技術的なシステムも、社会システム、組織をシステムとして捉えた場合も、同様のデザインプロセスで設計できます。大きな流れとしては、利害関係やニーズを把握し、技術的な仕様を定め、設計変数・選択肢や制約条件を整理し、複数の基準を複合的に評価しながらデザインプロポーザルを選出するという手順あるいはその繰り返しになります。
産業界では、前述の一般的な流れを商慣習に合わせた標準化を行い、情報システムによる効率化が行われます。このため、実務者は、システムデザインの一部の計算や評価についての専門性が高まる一方で、システム全体をデザインしているという認識が失われてしまう可能性があります。例えば、情報システムを開発しているエンジニアは、仕様が顧客のニーズから導かれたものであることを忘れて、仕様を実現する情報システムの開発のみに集中できるようになっています。船舶設計であれば、顧客がその船をどの航路でどのような荷物を運ぶために使うのかを忘れて、所定の船速で効率的に単位体積重量の荷物を運ぶことのできるエンジンの選択に集中できます。
We expect engineered system here, not general definition of system. We try to explain how systems thinking can support problem solving activities of complex systems.
We define an engineered system as a combination of components that work in synergy to collectively perform a useful function. The engineered system could, for example, wholly or in part constitute a new technology for a new product line a new manufacturing process, a technology to improve the delivery of a service, or an infrastructure system [ERC/NSF webpage].
Driving a car as one example of a system. We can think of a car as consisting of three elements: body, engine, and tires. The body holds people and cargo, the engine converts fuel into energy for rotation, and the tires convert the rotational energy of the axle into driving force for the body. When these three elements are combined, they provide a function of moving people and cargo that could not be expressed by the individual elements. In such a case, a car can be considered as a system.
The phenomenon of the new or larger functions being created from simple elements is called "emergence." When the performance of a function does not grow as expected due to the combination of each element, or when an undesirable phenomenon occurs, it is also considered as "undesirable emergence." For example, traffic congestion is a type of undesirable emergence that causes a decrease in performance even when each element (road and vehicle) is functioning properly. If we focus on emergence, we can also define a system as a collection of elements with emergence.
In many cases, the system solves a problem that involves uncertainty, or the system itself contains uncertainty.
There are two types of uncertainty: Aleatory and Epistemic. As an example of Aleatory, it is not possible to predict the color of a ball when it is randomly picked out of a box containing half of each of the red and white balls. This problem turns into an Epistemic problem when you don't know the proportion of red and white balls.
There is also the uncertainty that the type of color of the ball that is taken out is unpredictable in the first place, but we will not cover it here.
The framework of systems thinking is to deal with the uncertainty and complexity of society and industry as a system.