Zombie Politics: Evolutionary Algorithms to Counteract the Spread of Negative Opinions
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This paper is about simulating the spread of opinions in a society and about finding ways to counteract that spread. To abstract away from potentially emotionally laden opinions, we instead simulate the spread of a zombie outbreak in a society. The virus causing this outbreak is different from traditional approaches: it not only causes a binary outcome (healthy vs infected) but rather a continuous outcome. To counteract the outbreak, a discrete number of infection-level specific treatments is available. This corresponds to acts of mild persuasion or the threats of legal action in the opinion spreading use case. This paper offers a genetic and a cultural algorithm that find the optimal mixture of treatments during the run of the simulation. They are assessed in a number of different scenarios. It is shown, that albeit far from being perfect, the cultural algorithm delivers superior performance at lower computational expense.
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