Poker is a game that forces players to make decisions quickly and with little information. What’s more, these decisions are often based on something other than the cards in front of them – they are also based on what you think your opponent might do.
In this article I want to explore how this can be harnessed for good – by helping us make better educated decisions about the world around us.
It’s been said that “poker is the only game where luck matters more than skill” – which is true. But it’s not just a game of skill either. There are many elements involved in making a winning hand, from psychology to probability.
So what does this mean? The first thing it means is that poker is an ideal test bed for some of the most exciting areas of AI research today. To start with, poker will give us insight into how computers process visual data, how they react to uncertainty, and how they learn from experience. It will also allow us to explore how humans make decisions in real life situations.
The second thing it means is that poker will allow us to find out whether we can teach machines to make better decisions. This is important because we already know that our brains aren’t perfect decision makers. We all have biases and heuristics, and it’s possible that these could cause us to make bad decisions. If we can train machines to recognise patterns in human behaviour and to behave like people would under similar circumstances, then we may be able to overcome some of these biases.
This has huge potential outside of poker too. One example is automated medical diagnosis – where a computer program needs to diagnose disease from a patient’s symptoms. Another possibility is using machine learning to spot anomalies in security systems or to improve traffic flow.
Finally, there’s the obvious use in business – helping companies make better financial decisions. A few years ago, machine learning was used to analyse complex financial models and to come up with new ideas for businesses to try. These techniques were known as algos for short, and their ability to predict future outcomes was often astonishing.
One example was the company Amadeus, who created an algorithm which accurately forecast the stock market. The company ran its model through every single company in the S&P 500, and found that one particular company had a significant bias in its stock price compared with its competitors. So the company bought that company’s shares at a discount, and made $1 billion.
But while these algos work well in the short term, they are susceptible to sudden changes in market conditions. For example, if a large company announces a dividend, or goes bankrupt, the value of the algo will suddenly plummet.
However, what if we could build a system that learns from its mistakes over time? That way, it would know when to buy and sell stocks, rather than simply following the rules set down by the algo. In fact, this kind of approach is exactly what DeepMind did with AlphaGo, the algorithm which beat top Go player Lee Sedol.
If the player will have the motive of the okbet login then he needs to add the bank details with which he is more comfortable. The main motive of the players must be to go for the option that is convenient. As the time grows the players have the option to choose the cryptocurrency as the method of payment.
AlphaGo learned from over 1 million games played against itself, and eventually developed strategies which won against all comers. It’s likely that a similar strategy could be applied to many different problems in both finance and medicine.
We don’t need to wait for technology to catch up though; we can apply what we know about psychology to help us make fact-based decisions right now.
For example, in poker, there are so many factors to consider that it’s easy to get caught up in emotion.
You see a great hand, but you realise that your opponent is playing really tightly. You know that you can turn things around, but the problem is that you’re nervous and you’re scared of losing. And even though you know that you have a strong hand, you know that your opponent has a worse one.
So instead of thinking clearly, you start to sweat and play weakly. This is called emotional reasoning, and it happens very regularly in poker.
If we could train ourselves to avoid being distracted by emotions, we could become a lot sharper decision makers. So let’s take a look at how this works.
First, we need to understand why we get distracted by emotion.
Psychologists call this the availability heuristic. Simply put, we tend to remember events which are more frequent. So if we hear about two incidents of violence, we tend to believe that violence is more common than it actually is.
Likewise, we tend to remember positive events far more frequently than negative ones. So if someone says ‘I’m excited about my new job’, we tend to assume that everyone else is equally happy, whereas if someone says ‘I hate my job’, we tend to assume that everyone hates their jobs.
This is great for us to remember how many times we’ve heard certain news stories, and helps us form opinions about what is going on in the world. However, it also means that we’ll have trouble recognising and correcting our assumptions.
If you remember back to the example above, you probably thought that your opponent was getting nervous because he had a terrible hand. Yet you also knew that he had a much worse hand.
Now imagine that you’d seen him play before, and that you knew that he had a very strong bluffing style – one that relies on trying to convince people to fold. Then you’d know that his hand wasn’t terrible, and that he was probably bluffing. But if you were nervous, then you might still incorrectly believe that he was bluffing.
So next time you play poker, try to remind yourself that you should always think rationally. Remember that you should never rely on your intuition alone, because you can’t trust your own judgement. Instead, you should always base your decisions on facts.