Balancing Reasoning and Experience to Achieve Optimal Behavior

Unlock Your Potential: How Reasoning and Experience Shape Optimal Behavior

Elliot Brynn in Mind & Education August 2025 • 4 min read.

"Discover the powerful interplay between abstract thought and real-world encounters in achieving your best self."

We all strive to make the best decisions, whether it's in our careers, finances, or personal lives. But how do we actually learn to make optimal choices? For years, researchers in both economics and cognitive science have been exploring this very question, recognizing that we learn through two primary channels: reasoning and experience. Reasoning involves thinking abstractly and deliberating on potential outcomes, while experience comes from observing the results of our past actions.

Imagine you're trying to decide whether to invest in a new stock. You could spend hours researching the company, analyzing market trends, and weighing the potential risks and rewards – that's reasoning. Or, you could simply observe how similar stocks have performed in the past and base your decision on those outcomes – that's experience. Both approaches offer valuable insights, but they also have limitations. Reasoning can be time-consuming and may not always reflect real-world complexities, while experience is limited to what you've already encountered.

Now, a new framework is emerging that combines the strengths of both reasoning and experience to help us understand how we can truly optimize our behavior. This approach, drawing on insights from reinforcement learning (a field within artificial intelligence), suggests that the key lies in recognizing and managing the uncertainty inherent in our decision-making process.

The Power of Combining Reasoning and Experience

Balancing Reasoning and Experience to Achieve Optimal Behavior

The core idea is that we don't always have perfect information. We face subjective uncertainty about the best course of action. Think of it like this: you might have a hunch about which marketing strategy will generate the most leads, but you can't be 100% sure until you actually try it. This uncertainty influences how we learn and adapt.

Here's where it gets interesting. According to this framework, our brains are constantly estimating the 'action value' of different choices – essentially, how much reward we expect to receive from each action. This estimation process is influenced by both our reasoning and our experiences. Reasoning helps us form initial beliefs about action values, while experiences provide feedback that either reinforces or challenges those beliefs.

Reasoning: This involves internal deliberation and mental simulations. It helps us form initial beliefs and understand potential outcomes, but it comes at a cognitive cost.
Experiences: These are the observed outcomes of our past actions. They provide valuable feedback that refines our beliefs and helps us adapt to new situations.
Uncertainty: This is the subjective feeling of doubt we have about our beliefs. It influences how much we rely on reasoning versus experience.

Critically, the effectiveness of both reasoning and experience depends on our level of uncertainty. When we're highly uncertain, we may rely more on reasoning to explore different possibilities. As we gain more experience, we can refine our beliefs and rely less on abstract thought.

Putting It All Together: Practical Implications

So, what does this all mean for you? The key takeaway is that learning to make optimal decisions is a dynamic process that involves balancing reasoning and experience. By recognizing and managing your uncertainty, you can become a more effective learner and decision-maker. Embrace both abstract thought and real-world feedback, and you'll be well on your way to unlocking your full potential.

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.2403.18185,

Title: Learning Optimal Behavior Through Reasoning And Experiences

Subject: econ.th

Authors: Cosmin Ilut, Rosen Valchev

Published: 26-03-2024

Everything You Need To Know

What are the two primary channels through which we learn to make optimal decisions?

According to research in both economics and cognitive science, we learn through two main channels: **Reasoning** and **Experience**. **Reasoning** involves abstract thought and analyzing potential outcomes, while **Experience** comes from observing the results of our actions. Both are vital for making informed decisions.

How does 'Uncertainty' affect our decision-making process, and what role does it play in balancing 'Reasoning' and 'Experience'?

**Uncertainty**, or the subjective doubt we have about our beliefs, significantly impacts our reliance on **Reasoning** versus **Experience**. When we are highly uncertain, we tend to rely more on **Reasoning** to explore various possibilities. As we gain more **Experience**, our beliefs become more refined, and we can rely less on abstract thought. Effectively managing **Uncertainty** is key to making optimal decisions, as it dictates how we integrate our internal deliberations (Reasoning) with real-world feedback (Experience).

Can you explain the concept of 'action value' and how it's influenced by 'Reasoning' and 'Experiences'?

The framework suggests our brains constantly estimate the 'action value' of different choices, which is the expected reward from each action. This estimation is influenced by both **Reasoning** and **Experiences**. **Reasoning** helps form initial beliefs about these action values through internal deliberation and mental simulations. In contrast, **Experiences** provide the feedback that either validates or challenges those initial beliefs, thereby refining our understanding of the action values over time. The balance between these two factors enables us to make better decisions.

What are the practical implications of balancing 'Reasoning' and 'Experience' for improving decision-making in real life?

The key takeaway is that learning to make optimal decisions involves a dynamic balance between **Reasoning** and **Experience**. By recognizing and managing your **Uncertainty**, you can become a more effective learner and decision-maker. Embrace both abstract thought (Reasoning) and real-world feedback (Experiences) by actively seeking experiences to test your assumptions. This iterative approach helps refine your beliefs and improve your decision-making abilities over time.

How does 'Reinforcement Learning' relate to the process of optimizing behavior by combining 'Reasoning' and 'Experience'?

The framework draws insights from reinforcement learning, a field within artificial intelligence, to explain how we optimize our behavior. It suggests the key lies in recognizing and managing the **Uncertainty** in our decision-making. In reinforcement learning, an agent learns to make decisions in an environment to maximize a reward. Similarly, by integrating **Reasoning** (which helps form initial beliefs) and **Experiences** (which provide feedback), we are, in effect, reinforcing the actions that lead to positive outcomes and modifying those that don't, much like an agent in a reinforcement learning system.