**Why entropy?**

Data analytics/science is all about reducing uncertainty. Entropy is how uncertainty is defined and quantified. Understanding and reducing entropy, enables more informed and therefore more confident choices. This article will explain what entropy is, using ideas from math, statistics, and physics and show how it helps us solve problems and make better decisions.

Entropy is a concept that shows up everywhere, from tiny particles to huge systems. It’s often linked to disorder and randomness, but it’s really about understanding uncertainty. For anyone who loves data, grasping entropy can change how we make decisions, especially in business. I decided to write about entropy because I recently realised how it affects so many parts of our lives.

**Understanding Entropy: A Primer**

Before diving into the intricacies of entropy, let’s start with a basic understanding of the concept. In simple terms, entropy is a measure of uncertainty or randomness in a system. The higher the entropy, the greater the disorder and unpredictability. Conversely, lower entropy indicates more order and predictability.

Let’s unpack this further. Every decision we face, from mundane choices to complex business strategies, is essentially an optimisation problem. We have a goal, a target variable, and a set of options. In business, this target variable often translates to maximising profit, minimising risk, or achieving a specific growth metric. But here’s the twist: entropy works against us. It constantly seeks to introduce uncertainty, to increase the ‘disorder’ of the system by making outcomes less predictable.

Every organism, from the simplest bacteria to the most complex mammals, is constantly fighting against the tide of entropy. In fact, the very process of evolution is driven by the fight against entropy. Over millions of years, living things have developed incredibly sophisticated ways of capturing and using energy to maintain their ordered state. From photosynthesis in plants to the complex metabolic pathways in animals, every aspect of life is geared towards keeping entropy at bay. And the organisms that are most successful at doing this are the ones that survive and pass on their genes to the next generation.

**Entropy in Thermodynamics**

The concept of entropy was first introduced in the context of thermodynamics by the German physicist Rudolf Clausius in the 19th century. In this context, entropy is a measure of the amount of energy in a system that is not available to do work. As a system evolves, its entropy tends to increase, leading to a state of maximum disorder, known as thermal equilibrium.

Although thermodynamics tells us that closed systems (like our business environment) naturally tend towards increasing entropy (i.e. increasing randomness), it doesn’t mean there’s no hope – open systems can exchange energy with the outside world, potentially creating order. This translates to the importance of gathering external data, incorporating market trends and staying adaptable in our decision-making processes.

**Entropy in Information Theory**

In the mid-20th century, Claude Shannon, an American mathematician and electrical engineer, extended the concept of entropy to information theory. Shannon entropy quantifies the uncertainty in a set of possible outcomes. For example, in a fair coin toss, the entropy is high because there is equal uncertainty about whether the outcome will be heads or tails.

**The Philosophical Perspective: Life as an Optimisation Problem**

From a philosophical standpoint, life can be viewed as a series of optimisation problems. Every decision we make, whether personal or professional, involves selecting the best possible option from a set of alternatives. The target variable in these optimisation problems is often to reduce entropy, thereby minimising uncertainty and maximising predictability.

**The Role of Entropy in Decision-Making**

When making decisions, especially in a business context, reducing entropy translates to reducing uncertainty. By minimising uncertainty, we can make more informed decisions, allocate resources more efficiently and achieve better outcomes. This is where the concept of entropy becomes invaluable.

**Mathematical Foundations: Entropy and Optimisation**

Mathematics provides a robust framework for understanding and applying the concept of entropy in decision-making. Let’s explore some key mathematical principles that underpin entropy and optimisation.

This formula captures the essence of uncertainty: the more evenly distributed the probabilities, the higher the entropy. If you don’t know what this gibberish mathematical notation above is, don’t worry, I’ve got you covered. All you need to know is this:

Imagine you have a bag of marbles, and you want to know how ‘uncertain’ you are about which marble you’ll pick if you reach into the bag without looking.

• If all probabilities are equal (i.e. perfectly distributed probabilities), the entropy is high because we are very uncertain about which marble we’ll pick.

• If one type of marble had a much higher probability (say, 5 red marbles and 1 blue marble – not as evenly distributed), the entropy would be lower because we are more certain we’ll pick a red marble.

**Statistical Insights: Entropy and Information Gain**

In statistics, entropy plays a crucial role in various methods and algorithms, particularly in the field of machine learning. One of the key concepts related to entropy is information gain.

**Information Gain**

Information gain measures the reduction in entropy achieved by partitioning a dataset based on a certain attribute. It is widely used in decision tree algorithms to select the best attribute for splitting the data. The attribute that results in the highest information gain is chosen, as it reduces the uncertainty the most.

For a dataset (D) with entropy (H(D)), the information gain (IG(D,A)) for an attribute (A) is defined as:

**How to understand information gain as a decision maker:** Imagine you’re a detective investigating a robbery. You have some initial information – let’s say you know a crime occurred at a specific location. This is your starting point, with a moderate amount of uncertainty about the culprit and the details.

Now, let’s say a witness comes forward and tells you they saw someone leaving the scene in a red car. This new piece of information acts like a gain in entropy – it reduces the overall uncertainty in the case. You now have a clearer picture, narrowing down the possibilities of who might be responsible.

Here’s how it translates to business:

• **Data is like gathering clues:** The more data you have about your target market, customer behavior, or competitor strategies, the more ‘information gain’ you achieve.

• **Focus on relevant data:** Not all information is created equal. Just like a witness statement is more helpful than a random person’s grocery list, focus on data that directly addresses your decision-making needs.

• **Information gain refines your focus:** With a clearer picture, you can make more targeted decisions. The detective might now focus on tracking down red cars, similar to how a business with strong customer data can tailor its marketing campaigns for maximum impact.

By understanding information gain and actively seeking relevant data, you can significantly reduce the uncertainty surrounding your decisions and achieve optimal results.

**Physical Insights: Entropy in Quantum Physics**

Quantum physics, with its mind-bending concepts, also throws in its two cents. The uncertainty principle tells us that certain properties, like a particle’s position and momentum, cannot be known with perfect accuracy simultaneously. This translates to an inherent level of ‘fuzziness’ in some business situations. Accepting this underlying uncertainty and incorporating it into our models can be a powerful strategy.

**How to understand the uncertainty principle as a decision maker:** To better understand the uncertainty principle, imagine you’re a party planner trying to predict how much food to order. You know roughly how many guests are coming (position), but you can’t be sure exactly how much each person will eat (momentum). Some might be ravenous; others might be picky. This inherent uncertainty is like the fuzziness of the particle’s properties.

Here’s how it translates to business:

• **The ‘perfect’ plan is often an illusion:** Just like perfectly knowing a particle’s position and momentum, there might not be a single, perfect solution in business.

• **Focus on ranges instead of absolutes:** Instead of just predicting a single sales figure, consider a range of possibilities based on historical data and market trends. This acknowledges the inherent fuzziness.

• **Build in flexibility:** Don’t get caught flat-footed by unexpected situations. Just like you might have extra snacks on hand for hungry guests, have contingency plans or adaptable strategies in your business model.

By embracing this ‘fuzziness’ and incorporating it into your planning, you become a more prepared and adaptable business leader, ready to navigate the ever-changing market landscape.

Entropy, a concept rooted in thermodynamics, information theory and quantum physics is at the heart of decision-making processes. By understanding and applying the principles of entropy, data enthusiasts and decision makers can transform how they approach optimisation problems, particularly in a business context. Reducing entropy translates to reducing uncertainty, leading to more informed decisions, efficient resource allocation and better outcomes.

P.S. If you didn’t know, the term ‘quantum’ in quantum physics is derived from the Latin word ‘quanta’, which means discrete. This implies that everything is quantised or exists in discrete units rather than being continuous.

As we continue to navigate the complexities of the modern world, embracing the concept of entropy can provide a powerful lens through which to view and solve the myriad challenges we face. Whether in mathematics, statistics, physics, or business, entropy remains a central and unifying principle that guides us towards greater understanding and more effective decision-making.

*George Vlachos started his journey as a consultant to then becoming an AI and Data Engineer and now evolving into a role where creativity and technology intersect. George currently spends his time working 50/50 between Digital Nachos and Onestack. He has been programming since he was 12 years old and is passionate about using information in creative ways. He believes that data is not just a tool for business, but a medium for creativity. He likes to find patterns where others see chaos and opportunities where others see obstacles. His goal is to use data to create, to innovate, to solve problems, and to make a difference.*