Computational Thinking — The Wisdom of Simplifying Complexity*

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*Computational Thinking — The Wisdom of Simplifying Complexity*

https://www.zaobao.com.sg/lifestyle/tech-science/story20251005-7582056?utm_source=android-share&utm_medium=app

Author: 童若轩 Roh-Suan Tung

(The author holds a PhD in Astrophysics from National Central University (Taiwan), conducted postdoctoral research at the Fermi Institute of the University of Chicago, was formerly an associate researcher at the Institute of Theoretical Physics, Chinese Academy of Sciences, and a researcher at the Institute of Advanced Studies, Nanyang Technological University. He is currently an advisor for the academic journal Classical and Quantum Gravity of the Institute of Physics, UK.)

Published: 2025-10-05

Lianhe Zaobao

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Faced with numerous problems in work and daily life, we need a smarter way of thinking. Computational thinking transforms intimidatingly complex problems into clear and feasible solutions, truly achieving the goal of simplifying complexity. Many countries have now incorporated computational thinking into their education systems. In Singapore, programming, data analysis, and problem-solving training are embedded throughout the primary and secondary school curricula.

When planning a trip, do you often feel stressed about booking flights and arranging itineraries? At work, when facing a complex project, do you sometimes not know where to begin? In fact, there is a scientific method of thinking called Computational Thinking (CT) that can help us organize our thoughts and simplify complexity. Originating from computer science, it is by no means exclusive to programmers. It is a fundamental form of intelligence that can be applied to daily life, scientific research, and even artistic creation.

*The Four “Secrets” of Computational Thinking*

Although computational thinking may sound sophisticated, its core ideas are highly practical and can be summarized into four key “secrets”:

1. Decomposition: This is the first step in simplifying complexity. Just like when planning a trip, we naturally break down the big task of “going on a trip” into manageable smaller tasks such as booking flights, planning daily itineraries, and packing luggage.

2. Pattern Recognition: This involves finding patterns in seemingly chaotic information. For example, when arranging your commuting route, you may notice that traffic is always congested during morning and evening peak hours, so you adjust your travel time to avoid those periods.

3. Abstraction: This means focusing on the core of the problem and ignoring unnecessary details. The MRT route map is a perfect example of abstraction. It removes the complex buildings, streets, and parks on the ground, retaining only key information such as stations, lines, and transfer points—allowing everyone to clearly see how to get from point A to point B at a glance.

4. Algorithm: This refers to designing a clear and feasible set of steps to solve a problem. A good recipe is an algorithm—it tells you what ingredients you need (input) and in what order, with what heat and timing to cook (processing steps), ultimately resulting in a delicious dish (output).

*Elegant Problem-Solving Strategies*

Beyond these four basic principles, computational thinking also involves a set of more refined problem-solving strategies. One example is the greedy algorithm.

Although “greedy” may sound negative, it is actually an intuitive and efficient strategy. Its core idea is: at each step of decision-making, choose the option that seems best at the moment, without considering its long-term consequences. It aims for the “locally optimal solution” at each step. However, the “local optimum” does not always lead to the “global optimum.”

Imagine going to an all-you-can-eat buffet with the goal of eating the highest total value of food within the limits of your stomach capacity. If you use a greedy strategy, you might immediately fill your plate with lobster at the entrance because it looks valuable. By the time you finish it and are half full, you discover that there are even more valuable delicacies on the other side of the restaurant. Because of your initial “greedy” choice, you no longer have enough “stomach capacity” to enjoy the wider variety of high-value dishes. A better (non-greedy) strategy might be to spend a few minutes surveying the entire spread (collecting information), mentally planning what to eat and in what order (developing a global strategy), instead of fixating on the first tempting option you see.

In computer science, there is a classic model called the knapsack problem. An explorer’s backpack can hold at most 10 kilograms. Inside a cave, they find three treasures:

Treasure A (6 kg, worth $20)

Treasure B (5 kg, worth $18)

Treasure C (5 kg, worth $18)

If the explorer uses a greedy strategy based on “highest single value,” they will unhesitatingly choose Treasure A. But after putting it in the backpack, only 4 kg of capacity remains, not enough to carry anything else. The total value is $20.

However, if the explorer abandons the most tempting Treasure A and chooses Treasures B and C instead, their combined weight is 5 + 5 = 10 kg, exactly filling the backpack, and their total value is $18 + $18 = $36!

This example shows the limitation of the greedy algorithm: shortsighted, locally optimal decisions can cause one to miss out on the globally optimal combination. When faced with such problems, scientists use more sophisticated algorithms such as dynamic programming or backtracking to systematically find the truly optimal solution.

*The History of Computational Thinking*

Computational thinking did not appear out of nowhere—its conceptual seeds can be traced back hundreds of years. Its development into a universal literacy is largely thanks to the efforts of modern educators. In 1980, Seymour Papert of MIT used simple programming languages and Mindstorms building blocks to help children naturally understand abstract computational concepts through hands-on practice.

In 2006, Professor Jeannette Wing of Carnegie Mellon University published a highly influential article explicitly stating that “computational thinking is a fundamental skill for everyone,” which greatly promoted its integration into education systems worldwide.

Many countries have incorporated computational thinking into their education frameworks. For example, Singapore has long embedded programming, data analysis, and problem-solving training into primary and secondary school curricula. It also emphasizes the legal, ethical, and social implications of new technologies, nurturing the next generation’s logical thinking and innovative capabilities.

In this highly digital era, computational thinking is no longer the privilege of a few but a way of thinking that everyone can learn and apply everywhere. It helps us solve complex problems in life more efficiently and allows us to better understand the core logic behind artificial intelligence, big data, climate modeling, and genetic science.

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(The author holds a PhD in Astrophysics from National Central University (Taiwan), conducted postdoctoral research at the Fermi Institute of the University of Chicago, was formerly an associate researcher at the Institute of Theoretical Physics, Chinese Academy of Sciences, and a researcher at the Institute of Advanced Studies, Nanyang Technological University. He is currently an advisor for the academic journal Classical and Quantum Gravity of the Institute of Physics, UK.)

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