Abstract illustration of a brain representing decision-making processes.

Decoding Decision-Making: How Variational Bayes Explains Our Choices

Mira Elwood in Mind & Education August 2025 • 4 min read.

"Explore how the Variational Bayes framework sheds light on human decision-making, from everyday choices to complex judgments."

Every day, we're faced with a myriad of decisions, big and small. From choosing what to eat for breakfast to making critical business moves, our choices shape our lives. But have you ever stopped to wonder why we make these decisions? What are the underlying processes that drive our preferences and actions?

Traditional models often fall short in capturing the nuances of human decision-making. They assume we're perfectly rational beings, weighing all options with flawless logic. Yet, anyone who's ever made an impulsive purchase or stubbornly stuck to a losing strategy knows that our decisions are often far from rational.

Enter Variational Bayes, a powerful framework that offers a more realistic perspective. By blending statistical inference with cognitive psychology and behavioral economics, Variational Bayes provides a compelling explanation for how we navigate uncertainty, update our beliefs, and ultimately, make choices. It suggests that our brains are constantly trying to strike a balance between what we already believe (our priors) and new information we encounter.

Bayesian Updating: The Foundation of Belief

Abstract illustration of a brain representing decision-making processes.

At the heart of Variational Bayes lies the concept of Bayesian updating. Imagine you have an initial belief about something – let's say, the probability of rain tomorrow. This initial belief is your 'prior.' As you gather new information – the weather forecast, the look of the sky – you adjust your belief. This adjustment process is Bayesian updating.

In mathematical terms, Bayesian updating uses a formula to calculate the 'posterior' belief – your updated belief after considering the new evidence. This formula takes into account your prior belief, the likelihood of the evidence given different possibilities, and a normalizing factor. While seemingly straightforward, the computation can become incredibly complex, especially when dealing with continuous data or intricate models. This is where Variational Bayes steps in to simplify the process.

Variational Bayes offers a practical approach to Bayesian updating by:

Approximating complex probability distributions.
Transforming the problem into an optimization task.
Finding the 'closest' distribution to the true posterior.

In essence, Variational Bayes suggests that our brains are constantly engaging in a simplified form of Bayesian updating, allowing us to efficiently process information and make decisions in a complex world. But what happens when we deviate from this 'ideal' Bayesian process?

The Future of Understanding Decision-Making

Variational Bayes offers a compelling framework for understanding the complexities of human decision-making. By acknowledging our cognitive limitations and incorporating the principles of Bayesian inference, this approach provides a more realistic and nuanced perspective than traditional models. As research continues to evolve, Variational Bayes holds the key to unlocking deeper insights into the choices we make and the forces that shape our behavior.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2405.08796,

Title: Variational Bayes And Non-Bayesian Updating

Subject: econ.th econ.em

Authors: Tomasz Strzalecki

Published: 14-05-2024

Everything You Need To Know

What is Variational Bayes, and how does it help explain human decision-making?

Variational Bayes is a framework that blends statistical inference with cognitive psychology and behavioral economics to explain how we make decisions. It offers a more realistic perspective than traditional models by acknowledging our cognitive limitations and incorporating the principles of Bayesian inference. This means it considers how we navigate uncertainty, update our beliefs based on new information, and ultimately, make choices. Variational Bayes suggests our brains are constantly trying to balance what we already believe (priors) and new information, which is a simplified form of Bayesian updating.

How does Bayesian updating work, and what role does it play in Variational Bayes?

Bayesian updating is the core of Variational Bayes. It's a process where we adjust our initial beliefs (priors) based on new information (evidence). In mathematical terms, it uses a formula to calculate the posterior belief, which is our updated belief. The formula considers the prior belief, the likelihood of the evidence, and a normalizing factor. This process allows us to update our understanding of the world as we receive new data. In the context of Variational Bayes, this process is simplified to enable efficient information processing and decision-making in complex scenarios.

What are the limitations of traditional models of decision-making that Variational Bayes addresses?

Traditional models often assume humans are perfectly rational, weighing all options with flawless logic. However, these models fail to capture the nuances of real-world decision-making, like impulsive purchases or sticking to losing strategies. Variational Bayes provides a more realistic perspective by acknowledging that we don't have perfect information processing capabilities and that our choices are influenced by factors beyond pure rationality. It helps explain how we make decisions under uncertainty, a key aspect of human behavior often overlooked by traditional models.

How does Variational Bayes simplify the process of Bayesian updating?

Bayesian updating, while theoretically sound, can become computationally complex, especially with continuous data or intricate models. Variational Bayes simplifies this process by approximating complex probability distributions. This approach transforms the Bayesian updating problem into an optimization task, allowing it to find the 'closest' distribution to the true posterior. This simplification allows the brain to efficiently process information and make decisions in a complex world, aligning with the idea that our brains are constantly engaged in a simplified form of Bayesian updating.

In what ways can understanding Variational Bayes improve our understanding of human behavior and choices?

Variational Bayes offers a compelling framework for understanding the complexities of human decision-making by incorporating the principles of Bayesian inference and acknowledging cognitive limitations. This approach provides a more nuanced perspective than traditional models by recognizing that our brains are not perfect processors of information and that our choices are influenced by prior beliefs and new evidence. By studying Variational Bayes, we can gain insights into why we make certain choices, how we learn and adapt to new information, and the factors that drive our behavior. It helps us understand the underlying processes that shape our preferences and actions, offering a deeper understanding of human behavior.