Modelling human behaviour in coupled human and natural systems

Koen de Koning

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

38 Downloads (Pure)

Abstract

Humans have a major impact on what the world looks like today. The challenge for modern humans is to counter the undesirable changes that affect our collective well-being, such as climate change, land degradation and rapid decline of global biodiversity. For scientists it implies that we must try to understand how human behaviour affects the natural environment and vice versa. We study this in systems referred to as coupled human-and-natural systems (CHANS). Agent-Based Modelling (ABM) is one of the tools that is increasingly adopted in studying CHANS for various applications. ABM is particularly powerful in capturing all aspects of complexity of CHANS. Yet, the field of ABM is still far from mature. The complex dynamics of human behaviour and its impact on the environment is still underrepresented in CHANS prediction models, and there are many degrees of freedom in terms of how to model human behaviour in ABMs of CHANS. In this methodological thesis I address these issues with the goal to improve the modelling of human behaviour in ABMs designed to explore the undesired environmental changes that affect our well-being. I use a case study of the housing market in flood-prone areas that are increasingly at risk as a consequence of climate change. Throughout the thesis I make step by step changes to an existing ABM that is designed to predict how households will respond to increasing flood risk and how demographics of coastal cities may change as a consequence of that.
The findings of my thesis contribute to current modelling practices in 3 ways. First, my thesis contributes to finding the sweet spot between complicatedness and simplicity in modelling CHANS with ABMs. I show on specific levels of the model (micro-macro) where the model needs to be simplified and where it needs more detail. Secondly, my thesis contributes to the question what kind of data to use and which behavioural theories to select in order to formulate human behaviour in our models. Thirdly, I propose a number of steps that every modeller should undertake when trying to improve the formalisation of human behaviour in ABMs of CHANS.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Filatova, Tatiana , Supervisor
  • Need, Ariana , Supervisor
Award date4 Apr 2019
Place of PublicationEnschede
Publisher
Print ISBNs978-94-632-3554-9
DOIs
Publication statusPublished - 4 Apr 2019

Fingerprint

human behavior
modeling
climate change
housing market
land degradation
environmental change
thesis
biodiversity
prediction

Cite this

de Koning, Koen . / Modelling human behaviour in coupled human and natural systems. Enschede : University of Twente, 2019. 229 p.
@phdthesis{ac384fcab8794dc99400d1a42691d532,
title = "Modelling human behaviour in coupled human and natural systems",
abstract = "Humans have a major impact on what the world looks like today. The challenge for modern humans is to counter the undesirable changes that affect our collective well-being, such as climate change, land degradation and rapid decline of global biodiversity. For scientists it implies that we must try to understand how human behaviour affects the natural environment and vice versa. We study this in systems referred to as coupled human-and-natural systems (CHANS). Agent-Based Modelling (ABM) is one of the tools that is increasingly adopted in studying CHANS for various applications. ABM is particularly powerful in capturing all aspects of complexity of CHANS. Yet, the field of ABM is still far from mature. The complex dynamics of human behaviour and its impact on the environment is still underrepresented in CHANS prediction models, and there are many degrees of freedom in terms of how to model human behaviour in ABMs of CHANS. In this methodological thesis I address these issues with the goal to improve the modelling of human behaviour in ABMs designed to explore the undesired environmental changes that affect our well-being. I use a case study of the housing market in flood-prone areas that are increasingly at risk as a consequence of climate change. Throughout the thesis I make step by step changes to an existing ABM that is designed to predict how households will respond to increasing flood risk and how demographics of coastal cities may change as a consequence of that.The findings of my thesis contribute to current modelling practices in 3 ways. First, my thesis contributes to finding the sweet spot between complicatedness and simplicity in modelling CHANS with ABMs. I show on specific levels of the model (micro-macro) where the model needs to be simplified and where it needs more detail. Secondly, my thesis contributes to the question what kind of data to use and which behavioural theories to select in order to formulate human behaviour in our models. Thirdly, I propose a number of steps that every modeller should undertake when trying to improve the formalisation of human behaviour in ABMs of CHANS.",
author = "{de Koning}, Koen",
year = "2019",
month = "4",
day = "4",
doi = "10.3990/1.9789463235549",
language = "English",
isbn = "978-94-632-3554-9",
publisher = "University of Twente",
address = "Netherlands",
school = "University of Twente",

}

de Koning, K 2019, 'Modelling human behaviour in coupled human and natural systems', Doctor of Philosophy, University of Twente, Enschede. https://doi.org/10.3990/1.9789463235549

Modelling human behaviour in coupled human and natural systems. / de Koning, Koen .

Enschede : University of Twente, 2019. 229 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

TY - THES

T1 - Modelling human behaviour in coupled human and natural systems

AU - de Koning, Koen

PY - 2019/4/4

Y1 - 2019/4/4

N2 - Humans have a major impact on what the world looks like today. The challenge for modern humans is to counter the undesirable changes that affect our collective well-being, such as climate change, land degradation and rapid decline of global biodiversity. For scientists it implies that we must try to understand how human behaviour affects the natural environment and vice versa. We study this in systems referred to as coupled human-and-natural systems (CHANS). Agent-Based Modelling (ABM) is one of the tools that is increasingly adopted in studying CHANS for various applications. ABM is particularly powerful in capturing all aspects of complexity of CHANS. Yet, the field of ABM is still far from mature. The complex dynamics of human behaviour and its impact on the environment is still underrepresented in CHANS prediction models, and there are many degrees of freedom in terms of how to model human behaviour in ABMs of CHANS. In this methodological thesis I address these issues with the goal to improve the modelling of human behaviour in ABMs designed to explore the undesired environmental changes that affect our well-being. I use a case study of the housing market in flood-prone areas that are increasingly at risk as a consequence of climate change. Throughout the thesis I make step by step changes to an existing ABM that is designed to predict how households will respond to increasing flood risk and how demographics of coastal cities may change as a consequence of that.The findings of my thesis contribute to current modelling practices in 3 ways. First, my thesis contributes to finding the sweet spot between complicatedness and simplicity in modelling CHANS with ABMs. I show on specific levels of the model (micro-macro) where the model needs to be simplified and where it needs more detail. Secondly, my thesis contributes to the question what kind of data to use and which behavioural theories to select in order to formulate human behaviour in our models. Thirdly, I propose a number of steps that every modeller should undertake when trying to improve the formalisation of human behaviour in ABMs of CHANS.

AB - Humans have a major impact on what the world looks like today. The challenge for modern humans is to counter the undesirable changes that affect our collective well-being, such as climate change, land degradation and rapid decline of global biodiversity. For scientists it implies that we must try to understand how human behaviour affects the natural environment and vice versa. We study this in systems referred to as coupled human-and-natural systems (CHANS). Agent-Based Modelling (ABM) is one of the tools that is increasingly adopted in studying CHANS for various applications. ABM is particularly powerful in capturing all aspects of complexity of CHANS. Yet, the field of ABM is still far from mature. The complex dynamics of human behaviour and its impact on the environment is still underrepresented in CHANS prediction models, and there are many degrees of freedom in terms of how to model human behaviour in ABMs of CHANS. In this methodological thesis I address these issues with the goal to improve the modelling of human behaviour in ABMs designed to explore the undesired environmental changes that affect our well-being. I use a case study of the housing market in flood-prone areas that are increasingly at risk as a consequence of climate change. Throughout the thesis I make step by step changes to an existing ABM that is designed to predict how households will respond to increasing flood risk and how demographics of coastal cities may change as a consequence of that.The findings of my thesis contribute to current modelling practices in 3 ways. First, my thesis contributes to finding the sweet spot between complicatedness and simplicity in modelling CHANS with ABMs. I show on specific levels of the model (micro-macro) where the model needs to be simplified and where it needs more detail. Secondly, my thesis contributes to the question what kind of data to use and which behavioural theories to select in order to formulate human behaviour in our models. Thirdly, I propose a number of steps that every modeller should undertake when trying to improve the formalisation of human behaviour in ABMs of CHANS.

U2 - 10.3990/1.9789463235549

DO - 10.3990/1.9789463235549

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-94-632-3554-9

PB - University of Twente

CY - Enschede

ER -