Often these rituals would include mental practices such as meditation, ascetic practices such as fasting, and even medication in the form of hallucinogens derived from herbs or mushrooms. Given recent research into meditation and psychedelics and their applications in the treatment of various psychological disorders, it doesn’t seem too far fetched to say these shamans may have accessed genuine latent information through altered states of consciousness.
The problem with these predictions is that there was a high degree of subjective interpretation, whether reading pig entrails or describing psychedelic dreams, it left a lot to the imagination. Thus life-and-death questions about when to pick the year’s harvest, or whether to attack a neighbouring tribe were based on rather suspect information, resulting in a morbid and fateful toss of the coin.
Basis : wisdom, intuition, history
Content : high utility, medium relevance
One might think of the Oracle as a more refined, institutionalized version of the Shaman. While the term originates from Ancient Greece, we might include the Egyptian priests in this class given their similarities. While very little is documented for us to know about their specific practices and methodology, there are some second-hand accounts in Greek literature. What is clear is that the interpretation is still a part of the show, but the expectations have evolved from outright miracles to sage advice to the rulers of the day. In that regard, Oracles could be seen as a precursor to the specialist advisory teams that surround modern-day world leaders. Just with a little extra fanfare and mystique.
Basis : positions of planets and stars at specific times
Content : medium utility, no relevance
I won’t spend much time here, rather than point out that the futility of “reading the stars” is well refuted by science. Frankly, the existence of twins without identical life paths is already a big problem for the methodology. Besides, astrology already scores lower on utility, as it’s relegated more to gossip-column level speculation about your love life.
Astrological birth chart for Emperor Nero of Rome. Source: Wikimedia Fortune tellers
Basis : psychology, common sense
Content : medium utility, low relevance
The only difference between fortune-telling and astrology is that fortune-telling actually does have some scientific basis. No, it’s not your palm or the cards. Fundamentally, the fortune teller is reading you as a person. The way in which you respond to questions, your choice of words, your emotional state, your body language, and your appearance.
So in some sense, using some homespun psychology and simple common sense, a good fortune teller could be more beneficial than a bad psychiatrist! The fortune-teller has no new information to give, but they can tease out subtle truths that you refuse to acknowledge due to your own biases and emotional charge. Then again, it’s equally possible the fortune teller is specifically taking advantage of your biases to tell you precisely what you want to hear, reinforcing bad decisions. So maybe don’t cancel your shrink just yet.
Current approaches to predicting the future
Moving forward from things that don’t actually work, we arrive in the domain of modern science and mathematics. Let’s examine what can be done with the best of today’s technologies?
The Human Brain
Basis : sensory data, memory, hormones
Content : low utility, high relevance
Didn’t see this one coming, did you? Well, as it turns out, none of us would be around if we weren’t pretty good at predicting the future. Let me explain. There are actually several ways in which we predict the future inside our brain, that we know of, at least.
Firstly, think of catching a ball with your hand. The only reason you can catch it is that your eyes and hands collaborate, facilitated by the brain, to predict where the ball is going to be at a specific moment in time. We forget how hard such a thing is until we have to create it ourselves with robots. Robots suck at picking things up, let alone playing catch with dad.
One such mechanism in our brain that allows this is called anticipatory timing. Without it, you couldn’t walk, because you need to (mostly subconsciously) predict a complicated series of events to know how body mechanics, friction, and gravity will impact whether you slip or faceplant. This explains why babies do a lot of slipping and faceplanting, as they refine their motor skills algorithms just like a miniature robot.
Source: Darici, O., Temeltas, H. & Kuo, A.D. Anticipatory Control of Momentum for Bipedal Walking on Uneven Terrain. Sci Rep 10, 540 (2020). https://doi.org/10.1038/s41598-019-57156-6
The second kind of prediction feels pretty useless, almost harmful sometimes. It’s our ability to imagine potential scenarios or ruminate on them endlessly lying in bed at night. What happens if you miss the bus in the morning? Why did I drop the glass earlier? What could have been if you went on that second date after all? We can conjure up entire lifetimes of complicated events with real or imaginary people in them.
There are other types of prediction happening in our brains, such as from how our eyes and brain together interpret the world around us based on what we want to see. The prediction seems to be a fundamental capability, and we may have only scratched the surface of what’s really going on with things like consciousness. I’ve covered that whole chestnut before, so bookmark
that post for later. CIA “Super Forecasters”
Basis : data, intuition, common sense
Content : high utility, medium relevance
Following on from our ability to rationalize and simulate different scenarios in our mind, it’s logical to think that there might be certain innate abilities or skill development that determine how good or bad one is at this type of thinking. In fact, in 2011, the CIA tried to answer that very question. They set up a competition of sorts, to pick out people from different walks of life to go up against their best analysts.
To set the scene for context, what an intelligence analyst would do to predict outcomes like presidential voting in Venezuela, or chance of war in the Middle East next year, is build models. Lots of models. The models would be based largely on existing data, either public or extracted by the CIA themselves, and try to extract various statistical metrics, relying on rational hypotheses, to judge the overall probability of specific outcomes. The problem is, that there’s a lot of human judgement in that process, in terms of picking good reference data, the treatment of data, framing hypotheses, and the metrics used to extract predictions.
Shockingly at the time, the hobbyist “Super Forecasters” beat the analysts. It wasn’t even close. There’s quite a lot of good
material and podcasts on the topic if interested, but the short of it is this. These people had much simpler models than the analysts, who had the best tech and large teams of mathematicians.
Instead, the hobbyists had better intuition about choosing good reference data, and most importantly, they were focused on the art of being a good forecaster. In some sense, they weren’t overthinking the problems, but we’re very focused on a consistent process honing their prediction skills, and that frequently resulted in better results. Mind you, we aren’t talking 90% accuracy. More like going from 51% to 55%, so overall, just marginally better than tossing a coin!
More recently, we’ve seen the very same phenomenon as hobbyists have regularly outscored government agencies in predicting the spread of COVID-19.
Basis : data, logic, common sense
Content : medium utility, high relevance
Isn’t it peculiar, that the most boring industry is probably the best at predicting the future? That seems like a cool ability, possibly justifying the use of a cape. Well, the issue is that the scope of those predictions is extremely narrow. Hence this work is carried out in cubicles, not secret underground lairs. By definition, insurers only focus on things that drive risk, because they have to foot the bills if they get it wrong.
Here’s an example of how actuarial tables can predict whether people of various ages (x-axis) will live another 5 to 25 years. It’s interesting to note how the curves slow down if you make it past 75. Source: Wikipedia
While the obvious datasets relate to things like health and longevity that determine how much we pay for health and life insurance premiums, the origin story of these predictions was insuring ocean voyages for ships. Sadly, it was quite common for ships to not return, and so money was put up almost as a bet against success, which would then be paid out to widows of sailors and the owner of the ship. Nowadays, more complex models can calculate probabilities for your iPhone screen cracking, or your flight getting delayed.
Fundamentally, the probability is a prediction. Insurers literally bet your life on those odds.
Hedge Fund Quants
Basis : data, logic, common sense
Content : medium utility, medium relevance
Besides insurers, hedge funds also make money with predictions. In fact, that’s the only thing they care about. They purely focus on maximizing investment returns, by any means necessary, while
mostly staying within legal and regulatory limitations. As far as we can know, that is. The methods vary, but fundamentally these companies are trying to find an information edge.
At some point, for example, hedge funds were paying for satellite imagery to calculate precise crop yields and count cars in parking lots of shopping malls. Why? Because they wanted to know information on how various commodities and businesses were performing before that data was published in the next quarterly reports. If you knew in advance, you could make a high probability bet on the asset going up or down when that data became public. Yes, it’s effectively legalized cheating.
While making lots of money is useful, that is a pretty narrow use-case. Knowing the price of copper tomorrow won’t help with your life problems, other than that you’ll have more money to spend on worrying about them.
Basis : data alone
Content : medium utility, high relevance
In case you weren’t aware, the Artificial Neural Networks powering most modern A.I. solutions were directly inspired by the brain. While we don’t understand the brain fully, or particularly well, we’ve had good progress in using a few basic building blocks to train computers in learning useful tasks. This began with identifying handwriting in the nineties and has evolved into beating humans at games like Chess and Go and driving autonomous cars in urban traffic.
Much like the human brain, we covered earlier, some applications of decision making involve the ability to predict the future. Driving cars is pretty self-explanatory in that sense. Firstly, the A.I. needs to know what various systems of the car are doing every millisecond, and how they will coordinate something like an emergency braking situation on an icy road, should that be necessary. But even more complicated is the requirement to predict what humans will do. Remember, other cars are being driven by erratic, emotional hairless apes, us. Not to mention pedestrians doing random things like jaywalking. Don’t get me started on cyclists.
No wonder Tesla’s Autopilot is one of the most powerful A.I. systems on the planet. It comes with 8 cameras giving it a complete 360-degree view, plus sonar. Yes, like whales. The computer hardware running their own neural nets is also now built by Tesla from scratch. Most importantly though, is the data. That explains why they continue to dominate the category, as no one else has a decade’s worth of on-road data from thousands of cars across billions of miles. No data, no A.I., no self-driving. It’s that simple. Read more about all that stuff
Board games are another good example because a key part of successful gameplay is to know what your opponent can and will do. Given the latest generation, AlphaZero has a 99% win rate in both Chess and Go against humans, I’d say they’ve got us figured out pretty good. Before it happened, most experts thought it might never happen. That’s how big of a deal it was when Deepmind
crushed humanity’s last hope in Lee Sedol in 2016.
The thing to take note of here is that A.I. models are purely and entirely based on the data. So it really matters what data is used. Random data will result in no learning, and biased data will result in biased decision-making.
While the current approaches all have various real-world applications, there are several fields of academic study that examine human populations to understand them. In some sense, the highest level of understanding is predicting, because that gives you a model that functionally represents the data. Seeing is believing, after all.
I never studied the humanities, so I’ll be brief here. Not only have we studied ourselves in fields like psychology and biology, but there are whole fields of study around human behaviour in groups large and small. While we all think of ourselves as highly individualistic, with free will (*
cough*) to do as we please, the reality we also admit to is that our lives are largely dictated by routines. We all eat and sleep, and some of our work in between those mandatory activities.