SYSTEM THINKING

SYSTEM THINKING

            System thinking is comprehending how matter or things affect each other. It is also used in the field of problem solving, by considering problems as a component of a whole system. It is founded on the belief that a module or an element can be comprehended by considering its relationships and interactions with other systems and other components.

            A discrete system may be described as an exact mathematical model since this system carries with it states that are numerically countable. [i]A discrete system is a system with a countable number of states. According to a computational theory, this system is often formed with a directed graph and evaluated for preciseness and complexity.

            A continuous system is also known as a continuous time-signal system to which its inputs and outputs can alter at any time.

            A computer may be cited as an example of a finite system because it is a finite state machine. However, a computer models not only the discrete system but also the continuous system. Due to this, techniques have been extended to correspond  to real-world continuous system as discrete system.  One  illustration entails modeling a continuous signal at discrete time gap.

            The AD (analog-digital) converter is another example. It  alters an analog signal to a digital signal. A speed sensor or temperature can be the voltage signal.

            No randomness is contained in the future states’ development in a deterministic system. In its starting stage or initial condition, the same output will always be  generated and produced.

            A finite state machine may be deterministic. An example is an elevator. An elevator can be in any of the floors with two buttons representing the two values of this machine which  manipulates the direction of the elevator’s path.  Likewise, although a pseudorandom number generator is intentionally made difficult to forecast or predict, it is also a deterministic algorithm. This pseudorandom number generator is fundamental in cryptography and the Monte Carlo method (helpful in replicating systems that have degrees of freedom as liquids or solutions, structures of cells, and disordered materials among others.

            A boundary  sets the limit of a volume that is finite. Real or notional, it may separate a system from its environment.  If  matter is flowing inside to outside  of the boundaries, it is called an open system. An example is a piston with fluid that pushes mass outside the boundary. Another example is the piston’s movement towards turning a pulley to raise water.

If there are no transfer  or movement of mass along the boundaries, it is said to be a closed system.   An example of a closed system is fluid in a cylinder, and a bomb calorimeter

Probabilistic system states that [ii]if one randomly chooses objects from a specified class, the probability that the result is of the prescribed kind is more than zero.

An example of a probabilistic system is: If we have a hundred  coins, and ninety-nine are partial to heads and only one favors tails,  a coin is picked at random and is flipped, the coin will land tails. [iii]…”by using two random variables: C (the type of coin) with values bh and bt (biased towards heads and biased towards tails) and R (the result of the toss), with values h and t. A priori, the probability that we picked a coin that is biased towards heads is very high: P(C=hh)= 0.99. After receiving the evidence of the coin landing tails, we find out that P(C=hh)(R=t) is close to 0.92-less than the prior on C=hh but still very high…Clearly, the casual structure in this situation is known: there is a casual relation between C and R. Since the casual structure is known, the allowable explanation can be identified with C=hh and C=bt.”

[i] En.wikipedia.org

[ii] En.wikipedia.org

[iii] www.cs.cornell.edu

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