Joseph Nathan Cohen

Department of Sociology, CUNY Queens College, New York, NY

A Rational Decision-Making Model

This video was part of the “Teaching through YouTube” project of 2011 – 2013. The video was an attempt to impart the concepts related to the ideal-typical rational calculation model of human action.

To see more about the “Teaching through YouTube” series.  For more, see this post.

Original Video Description

This video introduces a simple model of rational decision-making, in which we try to select choices that deliver us the highest expected net benefit. The video intends to impart the notion that our estimates about costs and benefits, and our estimates of future events, colors our decision-making. CLOSED CAPTIONS AVAILABLE ON THIS VIDEO – USE THEM IF YOU HAVE TROUBLE FOLLOWING! This video was produced in January 2013 for my research methods seminar (SOC 334) at Queens College in the City University of New York. If you are enrolled in this class, you must also complete the assigned readings and exercises. Instructions will be posted on my web site and on Blackboard. This video is part of an experiment in teaching with technology. In the coming semester, I plan on releasing other videos and an overview of this experiment. If you are interested, please visit my web site ( and share your questions, corrections, thoughts or criticisms. I appreciate any feedback or advice on the video’s content (admittedly poor) production, or the format of moving my lectures to sets of short (5-10 minute) streaming videos.

Transcription (Auto-Generated)

In this video, I will describe a simple model of rational decision-making. This model is calculative and probabilistic. It’s calculative because it uses mathematics and probabilistic because it relies on our estimates of the likelihood of future events. The primary focus of this method is to guide us towards choices that maximize our expected net benefits. However, it’s worth noting that this isn’t the only way to make decisions. It’s just one method. Many of us assume this is how decisions are made, and many social-scientific theories are based on this assumption. In reality, our decision-making isn’t always rational, calculated, or deliberate. Recall our previous discussions where a decision is made when we need to select a course of action or thought. Decisions encompass goals, choices, payoffs, risks, and decision rules. Here, our attention is on payoffs and risks. Payoffs refer to the positive outcomes a choice can bring if successful, while risks consider the possible negative consequences. Now, when we mention the term “expected net benefit,” what do we mean? When faced with such terminology, breaking it down can be helpful. The term “benefit” equates to a payoff. This is what we’re aiming for when making a choice. But it’s crucial to remember that decisions inherently carry risks. The expected net benefit essentially measures potential payoffs minus potential risks. It evaluates the benefits against the costs. Consider a simple example. Imagine buying a lottery ticket with the goal of accumulating money. If you win, you gain, say, $100 million. If you lose, you’re down by the cost of the ticket. Without buying the ticket, there’s no potential gain or loss. Simply comparing the potential outcomes, buying a ticket seems lucrative. However, it misses a vital aspect: the likelihood of each scenario. This is where the “expected” in “expected net benefit” comes into play. It refers to our genuine estimates about the probability of outcomes. For instance, consider the Powerball lottery. The chances of winning are minuscule, but the payoff is huge. When you compute the expected benefit (probability multiplied by the payoff), the result might not seem as appealing. Subtracting the expected benefit from the potential loss provides the expected net benefit. Using this method, you might find that not buying the lottery ticket offers the highest expected net benefit. In essence, this model directs us to make decisions that maximize our expected net benefit. However, this example is straightforward. Real-life scenarios often have multiple goals, more choices, and more complex outcomes. While the actual calculations might be more challenging, this model highlights the importance of our expectations in decision-making. Improving our estimates can potentially lead to better choices.