Электронный учебно-методический комплекс по учебной дисциплине Философия и методология науки для студентов, слушателей, осваивающих содержание образовательной программы высшего образования 2 ступени

into areas that might include psychology, finance and sociology. The design of any artifact will then take account of the conditions under which it will be manufactured, the conditions under which it will be used, and the conditions under which it will be disposed. Engineers can consider such "life cycle" issues without losing the precision and rigor necessary to design functional systems.

There are many different types of models expressed in a diverse array of modeling languages and tool sets. This article offers a taxonomy of model types and highlights how different models must work together to support broader engineering efforts.

There are many different types of models and associated modeling languages to address different aspects of a system and different types of systems. Since different models serve different purposes, a classification of models can be useful for selecting the right type of model for the intended purpose and scope.

Since a system model is a representation of a system, many different expressions that vary in degrees of formalism could be considered models. In particular, one could draw a picture of a system and consider it a model. Similarly, one could write a description of a system in text, and refer to that as a model. Both examples are representations of a system. However, unless there is some agreement on the meaning of the terms, there is a potential lack of precision and the possibility of ambiguity in the representation.

The primary focus of system modeling is to use models supported by a well-defined modeling language. While less formal representations can be useful, a model must meet certain expectations for it to be considered within the scope of model-based systems engineering. In particular, the initial classification distinguishes between informal and formal models as supported by a modeling language with a defined syntax and thesemantics for the relevant domain of interest.

The United States ―Department of Defense Modeling and Simulation Glossary‖ asserts that ―a model can be [a] physical, mathematical, or otherwise logical representation of a system‖. This definition provides a starting point for a high level model classification. A physical model is a concrete representation that is distinguished from the mathematical and logical models, both of which are more abstract representations of the system. The abstract model can be further classified as descriptive or analytical.

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A descriptive model describes logical relationships, such as the system's whole-part relationship that defines its parts tree, the interconnection between its parts, the functions that its components perform, or the test cases that are used to verify the system requirements. Typical descriptive models may include those that describe the functional or physical architecture of a system, or the three dimensional geometric representation of a system.

An analytical model describes mathematical relationships, such as differential equations that support quantifiable analysis about the system parameters. Analytical models can be further classified into dynamic and static models. Dynamic models describe the time-varying state of a system, whereas static models perform computations that do not represent the time-varying state of a system. A dynamic model may represent the performance of a system, such as the aircraft position, velocity, acceleration, and fuel consumption over time. A static model may represent the mass properties estimate or reliability prediction of a system or component.

A particular model may include descriptive and analytical aspects as described above, but models may favor one aspect or the other. The logical relationships of a descriptive model can also be analyzed, and inferences can be made to reason about the system. Nevertheless, logical analysis provides different insights than a quantitative analysis of system parameters.

Both descriptive and analytical models can be further classified according to the domain that they represent. The following classifications are partially derived from the presentation on OWL, Ontologies and SysML Profiles: Knowledge Representation and Modeling & Systems Modeling Language:

properties of the system, such as performance, reliability, mass properties, power, structural, or thermal models;

design and technology implementations, such as electrical, mechanical, and software design models;

subsystems and products, such as communications, fault management, or power distribution models; and

system applications, such as information systems, automotive systems, aerospace systems, or medical device models.

The model classification, terminology and approach is often adapted to a particular application domain. For example, when modeling organization or business, the behavioral model may be referred to as workflow

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orprocess model, and the performance modeling may refer to the cost and schedule performance associated with the organization or business process.

A single model may include multiple domain categories from the above list. For example, a reliability, thermal, and/or power model may be defined for an electrical design of a communications subsystem for an aerospace system, such as an aircraft or satellite.

System models can be hybrid models that are both descriptive and analytical. They often span several modeling domains that must be integrated to ensure a consistent and cohesive system representation. As such, the system model must provide both general-purpose system constructs and domain-specific constructs that are shared across modeling domains. A system model may comprise multiple views to support planning, requirements, design, analysis, and verification.

Wayne Wymore is credited with one of the early efforts to formally define a system model using a mathematical framework in A Mathematical Theory of Systems Engineering: The Elements. Wymore established a rigorous mathematical framework for designing systems in a model-based context. A summary of his work can be found in A Survey of Model-Based Systems Engineering Methodologies.

The term simulation, or more specifically computer simulation, refers to a method for implementing a model over time. The computer simulation includes the analytical model which is represented in executable code, the input conditions and other input data, and the computing infrastructure. The computing infrastructure includes the computational engine needed to execute the model, as well as input and output devices. The great variety of approaches to computer simulation is apparent from the choices that the designer of computer simulation must make, which include

stochastic or deterministic;

steady-state or dynamic;

continuous or discrete; and

local or distributed.

Other classifications of a simulation may depend on the type of model that is being simulated. One example is an agent-based simulation that simulates the interaction among autonomous agents to predict complexemergent behavior. They are many other types of models that could be used to further classify simulations. In general, simulations

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provide a means for analyzing complex dynamic behavior of systems, software, hardware, people, and physical phenomena.

Simulations are often integrated with the actual hardware, software, and operators of the system to evaluate how actual components and users of the system perform in a simulated environment. Within the United States defense community, it is common to refer to simulations as live, virtual, or constructive, where live simulation refers to live operators operating real systems, virtual simulation refers to live operators operating simulated systems, and constructive simulations refers to simulated operators operating with simulated systems. The virtual and constructive simulations may also include actual system hardware and software in the loop as well as stimulus from a real systems environment.

In addition to representing the system and its environment, the simulation must provide efficient computational methods for solving the equations. Simulations may be required to operate in real time, particularly if there is an operator in the loop. Other simulations may be required to operate much faster than real time and perform thousands of simulation runs to provide statistically valid simulation results. Several computational and other simulation methods are described in Simulation Modeling and Analysis.

Computer simulation results and other analytical results often need to be processed so they can be presented to the users in a meaningful way. Visualization techniques and tools are used to display the results in various visual forms, such as a simple plot of the state of the system versus time to display a parametric relationship. Another example of this occurs when the input and output values from several simulation executions are displayed on a response surface showing the sensitivity of the output to the input. Additional statistical analysis of the results may be performed to provide probability distributions for selected parameter values. Animation is often used to provide a virtual representation of the system and its dynamic behavior. For example, animation can display an aircraft‘s three-dimensional position and orientation as a function of time, as well as project the aircraft‘s path on the surface of the Earth as represented by detailed terrain maps.

Many different types of models may be developed as artifacts of a MBSE effort. Many other domain-specific models are created for component design and analysis. The different descriptive and analytical models must be integrated in order to fully realize the benefits of a model-based approach. The role of MBSE as the models integrate

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across multiple domains is a primary theme in the International Council on Systems Engineering INCOSE Systems Engineering Vision 2020.

As an example, system models can be used to specify the components of the system. The descriptive model of the system architecture may be used to identify and partition the components of the system and define their interconnection or other relationships. Analytical models for performance, physical, and other quality characteristics, such as reliability, may be employed to determine the required values for specific component properties to satisfy the system requirements. An executable system model that represents the interaction of the system components may be used to validate that the component requirements can satisfy the system behavioral requirements. The descriptive, analytical, and executable system model each represent different facets of the same system.

The component designs must satisfy the component requirements that are specified by the system models. As a result, the component design and analysis models must have some level of integration to ensure that the design model is traceable to the requirements model. The different design disciplines for electrical, mechanical, and software each create their own models representing different facets of the same system. It is evident that the different models must be sufficiently integrated to ensure a cohesive system solution.

To support the integration, the models must establish semantic interoperability to ensure that a construct in one model has the same meaning as a corresponding construct in another model. This information must also be exchanged between modeling tools.

One approach to semantic interoperability is to use model transformations between different models. Transformations are defined which establish correspondence between the concepts in one model and the concepts in another. In addition to establishing correspondence, the tools must have a means to exchange the model data and share the transformation information. There are multiple means for exchanging data between tools, including file exchange, use of application program interfaces, and a shared repository.

The use of modeling standards for modeling languages, model transformations, and data exchange is an important enabler of integration across modeling domains.

It is often held that technology itself is incapable of possessing moral or ethical qualities, since "technology" is merely tool making. But many now believe that each piece of technology is endowed with and radiat-

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ing ethical commitments all the time, given to it by those that made it, and those that decided how it must be made and used. Whether merely a lifeless amoral 'tool' or a solidified embodiment of human values "ethics of technology" refers to two basic subdivisions:

The ethics involved in the development of new technology – whether it is always, never, or contextually right or wrong to invent and implement a technological innovation.

The ethical questions that are exacerbated by the ways in which technology extends or curtails the power of individuals – how standard ethical questions are changed by the new powers.

In the former case, ethics of such things as computer security and computer viruses asks whether the very act of innovation is an ethically right or wrong act. Similarly, does a scientist have an ethical obligation to produce or fail to produce a nuclear weapon? What are the ethical questions surrounding the production of technologies that waste or conserve energy and resources? What are the ethical questions surrounding the production of new manufacturing processes that might inhibit employment, or might inflict suffering in the third world?

In the latter case, the ethics of technology quickly break down into the ethics of various human endeavors as they are altered by new technologies. For example, bioethics is now largely consumed with questions that have been exacerbated by the new life-preserving technologies, new cloning technologies, and new technologies for implantation. In law, the right of privacy is being continually attenuated by the emergence of new forms of surveillance and anonymity. The old ethical questions of privacy and free speech are given new shape and urgency in an Internet age. Such tracing devices as RFID, biometricanalysis and identification, genetic screening, all take old ethical questions and amplify their significance.

7.1.36. Technoethics

Technoethics (TE) is an interdisciplinary research area that draws on theories and methods from multiple knowledge domains to provide insights on ethical dimensions of technological systems and practices for advancing a technological society.

Technoethics views technology and ethics as socially embedded enterprises and focuses on discovering the ethical use of technology, protecting against the misuse of technology, and devising common principles to guide new advances in technological development and applica-

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tion to benefit society. Typically, scholars in technoethics have a tendency to conceptualize technology and ethics as interconnected and embedded in life and society. Technoethics denotes a broad range of ethical issues revolving around technology – from specific areas of focus affecting professionals working with technology to broader social, ethical, and legal issues concerning the role of technology in society and everyday life.

Technoethical perspectives are constantly in transition as technology advances in areas unseen by creators, as users change the intended uses of new technologies. Humans cannot be separated from these technologies because it is an inherent part of consciousness and meaning in life therefore, requiring an ethical model. The short term and longer term ethical considerations for technologies do not just engage the creator and producer but makes the user question their beliefs in correspondence with this technology and how governments must allow, react to, change, and/or deny technologies.

Using theories and methods from multiple domains, technoethics provides insights on ethical aspects of technological systems and practices, examines technology-related social policies and interventions, and provides guidelines for how to ethically use new advancements in technology. Technoethics provides a systems theory and methodology to guide a variety of separate areas of inquiry into human-technological activity and ethics. Moreover, the field unites both technocentric and bio-centric philosophies, providing "conceptual grounding to clarify the role of technology to those affected by it and to help guide ethical problem solving and decision making in areas of activity that rely on technology." As a bio-techno-centric field, technoethics "has a relational orientation to both technology and human activity"; it provides "a system of ethical reference that justifies that profound dimension of technology as a central element in the attainment of a 'finalized' perfection of man.'

Ethics address the issues of what is 'right', what is 'just', and what is 'fair'.

Ethics describe moral principles influencing conduct; accordingly, the study of ethics focuses on the actions and values of people in society (what people do and how they believe they should act in the world).

Technology is the branch of knowledge that deals with the creation and use of technical means and their interrelation with life, society,

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and the environment; it may draw upon a variety of fields, including industrial arts, engineering, applied science, and pure science. Though the ethical consequences of new technologies have existed since Socrates' attack on writing in Plato's dialogue, Phaedrus, the formal field of technoethics had only existed for a few decades. The first traces of TE can be seen in Dewey and Peirce's pragmatism. With the advent of the industrial revolution, it was easy to see that technological advances were going to influence human activity. This is why they put emphasis on the responsible use of technology.

The term "technoethics" was coined in 1977 by the philosopher Mario Bunge to describe the responsibilities of technologists and scientists to develop ethics as a branch of technology. Bunge argued that the current state of technological progress was guided by ungrounded practices based on limited empirical evidence and trial-and-error learning. He recognized that "the technologist must be held not only technically but also morally responsible for whatever he designs or executes: not only should his artifacts be optimally efficient but, far from being harmful, they should be beneficial, and not only in the short run but also in the long term." He recognized a pressing need in society to create a new field called 'Technoethics' to discover rationally grounded rules for guiding science and technological progress.

With the spurt in technological advances came technological inquiry. Societal views of technology were changing; people were becoming more critical of the developments that were occurring and scholars were emphasizing the need to understand and to take a deeper look and study the innovations. Associations were uniting scholars from different disciplines to study the various aspects of technology. The main disciplines being philosophy, social sciences and science and technology studies. Though many technologies were already focused on ethics, each technology discipline was separated from each other, despite the potential for the information to intertwine and reinforce itself. As technologies became increasingly developed in each discipline, their ethical implications paralleled their development, and became increasingly complex. Each branch eventually became united, under the term technoethics, so that all areas of technology could be studied and researched based on existing, real-world examples and a variety of knowledge, rather than just discipline-specific knowledge.

Technoethics involves the ethical aspects of technology within a society that is shaped by technology. This brings up a series of social and

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ethical questions regarding new technological advancements and new boundary crossing opportunities. Before moving forward and attempting to address any ethical questions and concerns, it is important to review the 3 major ethical theories to develop a perspective foundation:

Utilitarianism is an ethical theory which attempts to maximize happiness and reduce suffering for the greatest amount of people. Utilitarianism focused on results and consequences rather than rules.

Duty Ethics (Kant) notes the obligations that one has to society and follows society's universal rules. It focuses on the rightness of actions instead of the consequences, focusing on what an individual should do.

Virtue Ethics is another main perspective in normative ethics. It highlights the role and virtues that an individual's character contains to be able to determine or evaluate ethical behaviour in society.

Relationship ethics states that care and consideration are both derived from human communication. Therefore, ethical communication is the core substance to maintain healthy relationships.

7.1.37. Historical framing of technology

Greek civilization defined technology as techné. Techné is "the set principles, or rational method, involved in the production of an object or the accomplishment of an end; the knowledge such as principles of method; art." This conceptualization of technology used during the early Greek and Roman period to denote the mechanical arts, construction, and other efforts to create, in Cicero's words, a "second nature" within the natural world.

1.Modern conceptualization of technology as invention materialized in the 17th century in Bacon's futuristic vision of a perfect society governed by engineers and scientists in Saloman's House, to raise the importance of technology in society.

2.The German term "Tecknik" was used in the 19th-20th century. Technik is the totality of processes, machines, tools and systems employed in the practical arts and Engineering. Webber popularized it when it was used in broader fields. Mumford said it was underlying a civilization. Known as: before 1750: Eotechnic, in 1750-1890: Paleoethnic and in 1890: Neoethnic. Place it at the center of social life in close connection to social progress and societal change. Mumford says that a machine cannot be divorced from its larger social pattern, for it is the pattern that gives it meaning and purpose.

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3. Rapid advances in technology provoked a negative reaction from scholars who saw technology as a controlling force in society with the potential to destroy how people live. Heidegger warned people that technology was dangerous in that it exerted control over people through its mediating effects, thus limiting authenticity of experience in the world that defines life and gives life meaning. It is an intimate part of the human condition, deeply entrenched in all human history, society and mind.

Many advancements within the past decades have added to the field of technoethics. There are multiple concrete examples that have illustrated the need to consider ethical dilemmas in relation to technological innovations. Beginning in the 1940s influenced by the British eugenic movement, the Nazis conduct "racial hygiene" experiments causing widespread, global anti-eugenic sentiment. In the 1950s the first satellite Sputnik 1 orbited the earth, the Obninsk Nuclear Power Plant was the first nuclear power plant to be opened, the American nuclear tests take place. The 1960s brought about the first manned moon landing, ARPANET created which leads to the later creation of the Internet, first heart transplantation completed, and the Telstar communications satellite is launched. The 70s, 80s, 90s, 2000s and 2010s also brought multiple developments.

Technological consciousness is the relationship between humans and technology. Technology is seen as an integral component of human consciousness and development. Technology, consciousness and society are intertwined in a relational process of creation that is key to human evolution. Technology is rooted in the human mind, and is made manifest in the world in the form of new understandings and artifacts. The process of technological consciousness frames the inquiry into ethical responsibility concerning technology by grounding technology in human life.

The structure of technological consciousness is relational but also situational, organizational, aspectual and integrative. Technological consciousness situates new understandings by creating a context of time and space. As well, technological consciousness organizes disjointed sequences of experience under a sense of unity that allows for a continuity of experience. The aspectual component of technological consciousness recognizes that individuals can only be conscious of aspects of an experience, not the whole thing. For this reason, technology manifests itself in processes that can be shared with others. The integrative characteristics of technological consciousness are assimilation, substitution

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