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

1. An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com
HOW TO CONDUCT AGENT
BASED MODELLING IN
ECONOMICS FOR YOUR PHD
DISSERTATION

2. Introduction
Shift to ABM from cellular automata
Implications in Economics
Recent Research Topics IN ABM
RECENT RESEARCH STUDIES IN ABM
Conclusion
Outline
TODAY'S DISCUSSION

3. 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.
Introduction

4. 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.

5. 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.
Shift to ABM
from
cellular
automata

6. 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.

7. 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.
Implications
in
Economics

8. Figure territory of the Basilicata region in the south of Italy [2]

9. 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.

10. 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
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.

11. 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.

12. 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.

13. 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.
Recent
Research
Topics IN
ABM

14. 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 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.

15. 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]

16. RECENT
RESEARCH
STUDIES IN
ABM

17. 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.
Conclusion

18. 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.

19. 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.

20. Contact Us
UNITED KINGDOM
+44-1143520021
INDIA
+91-4448137070
EMAIL
info@phdassistance.com