Maze representing simple economic models with a distant, distorted exit symbolizing forecasting biases.

Are Simple Economic Models Leading Us Astray? Unpacking Biases in Economic Forecasts

Kai Mendoza in Business & Economy December 2025 • 3 min read.

"Dive into the complexities of economic forecasting as we explore how simple models can create biased predictions and what it means for understanding the future economy."

In an increasingly unpredictable global economy, experts and analysts constantly refine their methods to forecast future trends. Traditional economic models often assume that individuals can accurately predict economic outcomes, however, those forecasts often fall far from the mark. A new perspective suggests that individuals and institutions rely on simplistic models to make sense of the future, which can lead to biases.

Pooya Molavi's research offers a framework for understanding how these 'simple models' impact economic predictions. This approach examines agents constrained to use simplified frameworks for forecasting economic variables, assessing how these constraints lead to consistent biases. By understanding these biases, we can bridge the gap between theoretical models and real-world economic behaviors.

This article dives into the core of Molavi's work, translating academic findings into actionable insights for everyday economic observers. We'll explore how these simplified models work, what biases they introduce, and why recognizing these limitations is crucial for interpreting economic forecasts.

What Exactly are 'Simple Models' in Economics?

Maze representing simple economic models with a distant, distorted exit symbolizing forecasting biases.

Simple models, in this context, refer to state-space models that contain a limited number of states—represented by the variable 'd'. The number of states indicates how complex a model is over time. When agents use these models, they're employing a boundedly rational approach, meaning they use the best model available to them, even if it is too simple to fully capture the true economic process.

It's important to understand that in this framework, 'simple' doesn't necessarily mean unsophisticated. It defines models with limited moving parts to anticipate and project future outcomes. These models often emphasize the most persistent elements of the economy, sometimes at the cost of overlooking other factors.

Limited States: Models are constrained by the number of variables they can consider, simplifying the overall analysis.
Bounded Rationality: Agents use the best available model, even if it is an oversimplification.
Focus on Persistence: Emphasis is placed on the most stable and predictable components of economic activity.

This simplification isn't without consequences; it introduces specific biases into economic forecasts that can have broad implications.

Why Recognizing the Limits of Simple Models Matters

Understanding the biases inherent in simple economic models is essential for interpreting forecasts and making informed decisions. By acknowledging these limitations, individuals and institutions can develop a more nuanced view of economic trends. As the field evolves, integrating more sophisticated methods alongside traditional models may offer a more reliable view of the economic future.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2202.06921,

Title: Simple Models And Biased Forecasts

Subject: econ.th

Authors: Pooya Molavi

Published: 14-02-2022

Everything You Need To Know

What are 'Simple Models' in economics, and how do they influence economic forecasts?

In economics, 'Simple Models' refer to state-space models that use a limited number of states, indicated by the variable 'd'. These models are employed by agents to forecast economic variables. Because agents operate with 'bounded rationality,' they utilize the best model at their disposal, even if it is simplified. The key features of these models include limited states, a focus on persistence, and the assumption that agents are making the best possible decision with the information they have available. This framework simplifies the complex economic landscape but can introduce biases into forecasts.

How does the concept of 'bounded rationality' relate to the use of simple economic models?

The concept of 'bounded rationality' is central to understanding the use of simple economic models. Agents, be they individuals or institutions, are considered 'boundedly rational' because they don't have the cognitive capacity or access to information to make perfectly rational decisions. Instead, they use the best available model, even if it's a simplification. This means they might overlook certain economic factors. They prioritize the most stable components of economic activity to facilitate forecasts, which can lead to consistent biases in predictions.

What biases are introduced by the use of 'Simple Models' in economic forecasting, and why is it important to recognize them?

The use of 'Simple Models' introduces biases because these models inherently simplify complex economic processes. The reliance on a limited number of states and a focus on persistence can lead to overlooking other important factors. Recognizing these biases is crucial for interpreting economic forecasts because it allows individuals and institutions to develop a more nuanced understanding of economic trends. This recognition facilitates more informed decision-making by accounting for the limitations of the models used.

How does the number of states ('d') in a 'Simple Model' affect its complexity and potential for bias?

The number of states, represented by the variable 'd', directly impacts the complexity of a 'Simple Model'. 'd' indicates how many variables the model considers over time. A model with a small 'd' is simpler because it can only account for a limited number of factors, potentially overlooking critical elements and introducing bias. As 'd' increases, the model becomes more complex, which can potentially reduce bias by accounting for more variables; however, it also increases the chance that model will overfit the data and produce inaccurate predictions on unseen data.

How can we improve economic forecasting by acknowledging the limitations of 'Simple Models'?

Improving economic forecasting requires recognizing the inherent biases in 'Simple Models'. This includes understanding that agents often rely on these models due to 'bounded rationality.' To move forward, experts can integrate more sophisticated methods alongside traditional models. By acknowledging these limitations and developing a more nuanced view of economic trends, individuals and institutions can make more informed decisions and develop a more reliable view of the economic future.