I have often heard people say, “I’m a thinker” when discussing having to make a decision. I can relate, as I would label myself as one who routinely experiences paralyzation through analyzation in my decision-making process. I tend to think about the many possibilities that could potentially result from a single decision. Even worse, I precede every option or choice I can think of with “what if…” to ensure I can analyze the impact and the pros and cons of each. I don’t like surprises when it comes to what could happen from my decisions, so I make every attempt to be prepared. My way of thinking has its advantages and disadvantages. On one hand, I am very thorough and have a keen ability to identify potential outcomes. On the other hand, all my analyzation can prevent me from making decisions quickly.
People are faced with decisions every day dealing with their family, friends, careers, themselves, strangers, and everything and everyone else in between. Simply getting out of bed in the morning involves making a decision. Even not making a decision is, well, technically a decision. Most people usually do everything they can in their power to make the best decision possible which may include predicting the future result of today’s decision. Predicting the future is difficult in and of itself but becomes even more challenging when trying to solve dynamic decision problems. However, Hoch, Kunreuther, and Gunther (2001) provide an analysis to help in the decision-making process and create an optimal outcome. They discuss how researchers use dynamic programming to solve multistage decision problems using mathematics. And while useful to researchers who can obtain values for their formulas, it is not always effective, especially when information within multistage decision problems cannot be determined.
The foundations of optimal dynamic programming solutions are complete forward planning and optimal learning (Hoch, Kunreuther, & Gunther, 2001). In other words, forward planning is when we try to predict the future by investigating all possible choices and outcomes and optimal learning is utilizing past information to update present beliefs and future predictions. I believe most people engage in each of these intuitive assumptions to some degree. And while I think these assumptions are part of our human nature and help us navigate our decisions, Hoch et al. (2001) believe they are also problematic since “we have limited abilities to anticipate the future, we are poor at learning from the past, and even our perceptions of the present are distorted” (p. 49). Despite this grim deduction, people still manage to make good and sometimes even optimal decisions. This occurs because we are able to find the optimal answers through life experiences, decision environments are forgiving of mistakes, and are capable of learning via trial and error.
We were not created to be perfect and Hoch et al. (2001) validate that while there are methods to assist us in the decision-making process, no approach can consistently guarantee a perfect outcome. Additionally, Hoch et al. (2001) recognize that “the study of dynamic decision problems is littered with contradictions and paradoxes” (p. 57). Hoch et al. (2001) also suggest there is a lack of evidence that intuition routinely fails when making decisions and “we routinely face and resolve dynamic decision tasks, often with great apparent success” (p. 57). This is comforting because their findings lead me to accept my own decision-making ability in how I consciously or unconsciously employ intuition, experience, and trial and error.
Reference:
Hoch, S., Kunreuther, H., & Gunther, R. (2001). Wharton on making decisions. Hoboken, NJ: John Wiley & Sons Inc.
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