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PLEASE REBUTTAL, RESPOND, AND ANSWER EACH OF THE FOLLOWING QUESTIONS OR POST STATEMENTS. MUST BE 150 WORDS (PLEASE), WRITE IN 1st PERSON. MUST BE 150 to 200 WORDS (PLEASE), WRITE IN 1st PERSON. PLEASE MAKE SURE TO USE SCHOLARLY PEER REVIEWED ARTICLES AND PLACE EACH REFERENCE USED UNDER EACH ANSWER.
DQ1
I think that all of the techniques we learned in the course were important because many of the applications of the techniques built on each other. I can’t say that there was a single most important technique that I learned because I think the biggest takeaway from this course was that each of the techniques had pros and cons to their application and that which technique to use is really context specific.
This course also provided an opportunity to use the techniques and apply them to real situations we are encountering in public management and how this can be improved. I some ways while this discussion post provides an opportunity to reflect on the overall course, throughout the course we have been reflecting on the work that we do everyday. I appreciate my colleagues sharing their insights and learning from them as well. As John Dewey said in 1933, “We do not learn from experience. We learn from reflecting onexperience” (as cited in Di Stephano, Francesca, Piscano & Staats, 2016). This is even more true today in the fast paced life we live. Taking time to reflect is so important.
Positive change is achieved by continuously learning and making improvements based on new knowledge gained. Having learned these new techniques I will try to incorporate them when I can and where appropriate to improve the ways in which I practice public management in my career.
References
Di Stefano, G., Francesca, G., Pisano, G.P., & Staats, B.R. (2016). Making experience count:
The role of reflection in individual learning. Harvard Business School Technology and
Operation Management Unit Working Paper 14-093.
DQ2
Introduction. The most useful leadership technique that I learned is the simulation and agent-based modeling (ABM). This field remains largely untapped, and the power behind its computing algorithms is still in its infancy. With ABM, computer software agents interact in a simulated environment and variables are assigned and manipulated for the agents. They also can manipulate the variables themselves. Each agent follows programmed rules, which govern their behavior under. Each agent is programmed with a level of contentment, in which they seek equilibrium within the system. All measures are mathematically and probabilistically governed (Hammond, 2015).
Benefits and Risks of ABM. ABM captures emergent phenomena. That is, we can notice and hypothesize on unexpected behaviors by watching the simulation. ABM also provides a natural system description. That is, it is inductive instead of deductive, like Socrates. ABM is also very flexible, and there appears to be no limit to the programming applications (Bonabeau, 2002, p. 7280). Thus, by setting up a collection of heterogeneous agents in a computer simulation, interconnected by rules, I can program the agents to form mental models, biases, and beliefs, based on how they evolve their behaviors. The hypotheses I develop for their behavior are subjective and are based upon the agents’ temporarily fulfilled expectations, or contentment (Arthur, 1994, p. 407). These are clear benefits. The risks, however, stem from the power of the benefits themselves. With such strong programmability comes the opportunity to program bias into a system. Thus, one could hypothesize something quite altruistically and the results might display spurious results because of faulty assumptions governing agent behavior.
Considerations. The key considerations that I will apply revolve around the models. This is where we get the term, “modality.” Hammond (2015) provided a great discussion on ABM modalities. I take them quite seriously when deriving (or inducing) policies. With any non-profit I’m involved with, this will be critical for strategy and effective results-based efforts. If a policy model is “prospective,” it informs policy design by focusing on effects. If a policy model is “retrospective,” it informs policy design by focusing on extant performance. “Indirect” models are tangential models that can be used to inform policy.
Social Change. By modeling human behavior and economic scenarios with ABM, I feel that the follow-through of research can be accomplished much faster and more effectively than before. Much research examines why and how things happen. Not many go to the next step to determine how things might be better or worse when variables change. This is where ABM has its potential.
References
Arthur, W. B. (1994). Inductive reasoning and bounded rationality. American Economic Review 84(2), 406–411. (Seminal). Retrieved from http://multiagent.martinsewell.com/Arth94a.pdf
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
Hammond, R. A. (2015). Considerations and best practices in agent-based modeling to inform policy. In: Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation, Board on Population Health and Public Health Practice, Institute of Medicine, Wallace, R., Geller, A., & Ogawa, V. A. (Eds.), Assessing the Use of Agent-Based Models for Tobacco Regulation. Washington, DC: National Academies Press. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK305917/