Previously, I shared three ideas that I used to think about my career planning:
- Knowing your hard constraints
- Knowing your observed industry
- Knowing your strengths (what you are good at)
In this post, I will focus on two broad fields that I have incorporated into my life in general, but which I felt were also particularly useful in how I approached my career. These two fields are behavioural economics and Bayesian statistics.
Behaviour Economics — I am and will always be biased
Economics has traditionally been a mathematical endeavour with one irksome assumption; humans are perfectly rational. Assuming perfect rationality was a necessary evil for many mathematical economic models to have tractable and optimizable solutions, which was something many physic-envy economists wanted to achieve in their own field. However, recent research by economists such as Herbert Simon, Gary Becker, Richard Thaler, Dan Ariely, George Loewenstein, and many others have focused on modeling and mapping out the ‘irrational’ aspects of human decision making. While I didn’t take the only behavioural economics module taught in my university, there were still a lot of books that I could learn from (I am recommending some of them at the end of my post). Interesting human fallacies like sunk costs fallacy, anchoring effects and hyperbolic discounting have been explored extensively. Some behavioral fallacies have been used to improve public policy implementations, while others have been used in fields like marketing and behavioural finance.
Given all the vast research done by behavioural economists, my key personal takeaway was simple: humans are irrational, biased and easily manipulated (or nudged) by psychological tactics. Nowadays, I try to be mindful of the potential biases that I will have as a human. I am mindful of how anchoring effects and choice architects are affecting my consumption decisions, and how sunk cost fallacies will affect how I invest my time and money (I sometimes still stubbornly press on). In general, I like to think of myself as someone who does add some healthy dose of skepticism into my thought process when I go about my daily life. Unfortunately, I cannot be sure if I am being too overconfident about this aspect of my life.
Statistics — Everything is about probabilities
Most basic statistics courses teach the frequentist school of statistics, with concepts like sampling, normal distributions with their bell-curve shapes and the infamous p-values of statistical significance. However, another school of statistics that is more commonly taught in advanced classes is called Baynesian statistics.
My exposure to Bayesian statistics actually began from a book called “The Signal and The Noise”, by Nate Silver, who was a prominent statistician turned international best-seller. I highly recommend people to read his entire book to learn about how statistical concepts are being utilised in the real world. However, my biggest takeaway from his book was the intuition behind Bayesian statistics. Anyone interested in a deeper dive between the difference frequentist and Bayesian statistics can read this Medium post.
My own philosophical interpretation of Bayesian statistics is about having a base level understanding of the given scenario (an a priori probability), which can then be updated when new information comes in. And it was Nate Silver’s book that made me realise how I could apply Baynesian statistics to my life. In essence, I realised that at any moment in time, I am always living in some probability state, a.k.a the apriori state in Bayesian statistics. These probability states can be for anything, from the probability that I will learn something new at work, that I will change my job, to the probability that I will get hit by a bus or die from a heart attack.
As I go about my daily life, these probability states will change when ‘new information’ comes in. Here, I am abusing the terminology ‘new information’ to loosely mean a variety of things, from lucky incidents like chance meetings, changing marco-economic conditions affecting the job market sentiment, to self-discoveries or changing personal social-economic situations like hitting the lottery. Finally, these probability states are then used to decide on the many important choices in my life.
In terms of career planning
The field of behavioral economics and Bayesian statistics are important concepts that I have already applied to my life in general, and that included in areas of how I approached my career planning and decisions. Bayesian statistics gave me a good philosophical framework to approach many of my career decisions, especially since many of these decisions had to be made under situations with limited information and uncertain outcomes. Naturally, I wasn’t calculating actual probabilities of the different future states that I hope to achieve (probability of being a data scientist or working in a tech startup) because that would take too much time, and I have no idea on how to accurately provide probabilities to such outcomes. What I did was to come up with heuristic definitions of ‘high’ and ‘low’ probabilities for the different outcomes that I hope to achieve, and take actions to improve the probabilities of desirable outcomes happening. This would usually mean me laying out plans to either fill up my informational gaps with regards to certain situations or improve the odds of me achieving certain desirable outcomes.
I also reminded myself that while I can work towards improving my probabilities, getting the desired outcomes still largely depends on things beyond me. These probability states are after all, probabilities, and no one can be confident that they will definitely happen. And because I felt I could never be 100 percent certain about any outcome, Bayesian statistics got me to think about the contingency plans that I should have in case my desirable outcomes didn’t materialise.
On the other hand, ideas from behavioural economics helped me to be more mindful of my career decisions and plans. For example, while my initial efforts in picking up programming felt good, I wasn’t sure if the data science industry would give a self-learned coder a chance. The behavioural economics side of things got me to realise that given that I was willing to overcome present bias in pay cuts, stagnant career progressions and late nights coding, just for a potentially desirable but uncertain outcome, maybe I was really keen on this data science industry after all? However, on the other hand, I was also mindful that I could be committing to the data science field because I have committed too many hours to this endeavour (sunk cost fallacy), and didn’t want to give up all the effort that I have already put in.
I really love to read for knowledge, and after reading for a few years, I really want to share what I have read through some writings. Naturally, my views are a mix of my interpretations of the written material and how the context have intertwined into and affected my life thus far. For those interested in the source material, these are the books that I have mentioned in this post.
Books on Behavioural Economics
- Predictive Irrational — Dan Ariely
- The Upside of Irrationality — Dan Ariely
- The Honest Truth About Dishonesty — Dan Ariely
- Nudge — Richard Thaler
- Paradox of Choice — Barry Schwartz
Book on Statistics
- The Signal and the Noise — Nate Silver