Typically, the answer we believe to be right is based on the equilibrium point. The more we understand a game and the more we achieve "perfect knowledge of what [we] are doing and of what [our] opponents are doing" the higher our chances of winning.
Applied to a simple game that consists of only a few moves, we can easily figure out the optimal strategy to win. The more factors and choices are introduced, the more difficult a game is to understand and our decisions actions become less rational.
However, Tobias Galla from The University of Manchester and Doyne Farmer from Oxford University and the Santa Fe Institute suggest that "Equilibrium is not always the right thing you should look for in a game", simply because "people do not play equilibrium strategies."
"Instead what they do can look like random or chaotic for a variety of reasons, so it is not always appropriate to base predictions on the equilibrium model," Galla explained. The scientists compared the gaming model to financial markets where traders have virtually unlimited choices and face the ultimate complexity of a game, which currently assumes that traders are infinitely intelligent and rational - and that an equilibrium point exists.
"With trading on the stock market, for example, you can have thousands of different stock to choose from, and people do not always behave rationally in these situations or they do not have sufficient information to act rationally," Galla said. "This can have a profound effect on how the markets react."
Galla noted that their initial findings indicate that an increasing number of players reduces the chance of an equilibrium. "It could be that we need to drop these conventional game theories and instead use new approaches to predict how people might behave."
