Understanding Causality
October 23, 2008 – 11:44 by Mikko HämäläinenIt’s been a while since my last post, mainly due to work overload and house renovation. However, I’ve also been occupied with a couple of science-meets-philosophy type of questions, above all causality and it’s role in human decision making processes.
In essence causality can be described by means of logic so that from event A follows event B. To put it simply, from action follows reaction – the principle of cause and effect. Causality also raises a lot of philosophical questions, most importantly about the free will of humans, as the law of causality implies that effects are predetermined and such free of any decision making processes. The philosophical debate on causality has been going on for since the times of Aristotle so I’m not going to dig any deeper into that. I’ll just say that delving into the philosophy of causality is pretty mind bending and will cause a lot of sleepless nights.
So why I started thinking about this question in the first place? Well, I did not actively start thinking on it – rather the question asked itself when doing some thought experiments on business decision making processes, especially trying to predict the future conditions of given business.
Businesses put a lot of effort in crafting right investment portfolios and the process usually follows the same pattern: investigate market situation, try to predict how market develops, pretend to spot any white spots in the market and finally formulate a strategy and action plan according to aforementioned data. The problem with the process is that it never produces desired output. Why? We never really know for sure. A competitor did this, the market evolved as that and the consumer needs were those. But we really never know which bits of all things actually had an effect on our own market performance. We can try to understand, but in the end of the day, we do not know for sure.
The point is, it is totally impossible to try to predict the future using the numerical methods since it is also impossible to predict the past. Trying to tie things together using such coarse grained data is naive, as is even trying to analyze microscopic bits of data – there simply is too much information. Take consumer behavior. How is it possible to predict an adoption model for new technology? I’d say it is nearly impossible. It is not only the maturity of the technology itself, or its perceived usefulness to the consumer. There are so many microscopic level fluctuations that precise predictions are not possible. One can have absolutely fabulous technology and still fail in the marketplace and on another hand some mediocre offering can still beat the competition. Or both could fail due to lack of consumer interest, although the market data showed an explicit interest for our particular innovation. Well, maybe we asked the wrong people. Or maybe the consumers’ perception of the product was different from ours although we knew exactly what the consumer wanted using focus groups. Or or or. :-)
This was the thing that made me to try to understand causality. And out of that thinking I’ve come up with some conclusions, although my thinking is still evolving as I write this and I’m certain that I will change my mind about these as my own take on the subject matures.
First of all, instead of trying to predict, you should accept that trying to make precise predictions based on financial climate is quite useless, and in many ways dangerous. Secondly, market data gets you nowhere, it only limits your possibilities to succeed if you use market data as your dominant guide. Thirdly, you need visionary people in your organization in order to succeed. I’ll come to that later, as for me visionary means something else than majority of people. Or at least my understanding on the subject is starting to clarify.
I think I already described some of the problems with so called precise predictions. It is impossible to have precise view on what is going to happen if you do not even know how you got to the present situation. I’m not saying that one should not try to consider future possibilities, but one should not focus on finding the path instead of walking it. The problem with predictions is, that they will affect your day to day decision making processes and as predictions are based on fuzzy data, they will eventually guide you to the wrong place. And you do not even realize how you got there, since you trust the data.
To the second one is tougher one. Businesses tend to be overly focused on market data with monthly reporting of market share, tiny fluctuations in the cash flow and sales performance. While measuring business performance is of course very important thing, short sighted visibility of the data can easily lead too much to opportunism and making decisions with too much focus on current operative environment, lacking the possibility to evaluate the consequences on longer term. And it is the long term that really matters. It is you who should guide the company, not numbers or macroeconomic environment.
The third one is the toughest one. Many organizations focus on hiring the brightest talent in the market, with the delusion that having the brightest minds will produce the best outcome. This is usually wrong as leadership based purely on intellect will lead to implementing too much logic to the core of business, whereas humans, and such consumers and markets, are not logical. This leaves no room for risky innovation and easily leads to safe bets and protectionism. Instead, one should try to find people with a vision. Having a vision is not about having unrealistic view on the consumer needs, building a tunnel vision on opportunities, or egoism, but having true, intuitive, talent of seeing causal lines.
Since causality is something that you can not ever fully control, the only way to true success is the ability to see the future instead of predicting it. The ability to see causal lines is a gift, something some people (true visionaries) have and something that rarely, if ever, can be learned. Having a visionary leader also leads to have a solid vision and values, that resonate with the customers, to follow even when times get tougher. Visionary people tend to follow the vision, not market data. If one sacrifices the core values and vision of the company for short term suboptimal financial wins, the consumers will gradually be alienated and the game will sooner or later be over no matter what.
So the success very much depends on accepting the future as it formulates and adapting to the changes, not on trying to slow down market development and disruptions. Causality shares similar attributes with entropy in that it eventually wins the game.
So, that’s about it. I think this all started after so many healthy companies started destroying their future in the current economic turmoil and forgot why they actually exist, what is the purpose of the company. There are two exceptions to this, namely Apple and Google, that actually quite stubbornly follow their vision even in the slowing consumer demand and decline in media sales respectively. And they both are lead by visionaries. And they both are quite successful in what they do.