17th Feb

Back to Reality: From Corporate Life to Nash Equilibria

It has been a bit of a gear shift.

After spending 18 weeks in the corporate world at Accenture, trading a lanyard for a student card again feels slightly surreal. I have officially returned to SUTD for Term 6, and if I thought the pace would slow down after my internship, I was very wrong.

The “holiday” is definitely over. The past few weeks have been a deep dive into the core of Engineering Systems and Design, and honestly, the syllabus this term is intense. We are moving past the basics and getting into the heavy machinery of how systems think, learn, and interact.

Here is a look at what has been keeping me busy (and awake) since school started.

The Strategic Mind: Game Theory (40.316)

One of the most interesting modules this term is Game Theory. It is not just about board games; it is the mathematical study of strategy and decision-making. We have been exploring Nash Equilibrium (essentially, a state where no player benefits by changing their strategy if the others keep theirs unchanged) and the Prisoner’s Dilemma.

We also looked at Braess’s Paradox in selfish routing. It is a counter-intuitive concept where adding extra capacity or a new road to a network can actually reduce overall performance because of selfish behaviour by individual drivers. It makes you look at Singapore’s traffic (or the queue for food at the canteen) in a completely different light.

Building Worlds: Simulation Modelling and Analysis (40.015)

If Game Theory is about how agents compete, Simulation Modelling is about how systems evolve. This course feels like playing god with data. We have been working with System Dynamics, using stocks and flows to model complex feedback loops.

We started with Predator-Prey dynamics (mathematically modelling why the rabbit population crashes when the foxes get too numerous) and moved quickly into SEIR models. Given the last few years, modelling the Susceptible, Exposed, Infectious, and Recovered populations feels particularly relevant. We are also getting into Agent-Based Modelling, which allows us to simulate individual behaviours to see what macro-level patterns emerge.

The Brain: Statistical and Machine Learning (40.319)

This is the one that ties directly into my interest in data science. We are not just running import sklearn and hoping for the best; we are tearing apart the algorithms to see how they work.

We have covered the Bias-Variance Trade-off (the classic struggle between a model that is too simple and one that tries too hard) and Principal Component Analysis (PCA) for dimensionality reduction. Currently, we are tackling Neural Networks. It is fascinating to see the mathematical grounding behind the AI tools we use every day. We also touched on Monte Carlo simulations, which is a nice bridge between this course and the Simulation module.

Unanticipated Variables

And then there is the part I did not factor into my semester plan.

It was not a sudden shift. It was more of a slow burn, a realisation that quietly grew upon me while I was busy looking at data sets and payoff matrices.

Between the mad rush of grabbing meals in the small pockets of time between lectures and the quiet intimacy of late nights when someone sleeps over, I have found myself enjoying their presence more than I expected. In a term defined by chaotic systems and high-dimensional data, that someone has become a somewhat grounding force.

There is a specific comfort in that calming presence. It brings a sense of safety that makes the rigour of the workload feel a little more manageable. It is ambiguous, undefined, and surprisingly nice.

Looking Forward

Transitioning back to the academic grind from a professional environment is a challenge, but it is a welcome one. There is something satisfying about taking the practical skills I sharpened during my internship and applying them to these theoretical frameworks.

I am essentially spending my days modelling economies, simulating ecosystems, and training neural networks. It is going to be a heavy term, but I am ready for it.

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