Computational Thinking is a powerful problem-solving approach that simplifies complex issues by: breaking them down into manageable parts, recognizing patterns, abstracting essential information, and creating algorithmic solutions.

While often associated with computer science, computational thinking, if widely adopted, could offer great solutions to many of today’s societal challenges - ranging from improving healthcare and education to addressing environmental issues, urban challenges, and beyond.

Example 1: The London Tube Map  

One notable example of computational thinking is the redesign of the London Tube Map. The original map, while accurate and detailed, was overly complex and difficult for travelers to navigate.

By using an abstracted version, the map was simplified to focus on the most important elements, such as station locations and connections.

The computational thinking process for this redesign involved the following steps:  

1. Decomposition
The challenge was broken down into smaller parts: simplifying the geographic layout, improving readability, and maintaining the essential connections between stations.  

2. Pattern Recognition
Travelers were found to prioritize the relationships between stations and routes over geographic accuracy. Recognizing this pattern guided the map’s simplification, focusing on what travelers valued most.  

3. Abstraction  
Instead of including every geographical detail, unnecessary information, such as the exact positions of stations and distances between them, were removed. While the essential information - stations, routes, and connections - were kept.  

4. Algorithmic Thinking
A clear, step-by-step process was applied to create the redesigned map. For example, lines were straightened, and stations were placed at fixed intervals, regardless of actual geographical distances.  

Through this process, the London Tube Map was transformed into an efficient, user-friendly design that set a global standard. Since then, many other organizations have since adopted similar techniques.  

Example 2: The Internet and Access to Information  

The evolution of the internet has permanently reshaped human life. Information that was once limited to private institutions or specific locations is now widely accessible to anyone with an internet connection. Whether you are a child in a rural village in Congo, an aspiring entrepreneur in a developed country, or an individual facing economic difficulties, access to the internet opens up opportunities for learning and growth.  

However, access to information is only part of the equation. The real challenge lies in identifying and utilizing the vast amount of data available. In a world saturated with information, we need a way to cut through the noise. This is where computational thinking becomes essential. It is not just about learning facts but about developing the mental tools to process, analyze, and apply information in meaningful ways - a skill computational thinking emphasizes.

In essence, computational thinking can equips individuals with the ability to unlock their transformative potential in this digital age.  

How Computational Thinking Can Change Our Lives  

1. Decomposition
Computational thinking encourages breaking down large, complex problems into smaller, more manageable parts.  
For example, imagine a young person in a developing country wanting to start an e-commerce business. By applying decomposition, they can tackle the challenge step by step: understanding digital marketing, learning logistics and supply chains, navigating legal constraints, and observing other businesses in their area. This allows them to systematically assess how and what is needed to launch their business.  

2. Pattern Recognition
Recognizing patterns is another key aspect of computational thinking. Once patterns are identified, they can guide problem-solving.  
For instance, in agriculture, farmers can recognize seasonal patterns or soil quality patterns to improve crop yields. Similarly, meteorologists analyze large datasets to identify trends that influence weather conditions, that helps improve weather forecasts, which indirectly benefits farmers by providing better tools for agricultural planning.  

3. Abstraction 
Abstraction helps individuals focus on what truly matters by removing unnecessary details.  
For example, an university student looking to enhance their computing skills can use abstraction to concentrate on core course materials without being distracted by irrelevant technicalities. Similarly, his professor can optimize lecture materials by focusing on the most critical concepts to benefit students' development.  

4. Algorithmic Thinking
Once a problem is broken down, patterns are recognized, and essential details are abstracted, the final step is to create clear, step-by-step processes to solve the problem.  
For example, imagine a city planning to address growing waste issues. Computational thinking can help officials create a systematic process to optimize waste collection. This might begin with analyzing waste data, identifying high-waste areas, and implementing a route optimization algorithm for waste collection.  

The Multiplier Effect

When applied correctly, computational thinking generates solutions that extend beyond their original purpose, benefiting unrelated areas. For example, improved weather forecasts (from pattern recognition in meteorology) can help farmers make better decisions, ultimately increasing agricultural productivity. This multiplier effect fosters innovation and societal progress.  

Personal Experience  

Reflecting on my personal experience as a former sprinter for the Italian team, I now recognize that I was unknowingly applying computational thinking daily. In athletics, we constantly faced challenges that required breaking down complex problems - such as improving performance or optimizing race strategies - into smaller, more manageable parts.  

1. Decomposition
For instance, when preparing for a race, we decomposed the challenge into different training aspects: speed, explosiveness, strength, reaction time, and mental preparation. By focusing on each of these aspects individually, we enhanced overall performance.

2. Pattern Recognition
By analyzing past races, we identified patterns in performance or race conditions, which allowed us to adjust tactics and gain a competitive edge.  

3. Abstraction 
Abstraction involved simplifying race preparation into core areas, such as the start, acceleration phase, and finishing strategy. By focusing on these critical elements, we removed distractions and refined our performance.

4. Algorithmic Thinking 
During each race, we executed a well-planned, step-by-step process - from warming up to post-race recovery. This systematic approach allowed us to refine and improve our performance with every competition.  

Conclusion  

Computational thinking is not just a skill for computer scientists but a transformative approach that holds the potential to address some of the world’s most pressing challenges. Whether designing a metro map, optimizing waste management, or improving athletic performance, its principles - decomposition, pattern recognition, abstraction, and algorithmic thinking - can drive progress and innovation across diverse fields.  

By embracing computational thinking, individuals and societies can create effective solutions that lead to sustainable growth and development.