Suppose we have some algorithms which are trying to predict which team will win a game. People are going to bet money based on what the algorithms say, and teams benefit when more money is bet on them.<p>The red team plays dirty.<p>First they spread false rumours about the blue team. Most of the algorithms respond by reducing the blue team&#x27;s chances of winning against the red team, but algorithm #5 successfully predicts that the rumours were false and people who bet using algorithm #5 made a lot of money. Now lots of people use variants of algorithm #5 and thereafter the red team has no luck getting anyone to believe their false rumours.<p>In other words, if the algorithms are <i>incorrect</i> then we&#x27;re all better off to fix them.<p>Next the red team successfully lobbies the government to raise the blue team&#x27;s taxes (even though the red team makes more money). Now the blue team has less money for training and equipment, which has <i>actually</i> reduced their chances of winning the game. Most of the algorithms successfully predict this, and then the blue team loses the next game.<p>Your argument seems to be that the red team should be punished for this by having your algorithm stop preferring them. But now you&#x27;re screwing over everyone else relying on your algorithm, who weren&#x27;t responsible for the behaviour you&#x27;re trying to punish. And thereafter no one trusts you to provide true information.<p>If the red team is playing dirty and the math accurately predicts the result, the problem is not the math.