Background In recent years, multiple empirical studies have revealed the existence of segregated communities in online social media interaction [4, 7, 9, 14, 18, 39, 73], where likeminded people interact with little exposure to different viewpoints. This phenomenon is commonly referred to as the echo chamber effect. More concretely, the term echo chambers refers to a state where online interactions are conducted in a polarized pattern; within an online social network, there exist groups that correspond to a certain belief (e.g., political left or right), and most interactions happen between users from the same group. A study into the nature of these polarized communities is consequential; echo chambers has been increasingly studied as of late due to its association with significant political events such as Brexit and the surprise win of Donald Trump in the 2016 US election. It is also known to cause significant harms to the process of discussion and democracy [68, 69, 72]. At its worst, echo chambers can limit one's viewpoints, reinforce personal biases, and foster environments where hoaxes and misinformation thrives.

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in recent years, Procedural Content Generation (PCG) has become a popular alternative for creating video games content. In this paper, we attempt to create a PCG for valid video game racing tracks while optimizing the fun they give to players. To achieve this, we represent a racing track as a sequence of parameterized segments, evaluate it using a multi objective fitness function, and then search for the optimum track by using search algorithms. Two search algorithms are used as alternatives to compare the effect it has on the racing tracks generated. The result we report show that the tracks generated can successfully match the validity criteria of being closed, continuous, and non-intersecting. More than that, they can also successfully create a certain degree of fun for players, according to data acquired from a questionnaire given to testers.