Person drowning in data, reaching for a beacon of light.

Is More Information Always Better? Unveiling the Law of Diminishing Returns in Decision-Making

Kai Mendoza in Mind & Education August 2025 • 5 min read.

"Dive into the surprising world of information economics, where more data doesn't always lead to better choices. Discover how to navigate the complexities of information overload and make smarter decisions."

We live in an age of unprecedented access to information. From news articles and social media feeds to market reports and scientific studies, we're constantly bombarded with data. The intuitive assumption is that having more information at our fingertips leads to better, more informed decisions. However, economic theory suggests a more nuanced reality: the value of information doesn't always increase linearly. There's a point where additional information provides diminishing returns, and in some cases, can even hinder the decision-making process.

The idea that information might not always be beneficial isn't new. Economists have long grappled with the concept of 'non-concavity' in the value of information. This means that the marginal value of each additional piece of information decreases as the quantity of information grows. This concept challenges the common perception that more data automatically translates to improved outcomes.

This article explores the complexities of information value and the law of diminishing returns in decision-making. We'll delve into the theoretical underpinnings of this phenomenon, drawing on research in information economics and decision theory. Ultimately, we aim to provide practical insights into how to navigate the information age, make smarter choices, and avoid the pitfalls of information overload.

The Core Question: Does Quantity Equate to Quality?

Person drowning in data, reaching for a beacon of light.

At the heart of this discussion is how we quantify information. A common approach is to equate information with a 'Bayes-plausible distribution' over possible outcomes. Imagine you're trying to predict the success of a new product. Each piece of information you gather – market research, competitor analysis, consumer surveys – refines your understanding of the probabilities of success and failure. However, the key insight is that the 'quantity of information,' measured by the expected divergence of the posterior (your refined belief) from the prior (your initial belief), may not always translate into better decision-making.

Think of it this way: the first few pieces of information might drastically shift your perspective and lead to a much better understanding of the situation. But after a certain point, additional information may only offer marginal improvements, or even introduce noise and confusion. It's like adding spices to a dish – a little can enhance the flavor, but too much can ruin it.

Why might more information sometimes be detrimental?

Cognitive Overload: Humans have limited cognitive capacity. Too much information can overwhelm our ability to process and analyze data effectively, leading to poorer decisions.
Confirmation Bias: We tend to seek out and interpret information that confirms our existing beliefs, even if that information is flawed or incomplete. More information can exacerbate this bias, leading to skewed perceptions and suboptimal choices.
Analysis Paralysis: The sheer volume of information can lead to indecision and procrastination. We become so focused on gathering more data that we never actually make a decision.
Information Asymmetry: Not all information is created equal. Some sources are more reliable and accurate than others. Relying on low-quality information can lead to flawed decisions, even if you have a lot of it.

Economists Radner and Stiglitz highlighted that the marginal productivity of information depends heavily on how the quantity of information is measured. Many studies specify particular parametrizations of the quantity of information, which allows them to derive specific results about its value. These studies suggest that there isn't a universal rule; the relationship between information and decision quality depends on the context, the decision-maker, and the nature of the information itself.

Navigating the Information Age: Strategies for Smarter Decision-Making

So, how do we navigate the information age and make better decisions without falling victim to information overload? The key is to be more strategic about how we acquire and process information. This includes focusing on high-quality sources, filtering out noise, and being aware of our own cognitive biases. It also means recognizing that sometimes, the best decision is the one made with less information, but with a clearer understanding of our goals and values.

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.2404.0119,

Title: Call The Dentist! A (Con-)Cavity In The Value Of Information

Subject: econ.th

Authors: Mark Whitmeyer

Published: 01-04-2024

Everything You Need To Know

What is the central argument about information in the context of decision-making?

The central argument revolves around the idea that more information doesn't always lead to better decisions. The article explains that the value of information doesn't increase linearly. It highlights the concept of diminishing returns, where the marginal value of each additional piece of information decreases as the quantity of information grows. In some cases, additional data can hinder the decision-making process due to factors like cognitive overload, confirmation bias, analysis paralysis, and information asymmetry.

How is the quantity of information quantified, and why is this approach significant in decision-making?

The quantity of information is often quantified using a 'Bayes-plausible distribution' over possible outcomes. This approach is significant because it allows us to measure how each piece of information refines our understanding and adjusts our probabilities. For example, when predicting a new product's success, each piece of information from market research, competitor analysis, and consumer surveys refines your understanding. However, the article points out that the 'quantity of information' measured by the expected divergence of the posterior (refined belief) from the prior (initial belief) may not always translate into better decisions. Therefore, it emphasizes the importance of recognizing that more information doesn't always equate to better outcomes.

What are the main reasons why more information can sometimes be detrimental to the decision-making process?

The article outlines four primary reasons why more information can be detrimental. Firstly, 'Cognitive Overload' occurs when humans have limited cognitive capacity, and too much information overwhelms our ability to process and analyze data effectively, leading to poorer decisions. Secondly, 'Confirmation Bias' causes us to seek information that confirms our existing beliefs, which can skew perceptions. Thirdly, 'Analysis Paralysis' results from the sheer volume of information, leading to indecision and procrastination. Finally, 'Information Asymmetry' means that not all sources are reliable, and relying on low-quality information leads to flawed decisions even with a lot of data.

How do Radner and Stiglitz contribute to the understanding of information's impact on decision quality?

Economists Radner and Stiglitz highlighted that the marginal productivity of information significantly depends on how the quantity of information is measured. Their work underscores that there is no universal rule regarding the relationship between information and decision quality. They suggest that the outcome depends on several factors: the context, the decision-maker, and the nature of the information itself. Their work allows us to understand that there is no one-size-fits-all approach, and the impact varies depending on specific scenarios.

What strategies are recommended for smarter decision-making in the information age, and what key understanding should guide our approach?

To make smarter decisions in the information age, the article recommends strategic approaches. These strategies include focusing on high-quality sources, filtering out noise, and being aware of our own cognitive biases. The key understanding is recognizing that sometimes, the best decision is made with less information, but with a clearer understanding of our goals and values. This highlights the importance of being selective and critical about the information we consume, rather than simply accumulating as much data as possible. It's crucial to prioritize quality and relevance over quantity to avoid the pitfalls of information overload and make more informed choices.