Some great minds in finance, namely Nassim Nicholas Taleb and Charlie Munger have regularly lambasted economists with having physics-envy – or the envy of wanting more mathematically sound theories.
Although this may seem to imply some level of objectivity (or rigor), the reality is much different. Namely, we have issues of narrative fallacies, the unreliability of data, lack of controlled experiments, the assumption of rational consumers (see behavioral economics), and non-standard variables to deal with.
The Generational Data
To start off on a simple note, much of the statistical data used in economics is based on the past. What’s the problem? you may ask. Well, us millennials, for example, are perhaps diametrically opposed to our parent’s generation – the Generation X. Much of what we do, and how we behave, is highly unlike our predecessors. This has been true of every generation.
So how reliable is past data, when it is telling of the previous generations and not this one. What happens, when data is used from the 1960’s – 2010 to make economic decisions for today. How much of the data is representative of the population’s behavior today?
Well, what about sticking to the data that is only relevant to us millennials? You may ask. Well for one, when does that data start from? 1980’s? 1990’s? Then too, honestly ask yourself, is today’s iPhone generation the same as the 1980’s dial-up internet connection?
Behavioral Economics and the irrational consumer
Much of the Economics literature assumes, humans are rational creatures focused on ‘maximizing’ and ‘optimizing’ their outcomes. This is laughable. Although, I concede, much of the literature seems to be moving away from this assumption this has not always been the case.
The Nobel Prize winners, Daniel Kahneman and Amos Tversky conducted controlled experiments to assess the decision-making capabilities of your average human. What did they find? A lot. Some examples are below:
- Endowment Effect: If you buy something, and use it regularly for many years you will be highly unlikely to sell it. Why? Because of it’s nostalgic effect. Even though there is no more utility to be derived from that good, instead of selling it and ‘optimizing’ your position. You keep it, even though it’s useless.
- Framing: We are highly affected by how information is presented to us. In one of their studies, doctors were asked if they would practice a procedure with a 10% failure rate. The doctors said, ‘no’. Now, when framed differently, the doctors were asked if they would practice a procedure with 90% success rate. The doctors said, ‘yes’. It was the exact same procedure, presented differently: 90% success vs. 10% failure. The doctors were simply fooled by the presentation of data.
These are just some of the common errors made by humans. So ask yourselves, are we really wired to be rational? Do we really buy smartphones, laptops, cars, clothes and other products based on how they ‘maximize’ our position? Or because of some extremely attractive tall and blonde model standing next to the item? What about culture – oily food and alcohol, both are bad yet consumed because of your respective cultures – are we really rational?
Free will and the unpredictable human
Physicists have the undue blessing of working in the ‘hard sciences’. When they attempt to understand the universe, they often don’t have to deal with variables functioning differently. The structure of the atom, the formulas to calculate speed and distance, the laws of gravity, etc., do not change. Can the same be said of humans? Are we usually ‘predictable’ ‘static’ and ‘monotonous’ like those non-living things? Hardly.
Forget about the ‘generational gap’ I mentioned earlier (Part 1); we cannot even deduce our own behavior, by observing our own data. Let me explain: When I was 10, I used to buy comics every month for 5 years straight. The data would tell you, ‘this person buys comics all the time’. Almost a decade later, this is hardly the case – I rarely buy comics. Information from my own life itself cannot predict what I would do, yet many in the social sciences assume such data can be ‘telling’.
One of the drawbacks of economic theories, in comparison to the hard sciences, is the reliance on ‘observational data’. This brings the issues of ‘generational gaps’ and ‘unpredictable humans’ as already mentioned.
Hard sciences have the privilege of conducting ‘controlled experiments’. If you hold a hypothesis and want to test it out, you have a controlled environment and you can run the experiment multiple times and assess the probability of success. Can the Economists do the same? How many controlled environments can they test their hypothesis in? How many times can they conduct these tests, to measure the probability of success?
In Economics, if you have a theory, you need it to the entire country to conduct a test. If it worked out once, great! Can you convince us this wasn’t a fluke or a one time-wonder? It’s extremely difficult. This brings us, naturally, to narrative fallacies:
“The narrative fallacy addresses our limited ability to look at sequences of facts without weaving an explanation into them, or, equivalently, forcing a logical link, an arrow of relationship upon them.”
The lack of ‘controlled environments’, where experiments can be ‘repeated’ to ascertain the probability of success is lacking. This leads to theorizing and weaving of narratives from potentially unrelated facts.
Uncertainty and the unknown
This brings us to the final section. Now, you may say, of course, the Economists perhaps already apply some level of probabilistic measures in their forecasts. But does that actually help?
When it comes to a dice, we know there are 6 possible outcomes. But when it comes to the real world, how many outcomes are there? Can you actually assign probabilistic measures to the impact of economic policies on human behavior?
- You don’t know the number of outcomes. Even if you do, let’s entertain a thought experiment. What if, the odds of an event are 1 in 61. You wait till 62 instances and nothing happens. What do you do? Update the probability. Why? It’s based on past generation’s data.
- There also a gross negligence of the famous military term ‘unknown-unknowns’. As opposed to ‘known-unknowns’ – which is when you know you where you are lacking something. ‘Unknown-unknowns’ are when you have no idea you’re lacking something. Blinded by your own confidence. How do you account for such events in statistics? Maybe throw out the bell-curve as suggested Nassim Nicholas Taleb. But that only complicates things, and makes modeling impossible in some cases, resulting in multiple unanswered questions.
Economics is an art, perhaps closer to philosophy or (political) ideology. The overt quantifying and mimicry of ‘science-like’ has perhaps impeded the field – if not downright misguided many, leading to economic failures. The economists might get some jokes thrown at them from scientists, on the account that their subject is an art and not science, but perhaps it’s a bitter yet necessary pill worth swallowing.