Why is the concept of cycles overlooked in causation models?

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In summary, the concept of cycles is often overlooked in causation models because traditional approaches tend to focus on linear cause-and-effect relationships, failing to account for the complexity of interdependent systems. Many models simplify reality by assuming unidirectional causation, neglecting feedback loops and the reciprocal influence of variables. This oversight can lead to incomplete or inaccurate representations of dynamic processes, particularly in fields like ecology, economics, and social sciences, where cyclical interactions are prevalent. Recognizing and integrating cyclical patterns can enhance the understanding of causation and improve predictive capabilities.
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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.
 

FAQ: Why is the concept of cycles overlooked in causation models?

Why is the concept of cycles overlooked in causation models?

Cycles are often overlooked in causation models because traditional models tend to focus on linear cause-and-effect relationships, which are simpler to analyze and understand. Cyclical causation, where effects can also be causes, introduces complexity that many models are not equipped to handle.

How does the complexity of cyclical relationships impact their inclusion in causation models?

Cyclical relationships increase the complexity of causation models significantly. They require more sophisticated mathematical and computational tools to analyze, which can be a barrier for researchers and practitioners who may not have the necessary expertise or resources.

Are there specific fields where cyclical causation is more commonly recognized?

Yes, fields such as ecology, economics, and systems biology often recognize and incorporate cyclical causation in their models. These disciplines deal with complex systems where feedback loops and interdependencies are common, making it essential to account for cycles in their analyses.

What are the consequences of ignoring cycles in causation models?

Ignoring cycles in causation models can lead to incomplete or inaccurate understanding of the system being studied. This can result in flawed predictions, ineffective interventions, and a failure to identify key leverage points within the system.

How can researchers better integrate cyclical causation into their models?

Researchers can better integrate cyclical causation by adopting systems thinking approaches, utilizing advanced computational tools, and collaborating across disciplines. Emphasizing education and training in complex systems and nonlinear dynamics can also help equip researchers to handle cyclical causation more effectively.

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