A Day in the Life at London Business School

The possibility of attending one of the top business schools in the world is a dream for many prospective MBA students, but, with hard work and determination, many students do in fact fulfil this sought-after reality.

London Business School – ranked fourth in our Global MBA Rankings 2019 – is one such school, home to students who have sacrificed and excelled to earn their place at this coveted European institution.

I was given the opportunity to walk among the MBA students of this year’s cohort to find out what being an MBA student actually entailed.

With a humanities’ undergraduate degree, I thought I would feel out of place surrounded by students who had held positions in the US Navy, security organizations and medical technology. However, I was reassured by Dr Nicos Savva, Associate Professor of Management Science and Operations at LBS, that there’s a place for individuals from any background on its MBA program.

The location

I was welcomed to LBS’ Sammy Ofer Center by Dr S. Alex Yang, Assistant Professor of Management Science and Operations before observing his class ‘Data Analytics for Managers’.

LBS opened the center – situated at the Old Marylebone Town Hall, Central London – in September 2017. The three-storey building – a five minute walk from the original LBS campus – contains six lecture theatres, a library, 35 seminar rooms, staff and faculty suites, a student lounge, broadcast facilities and a new alumni centre.

I was transported into a learning environment like I’ve never experienced before.

A day in the life at LBS

Under the wing of Dr Yang, I was able to get a feel for what a quantitative MBA class entailed.

As it was the final day of his class, students were undertaking a case study, deciding in their ‘company’ plan whether their US$1,000 should be used for advertising or promoting the sale of meatloaf.

It was interesting to see how students delegated tasks among each other, as on a compacted course of this kind it must feel like a whirlwind at times. The camaraderie among peers was clear, all working towards a common goal, using each other’s strengths to complete the challenge.

The findings

To conclude the case study, some groups were given the opportunity to present their business plan.

Addressing the lecture theater, Yang says, “What do we mean by business model? Although statistically rigorous, it should be interpretable. We are building a model to make better decisions.”

Although there wasn’t necessarily a right or wrong answer, each group brought a different interpretation of the problem and its subsequent outcome to the table.

In this instance both advertising and promotion had a part to play, but seasonality also needed to be taken into account when deciphering why meatloaf had higher or lower sales at different intervals throughout the year.

The term ‘regression’ was used substantially. Apt in statistical modeling, regression analysis sets statistical processes for estimating the relationships among variables. Using this, the class created theoretical projection graphs to see whether their model should focus on advertising or promotion.

Four teams were chosen to present their findings to the lecture theater. One team says, “To decide whether to spend the US$1,000, first we looked at short term vs. long term strategy, and then we looked and the exponential effect of advertisement.

“Even though promotion has a long-term effect, US$1,000 investment in advertisement isn’t big enough to create an effect stronger than the negative effect during this quarter and in the future.

“our model suggests to always choose advertisement when investment is higher than US$140,000.”

My mind was blown (and felt like I’d absorbed a lot of new learning in one afternoon).

What sets LBS apart?

Yang earned his PhD in the US, but how does he think the environment at LBS differs to that of its peers? Yang says, “I think at LBS we focus on exceling in two dimensions.

“First we try to motivate students in different subjects to broaden their interests.

“I also think we take a more personable approach compared to other schools. We’re warmer and we have more personal interaction with the students.” From witnessing his teaching first-hand, I have to agree. The faculty at LBS were warm and charming.

He continues, “LBS used to be a very small school and the community was very close, but as the school expanded, I think the close relationships between students and faculty remained.”

Learning something new every day

Dr Savva’s ‘Managing Responsibility: Ethics in Work, Organizations and Society’ also demonstrated the amicable relationship faculty and students share.

Savva’s lecture shed light on how algorithms work and how they’re used in everyday life.

To begin Savva quipped, “What are algorithms? An Arabic word that data scientists use when they don’t want to tell you what you have done.” And with that, the class was hooked.

But for those of us who were unsure about algorithms, – I had to think back to high school maths – Savva demonstrated. Offering out an unsolved Rubix cube for a student to solve, he asked, ‘how do you manage to figure it out?’ One student said, ‘she uses a series of steps’, and Savva responded, “aaaa, so you have an algorithm.” I respond to visual learning more than any other kind, which leads us to Savva’s case study.

Are machines biased?

To demonstrate how algorithms are used in real-life situations, Savva introduced a case study about Eric Loomis – a man who was sentenced to prison in Wisconsin by a computer program called COMPAS.

At Loomis’ sentencing, the judge cited Mr Loomis’ high risk of recidivism as generated by COMPAS. The judge denied probation and gave an 11-year sentence that included six years in prison and five years of extended supervision.

But was this fair?

Savva says, “COMPAS is an assessment, it doesn’t say if someone is guilty or not. We’re asking if there is no unreasonable doubt they will offend again.”

To create software like COMPAS, a human has to build the original program. But this beggars the question, was there original bias?

Savva says, “Algorithms give us consistency, but it could be consistently bad if the starting product is biased.” Machine learning also comes into play in this instance, as although artificial intelligence (AI) allows a program to learn as it goes, could humans be unintentionally causing bias?

He outlined there are three potential solutions to algorithm bias: by using algorithm as part of the decision-making process; educate yourself and present results appropriately; audit algorithms and use statistical theory to assess fairness.

Algorithms and machine learning

Savva showed the class an online game that asks the player to draw an object. It could be a shoe, a glass, an oven, a stick of asparagus (some of his designs were more successful than others). The player had to close their eyes, think of the item mentioned and draw it. Simple enough, right? But what are you actually picturing? A sneaker? A stiletto? A boot? If more people choose to draw a sneaker, this will cause unintentional bias, as a computer algorithm then may not recognise a stiletto, for example.

But why is a course of this kind important? Savva says, “Since our students are going to be in managerial positions, things that were done by humans will be outputted to algorithms.”

He asks, “Why are we talking about algorithms now compared to 10 years ago?” To which students responded that it’s because there’s a lot more data in the world today, i.e. internet, social networks, computers.

Savva agreed, “Our processing power has improved dramatically”.

Looking back

So what did I learn? MBA students work incredibly hard – it’s essentially a full-time job with none of the financial stability.

But they enjoy themselves, work together and learn new things from faculty and peers.

It was a privilege to be a fly on the wall, speaking with faculty and students alike who have already achieved great things in their careers.

If someone could put in a good word for me at LBS’ admissions office I’d be ever so grateful.