Article 1
Jay Michael Lowmack
February 16, 2025
Understanding Simulations in Economics for Cybersecurity Decision-making
This paper offers a comprehensive understanding of the framework that utilizes
simulations in economic cybersecurity decision-making making, which emphasizes their
potential to manage risk and uncertainty while addressing the common failures that can
arise due to concepts like bookkeeping and abstractions
Relation to Social Science and Concepts
It connects to these three Social Science concepts: behavioral decision theory,
economic and social impact, and Technological change and society. Each of these
concepts relates in many ways. For example, technological change and society relate to
how up-to-date the systems are, what technological upgrades and changes were made
to them, and the influences of policies. This is even mentioned in the following quote
“Cybersecurity is crucial for protecting tangible and intangible assets in modern society.
However, achieving it is challenging, e.g. because of rapid technological development
[1], cognitive limitations [2], lack of a skilled workforce [3], a plethora of stakeholders [4],
and for many other reason… This hampers the ability of organizations to make rational,
evidence-based decisions (see, e.g. [18–21]) to safeguard their digital ecosystems,
even though we do not claim that all stakeholders need detailed quantitative data to
make evidence-based policies.”( Kianpour & Franke, 2025).
Research Questions, Methods, and Simulation Data
Analysis:
Research questions:
• Focusing on how the simulations can improve decision-making in
economics involving cybersecurity can help identify questions and
hypotheses. These hypotheses must explore the effectiveness of the
simulation to ensure it is worth using and to enhance its abilities about
probabilities and consequences, particularly under the risk of uncertainty.
Data and Analysis:
• Simulation data is used to analyze the information found to explore
potential outcomes in cybersecurity for decision-making. This is done by
analyzing simulation models to understand how these models predict the
outcomes and complex relationships between threats, vulnerabilities, and
decision outcomes.
Research methods used:
- Simulation modeling: Replicating real-world cybersecurity decision-making
scenarios helps analyze complex systems and evaluate potential
outcomes under different conditions, risks, and uncertainty. - Inductive Analysis: This is when data generated from predefined models
are examined to discover new insights rather than conforming theories
and hypotheses that were made.
Being able to distinguish two different types of knowledge:
• To understand decision-making frameworks, distinctions between two
types of knowledge are critical. This is shown through the following quote
“Decision theory traditionally distinguishes between two kinds of
knowledge needed for decision-making: knowledge of the
possible consequences in different scenarios and knowledge of
the probabilities of those scenarios. Despite its apparent simplicity, this
framework, depicted in Fig. 1 enables rich and subtle analysis of different
decision situations” (Kianpour & Franke, 2025).
Impact and Contributions to Society:
Simulations in economic decision-making for cybersecurity can potentially
significantly impact the future. It can offer practical solutions to manage risks and
uncertainty. They can contribute to the broader societal understanding of cybersecurity,
helping decision-makers assess and improve their strategies for protecting critical
systems in this complex field.
Conclusion
In conclusion, the importance of simulation-based frameworks in the economics
of cybersecurity decision-making is shown in many ways. These ways are social
sciences and their concepts, questions, methods, data, and societal impact. This is
shown by how simulation models can help us understand how these models predict
threats, vulnerabilities, and decision outcomes.
Work Cited
Kianpour, M., & Franke, U. (2025). The use of simulations in economic cybersecurity decision-making.
Journal of Cybersecurity, 11(1). https://doi.org/10.1093/cybsec/tyaf003
Article 2
Jay Michael Lowmack
April 6, 2025
The connection between AI and cybercrimes, explored through the examination of risks, impacts, and
potential solutions
This paper provides an understanding of the connection between AI and cybercrime by
highlighting the risks and societal impacts of Crimes driven by AI, such as deepfakes and social
engineering attacks., It also emphasizes the urgent need for preventive measures, policies, and
frameworks that can protect vulnerable individuals from these attacks or threats.
AI’s role in the world of cybercrime and the urgent
need for protective measures:
• Evolving Cybercrime Strategies:
o Study: “Integrated Model of Cybercrime Dynamics (IMCD) designed to analyze the
complex interactions between individual traits, online behaviors, environmental
influences, and the re- sulting cybercrime activities, both in terms of offending and
victimization (Smith, 2024). The study details the model’s conceptual foundations for
ongoing research in cybercrime and highlights the model’s flexibility that supports
diverse applications, including policy, education, and intervention strategies.” (Choi,
Deaden, & Parti, 2024).
o Explanation: The Integrated Model of Cybercrime Dynamics (IMCD) illustrates how
these online behaviors and environmental factors collectively contribute to the
development of cybercrime. Through a flexible framework for research and application
in policies, education, and intervention, it can inform the development of protective
measures.
• Proactive measures that must be used:
o Protective measures that can be used to prevent cybercrimes are awareness, training,
implementation of AI-driven security solutions, and polices. These measures can help
people recognize and respond to the occurrence of one of these attacks. Also, this can
lead to less manipulation of AI.
The impact on those in society as well as those in
marginalized groups:
• The rising vulnerability for those in marginalized groups:
o Marginalized groups like the elderly and low-income individuals are more susceptible to
cybercrime due to their limited information and resources to protect themselves from
these threats.
• The manipulation that occurs through social engineering and its impact on the public’s trust:
o Through social engineering, cybercriminals can exploit people’s trust by leading them
down a path that reduces their confidence in various apps and devices.
The primary purpose of this article:
• The purpose of this article is to show as well as explore the connections between AI and
cybercrime by focusing on the technologies that are exploited by these cybercriminals and what
the necessary measures are to prevent them.
Conclusion:
In conclusion, AI has become a tool in cybercrime, making the digital space increasingly more dangerous
over time. Therefore, leading to studies and proactive protection plans to keep vulnerable groups like
the elderly and low-income individuals safe from these threats. As time passes, more research and
awareness help foster a more vigorous defense against these ever-evolving threats.
Work Cited
Choi, S., Dearden, T., & Parti, K. (2024). Understanding the use of artificial intelligence in cybercrime.
International Journal of Cybersecurity Intelligence & Cybercrime, 7(2). https://doi.org/10.52306/2578-
3289.1185. Accessed April 6, 2025.