entities, ABMs provide the most natural environment. ABMs often use naive deterministic algorithms, which are rule-based, to simulate behavioural change in agents. While agents in ABMs are sometimes endowed with memory, the actual learning in machine learning style is rarely implemented. The endogenous switching of expectations formation strategies using learning algorithm is underdeveloped in ABMs.The goal of my PhD research is to systematically test the effects of implementing social and environmental intelligence on the dynamics and emergent outcomes of spatial ABM. Spatial ABMs often use spatial data (GIS data) to construct real geographic environments in which agents are situated. Agents need to take changes in the spatial environment into account and adjust their behaviour accordingly. In this PhD research, intelligence, rational, and risk perception are playing an important role in the decision making of agents. Understanding the learning processes of agents in the spatial ABM can assist developing better strategies in problem-solving and coordination mechanisms.

AB - Complexity in human behaviour can play a crucial role in socio-environmental processes like disease diffusion. An example of such complex behaviour is risk perception, and behavioural change due to perceived risk. Computational models, and in particular Agent-based models (ABMs), have evolved as tools for simulating complex real-world processes. ABMs for describing and simulating a system composed of behavioural entities, ABMs provide the most natural environment. ABMs often use naive deterministic algorithms, which are rule-based, to simulate behavioural change in agents. While agents in ABMs are sometimes endowed with memory, the actual learning in machine learning style is rarely implemented. The endogenous switching of expectations formation strategies using learning algorithm is underdeveloped in ABMs.The goal of my PhD research is to systematically test the effects of implementing social and environmental intelligence on the dynamics and emergent outcomes of spatial ABM. Spatial ABMs often use spatial data (GIS data) to construct real geographic environments in which agents are situated. Agents need to take changes in the spatial environment into account and adjust their behaviour accordingly. In this PhD research, intelligence, rational, and risk perception are playing an important role in the decision making of agents. Understanding the learning processes of agents in the spatial ABM can assist developing better strategies in problem-solving and coordination mechanisms.

KW - Risk perception

KW - Machine learning

KW - Bayesian networks

KW - Infectious diseases

KW - Adaptation

KW - Human behaviour

U2 - 10.3990/1.9789036547482

DO - 10.3990/1.9789036547482

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-90-365-4748-2

PB - University of Twente

CY - Enschede

ER -