Algorithmic predictions and resource allocation.

AI in Decision Making: Are Better Predictions Always Worth It?

Theo Raines in Tech & Innovation December 2025 • 4 min read.

"Explore the hidden complexities of algorithmic decision-making and discover whether improving prediction accuracy truly delivers the best outcomes in public resource allocation."

Algorithms are increasingly shaping critical decisions about distributing resources and interventions in areas that impact the public. At their core, these algorithms offer predictions—insights into the likelihood of future events—that stakeholders use to make informed decisions and enhance social welfare. The promise is clear: better predictions lead to better decisions, which, in turn, boost overall well-being.

But maximizing welfare is more complex than simply improving the accuracy of predictions. Social planners have many levers at their disposal, such as expanding access to available resources and boosting the impact of interventions. In the real world, schools use algorithms to identify at-risk students, and healthcare systems predict individual health risks to prioritize treatments. The assumption is that better predictions will ensure that limited resources reach those who need them most.

It's easy to assume that better predictions automatically lead to better outcomes, but this isn't always the case. Consider the bigger picture: What if you could improve outcomes more effectively by simply making existing resources more accessible or enhancing the quality of the interventions themselves? This article explores a fundamental question: In algorithmic decision-making, what is the real value of prediction compared to other strategies for improving welfare?

The Prediction-Access Ratio: A New Way to Think About Resource Allocation

Algorithmic predictions and resource allocation.

To understand the real value of prediction, we need a way to compare it to other methods of improving social welfare. This is where the concept of the Prediction-Access Ratio (PAR) comes in. The PAR is simply the ratio of the marginal improvement in social welfare achieved by improving the predictor (the algorithm) to the improvement gained by expanding access to resources.

Imagine that by making the predictor more accurate, we are increasing the quality of decision-making and we also are increasing the pool of resources that are available. The PAR helps determine whether focusing on better predictions or broader access provides a bigger boost to social welfare.

Improving Prediction: This involves enhancing the algorithm to generate more accurate insights. Think of it as fine-tuning the tool to better identify who needs help.
Expanding Access: This focuses on making existing resources more available. For example, hiring more counselors in schools or increasing the availability of vaccines.

Calculating the PAR is a crucial step in determining the most cost-effective way to improve overall welfare. The central question becomes: is it more worthwhile to invest in refining our predictions, or to put those resources into expanding access? To solve this, consider the ratio between the marginal cost of improving prediction vs. the marginal cost of expanding access, multiplied by PAR.

Making Smarter Choices for a Better Future

The key takeaway is that improving predictions isn't always the best way to improve social welfare. Expanding access to resources can often provide a greater boost, especially when resources are limited. By carefully considering the Prediction-Access Ratio and factoring in the costs of different strategies, decision-makers can make more informed choices that lead to better outcomes for everyone. It’s about using AI not just to predict, but to create a fairer and more equitable world.

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.2312.08511,

Title: The Relative Value Of Prediction In Algorithmic Decision Making

Subject: cs.cy cs.lg econ.th stat.ml

Authors: Juan Carlos Perdomo

Published: 13-12-2023

Everything You Need To Know

How are algorithms being used in decisions about public resources, and what's the main goal?

Algorithms are increasingly used to shape critical decisions about distributing resources and interventions in areas that impact the public. These algorithms offer predictions about the likelihood of future events, which stakeholders then use to make informed decisions and enhance social welfare. The main goal is to use these predictions to ensure resources reach those who need them most, ultimately boosting overall well-being. However, it's crucial to remember that improving prediction accuracy is not the only pathway, and social planners must consider the impact of expanding access to available resources and boosting the impact of interventions.

What is the 'Prediction-Access Ratio' (PAR), and how does it help in resource allocation?

The Prediction-Access Ratio, or PAR, is a method of comparing improving a prediction algorithm's accuracy versus expanding access to resources when determining social welfare. PAR helps decide if investing in improving an algorithm, the 'predictor', or increasing resource accessibility provides a bigger boost to social welfare. By understanding the ratio between the marginal cost of improving prediction versus the marginal cost of expanding access, multiplied by PAR, decision-makers can make smarter choices for a better future.

Why isn't improving prediction accuracy always the best approach for enhancing social welfare?

Improving prediction accuracy isn't always the optimal approach because there may be more effective strategies, such as expanding access to existing resources or enhancing the quality of interventions. Focusing solely on refining predictions can overlook the potential benefits of making resources more accessible or improving the impact of interventions. Improving access or interventions can provide a greater boost, especially when resources are limited. Decision-makers should consider the trade-offs between improving predictions and expanding access to achieve fairer and more equitable resource allocation.

What are some practical examples of improving prediction versus expanding access, and how do these relate to social welfare?

Improving prediction might involve fine-tuning an algorithm used by schools to identify at-risk students or enhancing algorithms used by healthcare systems to predict individual health risks. Expanding access, on the other hand, could mean hiring more counselors in schools or increasing the availability of vaccines. When considering the PAR, if the cost of improving the prediction algorithm is high and the impact is relatively small compared to the cost and impact of hiring more counselors, for instance, then expanding access would be the better strategy. Balancing these approaches ensures a more equitable and effective distribution of resources, ultimately improving social welfare.

What considerations should decision-makers keep in mind to ensure AI contributes to a fairer and more equitable world, especially when prediction algorithms are involved?

Decision-makers should carefully consider the Prediction-Access Ratio (PAR) and factor in the costs of different strategies, ensuring they don't automatically assume that better predictions lead to better outcomes. It is essential to evaluate whether expanding access to resources can provide a greater boost to social welfare than refining prediction algorithms. Using AI not just to predict but to create a fairer and more equitable world requires balancing prediction accuracy with the accessibility and impact of resources and interventions. Stakeholders should prioritize strategies that address the root causes of inequities and promote inclusivity.