How to conduct Agent Based Modelling In Economics for your PhD Dissertation - Phdassistance
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The document outlines the significance and methodologies of agent-based modeling (ABM) in economics, tracing its origins from cellular automata and its emergence as a popular modeling tool for complex systems. It details various approaches to ABM, including deductive, methodological, and phenomenological, and emphasizes its application in fields like small-scale irrigation and drought adaptation. The conclusion stresses the need for further research to enhance ABM's calibration and validation in socio-hydrological contexts.
![Copyright © 2021 PhdAssistance. All rights reserved 2
It is of no doubt that the Agent Based
Modelling has its origins in the cellular
automata. The cellular data which had a
computational perspective was combined
with an adaptive individualism of
methodology, established the ABM. With
the Academic Success of the SFI and
increasing computerization of science, the
success of the Agent Based Modelling is
explicitly associated with O’Sullivan and
Haklay.
The ABM has been used in a large number
of fields like that of voting, military tactics,
epidemics etc. impossible to keep a count.
III. IMPLICATIONS IN ECONOMICS
With its origin in the SFI in the mis 80’s, the
ABM is now the most widely used tools for
capturing the complexity of the Economy.
This approach allows economists to
associate human behaviors with algorithms
of abstract nature to describe agents’
fundamental behavior. That is to say,
formulation of models in terms of computer
programs are done for which inputs are
taken as characteristics of agent behavior.
Then there is association of outputs with
macro level which in turn is a result of
micro interactions of the agent.
Figure territory of the Basilicata region in
the south of Italy [2]
The works done using ABM in economics
can hence be methodologically classified in
the following manner: The deductive
approach: the classical and methodological
individualism used in economics is the
perfectly rational Agent Based Modelling.
An optimization of theoretical constraints in
a rational manner defines the rules of
interaction. The addition of individual’s
characteristics deduces the system’s
macroscopic behavior.
An intuitive or deductive framework
chooses the assumptions for the
determination of a set of interactions that is
defined mathematically. These set of
interactions are then combined with perfect
additivity of agents which is assumed, for
the estimation of aggregative rule at the
macro level.
The methodological approach: in contrary to
the earlier perfectly rational approach of](https://image.slidesharecdn.com/howtoconductagentbasedmodellingineconomicsforyourphddissertation-phdassistance-210424101202/85/How-to-conduct-Agent-Based-Modelling-In-Economics-for-your-PhD-Dissertation-Phdassistance-2-320.jpg)
![Copyright © 2021 PhdAssistance. All rights reserved 3
Agent Based Modelling, this approach
makes use of a large number of iterations
made in the computers for deduction of
macro level. But this is the actual approach
that associates with the origin of Agent
Based Modelling in the SFI in the 80’s. In
this approach, there is integration of
heterogeneity and autonomy of agents. This
approach doesn’t need the perfect rationality
condition and assumptions are made by an
“intuitive plausibility” which means there is
calibration of micro interactions for meeting
heterogeneity of agents that are observed.
The phenomenological approach: this last
category refers to Research aiming at
reproducing of existing data of statistics. In
the previous categories, authors have
depended on empirical studies which have
previously shown a persistent statistical and
economic data. The association of the macro
statistical pattern observation is with the
identification of a phenomenon which is
discernible discernable and noteworthy.
Once identification of the phenomenon is
done, statistical macro properties are entered
as input for micro interaction calibration
after which, macro patterns initially
observed are generated. In other words,
empirical determination of assumptions is
done to fit the data. The real target is the
definition of potential micro interaction that
are likely to generate macro patterns that are
initially observed.
IV. RECENT RESEARCH TOPICS IN
ABM
Small-scale irrigation has been promoted in
recent years because it not only tackles long-
term neglect and underinvestment by
government authorities, but it has also been
shown to be more cost-effective than large-
scale irrigation [6]. The increased autonomy
of individual irrigators, which motivates the
creation of the agent-based modeling tool in
this research, is one of the advantages of
small-scale irrigation. To advance small-
scale irrigation development planning and to
expand on the work of, we present a
groundbreaking agent-based model that
overcomes the limitations of the previous
optimization-based approach. By
decomposing the real-world system into a
large number of agents and simulating the
actions of agents at the individual level,
agent-based modeling offers a bottom-up
approach to modeling complex coupled
natural-human systems. As a result, the
decision to use small-scale irrigation for dry-
season agricultural production is simulated
at the farm level, and irrigation growth
capacity is estimated based on the spatial](https://image.slidesharecdn.com/howtoconductagentbasedmodellingineconomicsforyourphddissertation-phdassistance-210424101202/85/How-to-conduct-Agent-Based-Modelling-In-Economics-for-your-PhD-Dissertation-Phdassistance-3-320.jpg)
![Copyright © 2021 PhdAssistance. All rights reserved 4
trend of small-scale irrigation adoption that
emerges from the micro-level simulation of
irrigation adoption behaviors. We consider
agent-based modeling as a more suitable
technique to simulate small-scale irrigation
because small-scale irrigation is basically a
distributed system and there is no central
agency in charge of the adoption decisions
of small-scale irrigation technologies.
ABMs are simulations in which artificial
entities communicate over time in
personalized environments [3]. That is,
ABMs attempt to replicate individual
processes such as movement, actions, birth,
development, and death based on a
collection of data such as genotype, history,
and agent position (i.e., crucial components
of the analysis). Furthermore, ABMs
provide unrivalled control and statistical
power by allowing any number of agents to
be defined and their interactions observed
over time. [5]
V. RECENT RESEARCH STUDIES IN
ABM
Refe
renc
e
Problem
Identific
ation
Deve
lope
d
Mod
Simul
ation
tool
Soluti
on
el
[1] Investme
nt in
small-
scale
irrigation
may
provide a
solution
to
address
the
challeng
e by
extendin
g crop
producti
on into
the dry
season.
Agen
t-
base
d
mode
ling
appr
oach
with
an
appli
catio
n to
Ethio
pia
Enviro
nment
al
suitabi
lity
analysi
s,
SWAT
simula
tion
and
SPAM
Reduc
e the
negati
ve
enviro
nment
al
impact
s of
small-
scale
irrigati
on
develo
pment
[3] Paradox
requires
an
understa
nding of
human
adaptatio
n to
drought
by
mapping
Agen
t-
base
d
agric
ultur
al
water
dema
nd
(AB
Netlog
o
ABAD
model
reveal
ed a
mappi
ng
betwee
n the
basins
cale
reboun](https://image.slidesharecdn.com/howtoconductagentbasedmodellingineconomicsforyourphddissertation-phdassistance-210424101202/85/How-to-conduct-Agent-Based-Modelling-In-Economics-for-your-PhD-Dissertation-Phdassistance-4-320.jpg)
![Copyright © 2021 PhdAssistance. All rights reserved 5
individua
l
farmers’
water
conserva
tion
decisions
to the
dynamic
s of the
basin-
wide
water
demand
AD)
mode
l
d
pheno
menon
and
the
socio-
econo
mic
factors
in
individ
ual
farmer
s’
water
conser
vation
decisio
ns,
which
can
help
unders
tand
the
reboun
d
pheno
menon
and
possibl
y
control
it
throug
h
future
long-
term
water
policie
s.
[4] new
challeng
es related
to the e-
commerc
e
integrati
on—the
customiz
ation in
order to
be
competiti
ve
enough,
a
sustainab
le
logistic
process
Agen
t-
base
d
simul
ation
mode
l
Individ
ual-
based
simula
tions
Use of
horizo
ntal
cooper
ation
clearly
impro
ves the
econo
mic
and
service
quality
perfor
mance
of the
e-
grocer
y
distrib](https://image.slidesharecdn.com/howtoconductagentbasedmodellingineconomicsforyourphddissertation-phdassistance-210424101202/85/How-to-conduct-Agent-Based-Modelling-In-Economics-for-your-PhD-Dissertation-Phdassistance-5-320.jpg)
How to conduct Agent Based Modelling In Economics for your PhD Dissertation - Phdassistance
- 1. Copyright © 2021 PhdAssistance. All rights reserved 1 How to conduct Agent Based Modeling In Economics for your PhD Dissertation Dr. Nancy Agnes, Head, Technical Operations, Phdassistance info@phdassistance.com Keyword: Agent-Based Modeling in Economics, Agent Based Models in Economics and Finance, PhD Economic Dissertation, Economic Dissertation In PhD Help, Department of Economics in PhD, PhD Studentship in Economic Modelling, PhD student for Modeling Processes, PhD Thesis Writing Help, Agent Modelling In Economics, I. INTRODUCTION The Emergence of a New Scientific term, the “complexity science” have been witnessed in the last three decades of this era. It is a concept of varying perspectives and is somewhat an amalgamation of several methods, models and metaphors from different disciplines. Hence, with time complexity have been studied in a variety of subjects, Including Economics.The complexity era in economics gave rise to a lot of studies with actions being generated from abstract rules. These studies in turn gave rise to a class of models that simulate the actions and interactions of a number of autonomous agents in a complex situation, the Agent Based Modelling (ABM). In several disciplinary contexts, ABM has become a fashionable Methodology while it has also opened up a lot of debate in the field of economics. II. SHIFT TO ABM FROM CELLULAR AUTOMATA Automata was initially developed by Von Neumann in the year 1951. The agent-based modelling approach finds its roots in the cellular automata. Cellular automata was further studied in the 70’s by Conway and Tuffoli. Automata actually grew further in the 80’s when Wolfram, the founder of Santa Fe Institute worked on it, which worked like a real catalyst in his computerized complexity.
- 2. Copyright © 2021 PhdAssistance. All rights reserved 2 It is of no doubt that the Agent Based Modelling has its origins in the cellular automata. The cellular data which had a computational perspective was combined with an adaptive individualism of methodology, established the ABM. With the Academic Success of the SFI and increasing computerization of science, the success of the Agent Based Modelling is explicitly associated with O’Sullivan and Haklay. The ABM has been used in a large number of fields like that of voting, military tactics, epidemics etc. impossible to keep a count. III. IMPLICATIONS IN ECONOMICS With its origin in the SFI in the mis 80’s, the ABM is now the most widely used tools for capturing the complexity of the Economy. This approach allows economists to associate human behaviors with algorithms of abstract nature to describe agents’ fundamental behavior. That is to say, formulation of models in terms of computer programs are done for which inputs are taken as characteristics of agent behavior. Then there is association of outputs with macro level which in turn is a result of micro interactions of the agent. Figure territory of the Basilicata region in the south of Italy [2] The works done using ABM in economics can hence be methodologically classified in the following manner: The deductive approach: the classical and methodological individualism used in economics is the perfectly rational Agent Based Modelling. An optimization of theoretical constraints in a rational manner defines the rules of interaction. The addition of individual’s characteristics deduces the system’s macroscopic behavior. An intuitive or deductive framework chooses the assumptions for the determination of a set of interactions that is defined mathematically. These set of interactions are then combined with perfect additivity of agents which is assumed, for the estimation of aggregative rule at the macro level. The methodological approach: in contrary to the earlier perfectly rational approach of
- 3. Copyright © 2021 PhdAssistance. All rights reserved 3 Agent Based Modelling, this approach makes use of a large number of iterations made in the computers for deduction of macro level. But this is the actual approach that associates with the origin of Agent Based Modelling in the SFI in the 80’s. In this approach, there is integration of heterogeneity and autonomy of agents. This approach doesn’t need the perfect rationality condition and assumptions are made by an “intuitive plausibility” which means there is calibration of micro interactions for meeting heterogeneity of agents that are observed. The phenomenological approach: this last category refers to Research aiming at reproducing of existing data of statistics. In the previous categories, authors have depended on empirical studies which have previously shown a persistent statistical and economic data. The association of the macro statistical pattern observation is with the identification of a phenomenon which is discernible discernable and noteworthy. Once identification of the phenomenon is done, statistical macro properties are entered as input for micro interaction calibration after which, macro patterns initially observed are generated. In other words, empirical determination of assumptions is done to fit the data. The real target is the definition of potential micro interaction that are likely to generate macro patterns that are initially observed. IV. RECENT RESEARCH TOPICS IN ABM Small-scale irrigation has been promoted in recent years because it not only tackles long- term neglect and underinvestment by government authorities, but it has also been shown to be more cost-effective than large- scale irrigation [6]. The increased autonomy of individual irrigators, which motivates the creation of the agent-based modeling tool in this research, is one of the advantages of small-scale irrigation. To advance small- scale irrigation development planning and to expand on the work of, we present a groundbreaking agent-based model that overcomes the limitations of the previous optimization-based approach. By decomposing the real-world system into a large number of agents and simulating the actions of agents at the individual level, agent-based modeling offers a bottom-up approach to modeling complex coupled natural-human systems. As a result, the decision to use small-scale irrigation for dry- season agricultural production is simulated at the farm level, and irrigation growth capacity is estimated based on the spatial
- 4. Copyright © 2021 PhdAssistance. All rights reserved 4 trend of small-scale irrigation adoption that emerges from the micro-level simulation of irrigation adoption behaviors. We consider agent-based modeling as a more suitable technique to simulate small-scale irrigation because small-scale irrigation is basically a distributed system and there is no central agency in charge of the adoption decisions of small-scale irrigation technologies. ABMs are simulations in which artificial entities communicate over time in personalized environments [3]. That is, ABMs attempt to replicate individual processes such as movement, actions, birth, development, and death based on a collection of data such as genotype, history, and agent position (i.e., crucial components of the analysis). Furthermore, ABMs provide unrivalled control and statistical power by allowing any number of agents to be defined and their interactions observed over time. [5] V. RECENT RESEARCH STUDIES IN ABM Refe renc e Problem Identific ation Deve lope d Mod Simul ation tool Soluti on el [1] Investme nt in small- scale irrigation may provide a solution to address the challeng e by extendin g crop producti on into the dry season. Agen t- base d mode ling appr oach with an appli catio n to Ethio pia Enviro nment al suitabi lity analysi s, SWAT simula tion and SPAM Reduc e the negati ve enviro nment al impact s of small- scale irrigati on develo pment [3] Paradox requires an understa nding of human adaptatio n to drought by mapping Agen t- base d agric ultur al water dema nd (AB Netlog o ABAD model reveal ed a mappi ng betwee n the basins cale reboun
- 5. Copyright © 2021 PhdAssistance. All rights reserved 5 individua l farmers’ water conserva tion decisions to the dynamic s of the basin- wide water demand AD) mode l d pheno menon and the socio- econo mic factors in individ ual farmer s’ water conser vation decisio ns, which can help unders tand the reboun d pheno menon and possibl y control it throug h future long- term water policie s. [4] new challeng es related to the e- commerc e integrati on—the customiz ation in order to be competiti ve enough, a sustainab le logistic process Agen t- base d simul ation mode l Individ ual- based simula tions Use of horizo ntal cooper ation clearly impro ves the econo mic and service quality perfor mance of the e- grocer y distrib
- 6. Copyright © 2021 PhdAssistance. All rights reserved 6 (i.e., guarante e the optimum provision ing and delivery of goods), and a company internati onalizati on ution VI. CONCLUSION This term Agent Based Modelling is now widely used in the Scientific Literature and has almost become a buzzword. The work profusion dealing with Agent Based Modelling needs to be clarified for the better understanding of the lines of, which this approach has paved in economics. The structural sensitivity of the agent-based agricultural water demand (ABAD) model will need to be assessed in future studies. In other words, comparing the model's success under the theory of bounded rationality to other social theories in the BRB is important Furthermore; research is required to collect more data on the ABAD model's socio- economic factors in order to improve model calibration and validation. We assume that socio-hydrological problems are location- specific, making the creation of a generic model or transferable methods difficult. However, since the ABAD model is based on a general social theory (i.e., bounded rationality theory), applying it to other case studies will help with comparative analysis and the development of a more generalized model. The calibration and evaluation of future simulation-based researches will be aided by the ad hoc concept of horizontal cooperation policies that are currently in place on logistics-related services. Practical problems, on the other hand, should be investigated and incorporated into optimization models. This applies to how the coalition shares advantages and costs, as well as how the coalition works together. REFERENCES 1. Xie, H., You, L., Dile, Y. T., Worqlul, A. W., Bizimana, J. C., Srinivasan, R., ... & Clark, N. (2021). Mapping development potential of dry- season small-scale irrigation in Sub-Saharan African countries under joint biophysical and economic constraints-An agent-based modeling approach with an application to Ethiopia. Agricultural Systems, 186, 102987. 2. Saganeiti, L., Mustafa, A., Teller, J., & Murgante, B. (2021). Modeling urban sprinkling
- 7. Copyright © 2021 PhdAssistance. All rights reserved 7 with cellular automata. Sustainable Cities and Society, 65, 102586. 3. Ghoreishi, M., Razavi, S., & Elshorbagy, A. (2021). Understanding Human Adaptation to Drought: Agent-Based Agricultural Water Demand Modeling in the Bow River Basin, Canada. Hydrological Sciences Journal. 4. Serrano-Hernandez, A., de la Torre, R., Cadarso, L., & Faulin, J. (2021). Urban e-Grocery Distribution Design in Pamplona (Spain) Applying an Agent-Based Simulation Model with Horizontal Cooperation Scenarios. Algorithms, 14(1), 20. 5. Jackson, J. C., Rand, D., Lewis, K., Norton, M. I., & Gray, K. (2017). Agent-based modeling: A guide for social psychologists. Social Psychological and Personality Science, 8(4), 387-395. 6. Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. Journal of Simulation, 10(2), 144- 156.