Can algorithms help you live a better life?

Can mathematics and algorithms be applied to your problems to improve your life, help you find love, organise your schedule, and make the best choices? Brian Christian and Tom Griffiths, two cognitive scientists, explore this idea in their book, Algorithms to Live by: The Computer Science of Human Decisions. The Mancunion asks if applying some of these algorithms to student life can improve your university experience.

What are algorithms?

An algorithm is a set of instructions given to a computer to solve a problem or make a decision. You can think of it like a recipe: a set of instructions that when followed produce a desired outcome, whether that is a bowl of Spaghetti Bolognese or a sat-nav route.

Computers use algorithms, ranging from the simple to the complex, to determine an optimal choice. A good algorithm will balance a high success rate with a short running time. So how can we, as students, employ this idea to help take the stress out of decision-making?

Find the love of your life using maths

The problem of finding the perfect partner will be familiar to many students, as will endlessly scrolling through dating apps and spending half of your student loan on pizzas for two at Haus. Many issues around romantic commitment are based on the feeling that there could always be someone better out there for you or that if you leave you won’t find something better than what you already have.

What if a mathematical approach could ensure you make the right choice whether to accept or reject each relationship “offer” before seeing the next? The answer lies in something called the 37% rule. In this model, you cannot see multiple “offers” at the same time because The Mancunion does not condone cheating. We also assume there is no opportunity to return to previous best offers; once you have rejected someone they are gone.

If you want to find the perfect partner by the time you finish your three years at university, you should spend 37% of your time at university (approximately 406 days) dating without committing; “rejecting” each relationship “offer” in order to build up your knowledge of what a good relationship is. After this, you should accept the next offer that is better than all the ones you have seen so far. This will lead you to select the best offer in the set of all offers 37% of the time. Now this doesn’t seem very high, but is the best approach statistically, far better than making a random choice!

We can revise the model by allowing you to revisit previous partners, assuming that the chance of someone accepting your proposal decreases to 50% when asking them out after previously rejecting them. In this case, the best approach, statistically speaking, is to date without committing for 61% of the time, then accept the next offer that is better than all previous options. If you reach the end of the set and haven’t found anyone better, then you can return to the best option that you previously rejected. This approach now leads to you finding the optimal partner 61% of the time.

While the best approach may not be these statistically driven methods, sometimes it is good to take a step back, explore other options, and not leap on the first offer that comes along if it is not right for you. This could be applied to a whole range of decisions, from house-hunting to dating or making career choices.

Sort your life out!

Student life is riddled with disorder. Whether it be your stack of unorganised lecture notes or piles of unpaired socks, it can often feel like you are drowning under the inevitable weight of entropy. The problem with sorting is that as you try and sort larger sets of data, the time taken to sort the data rises at a rapid rate. Most algorithms have a quadratic dependence on the number of data values needed to be sorted. This means that the time taken to sort a pile of sheets of paper into date order grows proportional to the number of sheets squared, posing a significant issue when trying to sort a large number of items.

One way around this is to sort as often as possible using smaller samples. We all know it is easy for life to get on top of you, but a very efficient and easy method to organisation is called ‘Merge sort.’ Say you need to organise a stack of 100 lecture notes into date order, the merge sort algorithm tells you to divide the stack into a few piles (say five), then organise each smaller pile by date order. Then you take the earliest sheet that is on top of the five piles and start a new sorted pile, and repeat this process, selecting the next earliest sheet from one of the five piles and placing it on the new pile. After you have moved all the sheets onto your new sorted pile you will have all 100 sheets ordered by date. The reason this works well is that all the serious sorting occurs when you originally order the five piles, each with 25 sheets (not the original 100). Therefore, it will take less time to order.

Can computer algorithms really make your life better?

While algorithms may give us the best statistical approach to problems, it is likely that humans are capable of making better decisions, as they have a better understanding of their feelings and how their brains work. However, there can be some important lessons learned from “thinking like a computer:” to take a step back and not dive on the first offer that comes along, to regularly organise yourself, and not let things get overwhelming by taking organisation in bite-size chunks.

If you find the algorithmic approach to life helpful or interesting, many more examples and computer science complexities can be found in the excellent book Algorithms to Live by: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths.