Chess game on a stormy sea, symbolizing robust pricing strategy.

Robust Pricing: How to Make Decisions When You Don't Know Everything

Elliot Brynn in Business & Economy September 2025 • 3 min read.

"Navigating Uncertainty: A guide to robust decision-making in pricing strategies, ensuring resilience in the face of incomplete information."

In the world of business, decisions often need to be made without all the facts. This is particularly true for pricing strategies, where understanding market dynamics and customer behavior is critical, but rarely complete. When uncertainty is approached using probabilities, this is known as stochastic optimization. However, what happens when it's impossible to know the true probabilities?

Robust decision-making is a way of dealing with uncertainty. It acknowledges that decision-makers often know something, but not everything. Instead of trying to predict the future exactly, robust decision-making focuses on creating strategies that perform well across a range of possible future scenarios.

This method has roots in Statistics, Operations Research, Computer Science, and Economics, with significant efforts devoted to developing solid criteria. But how do you choose the 'best' robust approach? What if different robust strategies suggest different actions?

Understanding Robust Optimization Criteria: Finding the Best Fit

Chess game on a stormy sea, symbolizing robust pricing strategy.

Several robustness criteria exist to guide decision-making under uncertainty. These include:

Each criterion offers a unique perspective and aims to address specific concerns:

Maximin Performance: This seeks to maximize the minimum possible outcome. It is about ensuring a certain baseline level of performance, no matter what happens.
Minimax Regret: This aims to minimize the worst-case 'regret,' or the difference between the decision made and the best decision that could have been made in hindsight.
Maximin Ratio: This maximizes the worst-case ratio between the achieved performance and the best possible performance. It ensures that the outcome is never too far from what could have been achieved.

While all three criteria are reasonable, they don't always point to the same decision. In practice, you might have to ask which criterion you should trust? Different scenarios can lead you to favor one criterion over another, highlighting the risk of 'overfitting' to a particular way of thinking about uncertainty.

The Best of Many Robustness Criteria: Finding Stability in Pricing Strategy

Rather than get stuck on just one method, remember that you can create pricing strategies that can meet business goals across a range of scenarios. By understanding the strengths and weaknesses of these three criteria, decision-makers can design mechanisms that achieve good performance across diverse criteria simultaneously.

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.2403.1226,

Title: The Best Of Many Robustness Criteria In Decision Making: Formulation And Application To Robust Pricing

Subject: math.oc econ.th

Authors: Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes

Published: 18-03-2024

Everything You Need To Know

What is robust decision-making, and how does it help with pricing strategies?

Robust decision-making is a strategic approach to making decisions under uncertainty, especially when dealing with pricing strategies where complete information is rarely available. It acknowledges that decision-makers have some knowledge but not all the facts. Instead of predicting the future, robust decision-making focuses on creating pricing strategies that perform well across various potential future scenarios. This helps businesses navigate market dynamics and customer behavior without having to know everything about the future. The goal is to build resilience and capitalize on opportunities despite incomplete information.

What are the key robust optimization criteria, and what makes them unique?

There are three key robust optimization criteria: Maximin Performance, Minimax Regret, and Maximin Ratio. Maximin Performance aims to maximize the minimum possible outcome, focusing on a baseline level of performance. Minimax Regret seeks to minimize the worst-case 'regret,' or the difference between the decision made and the best decision that could have been made in hindsight. Finally, Maximin Ratio maximizes the worst-case ratio between the achieved performance and the best possible performance, ensuring outcomes are not too far from the best possible. Each criterion offers a unique perspective on managing uncertainty, but they don't always suggest the same action.

Why is it important to consider multiple robust optimization criteria when making pricing decisions?

Considering multiple robust optimization criteria is important because it allows decision-makers to create pricing strategies that can meet business goals across a range of scenarios. Each criterion, such as Maximin Performance, Minimax Regret, and Maximin Ratio, has its strengths and weaknesses. Relying on just one method can lead to 'overfitting' to a particular way of thinking about uncertainty, potentially leading to suboptimal outcomes in different scenarios. Understanding the interplay between these criteria helps create more stable and effective pricing strategies.

How does Maximin Performance differ from Minimax Regret in the context of pricing strategies?

Maximin Performance focuses on maximizing the minimum possible outcome, essentially ensuring a certain baseline level of performance. In contrast, Minimax Regret aims to minimize the worst-case 'regret,' which is the difference between the decision made and the best decision that could have been made if the future were known. The key difference is that Maximin Performance prioritizes securing a minimum acceptable result, whereas Minimax Regret prioritizes minimizing the potential for a significant loss relative to the best possible outcome. Both criteria offer valuable perspectives, but they address different aspects of risk management in pricing.

What are the practical implications of applying robust decision-making in pricing, and what challenges might arise?

The practical implications of applying robust decision-making in pricing involve creating strategies that perform well across a range of future scenarios, allowing businesses to adapt to market changes and customer behavior. However, a challenge is selecting which robust optimization criterion to trust, as Maximin Performance, Minimax Regret, and Maximin Ratio might suggest different pricing decisions. Also, there's the risk of 'overfitting' to a specific criterion, which could lead to suboptimal outcomes in various market conditions. Therefore, a deep understanding of each criterion, and a balanced approach, is essential to develop robust and adaptive pricing strategies that help protect against risks and capitalize on opportunities.