Simpler explanations are more likely to be correct; avoid unnecessary or improbable assumptions. ## On Parsimony Yes, I am familiar with the concept of parsimony. In general, parsimony refers to the principle of using the simplest or most frugal explanation, solution, or method when faced with competing alternatives. This principle can be applied in various disciplines, such as philosophy, biology, and computational sciences. In the context of philosophy, parsimony is often related to Occam's razor, a principle that states that the simplest explanation that accounts for all the available data is preferable to more complex ones. Occam's razor helps guide decision-making and theoretical development by encouraging the selection of hypotheses or theories with the fewest assumptions. In biology, the principle of parsimony is used in phylogenetics, the study of evolutionary relationships among organisms. When constructing phylogenetic trees, scientists often prefer the tree that requires the fewest evolutionary changes, as it is considered the most parsimonious and likely to be the most accurate representation of evolutionary history. In computational sciences, parsimony can refer to creating simple, efficient algorithms or models that can explain the data with the fewest parameters. The idea is that simpler models are less prone to overfitting and are more likely to generalize well to new data. It can be frustrating to understand a model or explanation when it presents a lot of types of things but doesn't clearly describe the causal relationships between them because: 1. Lack of clarity: Without a clear understanding of how the different components in a model or explanation are causally connected, it can be difficult to grasp the overall concept or mechanism. A good explanation should provide a clear and concise account of the relationships between the components. 2. Difficulty in applying the knowledge: Without knowing the causal relationships, it can be challenging to apply the knowledge to real-world situations or problems. Understanding how things are causally connected helps in making predictions, solving problems, and making informed decisions. 3. Incompleteness: An explanation that doesn't provide causal relationships might seem incomplete, leaving you with unanswered questions and a sense that something is missing. This can lead to frustration, as you may need to seek out additional information to fill in the gaps. 4. Limited understanding of underlying principles: Without knowing the causal relationships, it's harder to understand the underlying principles or fundamental concepts that govern the model or explanation. This makes it difficult to generalize the knowledge and apply it to other related scenarios or problems. To alleviate this frustration, it can be helpful to seek out resources that provide clear, concise explanations of the causal relationships between the components of a model or explanation. This can include supplementary materials, videos, or expert opinions that can help you better understand the subject matter.