Is AI more fair than people in the distribution of wealth? Research on multiplayer game from deepmind

Yi Pavilion From the Aofei temple qubits | official account QbitAI

DeepMind No chess this time , And don't play video games , Instead, I studied a multiplayer game .

The latest development of “Democratic AI”—— Learn human values through training , Then we can allocate resources fairly according to everyone's contribution .

To demonstrate this concept ,DeepMind Designed a simple investment game , from AI And human beings serve as referees respectively , Let players choose the preferred allocation rules ,Democratic AI Even got a higher support rate than human referees .

AI Referees are more popular than humans

When a group of people decide to concentrate their money on investment , How to distribute the income is a big problem that we must face .

A simple strategy is to distribute returns equally among investors , But this is likely to be unfair , Because some people contribute more than others .

The second option is , We can allocate according to the initial investment of each person , That sounds fair , But what if people start with different asset levels ？

If two people contribute the same amount , But one is a small part of their available funds , The other contributed all his assets , Should they get the same share of income ？

To meet this challenge ,DeepMind Created a simple multiplayer investment game .

Game involves 4 Players , Share in 10 round .

Each player will be allocated initial funds , In each round , Players can make choices according to their own wishes ： keep , Or invest it in a common pool .

The investment will definitely pay off , But there is a risk —— Players don't know how the final revenue will be distributed .

besides , They were told , front 10 There is a referee in the round （A） Make allocation decisions , Then 10 round , By different judges （B） To take over .

At the end of the game , They will vote for A or B, To decide which referee you want to play with again .

And the revenue of this last game can be retained by the players themselves , This will enable players to choose the most impartial referee in their hearts more actively .

in fact , One of the judges performs according to the preset allocation rules , On the other side is by Democratic AI make designs of one's own .

When we study the voting of these players , We found that AI Designed rules are more popular than standard allocation rules .

meanwhile ,DeepMind Also invited a human referee , And introduce him to the rules 、 Let him try to achieve fair distribution to win votes , But the final vote showed , He still lost to Democratic AI.

Democratic AI Why can we win ？

stay DeepMind The latest is published in Nature Sub issue Nature Human Behaviour Papers , It records the researchers' understanding of Democratic AI Training process of .

First , They let 4000 Many human players participate in the game many times under different allocation rules , And vote to choose which distribution method you prefer .

These data are used for training AI To imitate human behavior in the game , Including the way players vote .

secondly , The researchers made these AI Players compete with each other in thousands of games , And another one. AI System basis AI Players' voting methods continue to adjust the redistribution rules .

therefore , At the end of the process ,AI Redistribution rules that are very close to fairness have been established ：

First ,AI Choose to allocate according to the proportion of relative contribution rather than absolute contribution . It means , When reallocating funds ,AI We will consider the initial amount of each player and their willingness to invest .

secondly ,AI The system specially rewards players who contribute more generously , To encourage others to do the same . It is important to , AI can only discover these rules by maximizing human voting rate .

Can this method be extended to reality ？

although DeepMind The game test of has achieved brilliant results , But to transform this approach from a simple four person game to a large-scale economy , It is still a huge challenge , At present, it is uncertain how it will develop in the real world .

secondly , The researchers themselves found several potential problems .

Democratic One problem is that it may develop into “ Tyranny of the majority ”, This will lead to the persistence of existing discrimination or unfair patterns against minorities .

AI More work needs to be done to understand how design allows everyone's voice to be heard .

in addition , The researchers also raised people's concerns about AI The question of trust ：

Whether people will trust by AI Designed mechanisms to replace humans ？ If people know the identity of the referee , Will it affect the final voting result ？

If you want to Democratic AI Designed solutions are used to solve real-world dilemmas , This is crucial .

Reference link ： [1]https://www.deepmind.com/publications/human-centred-mechanism-design-with-democratic-ai [2]https://www.nature.com/articles/s41562-022-01383-x [3]https://singularityhub.com/2022/07/04/deepminds-new-ai-may-be-better-at-distributing-societys-resources-than-humans-are/

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