The new results were obtained after a re-run of the 2001 well-known IBM experiment where human traders competed against state-of-the-art computerised trading agents - and lost. Now 10 years on, experiments carried out by Marco De Lucas (left) and Professor Dave Cliff (right)  of the University of Bristol have shown that AA is now the leading strategy, able to beat both robot traders and humans.

Dr Krishnan Vytelingum, who designed the AA strategy along with Professor Dave Cliff and Professor Nick Jennings (left) Southampton University  in 2008, commented:  “Robot traders can analyse far larger datasets than human traders. They crunch the data faster and more efficiently and act on it faster.

Robot trading is becoming more and more prominent in financial markets and currently dominates the foreign exchange market with 70% of trade going through robot traders.”

Professor Jennings, Head of Agents, Complexity and Interaction research at the University of Southampton, commented:  “AA was designed initially to outperform other automated trading strategies so it is very pleasing to see that it also outperforms human traders. We are now working on developing this strategy further.”

ROOM FOR THE REACTIVE  PARAMETER?
Parameter scheduling & adaptive  control of nonlinear processes (gases, liquids, sluries) are also used for optimisation)

It is perhaps worth noting that a 2010 suggested tablet revenue model (MediaIDEAS) offers reactive to the aggressive and reactive partnership theory.

The pictured Action Cycle shows the three primary tablet revenue models that will guide the delivering of digital magazine content to users are: Content-based, App based or Ad-based.

A publisher’s technological Aggressive, Adaptive, or Reactive approach combined with the technology’s development over time, will make one of these models or combinations of models a better, more appropriate fit.