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

Rational preference: The consistent and logical prioritization of choices by an individual based on a coherent set of criteria or values aimed at maximizing utility or satisfaction.

Bounded rationality: A concept introduced by Herbert Simon that suggests individuals make decisions within the constraints of limited information, cognitive limitations, and finite time. As a result, they make decisions that are simply good enough rather than the best.

Decision theory: A branch of mathematics, economics, and philosophy that studies how individuals, groups, or organizations make decisions.

Satisficing: A decision-making strategy or cognitive heuristic in which an individual evaluates all available alternatives until an acceptability threshold is reached. 

Maximizing: Unlike satisfiers, maximizers look to select the best possible option out of the array of choices. Seeking to maximize, an individual may spend more time on deliberation—which can be inefficient. 

Heuristics: Mental shortcuts that allow us to make quick judgment calls based on generalizations. While heuristics may reduce cognitive load, they can lead to biases that hinder our decision-making.

For centuries, people have been studying and exploring decision-making in their quest for answers to “why humans do what they do.” It would be somewhat impossible to cover all of the individuals who have impacted our understanding of decision-making, but we can provide an overview of some key players. 

We can trace decision analysis back to ancient philosophical inquiries about human nature and rationality, with significant contributions from Greek philosophers who emphasized the role of reason and virtue in making choices. Herodotus, the historian and geographer known for writing Histories, explored the concepts of “right” (rational) and “contrary to good counsel” (irrational). Additionally, the renowned philosopher and polymath Aristotle expanded on the concept of “rational preference,” which refers to the choices made by rational individuals. In Book III of his Topics, Aristotle wrote: If A is unconditionally better than B, then the best member of A is also better than the best member of B.6

During the following centuries, the study of decision-making evolved, incorporating insights from the Enlightenment's emphasis on rational thought and the empirical approaches of modern science. In the 1600s, philosophers such as John Locke and David Hume significantly advanced our understanding of human cognition and decision-making. Locke’s theories on empiricism and human understanding laid a foundation for considering how sensory information and experience influence our thinking. Later, Hume introduced the idea that many emotions play a crucial role in decision-making, a concept that remains prevalent in modern psychology. 

 The 20th century marked a pivotal period with the advent of decision theory, behavioral economics, and cognitive psychology, which provided a deeper understanding of how humans process information, assess risks, and navigate uncertainties. Perhaps the most influential development during this time was the formalization of decision theory as a multidisciplinary framework dealing with making choices under uncertainty. Although the work of various individuals laid the theoretical groundwork for decision theory during the 18th and 19th centuries, it came to prominence in the 20th century through the work of Frank Ramsey, Bruno de Finetti, Leonard Savage, and Richard Jeffrey.7

 Decision-making research expanded far beyond philosophy when Daniel Kahneman and Amos Tversky introduced Prospect Theory. Their ideas disrupted the long-held assumption of rational choice, demonstrating that human decisions are influenced by cognitive biases, heuristics, and emotional factors rather than pure logic. This breakthrough paved the way for the exploration of behavioral economics, cognitive psychology, and real-world decision-making processes, influencing fields such as finance, public policy, healthcare, and artificial intelligence. 

Humans have also started using their understanding of decision-making processes to influence the decisions and behaviors of other people in subtle ways. First introduced by economist Richard Thaler in 2008,9 nudging strategies are now widely used by governments and organizations to guide people to make better decisions about their health, finances, and social practices without restricting their freedom of choice (although this approach is not without its controversies, as we’ll see below). 

 Today, decision-making continues to evolve by integrating advanced technologies such as artificial intelligence and big data analytics, which offer new tools and methodologies for improving accuracy, efficiency, and personalization in decision-making processes across various domains. In 2022, decision management—the method of designing, building, and managing automated decision-making systems—was worth USD$4.66 billion and is expected to grow to USD$15.49 billion by 2030.8

John Locke

English philosopher and physician widely regarded as one of the most influential Enlightenment thinkers. His ideas and writings significantly shaped Western philosophy, political theory, and education.

David Hume

Scottish philosopher, historian, economist, and essayist known for his influential contributions to Western philosophy and the Enlightenment. His works encompassed various topics, including epistemology, metaphysics, ethics, political philosophy, and aesthetics.

Herbert Simon

Influential American economist, political scientist, cognitive psychologist, and computer scientist renowned for his pioneering work in multiple disciplines, particularly in the study of decision-making and artificial intelligence. He was awarded the Nobel Prize in Economic Sciences in 1978 for his research on bounded rationality, which challenged traditional economic models of human decision-making.

Richard Thaler

American economist known for his pioneering work in behavioral economics and his significant contributions to the understanding of how psychological factors influence economic decisions and market outcomes.

Amos Tversky

Israeli cognitive psychologist known for his pioneering work in behavioral economics, decision-making, and cognitive biases. Along with his lifelong colleague, Daniel Kahneman, he developed Prospect Theory, shaping the understanding of human judgment under uncertainty.

Daniel Kahneman

Israeli-American psychologist and Nobel laureate famous for his pioneering work in cognitive science and decision-making. Kahneman’s work challenged the long-standing assumption of human rationality in modern economic theory, paving the way for behavioral economics, which integrates psychological insights into economic decision-making. His book Thinking, Fast and Slow popularized the concepts of System 1 and System 2 thinking.

Gerd Gigerenzer

German psychologist and researcher best known for his work in decision-making, heuristics, and ecological rationality. His theory of ecological rationality challenges the traditional view that heuristics lead to irrational biases, instead arguing that simple decision-making strategies can be highly effective when matched to real-world environments.

John Von Neumann

Hungarian-American mathematician, physicist, and polymath whose work shaped multiple fields, including computing, quantum mechanics, and economics. Alongside his colleague Oskar Morgenstern, his work laid the foundation for the development of game theory. 

Oskar Morgenstern

Austrian-American economist best known for his work in game theory alongside John von Neumann. His research transformed economics by introducing mathematical models of strategic decision-making, which became central to fields like business, military strategy, and political science.

Since we’ve established that decision-making isn’t the most straightforward process—you probably aren’t surprised to find out that there are plenty of theories about how and why we come to make certain choices. Over the decades, psychologists, behavioral scientists, and economists have developed several different models of human decision-making (the following are just a handful of examples). 

Bounded Rationality 

This model, originally developed by Herbert A. Simon, challenges the traditional assumption that humans are perfectly rational decision-makers. Instead, the bounded rationality model suggests that people face cognitive limitations, incomplete information, and time constraints, all of which prevent them from making purely optimal decisions. Engaging in ‘satisficing,’ we look for a “good enough” solution that meets our needs rather than evaluating every possible option to find the absolute best one. 

Dual-process theory 

Developed by Amos Tversky and Daniel Kahneman (with additional contributions from Jonathan Evans and Keith Stanovich), dual-process theory is a psychological framework that explains how people think and make decisions based on two distinct cognitive systems. 

System 1 thinking is fast, intuitive, and automatic and operates effortlessly and without conscious control. It relies on heuristics (mental shortcuts) and emotions, enabling quick judgments like recognizing a familiar face or making a snap decision under pressure. This system, however, often leads to biases and errors because accuracy is often sacrificed for speed. System 2 thinking is slow, deliberate, and analytical, requiring effort, focus, and logical reasoning. We engage in System 2 thinking when we’re solving complex problems, weighing up pros and cons, or questioning initial assumptions. While more reliable, System 2 is also energy-intensive and not always used when needed, as people often default to the more effortless System 1. 

Heuristics

Heuristics, or the mental shortcuts our brains use to make decisions or process information quickly, are a concept originally developed by Herbert A. Simon in the middle of the last century. In the 1970s and 1980s, Tversky and Kahneman studied heuristics in human decision-making, challenging the traditional assumption that humans are fully rational decision-makers. Common heuristics that influence our decision-making include the availability heuristic, anchoring effect, affect heuristic, representativeness heuristic, and recognition heuristic.

Decision trees

A decision tree is a graphical tool used for decision-making, helping to break down complex choices into a structured, step-by-step format. The trees represent different possible outcomes, probabilities, and potential costs or benefits and can be used to analyze risks and rewards. There are four components of a decision tree (each with a corresponding shape): 

1. Decision Nodes (Squares) – Points where a decision must be made.
2. Chance Nodes (Circles) – Points where an outcome is uncertain and probabilities apply.
3. Branches (Lines) – Represent different choices or possible events.
4. End Nodes (Triangles or Empty Points) – Indicate final outcomes or payoffs.

The decision tree model has its roots (no pun intended) in decision theory and statistics. Mathematician John Von Neumann and economist Oskar Morgenstern were the first to apply decision trees in modern decision analysis as part of their game theory. Decision tree algorithms were later refined for machine learning in the 1980s by computer scientist Ross Quinlan and are now used widely in artificial intelligence and data science.

Do you find it difficult to make decisions? Perhaps you even go out of your way to dodge making those extra difficult ones?

Research on decision avoidance by Serena Hagerty and Kate Barasz shows that people are willing to put themselves in an objectively worse position to avoid making a tough decision.10 They also found that people hope for relatively worse news to pre-emptively avoid subjectively difficult decisions. For example, in one study, participants were asked to imagine a shoulder injury with differing levels of severity. In the case that the tear was more severe, surgery would be medically necessary and leave the participant with little choice and a long road to recovery. In the second scenario, the tear wasn’t so terrible that it endowed the participant with some level of autonomy, and they would be allowed to decide whether or not they were to go through with the procedure. Surprisingly, a significant number of participants preferred the idea of a more severe injury—simply so they didn’t have to make a challenging decision.11

There are several factors that can make the decision-making process hard. For instance, too many options can overwhelm us (choice overload), and unknown situations can make us wary of the outcomes. The perceived difficulty of decision-making also varies from person to person; what for you may feel like an impossible choice to make might be quite straightforward for someone else. 

So, how can we make it easier for ourselves to make decisions? 

Well, unfortunately, it’s probably not possible to consciously improve all decision-making processes. According to Harvard professor Gerald Zaltman, approximately 95% of our cognitive processes occur at the subconscious level.18 But for the decisions that we are making consciously, journaling may be an effective way to ensure that they’re the right ones. 

Amanda Reill, a Harvard Business Review author who writes on decision fatigue, highlights that the act of physically writing engages multiple cognitive processes, helping to articulate and clarify our thoughts. Writing allows us to balance analytical reasoning with creative exploration, ensuring that we consider both the factual details and the broader possibilities of a decision.18 However, it's important to write down not just the pros and cons but also your genuine thoughts and emotions—this process can help untangle complexity and bring clarity to difficult choices.

Being well-informed is crucial for good decision-making, but too much information can lead to overload and paralysis, making it harder to choose effectively. If you're deciding whether to take a new job, seeking input from a select few trusted individuals is often more helpful than consulting your entire social circle. 

Finally, your personal values can serve as a strong guiding framework for decision-making, helping you align choices with what truly matters to you. By considering your core beliefs, priorities, and long-term goals, you can make decisions that feel authentic and lead to greater satisfaction and fulfillment.

Sometimes, we go around in circles when we’re making decisions. We think we’ve made our choice, then change to another before returning to the original one. The study of decision-making may well just be doing that, too. 

The traditional view of decision-making suggests that humans make choices based on logical reasoning and a desire to maximize expected outcomes. Theories such as expected utility theory, developed by Neumann and Morgenstern, posit that individuals assess the probabilities and potential payoffs of different choices and make decisions that yield the highest expected utility. However, since the pioneering work of Tversky and Kahneman, the behavioral science view that humans are a bit irrational and easily swayed by emotions, heuristics, and biases has prevailed. 

But now some researchers believe that we aren’t homo irrationalis after all (phew!). Jens Madsen et al. argue that behavioral science should start from the assumption that people are reasonable rather than irrational or rational.19 Why? They believe that the assumption that we’re irrational as a default is problematic because it neglects the context and goals that shape behavior, and it risks treating biases as explanatory rather than descriptive. 

Instead, the authors define “reasonableness” as behavior that makes sense given a person’s knowledge, goals, and social context. This approach, they argue, considers the systemic environment in which we make decisions rather than just focusing on what’s going on inside our heads. By having a more nuanced and holistic understanding of human behavior, it’s believed we can foster better policy design. 

So, the rational vs. irrational debate continues. But there are other ideas on the table. The ecological rationality theory, first developed by German psychologist Gerd Gigerenzer, suggests human reasoning and heuristics are adaptive to specific environments rather than inherently biased or flawed.

This theory has been used to explore decision-making processes among older people.20 Aging is often linked to cognitive decline, such as reduced memory and executive function. However, ecological rationality challenges the idea that this decline inevitably leads to poor decision-making. In their paper, Mata et al. argue that successful decision-making depends on how well simple strategies fit the environment. In other words, older adults may compensate for cognitive decline by relying on experience and adaptive heuristics, allowing them to make effective decisions despite reduced mental processing capacity.

Other controversies surrounding decision-making refer to the extent to which humans can (and whether they should) influence the decisions of other humans. Nudging, choice architecture, and cognitive biases are widely used in public policy, business, and marketing to influence decision-making in subtle, often unconscious ways. While these techniques have proven to be effective in certain areas (the UK even established a ‘nudge unit’21), critics argue that they raise ethical concerns about manipulation and autonomy, especially when used without transparency. 

And after all that, some still argue that we don’t make decisions at all. The free will vs. determinism debate questions to what extent we have the autonomy to make our own choices or whether all behavior is predetermined by internal and external factors. But as Liam Monsell explains in his article “The Game of Life: Discussing Determinism in Behavioral Science,” no answer to this conundrum is ever satisfactory.

AI helping doctors make decisions

Humans aren’t infallible and can make bad decisions. Oftentimes, poor decisions aren’t life-threatening, such as taking a new route home from work and getting stuck in traffic. But sometimes bad decision-making can have harmful consequences. Take, for instance, medical professionals who have to evaluate complex evidence to make decisions about their patients’ health. One small error in their decision-making process can have catastrophic consequences. 

Thankfully, research shows that AI algorithms, particularly those involving machine learning and deep learning, can help medical practitioners by analyzing medical images (such as X-rays, MRIs, and CT scans) and providing them with suggestions about diagnoses. This technology is not new to the world of medicine; clinical decision support systems (CDSS) have been around since the 1980s but have recently witnessed a rapid evolution thanks to advances in the field of AI.12

For example, AI has been shown to improve the diagnostic performance of dermatologists in identifying skin cancers from images.13 Eleni Linos, Jiyeong Kim, and Isabelle Krakowski from Stanford University’s Medical School reviewed 67,000 evaluations of potential skin cancers to see whether AI impacted accuracy. They discovered that healthcare practitioners working without help from AI were able to accurately diagnose about 75% of people with skin cancer, while those with help from AI correctly diagnosed 81.1% of cancer cases. Although 6.1% may not seem like a large improvement, in reality, it equates to a lot more people receiving the correct treatment at the right time. 

However, doctors aren’t ready to hang up their lab coats and stethoscopes quite yet. The algorithms used in these contexts are still overseen by clinicians who assess the patient and choose whether to accept the computer’s suggestion. Ultimately, it’s the medical professionals who have the final say about the patient’s diagnosis. 

Ethical and moral decision-making 

Human decision-making isn’t always just about evaluating cold, hard data and coming to a conclusion. There are often intangible human factors that go into real-life decision-making, such as the ethical and moral considerations that inevitably guide our choices. These elements are learned through experience and our interactions with other human beings. 

The famous ‘trolley problem,’14 first discussed by English philosopher Philippa Foot in 1967 and later given its name by Judith Jarvis Thomson in 1976, highlights the important role of moral and ethical considerations in human decision-making. In the original version, a runaway trolley speeds down a railway towards five people tied to the tracks and unable to move. You, the decision maker, are standing some way away next to a lever; if you pull this lever, the trolley will switch to a different set of tracks and save the five people. However, on the new set of tracks is a single person tied down. You have to make a decision whether to do nothing and kill the five people on the main track or pull the lever and kill one person. Which course of action is more ethical? Which would you choose? 

Luckily, people from all disciplines have been ruminating on ethical and moral decision-making for decades and have come up with several frameworks to help navigate impossible choices. The Blanchard-Peale Framework, written about by Ken Blanchard and Norman Vincent Peale in their 1988 book The Power of Ethical Management,15 asks three questions: Is it legal? Is it fair? How does it make me feel? The Markkula Center for Applied Ethics at Santa Clara University suggests applying five dimensions or approaches when dealing with an ethical issue:16

1. Utilitarianism – the most ethical action is the one that provides the greatest amount of good to the largest number of people. 
2. Rights approach – the best decision is the one that preserves and protects human dignity and moral rights. 
3. Fairness – all humans should be treated equally.
4. Common good approach – actions should promote public life and the welfare of all.
5. Virtue approach – decision makers should ask, “What kind of person would I be if I take this action?”

Finally, according to the Issue-Contingent Model developed in 1991 by Thomas M. Jones, a professor at the University of Washington, there are four steps involved in making a decision: recognize the issue, make the judgment, establish moral intent, and engage in behavior.17

Although the future looks promising for incorporating AI into our decision-making processes, the issue of ethics and morals remains a major hurdle. Not only is it extremely difficult to teach AI about the subjectivities of morals and ethics, but determining who is ultimately responsible for the decisions made by AI is a complex ethical issue in and of itself.

Decision-Making Process

Eager to know more about decision-making? This article delves deeper into the process we go through when we are making a decision. 

Decision-Making Parallels Between Humans and Animals

Believe it or not, the decision-making process of animals is very similar to that of humans. That is, we both weigh the advantages and disadvantages of our choices and take actions based on that assessment. Read this article to learn more about the research behind animal decision-making. 

1. Attwood, D. A., Deeb, J. M., & Danz-Reece, M. E. (2004). Ergonomic Solutions for the Process Industries. Elsevier.
2. Reill, A. (2023, December 5). A Simple Way to Make Better Decisions. Harvard Business Review. https://hbr.org/2023/12/a-simple-way-to-make-better-decisions#:~:text=Various%20sources%20suggest%20that%20the,how%20we'll%20say%20it.
3. Wansink, B., & Sobal, J. (2007). Mindless Eating: The 200 Daily Food Decisions We Overlook. Environment and Behavior, 39, 106-123. DOI: 10.1177/0013916506295573.
4. Gonzalez, C. (2014). Decision-Making: A Cognitive Science Perspective. In Chipman, S. (Ed.), The Oxford Handbook of Cognitive Science [online]. DOI: 10.1093/oxfordhb/9780199842193.013.6.
5. Hunter, M., Wang, Y., & Ruhe, G. (2010). The Cognitive Process of Decision-Making. In Hunter, C. et al. (eds.) Strategic Information Systems: Concepts, Methodologies, Tools, and Applications. IGI Global, pp. 1452-1464.
6. Bergman, R. (2022, April 26). History of Decision-Making and Application to Dispute Resolution. Mediate. https://mediate.com/history-of-decision-making-and-application-to-dispute-resolution/.
7. Weirich, P. (2023, April 24). Decision Theory. Oxford Bibliographies. https://www.oxfordbibliographies.com/display/document/obo-9780195396577/obo-9780195396577-0207.xml.
8. Fortune Business Insights. (2024, June 3). Hardware & Software IT Services/Decision Management Market. Fortune Business Insights. https://www.fortunebusinessinsights.com/decision-management-market-107473#.
9. Thaler, R. H., & Sunstein, C. R. (2008). Nudge. New York: Penguin.
10. Barasz, K., & Hagerty, S. (2021). Hoping for the Worst? A Paradoxical Preference for Bad News. Journal of Consumer Research, 48(2), pp. 270-288.
11. Sawhney, V. (2021, August 19). Why Do We Try to Dodge Difficult Decisions? Harvard Business Review. https://hbr.org/2021/08/why-do-we-try-to-dodge-difficult-decisions.
12. Sutton, R., Pincock, D., Baumgart, D., Sadowski, D., Fedorak, R., & Kroeker, K. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digital Medicine, 3(17).
13. Conger, K. (2024, April 11). AI improves accuracy of skin cancer diagnoses in Stanford Medicine-led study. Stanford Medicine. https://med.stanford.edu/news/all-news/2024/04/ai-skin-diagnosis.html.
14. Andrade, G. (2019). Medical ethics and the trolley problem. Journal of Medical Ethics and History of Medicine, 12(3).
15. Blanchard, K., & Peale, N. V. (1988). The Power of Ethical Management. New York: William Morrow and Company.
16. Florida Tech. (n.d.). 3 Frameworks for Ethical Decision-Making. Florida Tech Online. https://www.floridatechonline.com/blog/business/3-frameworks-for-ethical-decision-making/.
17. Harrington, S. J. (1997). A Test of a Person – Issue Contingent Model of Ethical Decision-Making in Organizations. Journal of Business Ethics, 16, pp. 363-375.
18. Reill, A. (2023, December 5). A Simple Way to Make Better Decisions. Harvard Business Review. https://hbr.org/2023/12/a-simple-way-to-make-better-decisions.
19. Madsen, J., et al. (2024). Behavioral science should start by assuming people are reasonable. Trends in Cognitive Sciences, 28(7), 583-585.
20. Mata, R., et al. (2012). Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker. Frontiers in Neuroscience, 6(19).
21. Rutter, J. (2020, March 11). “Nudge Unit”. Institute for Government. https://www.instituteforgovernment.org.uk/article/explainer/nudge-unit.
22. Jankelová, N. & Puhovichová, D. (2020). Normative and descriptive perception of strategic decision making. SHS Web of Conferences, 83, 01027.
23. Fawzy Gad, A. (2024, September 23). Bayesian decision theory explained. DigitalOcean. https://www.digitalocean.com/community/tutorials/bayesian-decision-theory.