Of Toy Stores and Trade Wars: A Journey Through Tariffs, Instincts, and Evidence

 Imagine a little toy store tucked into the corner of a busy street in America. Its shelves are stacked with hand-crafted wooden cars, the kind that squeak slightly when rolled and carry the warmth of the maker's hand. Across the street, there's another shop—sleek, efficient, and stocked with plastic toy cars imported at half the price from abroad. Children and parents, drawn by the cost, flock there, leaving the warm little store quieter by the day.

This, in essence, is the world Donald Trump walked into when he assumed office. The quaint toy store? American manufacturing. The rival across the street? China. And Trump's answer? Tariffs—economic guardrails meant to shift the tide.

But what happens when you charge more for the toys across the street? Do people come back to your shop? Or do they simply stop buying toys altogether?

The Instinct Behind the Iron Wall

Trump's administration, in 2018, levied tariffs on $200 billion worth of Chinese goods, presenting them as a strategy to rebalance a rigged game. The idea wasn’t just economic; it was deeply emotional—a call to restore pride, jobs, and fairness. "They've taken advantage of us for too long," Trump said. In that phrase alone lies the heart of this move: not just a number game, but a gut reaction to perceived injustice.

Yet instinct, no matter how bold, begs for numbers to back it up. Reports from within the administration heralded short-term boosts in industries like steel and aluminum. But much like a sugar rush, the highs were quick, and the crash sharp. Farmers, once hopeful, were left grappling with plummeting exports as China struck back with tariffs of its own.

Echoes from a Troubled Past

To truly understand this gamble, we need to rewind to 1930. The world, staggering from economic collapse, watched as the U.S. passed the Smoot-Hawley Tariff Act. The goal? Protect American farmers and manufacturers by raising tariffs on over 20,000 imports. The result? A global trade war, a 66% drop in international trade, and an even deeper dive into the Great Depression.

Sound familiar?

Trump’s tariffs may not have matched Smoot-Hawley in scale, but they stirred the same ghosts: retaliation, isolation, and economic drag. The difference? In the 1930s, the damage was visible on bread lines. In the 2020s, it was buried in rising gadget prices, lost soybean exports, and slowed growth.

Between Belief and Evidence

One must ask: Was there a detailed playbook behind these tariffs, or just a businessman's instinct? The answer lies somewhere in the gray. Yes, internal reports projected gains, but they often lacked transparency and long-term modeling. Trump, seeing trade as a poker table, bet that his bluffs would force other nations to fold. And to an extent, he wasn’t wrong: NAFTA was renegotiated, and trade debates were reignited.

But economics isn't poker. It's people. It's prices. It's pressure that builds slowly, almost invisibly.

A Fork in the Road

Imagine our little toy store again. After a year of tariffs, the rival’s toys now cost more. Some customers return. But now, your own toys are also more expensive because the raw materials come from... the rival's country. Meanwhile, fewer children play with toys at all.

This is the paradox of protectionism: defend one castle, and you might accidentally set fire to the village.

What Now?

As the dust settles, one truth remains: tariffs are not mere taxes; they are instruments of ideology, belief, and sometimes desperation. The Trump administration's approach was part revenge, part rescue mission. It excited the base, challenged the status quo, and forced uncomfortable conversations in academia and policy rooms.

But did it work?

The answer isn't binary. Jobs were saved and also lost. Prices went up and manufacturing inched back. It wasn’t a triumph, nor was it a total failure. It was, above all, a reminder that economic policy is never just about numbers—it's about stories, values, fears, and dreams.

And maybe, just maybe, about how much we value the sound of a wooden toy car, rolling across the kitchen floor.

A Wedding Bride

A bride so new,

Actually, spectacularly beautiful,

Walked down the aisle.

The groom shed a tear, a tear of joy.

Hundreds and thousands of guests

Scattered the flowers with grace,

Yet it was the father

Whose eyes were wet.

He might have recalled

The night the bride was born,

And today he sees

She is walking down the aisle.

He refused to accept it at once

And looked at his wife,

She signaled to him to pull his shit together

And put on a fake smile of joy.

Being a man, a husband, and a dad,

He remembers a saint and his words:

"Daughters and sons are there as a part of life,

Yet they have their own stories to make for their lives."

Attachment is a source of suffering,

And yet it makes us wonder,

What if there was none?

Would life be the same, full of fights?

This is all that life makes,

So for a bride,

It's in the groom's eye

Where they see both their lives.

Smell of wind

The beast rang its bell,

Clouds are forming,

And I stared

At the blank canvas.

Mortified I was,

Yet calm in my mind,

Felt as thunder looming in,

Slowly, I felt fear.

Fear of the dark

With no hope of lights,

Yet there is a fragrance,

Wait, a smell, a smell of fragrance.

You and I,

Not so different,

Yet as old as one old wine,

Wait, it's definitely a smell, a smell of wind.

Stupid Brain

A wise man once said,

"There is no difference between

A loaf of bread and a brain."

Startled, I thought, "What did he say?"

Just leave them both:

Musk they will become, and mold they will get.

They lose touch with reality,

Making them unbearably rot.

I fear to use them both,

As they become pungent.

What if both were not real,

And it was something that my stupid brain made!

Logical Framework : A Brief Introduction

The logical framework approach is a way to think of plans and act in a more integrated fashion.

What does that mean? 

Well let's take a tool like Project management. Project management is great once you're clear about:

a. what the objectives are?

b. what the risks are?, and

you simply need to execute a timeline. But it's not very good at helping you come up with what the goals are? and neither they do have an integrated approach.

So, this is where logical framework steps in!

The logical framework combines really three types of thinking which are the best principles from strategic planning. It is scientific in nature and method. It does it in a fairly elegant way. It may sound like a big bunch of jargon words but it's actually quite simple.

To begin with, the basic three types of thinking in question form are:

1. What are we trying to accomplish and why ?
2. How will we measure success?

3. What other conditions must exist?
and even there is fourth question which is
4. How we are going to get there?

So first thing first lets observe the logical framework table:



Step 1: The objective column


For this:

a. We will write down our goal in the box given
b. We will then write the purpose/objective we expect to achieve
c. We will then write the outcomes
d. Finally in the Inputs box we will write down all the resources required.

Once you have written all those, the thought process should be from bottom to top. That means you need to check the inputs and ask yourself whether those inputs will give you the outcomes you have jotted. If outcomes are sure to achieve ask if that gives you required purpose and then if that leads to goals.

Step 2: Success measures and verification

Once Step 1 is done. Lets do Step 2. The question that rings after Step 1 is:

a. How will we measure success at each of these levels?
b. How will we know in advance that we've achieved these objectives?

In step 2, we will come up with measures, quantity, quality, time, cost, customer, as well as verification. The means of determining the success measure.

Step 3: Assumptions/Risk

In Step 3 we're going to ask two questions: 

a. What other conditions must exist? 

b. What assumptions do we have to make for this?

Step 4: Inputs

The fourth question is how do we get there now? 

Usually, while doing LFA, most people jump prematurely to the fourth question. I would say that's like trying to paint the house before you've built it. 

Well, If you are reading this, then Congratulations! You have almost quite figured out basic logical framework approach. You though will need lot of practice. So let me suggest! Go to google and type for LFA example. Take 1-2 of those and try to navigate along this write-up. I bet you wont get lost! 

Thank you reader for bearing with me ! Good day! 

Mathmatics- An overview

(Transcripted from the youtube video: https://www.youtube.com/watch?v=OmJ-4B-mS-Y&t=3s. I do not own this article. The Channel: Domain of Science is the rightful owner of the text here-within. If you want to go through the video you can click on the link above)

The mathematics we learn in school doesn’t quite do the field of mathematics justice. We only get a glimpse at one corner of it, but mathematics as a whole is a huge and wonderfully diverse subject.

My aim with this write-up is to show you all that amazing stuff. We’ll start back at the very beginning.

The origin of mathematics lies in counting. In fact, counting is not just a human trait, other animals are able to count as well and evidence for human counting goes back to prehistoric times with checkmarks made in bones.

There were several innovations over the years with the Egyptians having the first equation, the ancient Greeks made strides in many areas like geometry and numerology, and negative numbers were invented in China. And zero as a number was first used in India.

Then in the Golden Age of Islam Persian mathematicians made further strides and the first book on algebra was written. Then mathematics boomed in the renaissance along with the sciences.

Now there is a lot more to the history of mathematics then what I have just said, but I’m going jump to the modern age and mathematics as we know it now. Modern mathematics can broadly be broken down into two areas,
1. Pure maths: the study of mathematics for its own sake, and 

2. Applied maths: when you develop mathematics to help solve some real-world problems.

But there is a lot of crossovers.

In fact, many times in history someone’s gone off into the mathematical wilderness motivated purely by curiosity and kind of guided by a sense of aesthetics. And then they have created a whole bunch of new mathematics which was nice and interesting but doesn’t really do anything useful.

But then, say a hundred years later, someone will be working on some problem at the cutting edge of physics or computer science and they’ll discover that this old theory in pure maths is exactly what they need to solve their real-world problems! Which is amazing, I think!

And this kind of thing has happened so many times over the last few centuries. It is interesting how often something so abstract ends up being really useful. But I should also mention, pure mathematics on its own is still a very valuable thing to do because it can be fascinating and on its own can have real beauty and elegance that almost becomes like art.

Okay enough of this highfalutin, let's get into it.

Pure Maths
Pure maths is made of several sections. The study of numbers starts with the natural numbers and what you can do with them with arithmetic operations. And then it looks at other kinds of numbers like integers, which contain negative numbers, rational numbers like fractions, real numbers which include numbers like pi which go off to infinite decimal points, and then complex numbers and a whole bunch of others.

Some numbers have interesting properties like Prime Numbers, or pi or the exponential. There are also properties of these number systems, for example, even though there is an infinite amount of both integers and real numbers, there are more real numbers than integers. So some infinities are bigger than others.

The study of structures is where you start taking numbers and putting them into equations in the form of variables. Algebra contains the rules of how you then manipulate these equations. Here you will also find vectors and matrices which are multi-dimensional numbers, and the rules of how they relate to each other are captured in linear algebra. Number theory studies the features of everything in the last section on numbers like the properties of prime numbers.

Combinatorics looks at the properties of certain structures like trees, graphs, and other things that are made of discrete chunks that you can count. Group theory looks at objects that are related to each other in, well, groups. A familiar example is a Rubik’s cube which is an example of a permutation group.

And order theory investigates how to arrange objects following certain rules like, how something is a larger quantity than something else. The natural numbers are an example of an ordered set of objects, but anything with any two-way relationship can be ordered.

Another part of pure mathematics looks at shapes and how they behave in spaces. The origin is in geometry which includes Pythagoras and is close to trigonometry, which we are all familiar with from school.

Also, there are fun things like fractal geometry which are mathematical patterns that are scale-invariant, which means you can zoom into them forever and they always look kind of the same.

Topology looks at different properties of spaces where you are allowed to continuously deform them but not tear or glue them. For example, a Möbius strip has only one surface and one edge whatever you do to it. And coffee cups and donuts are the same things - topologically speaking.

Measure theory is a way to assign values to spaces or sets tying together numbers and spaces. And finally, differential geometry looks at the properties of shapes on curved surfaces, for example, triangles have got different angles on a curved surface, and brings us to the next section, which is changes.

The study of changes contains calculus which involves integrals and differentials which look at areas spanned out by functions or the behavior of gradients of functions.

And vector calculus looks at the same things for vectors. Here we also find a bunch of other areas like dynamical systems that look at systems that evolve in time from one state to another, like fluid flows or things with feedback loops like ecosystems.

And chaos theory which studies dynamical systems that are very sensitive to initial conditions. Finally, the complex analysis looks at the properties of functions with complex numbers.

This brings us to applied mathematics.

Applied Maths

At this point, it is worth mentioning that everything here is a lot more interrelated than I have drawn.

In reality, this map should look like more of a web tying together all the different subjects but you can only do so much on a two-dimensional plane so I have laid them out as best I can.

Okay, we’ll start with physics, which uses just about everything on the left-hand side(refer to the map below) to some degree. Mathematical and theoretical physics has a very close relationship with pure maths. Mathematics is also used in the other natural sciences with mathematical chemistry and biomathematics which look at loads of stuff from modeling molecules to evolutionary biology.

Mathematics is also used extensively in engineering, building things has taken a lot of maths since Egyptian and Babylonian times.

Very complex electrical systems like airplanes or the power grid use methods in dynamical systems called control theory. Numerical analysis is a mathematical tool commonly used in places where the mathematics becomes too complex to solve completely.

So instead you use lots of simple approximations and combine them all together to get good approximate answers. For example, if you put a circle inside a square, throw darts at it, and then compare the number of darts in the circle and square portions, you can approximate the value of pi.

But in the real world numerical analysis is done on huge computers. The game theory looks at what the best choices are given a set of rules and rational players and it’s used in economics when the players can be intelligent, but not always, and other areas like psychology, and biology.

Probability is the study of random events like coin tosses or dice or humans, and statistics is the study of large collections of random processes or the organization and analysis of data.

This is obviously related to mathematical finance, where you want to model financial systems and get an edge to win all those fat stacks.

Related to this is optimization, where you are trying to calculate the best choice amongst a set of many different options or constraints, which you can normally visualize as trying to find the highest or lowest point of a function.

Optimization problems are second nature to us humans, we do them all the time: trying to get the best value for money, or trying to maximize our happiness in some way. Another area that is very deeply related to pure mathematics in computer science and the rules of computer science were actually derived in pure maths and is another example of something that was worked out way before programmable computers were built.

Machine learning: the creation of intelligent computer systems uses many areas in mathematics like linear algebra, optimization, dynamical systems, and probability.

And finally, the theory of cryptography is very important to computation and uses a lot of pure maths like combinatorics and number theory.

So that covers the main sections of pure and applied mathematics, but I can’t end without looking at the foundations of mathematics.

Foundations of Mathematics:
This area tries to work out at the properties of mathematics itself and asks what the basis of all the rules of mathematics is.

Is there a complete set of fundamental rules, called axioms, which all of the mathematics comes from?

And can we prove that it is all consistent with itself? Mathematical logic, set theory, and category theory try to answer this and a famous result in mathematical logic are Gödel’s incompleteness theorems which, for most people, means that Mathematics does not have a complete and consistent set of axioms, which mean that it is all kinda made up by us humans. Which is weird seeing as mathematics explains so much stuff in the Universe so well.

Why would a thing made up by humans be able to do that? That is a deep mystery right there. Also, we have the theory of computation which looks at different models of computing and how efficiently they can solve problems and contains complexity theory which looks at what is and isn’t computable and how much memory and time you would need, which, for most interesting problems, is an insane amount.

Ending So that is the map of mathematics. Now the thing I have loved most about learning maths is that feeling you get where something that seemed so confusing finally clicks in your brain and everything makes sense: like an epiphany moment, kind of like seeing through the matrix.

In fact, some of my most satisfying intellectual moments have been understanding some parts of mathematics and then feeling like I had a glimpse at the fundamental nature of the Universe in all of its symmetrical wonder.

What are some ways you can break free from Product Life Cycle?

The Product life cycle is an inevitable situation. We can assure ourselves that almost every company has experienced it. One may be at the start, mid, or end of the Product life cycle. But interestingly we have to keep in mind that these inevitable situations do have way out. The time horizon can be manipulated if we have absolutely correct approaches to leverage. It's quite daunting for lots of firms in a competitive market where the competition to stay at the top is very high. Here in this article review, I will try to give you steps to follow to realize if you are under threat of the Product life cycle. Also, I will mention the steps to follow to overpass those and how to be on track.

STEP 1: REALIZATION OF PRODUCT LIFE CYCLE:

The bell curve which depicts the product life cycle is the first thing we need to ponder and understand very well. For our easiness, let us consider marketable product toothpaste, and let’s name it: Denti-fresh. The usual bell curve that defines this product will be somewhat as below figure depicts.

Product Life Cycle Model

                   As you can see the product sales with respect to time have a exponential growth till a point and eventually starts to decline. Well, this is something very inevitable. The graph is something we as an entrepreneur should have information. This is very critical in realizing the behavior of the market and planning accordingly.

STEP 2: POSITIONING IN START, MID OR TOP

After the realization of the product life cycle we need to focus on the position we fall under. Usually for an ease we will consider three positioning in the PLC that eventually manifest the outcome.

STEP 3: CASE_IF WE ARE AT THE TOP (REVERSE POSITIONING)

The first being the case what if the product we launched, Denti-fresh is at the top of bell curve. We know we have to face inevitable fall or decline. Also we have fierce competition. Is there a way back from this? Can we stale our PLC curve at here? These are the question we have to ask ourselves. The right question will direct us to right answers. Here in this case scenario we have to use something called Reverse Positioning. This positioning will help us somehow skip the fall until we hit another being to the top scenario. Consider we have the maximum sales for a year and we have started declining as time runs forward. At this moment, if we have innovate new formula for our Denti-fresh product line and get it tested from doctors. The brilliant review revives the production scale as it is marketed as customer is expecting it toothpaste to perform. Here, we have initiated another product life cycle and growth. The competitors are slow to respond. We eventually increase the share of our market and the sales and profit maximized until the falls looms us. This approach of positioning is Reverse Positioning.

STEP 4: CASE_IF WE ARE AT THE MID (BREAKAWAY POSITONING)

The second being the case what if the product we launched, Denti-fresh is at the mid of bell curve. We know we have to face inevitable uncertainty. Is there a way to prepare ourselves in a growing competition, when we have to win in marginal amount? These are the question we have to ask ourselves. As we mentioned the right question will direct us to right answers. Here in this case scenario we have to use something called Breakaway Positioning. This positioning will help us somehow alter the journey we were anticipating and get in track into new journey. Consider we have the tight one on one competition with our rival. Now in this situation we bring an idea to distinguish our product as per age group, the packaging for kid is different compare to adult. The child packaging comes with small toys of dolls and cars. This will eventually lead to marketing strategy being correctly used for maximum outflow of the toothpaste in market. Well the sales of Denti-fresh will sky rocketed when we are uncertain about the market behavior. This approach of positioning is Breakaway Positioning

STEP 5: CASE_IF WE ARE AT THE START (STEALTH POSITIONING)

The last but not the least being the case what if the product we launched, Denti-fresh is at the start of bell curve. We know we have to face inevitable question ‘what’s next?’ Well answer is in the positioning approach which is known as Reverse Positioning. This positioning will help us to maneuver ourselves in tenure of start. The education to the consumer about the product will help us focus in inclusion of new category. And this is the first thing we need to do when we are at the start. Suppose in our case of Denti-fresh we can firstly educate consumers about the organic way of keeping teeth healthy. Next slowly implying on how our toothpaste is most organic thing in the market. This realization on the market can be most effective way to overcome the latent market segmentation. This will eventually direct the sales in expected number. This approach of positioning is Stealth positioning

Hope the above steps will be handy in-case we are stuck in a paradigm of Product Life Cycle. As mentioned first is realization of PLC, then optimization of the position we are situated in PLC model and finally opting one out of three positioning method.