«Breaking News» Online shopping algorithms like Amazon's 'collude' with other sites to artificially inflate prices

Machine learning is becoming so smart that algorithms designed to set prices in online marketplaces are mirroring each others' behaviour to raise prices.    

Algorithms using self-learning AI are popular systems that have become adopted by Amazon to constantly learn and set the best prices in order to drive website profit. 

An experiment by researchers in Bologna used algorithms similar to those manipulated by online shopping sites and found they were able to 'collude' to artificially hike up prices. 

The researchers showed that this could happen entirely out of human control, as the independent AI systems were able to learn each others' behaviours.  

Machine learning is becoming so smart that online price setting algorithms are mirroring each others' behaviour to raise prices and with a goal to raise profits. Algorithms using self-learning AI on sites like Amazon to constantly learn and set the best prices to drive profit (stock) 

The so called 'reinforcement-learning' ability of these AI programmes is how autonomous algorithms learn from its own and others' behaviour. 

Such algorithms simulate different strategies in a way that uses penalties and rewards to drive the system and achieves its goal. 

For online shopping sites this goal could be as simple as to 'maximise profit'. 

The researchers of the study ran a number of AI price setting systems in a controlled environment and found that even simple algorithms, or 'agents' can participate in complex colluding systems. 

A team from the University of Bolognia, which included Emilio Calvano and Giacomo Calzolari, found their autonomous natures to be alarming. 

They determined there to be no communication necessary between the algorithms for behaviour to be learnt, even when multiple agents were involved. 

In contrast, human systems are incapable of the same. 

The researchers of the study who ran a number of AI price setting systems in a controlled environment found that even simple algorithms can participate in complex colluding systems. The blue and red lines show the price flux over time of two autonomous pricing algorithms. The red algorithm deviates from the collusive price in the first phase and is punished immediately, mirrored by the blue line drop that follows the red line drop. However, a return to the original competitive price sees the algorithms slowly push price back to the initial price

The research team added: 'The experimental literature with human subjects, by contrast,has consistently found that they are practically unable to coordinate without explicit communication save in the simplest case, with two symmetric agents and no uncertainty.'

The team said: 'Most worrying is that they learn to collude by trial and error, with no prior knowledge of the environment in which they operate, without communicating with one another, and without being specifically designed or instructed to collude.'

Online price setting has quickly evolved in the era of AI and the use of algorithms has become cheap and affordable  to the point where even small vendors are using it it to fix prices online. 

Google's  AlphaZero has defeated one of the best chess programs in the world after learning the game from scratch in just four hours. The 'superhuman' AlphaZero AI played 100 games against rival computer program Stockfish 8, and won or drew all of them(stock)

WHAT IS ALPHAGO ZERO?

The system starts off with a neural network that knows nothing about the game of Go.

It then plays games against itself, by combining this neural network with a powerful search algorithm.

As it plays, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games.

This updated neural network is then recombined with the search algorithm to create a new, stronger version of AlphaGo Zero, and the process begins again.

In each iteration, the performance of the system improves by a small amount, and the quality of the self-play games increases, leading to more and more accurate neural networks and ever stronger versions of AlphaGo Zero.

This technique is more powerful than previous versions of AlphaGo because it is no longer constrained by the limits of human knowledge.

Back in 2015, a third of the sellers on Amazon were using automated pricing AI systems, and that percentage is likely to be much higher now. 

What used to be a 'rule-based' system of price fixing has quickly moved towards the 'reinforcement-learning' system that constantly learns from both itself and others.  

A famous example of machine learning reinforcing profit driving AI behaviour is the AlphaGo.

The intelligent machine algorithm beat the best human players at the ancient board game Go, and learnt the best strategies during the game, with the goal of winning.

In the same way, price setting programmes are given the goal to maximise overall profit; then they experiment with different strategies in a simulated environment to find the optimal one. 

Link hienalouca.com

https://hienalouca.com/2019/02/13/online-shopping-algorithms-like-amazons-collude-with-other-sites-to-artificially-inflate-prices/
Main photo article Machine learning is becoming so smart that algorithms designed to set prices in online marketplaces are mirroring each others’ behaviour to raise prices.    
Algorithms using self-learning AI are popular systems that have become adopted by Amazon to constantly learn and set the best prices ...

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Dianne Reeves US News HienaLouca





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