Why is the concept of cycles overlooked in causation models?

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Twodogs
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The online Stanford Encyclopedia of Philosophy has a lengthy article on Causal Models. https://plato.stanford.edu/entries/causal-models/
In this article the word 'cycle' appears twice in non-substantive fashion. Given the prevalence of cycles in many kinds of dynamics, I am curious why it does not receive more attention as a key element in causation. That said, I am not certain I have posted this question in the appropriate forum. Thanks.
 
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Please give us more details of cycle you said, e.g. what elements form circle ?
 
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Twodogs said:
The online Stanford Encyclopedia of Philosophy has a lengthy article on Causal Models. https://plato.stanford.edu/entries/causal-models/
In this article the word 'cycle' appears twice in non-substantive fashion. Given the prevalence of cycles in many kinds of dynamics, I am curious why it does not receive more attention as a key element in causation. That said, I am not certain I have posted this question in the appropriate forum. Thanks.
This is not the correct forum for this discussion. It belongs in the probability and statistics forum. Also, causal models are not designed to model reversible processes like we typically find in classical mechanics. Instead, you typically want an irreversible process before causality models have much use and in practice they are typically applied in domains where it is impossible to model the complete system.

Also, I am not sure that all causal models disallow cycles. Even in the article I think they say that some formalisms do.
 
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Yes. Anywhere you look in 'nature' you find cycles of energy & materials driven by some sort of energy gradient. Put a rock in a laminar fluid flow and numerous eddies appear. Planets, pistons, pulse - cyclical dynamics is a numerous class. One expects that a thirty-six page article (that does mention 'acyclic' causality) would offer some discussion of this. Thanks for your thoughts.
 
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jbergman said:
This is not the correct forum for this discussion. It belongs in the probability and statistics forum. Also, causal models are not designed to model reversible processes like we typically find in classical mechanics. Instead, you typically want an irreversible process before causality models have much use and in practice they are typically applied in domains where it is impossible to model the complete system.

Also, I am not sure that all causal models disallow cycles. Even in the article I think they say that some formalisms do.
Thanks, I posted a reply before seeing your post. I would still like to find out more and will look into reversible processes.
 

1. Why is the concept of cycles important in causation models?

The concept of cycles is important in causation models because it allows for a more comprehensive understanding of the relationship between cause and effect. Cycles refer to the repeated patterns or sequences of events that occur over time, and they can greatly influence the outcomes of a causation model. By considering cycles, we can better account for factors such as feedback loops and long-term effects, which may not be captured in a linear cause-and-effect model.

2. How do cycles impact causation models?

Cycles can impact causation models in several ways. They can complicate the relationship between cause and effect, as they may involve multiple factors and feedback loops. Cycles can also introduce the concept of time, as they occur over a period and may have delayed or long-term effects. Additionally, cycles can change the direction of causality, as effects may also influence causes in a cyclical pattern.

3. Why are cycles often overlooked in causation models?

Cycles are often overlooked in causation models because they can be difficult to identify and measure. Unlike linear cause-and-effect relationships, cycles may involve multiple variables and feedback loops, making it challenging to determine which factors are causing which effects. Additionally, cycles may occur over a longer period, making it harder to observe and measure their impact. As a result, many causation models may simplify the relationship between cause and effect, omitting the complexity of cycles.

4. What are the consequences of overlooking cycles in causation models?

The consequences of overlooking cycles in causation models can be significant. By ignoring cycles, we may miss important factors that influence the relationship between cause and effect. This can lead to inaccurate or incomplete conclusions and may hinder our ability to predict and understand complex systems. Additionally, overlooking cycles may limit our ability to identify and address underlying issues that contribute to the cyclical patterns.

5. How can we incorporate cycles into causation models?

To incorporate cycles into causation models, we need to first identify and understand the cycles that are relevant to the system we are studying. This may involve collecting and analyzing data over a longer period and considering the feedback loops and multiple factors involved. Once we have a better understanding of the cycles, we can then modify our causation model to account for them. This may involve using more complex modeling techniques, such as system dynamics, to better capture the cyclical patterns and their impact on causality.

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