Understanding Causality: Is y(n) = x(n3) a Causal System?

In summary, causality in a scientific context refers to the relationship between two events, where one event leads to the occurrence of another. A system is considered causal if the output only depends on past or present inputs, and not future inputs. The notation y(n) = x(n3) represents a mathematical relationship between an input and an output signal, with the "n3" indicating that the output is equal to the input at that time, cubed. This system is causal because the output is only dependent on past or present inputs. Understanding causality is essential in scientific research as it allows researchers to identify and understand relationships between variables, make accurate predictions, develop effective treatments, and further our understanding of the world.
  • #1
caramello
14
0
Hi,

I need help in determining if a system is causal or not. So what I knew is that for a system to be causal is that it has to depend on past and present inputs. However, I don't completely understand the meaning of that definition. So for example if I have a system y(n) = x(n3) how do we know if its input depends on its past inputs or not? i mean obviously if we put in some values of n = 2, it will definitely affect the output of n3=23=8. So the system is then causal?

I am really confused about this. Thanks!
 
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  • #2
Yes, this system is causal. Causality means that the output of a system at any given time is only dependent on the present and past inputs and not on future inputs. In this particular example, the output at a given time step (n) will be determined by the input from the past three time steps (n-3, n-2, and n-1). Therefore, the system is indeed causal.
 
  • #3


I can provide some clarification on the concept of causality in systems. In order for a system to be considered causal, it must meet two criteria: 1) the output at any given time is only dependent on present and past inputs, and 2) the output cannot be affected by future inputs. This means that the system cannot have any knowledge of future inputs or events in order to produce its output.

In the case of the system y(n) = x(n3), we can see that the output at any given time n is only dependent on the present and past input values, which are represented by n3. This means that the system does not have any knowledge of future inputs and therefore meets the first criteria for causality.

To further understand this concept, let's look at an example. If we input n = 2, the system will output y(2) = x(2^3) = x(8). This output is dependent on the present input of 2 and the past input of 8. If we were to input a future value of n = 4, the system would still output y(4) = x(4^3) = x(64), which is only dependent on the present and past inputs of 4 and 64, respectively. The future input of n = 4 does not change the output of y(2) = x(8), thus fulfilling the second criteria for causality.

In conclusion, based on the definition of causality and the behavior of the system y(n) = x(n3), we can say that this system is indeed causal. It only depends on present and past inputs and is not affected by future inputs. I hope this helps clarify the concept for you.
 

What is causality in a scientific context?

Causality refers to the relationship between two events, where one event (the cause) leads to the occurrence of another event (the effect). In scientific research, causality is often studied to understand how changes in one variable affect changes in another variable.

How do we determine if a system is causal or not?

A system is considered causal if the output only depends on past or present inputs, and not future inputs. In other words, the output cannot be influenced by events that have not yet occurred.

What is the meaning of y(n) = x(n3)?

This notation represents a mathematical relationship between an input signal (x) and an output signal (y). The "n3" indicates that the output signal at a specific time (n) is equal to the input signal at that time, cubed.

Is y(n) = x(n3) a causal system?

Yes, this system is causal because the output at any given time is only dependent on past or present inputs, and not future inputs. The output y(n) is directly determined by the input x(n) at that same time, cubed.

How does understanding causality impact scientific research?

Understanding causality is crucial in scientific research as it allows researchers to identify and understand the relationships between variables. By establishing causality, researchers can make accurate predictions, develop effective treatments, and further our understanding of the world around us.

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