A632.1.4.RB - Multistage Decision-Making

On occasion I have been accused of being indecisive.  I like to call it analytical. It seems that any time I have a decision to make I stew over it for a long time.  Regardless of the decision, whether to apply for a new job, or purchase a sweater on Amazon.com I tend to use similar decision making techniques.  When I make decisions I try to clearly understand what it is that I am trying to decide, what is the question at issue? Once I have identified the question I try to get as much information about the topic as possible.  I peruse reviews on Amazon.com to find out what others have said about the sweater or I try to talk to other current or former employees of the company I am interested in to hear their experiences and opinions. I try to learn as much as I can about the options available from an objective, unbiased point of view.  Once I feel sufficiently informed I weigh the pros and cons to try to determine how the decision will affect me. I rely on intuition to a certain extent; if something doesn’t feel right, I abandon the choice. Once I make a decision I go for it! I follow through relying on my research and intuition and trust that I have done the best I could with the information I had.

Brodie (2007) suggested that there are six steps to being an effective decision maker.  These steps include defining the problem, assessing the implications, exploring different perspectives, clarifying the ideal outcome, weighing the pros and cons, and making a decision to act.  Although I previously have never taken the time to reflect on my own decision making process, the steps that I generally follow are consistent with Brodie’s recommendations. Brodie (2007) states that “At the end of the day there is no magic formula for decision making.  Following some simple steps and acting can however move you into the realm of effective decision maker” (para. 9).

Leaders are often required to make decisions that require anticipation of future conditions and trade-offs between short run and long run consequences.  This type of scenario is known as a dynamic decision problem (Hoch, Kunreuther, & Gunther, 2001).  Researchers solve such multistage problems by using estimated probabilities and complex equations to assist them in decision making.  However, Hoch, et al., (2001) stated that “optimal-decision methods . . .are too complex to offer much practical advice for how managers . . .might actually hope to solve such problems on their own” (p. 29).  It would be unrealistic to think that decision makers could use dynamic programming models to make their decisions. Leaders often confront time constraints and have to make decisions in real-time.

Despite the complexity of dynamic programming models, there are lessons that leaders and individuals can apply to their own decision making processes.  Hoch et al., (2001) indicated that optimal dynamic programming solutions are based on two fundamental assumptions; complete forward planning, and optimal learning.  Decision makers are assumed to have looked ahead “to all future periods and anticipate[d] all possible choices and outcomes” (p. 44). In addition, “Decision makers are assumed to fully utilize past information to update both current beliefs and future predictions” (p.44).  Leaders and individual decision makers can improve their decisions by following the same two rules: ensure complete forward planning, and optimize learning.

Hoch, et al., (2001) said “If we isolate a single critical fault in human abilities to act as efficient decision makers, it is often that we do not think ahead” (p. 44).  We are rarely able to think more than one or two steps into the future, and we typically want what we want and we want it now. I believe that awareness of this human weakness is the key to overcoming it.  When struggling through a challenging decision we should constantly be asking ourselves “Am I being myopic?” (Hoch, et al., 2001).

In addition to not becoming myopic and only considering the short term consequences of our decisions, it is important to ensure we are applying all of the information available to the situation and not discounting analogous experiences.  As additional new information becomes available we should update our current beliefs and our predictions about how they will affect the future. In short, we should not become blind to new information because we want a certain outcome so badly.  Decision makers should ask themselves the following questions to ensure optimized learning: “How appropriate is the starting analogy I am using to solve the problem?” And, “What am I learning from the feedback I am receiving?” (Hoch, et al., 2001, pp. 58-59).

Perhaps I would be more inclined to make a better decision about the sweater I purchase on Amazon.com if I consider more than the immediate time frame.  Do I need that sweater? How long will I use it for? Will it continue to fit me after I put on that “holiday weight?” Additionally, as I read reviews and become more informed about the decision I need to ensure that my choice is evolving appropriately.  Will I still make the purchase if I discover there are numerous negative reviews? These are examples of the lessons that can be applied to leaders and individuals from dynamic programming models.

Dholakia (2017) defines a good decision as “one that is made deliberately and thoughtfully, considers and includes all relevant factors, is consistent with the individual’s philosophy and values, and can be explained clearly to significant others.”  A good decision is made when we have considered all of the options, are well informed about the choices, and make a purposeful decision.

References

Brodie, D. (2007). 6 steps to better decision making. Retrieved from http://ezinearticles.com/?6-Steps-to-Better-Decision-Making&id=817450

Dholakia, U. (2017). What Is a “Good” Decision? Retrieved from https://www.psychologytoday.com/us/blog/the-science-behind-behavior/201707/what-is-good-decision

Hoch, S. J., Kunreuther, H. C., and Gunther, R. E. (2001). Wharton on making decisions. Hoboken, NJ: John Wiley & Sons, Inc