Researchers analyzed behaviors of 541 people in 'social dilemma' games

Then, they developed algorithm to classify them based on the responses

This revealed 4 types: envious, trusting, pessimistic, and optimistic 

Envious was found to be the most common, making up 30% of the group 

Most of the population can be categorized within four basic personality types, according to a new study.

Researchers in Spain analyzed the responses of hundreds of volunteers to various social dilemmas, and developed an algorithm to classify their behaviour.

The algorithm determined that 90 percent of people can be considered either optimistic, pessimistic, trusting, or envious – and the rest behave in a way that falls outside of the defined models.

The algorithm determined that 90 percent of people can be considered either optimistic, pessimistic, trusting, or envious – and the rest behave in a way that falls outside of the models. A stock image of a woman looking envious, the most common classification, is pictured

The algorithm determined that 90 percent of people can be considered either optimistic, pessimistic, trusting, or envious – and the rest behave in a way that falls outside of the models. A stock image of a woman looking envious, the most common classification, is pictured

FOUR PERSONALITY TYPES 

Optimistic (20%) - Optimists believe they and their partner will make the choice that’s best for both of them

Trusting (20%) - Individuals considered to be ‘trusting’ are collaborators, and work together with others without being overly concerned with whether they win or lose

Pessimistic (20%) - These people choose the option they consider to be the ‘lesser of two evils’

Envious (30%) - According to the researchers, these people didn’t mind what they achieved in the games, as long as the results were better than everyone else’s

Researchers from Universidad Carlos III de Madrid, and the universities of Barcelona, Rovira i Virgili and Zaragoza recruited 541 volunteers to participate in a number of social experiments.

These were formed in a way that could lead to collaboration or conflict, depending on the interests of the group and the individuals.

Participants were placed in pairs to partake in a series of social games.

And in each round, both the pairs and the games would change.

‘So, the best option could be to cooperate or, on the other hand, to oppose or betray,’ said Anxo Sánchez of Universidad Carlos III de Madrid, one of the study’s authors.

‘In this way, we can obtain information about what people do in very different social situations.’

The researchers then developed an algorithm to classify the participants based on their responses to these situations.

This revealed four dominant personality types, making up 90 percent of the group.

Participants considered to be envious were the largest group among this chunk, with 30 percent categorized in this way.

The researchers say these people were unconcerned with their own achievements, as long as they were better than everyone else’s.

The remaining part of this segment was split among optimists, who believe their team will make the best choice for both of them, pessimists, who gravitate toward the ‘lesser of two evils’ option, and ‘trusting’ individuals, who always cooperate with others, even if it means losing.

These were evenly divided, with 20 percent making up each group.

According to the researchers, there is also a fifth, undefined group, representing the 10 percent left unclassified by the algorithm.

These individuals responded in a way that fell outside of the outlined models.

‘The results go against certain theories; the one which states that humans act purely rationally for example, and, therefore, they should be taken into consideration in redesigning social and economic policies, as well as those involved in cooperation,’ said Yamir Moreno, an author on the study.

The researcher continued, ‘these types of studies are important because they improve existing theories on human behaviour by giving them an experimental base.’

While algorithms have been used in other fields, such as biology, for classification, the researchers say this is a ‘revolutionary’ tactic for the understanding of human behaviour.

According to Jordi Duch, a researcher at Universitat Rovira I Virgill, earlier studies ‘prefixed the behaviours 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 objective of using mathematics was precisely to guarantee impartiality.’

The findings shed light on the mechanisms that drive collective and individual interests.

Not only does the study have implications for improvements in business management, organization, and politics, but the researchers say this understanding can also help to male ‘robots more humanized.’

‘Previously, the experiments were performed by dozens of people,’ said Duch.

‘Now, with this platform, it is possible to significantly increase the volume of participants in the study, as well as being able to test using the heterogeneous population; this also allows us to record much more specific data on how the participants behave during the experiment.

‘This has opened up the door to setting up much more complex tests than those that have been carried out so far in this field.’ 

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