The Science of the Artificial and Herbert A. SimonThe Science of the Artificial and Herbert A. Simon
The Science of the Artificial (1969) is a book by Herbert A. Simon on the field of learning science and artificial intelligence, which has particularly influenced design theory. It is concerned with how artificial phenomena should be classified and discusses whether such phenomena belong to the domain of ‘science’.The Science of the Artificial (1969) is a book by Herbert A. Simon on the field of learning science and Artificial Intelligence, and it was particularly influential in the field of design theory. The book is concerned with how man-made phenomena should be categorised and discusses whether such phenomena belong in the realm of ‘science’.
The author, Herbert A. Simon (15 June 1916 – 9 February 2001), was an American economist, political scientist, psychologist, sociologist and computer scientist who became widely recognised for his pioneering work in various fields. He was particularly renowned for his work in decision theory, cognitive psychology and artificial intelligence, for which he was awarded the Nobel Prize in Economics in 1978.
His student, Allen Newell, implemented means-goal analysis through the development of the General Problem Solver using the artificial intelligence language LISP, which is also described in ‘Practical Common Lisp Reading Notes’. He was also a central figure in contributing to the development of artificial intelligence technology in its pioneering period.
Simon was awarded the Nobel Prize in Economics in 1978 for the following achievements The author, Herbert A. Simon (15 June 1916 – 9 February 2001), was an American economist, political scientist, psychologist, sociologist and computer scientist who became widely recognised for his pioneering work in a variety of fields. He was particularly renowned for his work in decision theory, cognitive psychology and artificial intelligence, for which he was awarded the Nobel Prize in Economics in 1978.
His student, Allen Newell, implemented means-goal analysis through the development of the General Problem Solver using the artificial intelligence language LISP, which is also described in ‘Practical Common Lisp Reading Notes’. He was also a central figure in contributing to the development of artificial intelligence technology in its pioneering period.
Simon was awarded the Nobel Prize in Economics in 1978 for the following achievements
- Bounded Rationality: he argues that people do not act with complete rationality in decision-making, but have ‘bounded rationality’ because of limitations in information and computational power, and explains that real-world decision-making often does not seek optimal solutions, but rather satisfying solutions (satisficing ) rather than seeking the optimum solution, he explains.
- Decision theory: he studied the decision-making process in organisations in detail and proposed the ‘managerial model’ as opposed to the ‘economic man model’, clarifying the process by which managers make decisions with limited information and resources, and showing that cognitive constraints and organisational rules play an important role in this process.
- Artificial Intelligence (AI): early work in the field of artificial intelligence with Allen Newell, particularly making significant contributions to ‘symbolic AI’, they developed an early AI programme, the Logic Theorist, in the 1950s, which became the basis for later AI research.
- Theories of scientific discovery: he studied the cognitive aspects of the process of scientific discovery and proposed a model to explain how scientists make new theories and discoveries, and his work emphasised the role of ‘heuristics’ (discovery methods) in discovery and creativity. Bounded Rationality (Bounded Rationality): people do not act with complete rationality in decision-making, he argued, but with ‘bounded rationality’ due to limitations in information and computational power, and explained that real-life decision-making often does not seek the optimal solution but rather the satisfying solution (satisficing) ) rather than seeking the optimum solution, he explains.
On The Science of the Artificial
The Sciences of the Artificial explores the differences between natural science and artificial systems (designed and created by humans) and discusses how the science of the artificial should be developed and understood.
A summary is given below.
1. the nature of the artefact: Simon explains that artefacts (human-made objects) reflect the intentions and purposes of their designers and have different characteristics from the natural world, and emphasises that to understand them, we need to consider how they are designed and in what environment they function It states.
2. design and decision-making: design is seen as ‘the process of finding means to achieve a goal’ and it is stated that design should be as rigorous as scientific enquiry. Design is highlighted as a process involving decision-making in the face of uncertainty, and as taking place under ‘limited rationality’, which assumes limited information and resources.
3. complex systems and hierarchy: complex systems are often described as having a hierarchical structure, which is key to enabling systems to be understood and managed. It is proposed in this book that designers of complex systems can divide the system hierarchically, designing each part independently and integrating them to control the whole The Sciences of the Artificial explores the difference between natural science and artificial systems (designed and created by humans). It explores the differences between natural science and artificial systems (designed and created by humans) and discusses how the science of the artificial should be developed and understood.
4. artificial intelligence and simulation: this book highlights Simon’s work in artificial intelligence (AI) research, and in particular discusses how simulation and modelling can help in understanding artefacts. He states that simulation is essential in the science of artefacts and is an important means of understanding the behaviour of complex systems.
5. evolution and adaptation: Simon states that artefacts can develop through a process of evolution and adaptation. He explains that artefacts adapt to the environment for which they are designed and are improved in response to changes so that they can function more effectively.
Simon emphasises that design is not just an aesthetic pursuit, but a logical and scientific process. This idea offers a new perspective on the understanding and creation of artefacts in the fields of design, engineering and computer science, and is considered an important work that has had a significant impact on the development of design and systems theory, showing how the scientific exploration of artificial systems should proceed It is regarded as.
Decision Support System:DSS
The following section describes Simon’s most focused system, the Decision Support System.
Decision Support Systems (DSS) are computer-based systems designed to support decision-makers in complex decision-making by companies and organisations, utilising techniques such as data analysis, modelling and simulation to It will support decision-makers to make more effective decisions.
Features of decision support systems include.
1. interactive systems: a DSS is designed to allow decision-makers to interact with the system, try out different scenarios and compare different options. This allows the decision-maker to derive the best decision for the situation.
2. data integration and analysis: a DSS integrates information from diverse data sources and analyses it for decision-making purposes. This data can include internal data within the organisation (e.g. sales data, inventory data) as well as external market data and competitive information.
3. modelling capabilities: the DSS provides models to simulate the decision-making process. This allows scenarios under different conditions to be examined and the consequences of each option to be predicted.
4. flexibility and adaptability: a DSS is designed to be flexible enough to accommodate different decision-making situations and can be customised according to the needs of decision-makers. This allows them to provide specific support for particular industries and business processes. The following section describes Simon’s most focused system, the Decision Support System.
5. user-centred design: the DSS is designed with an emphasis on ease of use and an intuitive interface, which allows users with little technical knowledge to use the system effectively.
The components of a decision support system include the following.
1. data management component: this part is responsible for collecting, storing and managing the data needed for decision-making and includes databases, data warehouses, data marts, etc.
2. model management component: this part manages various decision-making models and algorithms to support the decision-making process, including statistical models, optimisation models, simulation models, etc.
3. user interfaces: interfaces for users to access and manipulate the DSS, including graphical user interfaces (GUIs), dashboards, report generation tools, etc.
4. knowledge base: a database that stores knowledge and rules related to a specific decision-making domain to support the decision-making process.
Types of DSS include.
1. data-driven DSS: analyses large amounts of data to support decision-making, often using database management systems and online analytical processing (OLAP) tools. Examples include the analysis of financial data and customer behaviour.
2. model-driven DSS: supports decision-making through the use of models, which use optimisation or simulation models to help solve complex problems. Examples include logistics optimisation and production planning.
3. communication-driven DSS: This supports communication when teams or groups make joint decisions. Examples include meeting support systems and group discussion tools.4. Knowledge base: a database of knowledge and rules related to a specific decision-making area to support the decision-making process.
4. knowledge-driven DSS: uses expert knowledge to support decision-making in a specific domain, and expert systems and rule-based systems fall into this category. Examples include medical diagnosis support systems and legal decision support systems.
Advantages and challenges of DSS include
Advantages:
– Increased efficiency: DSS enables faster and more efficient decision-making and reduces the time spent analysing complex data.
– Increased accuracy: enables data-driven decision-making and reduces human bias.
– Consistency: consistent decision-making criteria can be maintained across the organisation.
Challenges:
– Cost: developing and maintaining an advanced DSS can be expensive.
– Data quality: DSS results depend on the quality of input data, and inaccurate data can lead to erroneous decision-making.
– Technology dependence: while systems are reliable, they can be severely affected in the event of system failure.
Decision support systems are powerful tools that utilise data and technology to support complex decision-making, and DSS can help decision-makers make informed decisions by utilising techniques such as data analysis, modelling and simulation. The tools are used in a variety of industries and sectors and play an important role in the strategic decision-making processes of organisations.
Reference information and reference books Reference information and reference books
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