Decision-Making

It has been estimated that people make tens of thousands of decisions per day, though the exact number depends on how we define “decision.” Many of our choices are habitual or unconscious—kind of like a reflex. These decisions don’t require much thought because, over the years, we’ve learned what choices will lead to the best outcomes in certain situations; when we cross the road, we don’t think about whether or not to step out when traffic is moving, we just know it’s best not to. But, of course, other decisions require more conscious thought, such as what to eat for breakfast or which movie to watch in the evening. 

 But what exactly are decisions, and how do we make them? Decision-making is a fundamental aspect of human and animal life and describes the cognitive process involved in selecting a course of action among multiple options. The decision-making process can be either “high level” or “low level” and builds upon other mental processes such as perception, memory, and attention. In making any decision, we are highly influenced by other cognitive processes that occur before a choice is made (perception, recognition, and judgment) and after (feedback and reinforcement learning).4

Normative and descriptive model of decision-making 

There are two distinct approaches to understanding decisions: normative and descriptive.22 

Normative decision theory examines how decisions are made in their most ideal form—maximizing rationality and utility. This theory characterizes the decision-maker as someone who is fully informed and operates under complete rationality. However, many argue that we’re irrational and don’t always make optimal choices. Descriptive decision theory, therefore, accounts for how decisions are actually made and how biases, emotions, and cognitive limitations influence our choices. 

Imagine a child coming home from a long day at school. They know they have a lot of homework to complete for the next day, but they also really want to watch their favorite programs on TV. Normative theory suggests that the child should complete their homework because this is the better choice for their long-term academic success. In reality, however, we know that the child is most likely going to watch TV because it’s more fun, provides instant gratification, and may help the kid procrastinate a difficult task they don’t want to do. This scenario is understood from the perspective of descriptive decision theory. 

The disconnect between what we should be doing and what we actually do is what drives many of our daily struggles, from procrastination and poor financial choices to unhealthy habits and irrational fears.

While psychologists and cognitive scientists may look at the mental processes behind our decisions, professionals from all areas are keenly interested in how decision-making impacts our actions.5 In economics, decision-making is studied to understand how individuals and organizations allocate resources; in political science, we seek to understand how individuals, groups, and institutions make choices related to governance, policy-making, and international relations; and in sociology and anthropology, practitioners look at how social structures, cultural norms, and interpersonal relationships influence decision-making. That is to say, decision-making is everyone’s business. 

Indecisive? Decision theory

Decisions are complex—that’s why we have an entire subdiscipline dealing with the study of how we come to make them and the reason why we made that particular choice. But decision-making doesn’t stop at subjective internal processes; in fact, there are many technical models of decision-making that can be leveraged for us to reach our particular goals. Decision theory stems from probability, mathematics, and analytical philosophy and employs several methods to determine the optimal choice in situations of uncertainty.7 

For example, Bayesian Decision Theory is a probabilistic approach to decision-making that incorporates uncertainty by updating beliefs based on new evidence. It applies Bayes' Theorem to revise probabilities and optimize decisions by minimizing expected loss or maximizing expected utility. This framework is widely used in fields like finance, machine learning, and medical diagnostics, where decisions must be made under uncertainty with incomplete information.23