Research shows that given a chance to win $100 or avoid losing $100, most people choose not to lose money rather than speculate. However, as the benefits of the potential gain rise, choices change. More people would choose to risk losing $100 if the prize was $1000. Many more would risk losing $100 if the prize was $10,000. And each week many people are prepared to risk around $20 for a chance to win billions in the lottery, even though the chance of winning is minute but hey, someone’s got to win, right?
Actually, no, there is no guarantee that the major prize is won each week, and in any event, the talk is about the $1.6 billion that someone could win, not the $20 you are almost certain to lose.
The results of extensive research to determine a mathematical relationship between risk and gain were surprising! Heavy computer number crunching showed that the relationship was non-linear between the two extremes, like the real-world examples, above. Not only that, and regardless of the application, the mathematical risk/gain relationship converged on a consistent value within a simple formula.
The value was 2.5:1, the general value of perceived risk over gain, basically confirming how we behave. We are a risk averse specie. When calculating the probabilities of future events as being on either side of 50:50, our humanized AI uses an adjusted probability of 72:28 in
favor of risk. The variables were found to be direct functions of the size of the potential risk versus gain relationships, independent of the application sectors.
When confronted with potential risk versus gain, human behavior and resulting decisions were showing high degrees of consistency for all applications, regardless of their apparent disconnectedness. And our computers found this to be the optimum ratio regardless of the application: like bets on football games, trading in the markets, predicting winning politicians and heart arrythmias, and even the direction of random number series.
The chart above shows the 69th play of the game between the Green Bay Packers and the Chicago Bears with Chicago on offense. The humanized AI curve for that play (1 day before the actual game) indicated that Chicago could choose potential gain (go for it) over risk (punt) given the high gain ratio of 144 to 1, compared to Green Bay’s defense ratio of 4.3 to 1. (Remember, games are all about going for the gains by winning). In actual fact, Chicago went for it with a 5-yard pass play an got a 1st down!
Hedging is an integral function in decision making and game playing. It’s the potential offset to failure. It changes the 72/28 ratio towards 50/50, increasing the prospect for gain. And, it can also attempt to eliminate all risk and gain. Chicago could have punted! The S&P 500 Futures Index is typically used to protect stock equity portfolios. But, hedging is never perfect. Some gain or loss always leaks through. Humanized AI knows this and can help by simulating us out of an unhappy future.
by Grant Renier, engineering, mathematics, behavioral science, economics