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. Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher. Learn More: https://bit.ly/3xn8INO Contact Us: Website: https://www.phdassistance.com/ UK NO: +44–1143520021 India No: +91–4448137070 WhatsApp No: +91 91769 66446 Email: info@phdassistance.com

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How to conduct Agent Based Modeling In Economics for your PhD
Dissertation
Dr. Nancy Agnes, Head,
Technical Operations, Phdassistance
info@phdassistance.com
Keyword:
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Agent Based Models in Economics and
Finance, PhD Economic Dissertation,
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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.

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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

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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

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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

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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

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(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.