System
Overview
A system is an organic whole in which multiple interacting components combine to perform functions and purposes beyond the sum of simple parts. It is not merely a collection of parts; rather, the relationships and interactions among elements, and the boundaries and exchanges with the external environment, are central. The concept of a system is used as a common language across the natural sciences, engineering, business administration, social sciences, and information technology, and it provides a framework of thought for understanding complex objects through relationships and overall patterns rather than splitting them into parts.
Main Content
Etymology and Definition
'System' originates from the Greek 'systema (something set up together)' and became established as the English word system via Latin. In Korean, it is also translated as chegye (체계), gyetong (계통), jojik (조직), jejo (제도), etc. A modern definition can be summarized as "a set of interacting elements for achieving a common purpose."
Basic Components of a System
- Input: Energy, matter, and information accepted from the environment
- Process: The action that transforms input to suit the purpose
- Output: Products sent out to the environment as a result of transformation
- Feedback: A return flow that sends part of the output back to adjust the next input
- Boundary: The limit that distinguishes the system from the environment
- Environment: Conditions outside the system that exchange influence
- Goal: The state or function toward which the system aims
General Systems Theory
Ludwig von Bertalanffy proposed 'General System Theory' in the 1950s by integrating similar concepts scattered across biology, physics, and sociology. Afterward, Norbert Wiener's cybernetics, Ross Ashby's law of requisite variety, Herbert Simon's 'near decomposability,' and Russell Ackoff's systems thinking followed, establishing it as an interdisciplinary field.
Classification of Systems
- Open system / Closed system: Whether it exchanges matter, energy, and information with the environment
- Natural system / Artificial system: Whether it arose naturally, like an ecosystem or the human body, or was designed, like a machine or organization
- Deterministic / Stochastic: Whether outcomes are predictable or appear as probability distributions
- Static / Dynamic: Whether the state changes over time
- Simple system / Complex system: The number of components and the degree of nonlinearity of interactions
- Soft / Hard system: Whether the problem is easy to quantify or entangled with stakeholders and interpretations
Systems Thinking and Methodologies
Systems thinking complements the limitations of reductionism and emphasizes wholeness, interdependence, circular causality, and emergent properties. Representative methodologies include systems engineering (requirements definition → design → verification → operation), the software development life cycle (SDLC), system dynamics (Jay Forrester), soft systems methodology (Peter Checkland), SSADM, and the V-model. Recently, model-based systems engineering (MBSE) and the digital thread have become standards.
Representative Examples
- Natural world: Ecosystems, the human body's circulatory, immune, and nervous systems, the climate system, the Earth system
- Engineering: Operating systems, embedded systems, power grids, transportation networks, system on chip (SoC)
- Social and economic world: Financial systems, legal systems, supply chains, urban infrastructure, organizational management
- Information world: Database management systems (DBMS), recommendation systems, distributed systems
Latest Trends
The trend in the systems field in 2024–2025 can be summarized as 'autonomy, intelligence, and connectivity.' First, as AI agents equipped with large language models (LLMs) enter the control and planning layers of systems, autonomous systems are spreading in industrial sites and software operations. Second, digital twins and simulation-based verification are becoming standardized in manufacturing, energy, and defense, and the 'digital thread,' which synchronizes real systems and virtual models in real time, is emerging. Third, cyber-physical systems (CPS), edge computing, and cloud-native architectures have combined to make software-defined systems commonplace. Fourth, in complex systems science, network science and research on emergent phenomena are being applied to climate, finance, and infectious disease modeling, and predicting system collapse (tipping points) has emerged as a key agenda. Fifth, 'system governance' and regulatory discussions dealing with the safety, security, and ethics of systems are being institutionalized around the EU AI Act and others, and securing explainability and accountability is required from the design stage.
Related Topics
- [[시스템 이론]]
- [[시스템 엔지니어링]]
- [[사이버네틱스]]
- [[복잡계]]
- [[운영체제]]
- [[디지털 트윈]]