Inductive reasoning and behavior is using simple models to fill in the gaps in our understanding given a lack of full definition of the problem (Arthur, 1994). Individuals, or agents, perceive stimuli and information differently, and react in different ways. This is a system of learning, and old hypothesis may be discarded for new ones as the agents perception is updated with repetition or clearer information. Arthur (1994), describes the inductive-reasoning system that consists of multiple belief-models or hypothesis that adapt to the environment as an adaptive complex system.

Agent-based modeling is a simulation modeling technique using a collection of autonomous decision makers called agents that individually assess their situation and make a decision based on a set of rules (Bonebeau, 2002). Agent-based modeling is a mindset that describes a system from the perspective of its constituent units (Bonebeau, 2002). According to Bonebeau (2002), agent-based modeling’s benefits are 1) ABM captures emergent phenomena, 2) agent-based modeling provides a natural description of a system, 3) agent-based modeling is flexible.

One example I can think of in the public sector is in the area of public gatherings. In my municipality, we put on a lot of free festivals and events that are open to the public. Often, it is a guess on how many people will attend these events, and what will be the peak times that require additional staffing. Using agent-based modeling and simulation may be useful in determining how many people may come to certain types of events, based on the attraction of that event and the location in the city. It may also be useful in determining what time periods in the event may have the highest and lowest attendance, based on factors such as lunch time, attractions, time of day and children-friendly attractions. There would be other factors that affect the agents to consider, such as season, temperature and weather, other competing events, and value. An example given by Bonebeau (2002) is in flow management, and the simulation of customer behavior in a theme park.

References

Arthur, W.B. (1994). Inductive reasoning and bounded rationality. Amer. Econ. Review (Papers and Proceedings), 84(406). Retrieved from http://tuvalu.santafe.edu/~wbarthur/Papers/El_Farol.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99(Suppl. 3), 7280-7287. Seminal. Retrieved from http://www.pnas.org/content/99/suppl_3/7280.full.pdf.

DQ2

The problem that I can envision where agent-based modeling (ABM) and simulation would be applicable and most useful is in the field of corrections and correctional healthcare. Scholarly literature suggests that agent-based modeling and simulation are applicable in a variety of fields and disciplines such as public health, marketing and agricultural policy. According to an illuminating article by Hammond (2015) he defines agent based computational modeling as “an approach to modeling complex social dynamics that has developed in recent decades, facilitated by increased computational power. They are given rules that govern their behavior, including adaptation and interaction with each other and with their environment through time and a starting configuration” (p. 1).

Agent-based modeling (ABM) would be implemented by incorporating its computations within the various types of scenarios that could happen within a correctional environment such as jails and or prisons. The uniqueness of this modeling tool is that provides flexibility, adaptation, spatial structure and heterogeneity. It’s important to note that these types of actors such as the inmates and inmate-patients are critical characters that create computational dynamics within the systems behaviors. In addition, the criminal mindset and mentality are agents that would dictate the types of negative and or positive outcomes that would be potentially foreseen.

Furthermore, agent-based modeling and simulation tools is more of a psychological mindset that “consists of describing a system from the perspective of its constituent units” (Bonabeau, 2002, p. 7280). The applicability of this tool would be extremely useful in capturing phenomena such as describing the natural description of a system, in this case prison (correctional environment) which actually drives what the organization does on a daily basis in managing these potentially violent actors and dangerous agents. In closing, this modeling tool captures the organizations, operational risks and liabilities as a state prison. These benefits would allow wardens, correctional healthcare managers and legislators to proactively manage and respond to crisis situations to where the actors could potentially be of grave danger to public safety and society as a whole.

Reginald L. Allen

References

Bonabeau, E. (2002. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99 (Suppl. 3), 7280-7287. Seminal. Retrieved on April 18, 2018, from http://www.pnas.org/content/99/suppl_3/7280.full.pdf.

Hammond, R. A. (2015, 07 15). Appendix A Considerations and Best Practices in Agent-Based Modeling to Inform Policy. Retrieved from National Library of Medicine, National Institutes of Health: https://www.ncbi.nlm.nih.gov/books/NBK305917/?report=printable