Each one of us is unique, which can mean studying our behavior can be a real pain for researchers. So, in a bid to solve this problem, a Spanish team has developed an algorithm-based framework to neatly characterize the array of human behavior in social situations. To their surprise, only four distinguishable groups were found.
The new research, led by the Charles III University of Madrid, has suggested that 90 percent of the population fit into these four groups: envious, optimistic, pessimistic, or trusting. Perhaps most surprisingly, they found that envious was the most common personality type.
The study gathered up 541 people and put them through a series of hypothetical “social games” that worked out how they manage social interactions. Each of their decisions then affects how the other individuals in the game respond.
Anxo Sánchez, one of the study’s researchers, explains a typical example of one of the games in a statement: “Two people can hunt deer together, but if they are alone, they can only hunt rabbits. The person belonging to the Envious group will choose to hunt rabbits because he or she will be at least equal to the other hunter, or maybe even better; the Optimist will choose to hunt deer because that is the best option for both hunters; the Pessimist will go for rabbits because that way he or she is sure to catch something; and the hunter who belongs to the Trusting group will cooperate and choose to hunt deer, without a second thought.”
The responses were then plugged through a computer algorithm, which sifted through the findings to see if there were any discernable patterns in the individual's behavior.
"The really funny thing is that the classification was made by a computer algorithm which could have obtained a larger number of groups, but which has, in fact, produced an 'excellent' rating in four personality types," explains Moreno.
The findings showed that 30 percent of people fell into the envious bracket, while the three other types were only found to account for 20 percent of the participants each. The researchers weren’t able to strictly classify the remaining 10 percent, as these responses did not appear to show any clear behaviors.
"This type of classification algorithm has previously been used with success in other fields, such as biology," explained study author Jordi Duch in the statement. "However, its application to the study of human behavior is quite revolutionary, given that previous works prefixed the behaviors expected before the experiment was carried out, instead of allowing an external system to then automatically give us information about which groupings were most logical."
The full study can be found in Science Advances.
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