antiheuristics

Antiheuristics are a concept in artificial intelligence and cognitive science that describes strategies or rules that intentionally lead away from an optimal or efficient solution. Unlike heuristics, which aim to find a good enough solution quickly, antiheuristics actively seek to avoid or delay reaching a desirable outcome. This might be done to explore alternative paths, to understand the limitations of a given problem-solving approach, or to intentionally generate suboptimal results for experimental purposes.

In some contexts, antiheuristics can be used as a debugging tool. By observing how a system behaves when directed by antiheuristics, researchers can gain insights into its underlying decision-making processes and identify potential flaws or biases. For example, if a pathfinding algorithm consistently chooses the longest possible route when instructed with antiheuristics, it can highlight specific conditions or weights that are being improperly interpreted.

Another application of antiheuristics is in the study of human decision-making. Humans sometimes exhibit behaviors that seem counterproductive or irrational, which could be interpreted as a form of antiheuristic thinking. This might occur when individuals are exploring novelty, engaging in creative thought, or deliberately trying to break free from established patterns of reasoning. Understanding antiheuristics can therefore contribute to models of both artificial and natural intelligence.