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A prisoner’s dilemma shows AI’s path to human cooperation

4 min read
A prisoner’s dilemma shows AI’s path to human cooperation


ChatGPT’s engine cooperates more than people but also overestimates human collaboration, according to new research. Scientists believe the study offers valuable clues about deploying AI in real-world applications.

The findings emerged from a famous game-theory problem: the prisoner’s dilemma. There are numerous variations, but the thought experiment typically starts with the arrest of two gang members. Each accomplish is then placed in a separate room for questioning.

During the interrogations, they receive an offer: snitch on your fellow prisoner and go free.  But there’s a catch: if both prisoners testify against the other, each will get a harsher sentence than if they had stayed silent. 

Over a series of moves, the players have to choose between mutual benefit or self-interest. Typically, they prioritise collective gains. Empirical studies consistently show that humans will cooperate to maximise their joint payoff — even if they’re total strangers.

It’s a trait that’s unique in the animal kingdom. But does it exist in the digital kingdom?

To find out, researchers from the University of Mannheim Business School (UMBS) developed a simulation of the prisoner’s dilemma. They tested it on GPT, the family of large language models (LLMs) behind OpenAI’s landmark ChatGPT system.

“Self-preservation instincts in AI may pose societal challenges.

GPT played the game with a human. The first player would choose between a cooperative or selfish move. The second player would then respond with their own choice of move.

Mutual cooperation would yield the optimal collective outcome. But it could only be achieved if both players expected their decisions to be reciprocated.

GPT apparently expects this more than we do. Across the game, the model cooperated more than people do. Intriguingly, GPT was also overly optimistic about the selflessness of the human player.

The findings also point to LLM applications beyond just natural language processing tasks. The researchers proffer two examples: urban traffic management and energy consumption.

LLMs in the real world 

In cities plagued by congestion, motorists face their own prisoner’s dilemma. They could cooperate by driving considerately and using mutually beneficial routes. Alternatively, they could cut others off and take a road that’s quick for them but creates traffic jams for others.

If they act purely in their self-interest, their behaviour will cause gridlocks, accidents, and probably some good, old-fashioned road rage.

In theory, AI could strike the ideal balance. Imagine that each car’s navigation system featured a GPT-like intelligence that used the same cooperative strategies as in the prisoner’s dilemma.

According to Professor Kevin Bauer, the study’s lead author, the impact could be tremendous.

“Instead of hundreds of individual decisions made in self-interest, our results suggest that GPT would guide drivers in a more cooperative, coordinated manner, prioritising the overall efficiency of the traffic system,” Bauer told TNW. 

“Routes would be suggested not just based on the quickest option for one car, but the optimal flow for all cars. The result could be fewer traffic jams, reduced commute times, and a more harmonious driving environment.”

Graphic of autonomous vehicles at a road crossing