A system is a set of interdependent parts that perform some function as a whole. A conceptual system is then a set of concepts, relations between those concepts, and the logic or processes of reasoning through which information or knowledge is processed.
A concept is an abstract representation of an entity or phenomenon. The Merriam-Webster dictionary defines a concept as “an abstract or generic idea generalized from particular instances.” Concepts are abstract in that they are ideas or mental pictures of a group or class of objects formed by combining all their aspects. A concept is an abstract idea or a mental symbol, typically associated with a corresponding representation in language or symbology, which denotes all of the objects in a given category or class of entities. Concepts are abstract in that they omit the differences of their instantiation, treating them as if they were identical.
A conceptual system firstly consists of a set of interrelated concepts, what may be called an ontology. In computer science and information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that actually or fundamentally exist for a particular domain. The term ontology has its origin in philosophy and comes from the Greek word meaning “being” “existence” or “that which is.” The concept of ontology in a philosophical context is the study of what exists and the relationship between those entities.
In a nonphilosophical context, the term ontology is used in a more specific sense to define exactly what exists within some given domain of interest and the relationship between those entities. For example, we might have an ontology of farming that defines all of the elements involved in agriculture, such as crops, harvesting, machinery, planting, farmers, etc. and how they relate to each other. The core meaning of an ontology within computer science is a model for describing the world that consists of a set of types, properties, and relationship types. There is also generally an expectation that the features of the model in an ontology should closely resemble the real world (related to the object).
“Things” or entities have properties that define them, a blue cup, a small car, a hard boiled egg, entities that have similar properties can be grouped into sets or what are called classes. A class is a set or category of things having some property or attribute in common and differentiated from others by kind, type, or quality. In information science, ontologies are understood in terms of classes and relationships. For example, a person is a class. Likewise, an organization would be a class. Classes are concepts in that they are abstract; they refer to a generic form of some entity, a real specific form of that class would be called an instance of the class. For example, Jane is an instance of the class person, and Sony Inc is an instance of an organization. Ontologies also consist of relationships between these classes or instances. For example, the relationship of employer might define the relation between Jane and Sony Inc; Jane has the employer Sony Inc.
An ontology then can be defined as the set of concepts within a conceptual system and the relationships between them. This is the basics of a conceptual system and can represent any conceptual system. For example, the knowledge within an organization or the entire body of scientific knowledge can be considered as an ontology. Likewise, an individual’s schema is another example of an ontology.
Systems perform functions; that is to say, they process inputs to generate outputs. A conceptual system then performs the function of processing information and/or knowledge. Before this can be done though the inputs to the system need to be verified to ensure their validity. The idea of “garbage in garbage out” applies equally to the functioning of conceptual systems. Incoming information needs to be filtered to ensure validity in some way, and the mechanism for doing this may be called an epistemology.
Epistemology is the domain of philosophy that deals with knowledge and aims to discover the means through which we can know something and thus what forms valid knowledge. How can we decipher valid information from invalid information and thus filter out the invalid information to ensure that the inputs to the conceptual system are valid? Inputs to the functioning of the system can be external to the system, such as the information being inputted via our senses, but they may also be internal, i.e. information or knowledge that we already have.
This processing of information and ideas may be called reasoning or thinking. Reasoning is the process through which we generate new information or knowledge. The process takes in some information, applies some set of instructions to it so as to produce some output. To reason, we need a set of rules or instructions as to what are valid and invalid processes of reasoning. This set of rules under which we perform the process of reasoning to generate new knowledge may be called logic.
Logic is reasoning conducted or assessed according to a coherent set of principles. Logic is the rules of inference, how we get from point A to point B through the process of reasoning. The root of the modern English word “logic” mean for example “speech” or “explanation” or an “account.” It is giving an account of something based upon a set of rules. Logic can be contrasted with faith or intuition in that logic represents an objective set of instructions, whereas other forms of thinking based on intuition or revelation are more subjective interpretations.
Inputs are processed through thinking to generate outputs. All systems perform their function only to some degree of efficiency. Efficiency is defined as a ratio between the degree of energy and entropy outputted. Entropy is the degree of disorder or uncertainty in a system. In this respect, it can refer to the level of uncertainty or distortion within the outputted information reducing its level of validity or relevance. Generally, validity may be described in terms of its internal coherence and the correspondence between the conceptual system and other systems. Correspondence is generally tested by empirical analysis and conditions of falsifiability.
Open and Closed Systems
Conceptual systems may be closed or open. Closed conceptual systems exchange limited information and knowledge with their environment. New knowledge and information are generated with reference to what already exists within the system. Open conceptual systems take in information and ideas from their environment and process this to generate an output back to the environment. A conversation where information is taken in and processed to generate a response would be an example of an open conceptual system functioning.
Conceptual systems are dynamic; they change over time. Through their development new knowledge can be generated, new information can be received from the environment, with new categories, and subcategories created in response to this. Throughout their development within some environment, they are subject to the process of evolution. Ideas and information are proven to be valid or invalid, effective or ineffective and thus retained and/or discarded. New ideas are generated and if proven successful are promulgated to form a greater part of the system within future life cycles as they are subjected to evolutionary forces from their environment.
Differentiation and Integration
During their process of development, conceptual systems go through a process of both integration and differentiation. New ideas are created, and existing ideas are broken down into subcategories as the system becomes more differentiated. However, equally, these concepts are integrated into broader systems. Ideas and knowledge are synthesized and reintegrated through the development of more abstract classes and patterns.
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