Avainsana-arkisto: anticipation

Hesiod, Hypnos, Foresight, and Anticipation

Jari Kaivo-oja:

Hesiod and Hypnos

Hypnos (Greek word: Ὕπνος) was the personification of a dream in Greek mythology, the Roman equivalent of which was Somnus. He was the twin brother of Nyks, the night, and Erebos, darkness, and of Thanatos, death. Hypnos is depicted in ancient art as either a naked young man with wings on the temples or a bearded man with wings on his shoulders.

Greet poet Hesiod, a Greek poet who wrote about Hypnos and his role in Greek mythology in his epic poem “the Birth of the Gods” (Θεογονία, Theogonia), describing the ancestry and lineages of the gods in Greek mythology. The poem comprises 1,020 verses. The literary form of the Birth of the Gods became established in the 500s before the dawn of time. At that time, new parts were added to the poem, such as verses 901–1020 at the end of the classic poem. Traditions continued to develop even after the work of Hesiod.  

However, the work of Hesiod has often been used as a reference work for Greek mythology. Hesiod is generally regarded by Western authors as the first written poet in the Western tradition to regard himself as an individual persona with an active role to play in his subject. We can easily find the relevance of this special topic in the actor-network theory of foresight. Among other things, Herodotus considered Hesiod an authority regarding the names of the gods and their attributes (Herodotos: Historiateos II.53, Herodotus 1993).

From the historical perspective, Hesiod should be referred to as a basic fundamental source of narrative storytelling in the fields of foresight and anticipation. Abductive reasoning (also called abduction, abductive inference, or retroduction) is an elementary issue in storytelling and narrative thinking, and it is a form of logical inference that seeks the simplest and most likely conclusion from a set of observations. Abductive reasoning was formulated and advanced by American philosopher Charles Sanders Peirce beginning in the last third of the 19th century (see good motivation of this issue in Peabody 1975. Josephson & Josephson 1994, Peirce 1998, Walton 2001, Carson 2009, Milojević & Inayatullah 2015, Syll 2023).

In the of economic thought, Hesiod can be seen as a central source of thinking before Adam Smith and some scholars keep Hesiod as a father of humanistic economics (see Brockway 2001, Gordon 1975).

Futures dialogues, future-oriented dreams, and visions can be defined to be forms of socio-therapy

Quality of sleep has a very significant impact on human thinking, as sleep is essential for brain function and cognitive performance. During normal sleep, the brain (1) consolidates memories, (2) processes information, and (3) repairs itself. When we don’t get enough good quality sleep, our cognitive performance can suffer in various ways. For example, our ability to focus, pay attention, and concentrate can be impaired. We may also experience memory problems and have difficulty learning and retaining new information. Research has shown that sleep deprivation can also negatively affect our mood and emotions, causing us to feel irritable, anxious, and depressed. In addition, lack of sleep can compromise our decision-making ability, judgment, and creativity. On the other hand, getting enough high-quality sleep has been shown to enhance cognitive performance, improve memory consolidation, and enhance overall brain function. (see e.g. Walker 2017, Nelson et al. 2022). Quality of sleep is thus very important for human creativity and human understanding.

We should also see the importance of dialogue as a form of socio-therapy. Futurists are creating all kinds of dialogues and from this perspective, we should understand in a better way that these dialogues can be interpreted to be a form of socio-therapy (see Bohm & Edwards 1991, Bohm 1992). Only a few futurists understand their societal role as socio-therapists.

Hypnos and dialogical narratives

Greek mythology can often be seen as a starting point for dialogical narratives. In the context of foresight and qualitative anticipative storytelling research, one can always lean on classic stories and myths. Greek mythologies can be associated with basic human problems, challenges, and humanism.  

There are many good reasons to analyze Hypnos and Hypnos´s relation to foresight and anticipation research. According to some sources, the personifications of dreams, thousands of oneiros, would be descendants of Hypnos, but they are reported in older sources to be descended directly from Nyks (Hesiod, trans. 1914). Among them is mentioned as Morpheus, who announced prophecies in dreams, both Fobetor and Fantasos, which were sources of false visions and dreams. (See Ovid Books, 1922). Thus, Hesiod can be linked also to the pre-history of visionary thinking, foresight, and anticipation.  

The ability to dream is not a self-evident issue

In many ways, Hypnos symbolizes the importance of dreams for people and their decision-making. The ability to dream is strongly associated with dreams and their quality. Sleep is crucial for our brain’s cognitive functions, including memory consolidation and problem-solving. Getting enough restful sleep can enhance our ability to think creatively and solve problems, which can lead to more visionary thinking during our waking hours. Dreams and sleep are important components of visionary thinking because they allow our minds to explore and imagine beyond the constraints of our waking reality.

Dreams and sleep are closely connected to visionary thinking because they allow our minds to enter a state of creativity and imagination that is often difficult to access during our waking hours. Normally, during the dreaming phase of sleep, our brains are highly active, and we are able to create vivid and often surreal experiences that can be interpreted as a form of visionary thinking. Dreams can be a source of inspiration for artists, writers, other creative artists, and scientists, as they often contain imagery and themes that are both symbolic and deeply personal.

Hypnos, the legacy of foresight research, and anticipation theory

Anticipation and foresight are closely related concepts, but there are some key differences between them. Very often these concepts remain undefined and are seen as the same things.

Normally, anticipation refers to the act of expecting or predicting something to happen in the future. It involves recognizing a future event and making plans or preparations based on that expectation. Anticipation can be based on past experiences, current trends or patterns, or intuition.

On the other hand, foresight activities involve a deeper level of thinking about the future. Foresight involves considering multiple potential scenarios, analyzing the potential consequences of each, and making strategic plans to prepare for those possibilities. The “fully-fledged foresight” includes the choice of foresight methods, networking and stakeholder analyses, and models of decision-making. Foresight requires a broader perspective and a willingness to consider multiple perspectives and possibilities. It requires also a basic understanding of stakeholders and networks and decision-making criteria. The choice of foresight methods can be based on the FAROUT criteria (future orientation, accuracy, resources, objectivity, usefulness, and timelines).

Perfect rationality, imperfect rationality, problematical rationality, or irrationality?

In other words, anticipation is more reactive, and focused on a specific event or outcome, while foresight is proactive, and focused on anticipating and planning for a range of possible outcomes and their consequences. This critical difference leads logically to different relations to the Hypnos issue and especially to visionary thinking and leadership. Actually, it may be not easy to be very visionary, if we are focused only on a specific issue or event. As noted by Robert K. Merton Professor of Social Sciences (Jon Elster 1978, 1979) there is a descending sequence from perfect rationality, through imperfect and problematical rationality, to irrationality in human thinking. Rational explanation is not the same analogous thing as understanding a thing or phenomenon. We have seen many very problematic examples of siloed anticipation studies where a potential balance between reductionist and holistic thinking is not much questioned or less critically discussed (see updated discussion of Anderson 2022, Jackson 2019).

In Figure 1 we can observe three basic scientific approaches: the fully holistic research approach, the mixed holistic and reductionist approach, and the fully reductionist approach. All futures researchers (and maybe all others researchers too) should be aware of these three basic methodological alternatives.

Figure 1. A holistic research approach and a reductionist research approach.

We should always define, whether are we going to explain or understand human behavior because these two human activities are different activities and lead us to different methodological choices. A holistic research approach typically supports understanding phenomena, but a reductionist approach may help in explaining phenomena. Boundaries between these methodological approaches (solutions) are not always very clear and there are also boundary options (mixed holistic and reductionist approaches).

In the future, we may be more visionary with “fully-fledged three foresight pillars” of methods, network analyses, and decision-making models. As many times noted in various scientific discussions, a loss of one or two foresight research pillars leads surely to an unsuccessful foresight process. A deep understanding of methodological choices, network/stakeholder context, and decision-making model with decision criteria, is surely needed for a successful foresight process.

To summarize, anticipation is about predicting a specific outcome and taking action to prepare for it, while foresight involves a more comprehensive and strategic approach to planning for the future, considering multiple possibilities and their potential consequences. Hopefully, our bright and dark journeys in the Hypnos world will lead us to think about many alternatives, even surprising options, wild card scenarios, and not business-as-usual alternatives.

Unfortunately, Business-As-Usual (BAU) scenarios often dominate siloed and highly theoretical and siloed anticipation studies. It is good to be aware that whenever we abandon the use of many perspectives in the context of future-oriented research, we do not make use of holistic thinking and end up with siloed reductionist studies.

The most low-quality foresight research is to present the BAU scenario and almost identical other scenarios as supposedly alternative scenarios. I have noticed these kinds of problematic methodological issues in the context of several foresight studies (see for example, Kaivo-oja, Keskinen & Rubin 1997, Kaivo-oja, Rubin & Keskinen 1998).

Jari Kaivo-oja

Research Director, PhD, Finland Futures Research Centre, Turku School of Economics, University of Turku;
Adjunct Professor (University of Helsinki, University of Lapland, and University of Vaasa);
Professor (Social Sciences), Kazimiero Simonavičiaus University, Vilnius, Lithuania


References and background literature

Anderson, Monica (2022) The Red Pill of Machine Learning. Experimental Epistemology. Web:

Bohm, David & Edwards, Mark (1991) Changing Consciousness: Exploring the Hidden Source of the Social, Political, and Environmental Crises Facing Our World. HarperCollins. San Francisco.

Bohm, David (1992) Thought as a System. Routledge. London and New York.

Brockway, George P. (2001) The End of Economic Man: An Introduction to Humanistic Economics, 4th edition (2001), p. 128.

Carson, David (2009) The Abduction of Sherlock Holmes. International Journal of Police Science & Management. 11 (2), p. 193–202.

Dosi, Roberto (2017) Introduction to Anticipation Studies. Anticipation Science 1. Springer.

Elster, Jon (1978) Logic and Society. Contradictions and Possible Worlds. John Wiley & Sons. Chichester and New York,

Elster, Jon (1979) Ulysses and the Sirens: Studies in Rationality and Irrationality. Cambridge University Press. Cambridge.

Evelyn-White, Hugh G. (1964) Hesiod, The Homeric Hymns and Homerica. Loeb Classical Library, Vol. 57, Harvard University Press, Boston, USA.

Gordan, Barry J. (1975) Economic Analysis Before Adam Smith: Hesiod to Lessius (1975), First Edition, Basingstoke and London, p. 3.

Griffin, Jasper (1986) Greek Myth and Hesiod, in J. Boardman, J. Griffin and O. Murray (eds.), The Oxford History of the Classical World. Oxford University Press, Oxford, p. 88.

Hardie, Philip, Barchiesi, Alessandro, and Hinds, Stephen (1991) Ovidian Transformations: Essays on Ovid’s Metamorphoses and its Reception. Series: Proceedings of the Cambridge Philological Society Supplementary Volume. Volume: 23. Cambridge Philological Society,

Herodotos: Historiateos II.53

Herodotus (1993) Historiae. Volume II: Books V-IX. Third Edition. Edited by K. Hude. Oxford Classical Texts. Oxford: Oxford University Press. Oxford.

Hesiod, Homeric Hymns, Epic Cycle, Homerica. Translated by Evelyn-White, H G. Loeb Classical Library Volume 57. London: William Heinemann, 1914.

Inkinen, Sam & Kaivo-oja, Jari (2009) Understanding Innovation Dynamics. Aspects of Creative Processes, Foresight Strategies, Innovation Media and Innovation Ecosystems. Finland Futures Research Centre. Turku School of Economics. FFRC eBooks 9/2009. Turku. 

Jackson, Michael J. (2019) Critical Systems Thinking and the Management of Complexity. Responsible Leadership for a Complex World. First Edition. John Wiley and Sons. UK and USA.

Josephson, John R. & Josephson, Susan G., (eds.) (1994) Abductive Inference: Computation, Philosophy, Technology. Cambridge University Press. Cambridge, UK; New York.

Kaivo-oja, Jari (2017) Towards Better Participatory Processes in Technology Foresight: How to Link Participatory Foresight Research to the Methodological Machinery of Qualitative Research and Phenomenology? Futures, Volume 86, February 2017, p. 94–106.

Kaivo-oja, Jari, Keskinen, Auli & Rubin, Anita (1997) Eurooppa-selonteko ja tulevaisuudentutkimus [European reporting and futures research]. FUTURA, Vol. 16. No. 1, p. 6–15.

Kaivo-oja, Jari, Rubin, Anita & Keskinen, Auli (1998) Proaktiivisa toimijoita vai koekaniineita Euroopan tietoyhteiskuntalaboratoriossa? Lipposen hallituksen tulevaisuusselonteon kommentointia tulevaisuudentutkimuksen näkökulmasta [Proactive Actors or Guinea Pigs in the European Information Society Laboratory? Commenting on Lipponen’s Government Report on the Future from the Perspective of Futures Research]. Tulevaisuuden tutkimuskeskus. Turun kauppakorkeakoulu. FUTU-julkaisu 4/98, Turku.

Kaivo-oja, Jari & Roth, Steffen (2023) Strategic Foresight for Competitive Advantage: A Future-oriented Business and Competitive Analysis Techniques Selection Model.  International Journal of Forensic Engineering and Management. Forthcoming. ©Inderscience Publishers.

Keenan, Michael, Loveridge, Dennis, Miles, Ian & Kaivo-oja, Jari (2003) Handbook of Knowledge Society Foresight. Prepared by PREST and FFRC for the European Foundation for the Improvement of Living and Working Conditions. Final Report, Annex B, European Foundation. Dublin. Web:

Milojević, Ivana & Inayatullah, Sohail (2015) Narrative Foresight. Futures, Volume 73, October 2015, p. 151–162.

Moe, Sverre and Kaivo-oja, Jari (2018) Model Theory and Observing Systems. Notes on the Use of Models in Systems Research. Kybernetes, Vol. 47, Issue: 9, p. 1690–1703. 

Nelson, Kathy L., Davis, Jean E. & Corbett, Cynthia F. (2022) Sleep Quality: An Evolutionary Concept Analysis. Nursing Forum. Vol. 57(1), p. 144–151.

Ovid (1922) Metamorphoses. Translated by More, Brookes. Boston, Cornhill Publishing Co.

Peabody, Berkley (1975) The Winged Word: A Study in the Technique of Ancient Greek Oral Composition as Seen Principally Through Hesiod’s Works and Days. State University of New York Press.

Peirce, Charles Sanders (1998) On the Logic of Drawing Ancient History from Documents. The Essential Peirce, Volume 2, Selected Philosophical Writings (1893-1913), Peirce Edition Project, Indiana University Press, Bloomington and Indianapolis, p. 107–9.

Rothbard, Murray N. (1995) Economic Thought Before Adam Smith: Austrian Perspective on the History of Economic Thought, vol. 1, Cheltenham, UK: Edward Elgar Publishing (1995), p. 8;

Syll, Lars P. (2023) Deduction, Induction, and Abduction. In Jesper Jespersen, Victoria Chick, Bert Tieben (Eds.) Routledge Handbook of Macroeconomic Methodology. Routledge, London.

Walker, Matthew (2017) Why We Sleep. The New Science of Sleep and Dreams. Simon and Schuster Inc. New York, USA.

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Picture: Pixabay.com

An Emerging Technology Challenge: Digital Twins

Mikkel Stein Knudsen and Jari Kaivo-oja:

With Digital Twins, organisations can not only create mirrors of real-world objects and processes but integrate physical and virtual worlds through bidirectional flows of information. With real-time simulations and intelligent algorithms, Digital Twins shifts the focus of data-driven operations from ex-post monitoring to ex-ante predictions and optimization in increasingly complex environments. Digital Twins are said to revolutionize the manufacturing industry, but it may also have major impacts on future studies and the general ways organisations anticipate the future. A Digital Twin development can be a new advanced form of scenario planning.

Finland Futures Research Centre takes part in the project Manufacturing  4.0  (2018–2020) for the Strategic Research Council at the Academy of Finland. Our contribution includes technological foresight related to the identification of promising technologies for the future manufacturing landscape in Finland. The concept of digital twins has featured heavily in our early scanning, as one of the most enterprising new advanced manufacturing technologies.

As the embodiment of Cyber-Physical Systems, Digital Twins has become one of the most hyped technologies of the so-called Industry 4.0. According to a comment in Nature in September 2019, “Digital twins – precise, virtual copies of machines or systems – are revolutionizing industry” (Tao & Qi, 2019). Many major companies already use digital twins, while half of all corporations may use them by 2021 (Gartner). For example, in Finland, Nokia is focusing strongly on Digital Twin challenges.

Digital twins are part of a vast shift in the world’s economy towards ‘mirror worlds’ as a new dimension of human life based on and fuelled by data. The emergence of these mirror worlds will bring about a distinct economy, and require new markets, infrastructure, institutions, businesses, and geopolitical arrangements, according to a recent special report in The Economist.

What are Digital Twins?

In its original form, a Digital Twin is the virtual model of a process, product or service, or ‘a digital representation that mirrors a real-life object, process or system’ (Panetta, 2018). While no general and precise definition of the features and scopes of Digital Twins has been reached (Cimino et al., 2019), a consensus appears of certain characteristics distinguishing ‘real’ Digital Twins from related virtual replications.

Kritzinger et al., 2018 operate with concepts of Digital Models, Digital Shadows, and Digital Twins (see figure 1). What sets Digital Twins apart is the real-time automatic dataflow in both directions between the physical and digital objects.


Fig. 1. From Digital Model to Digital Twin (own representation, after Kritzinger et al., 2018)

Talkhestani et al., 2019 elaborates this further including four necessary features in their definition of a Digital Twin: (1) A Digital Twin has to be a digital representation of a physical asset, including as realistic as possible models and all available data on the physical asset. (2) The data has to contain all process data, acquired during operation as well as organizational and technical information created during the development of the asset. (3) A Digital Twin has to always be in sync with the physical asset. (4) It has to be possible to simulate the Digital Twin of the behavior of the physical asset.

Fig 2. The conceptualisation of Digital Twin.

Summarily, as seen in Figure 2, we conceptualize digital twins as possessing five constitutive features separating them from other virtual models: They need to have counterparts or equivalents in the physical world (be it a person, an organ, a product, a machine, a traffic system, or an organisational environment/infrastructure). They need to provide a fair (= as precise as possible) representation of its equivalent characteristics. They must be updated automatically and continuously (in real-time, or close-to-real-time). It must be possible to simulate the environment of the real-world counterpart on the digital twin (achieving what Qi et al. deem ‘integration between entity DT and scenario DT’). Finally, there must be synchronization directly from the digital twin to its counterpart, so that the physical asset can also mirror new directions or alterations happening virtually to the digital twin.

Digital twins in systems theory and modeling

Until now, digital twins can roughly be separated into two categories (Zhidchenko et al., 2018). Either, they assist with the analysis of very complex systems (like transportation systems), or they provide real-time analysis of relatively small systems (like a vehicle). For complex systems, digital twins are means of providing a safe space for simulation of various potential developments and impacts. The idea of digital twins is thus born out of being a (much cheaper) virtual replacement of the identical twin spacecraft NASA always produced to have suitable test spaces. By using digital twins, it becomes possible to perform simulations using the real-time characteristics of their physical counterparts, even while these are in operation. This has enormous potential to limit operational risks, as well as for optimization issues.

However, the distinction between the two categories of digital twins is increasingly blurred. Digital twins for real-time analysis is being applied to more and more complex systems. If future digital twins, as seems likely, acquire functionalities – enabled by artificial intelligence and machine learning – which allow them to interact with other digital twins in a ubiquitous environment, even digital twins of simple systems will themselves be entities forming complex systems.

Personal Digital Twins

Digital twins are also moving out of the manufacturing halls and on its way into, well, you. Signals regarding digital twins for health are already appearing manifold. Last month, The Economist reported on ambitious cardiac-research plans to create digital twins of human patients’ hearts. Digital twins are also potentially important enablers for personalised medicine through “high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient (Björnsson et al., 2020).

In Finland, novel ideas of personal digital twins also appear in the context of the national AuroraAI-programme, which aims for Finland to ‘enter the AI age in a human-centric and ethically sustainable way’. A YouTube-video from the projects illustrates how you can ‘Let your digital twin empower you’. Futurist Osmo Kuusi has been a key contributor to development (cf. Kaivo-oja et al., 2019). Various technologies in the EU Foresight report “100 Radical Innovation Breakthroughs for the Future – The Radical Innovation Breakthrough Inquirer” indicate the high technical feasibility of digital twins.

Finding Finland’s niches

Manufacturing 4.0 believes Finland has the potential to be a leader in the Digital Twin-revolution, e.g. by developing and applying Digital Twins in strong niche markets. Department of Futures Technologies at the University of Turku has developed world-class digital models utilizing virtual reality/augmented reality, now commercialised through the spin-off company CTRL Reality. Researchers demonstrated their model of a virtual forest at the Finland Futures Research Centre’s Futures Fair in December 2017; this model was recently highlighted in the journal Nature calling for the world to Make more digital twins.

Other projects in Finland related to national strengths involve the environmental impacts of mining and mobile cranes. Usage of Digital Twins is also high on the agenda for the development and modernizations of ports in Pori and Rauma and as an element of the further take-off of the Robocoast-cluster. In Turku, digital twins are integral to the vision of creating a Smart and Wise Turku. Smart City Digital Twins is a major global research and investment area, and Finnish pioneer cities like Turku lead the way. Relying on a massive amount of data collected at high-speed from millions of sensors, there are finally also clear links between the rollout of digital twins and new demands for 5G and 6G networks.

What Digital Twins mean for futures studies?

The rapid trajectory of Digital Twins links with the disciplines of foresight and futures studies in several ways. First, it is in itself a trend to follow, analyse and speculate about the consequences of. It is also an embodiment of megatrends such as digitalisation, personalisation, and altered human-machine interactions.

At the same time, the very idea and definition of a digital twin as a vehicle of simulating the future makes digital twins an inherently futures-related technology. Futures studies-researchers and foresight practitioners must learn to utilize this as an important new tool in their toolbox. As one of its founding ideas, digital twins can provide a “safe simulation environment” to test future novelties, new products, new services, new organizational structures, new medicines, and smart infrastructures. This safety-oriented futures approach may be the most desirable way to use and apply digital twins systems thinking, and it could develop into a great futurist tool. How digital twins can supplement the identification of weak signals would seem like another fruitful avenue of investigation for future futures research.

Simultaneously, the theoretical baggage of futurists and system thinkers may be useful in shaping the field of digital twins. At present, the field seems primarily occupied by engineers and technology optimists, who may not always be aware of potential blind spots in their simulation models. A push might be needed to functionally integrate weak signals and wild cards into the simulations of digital twins. This will be another great topic for futures researchers in the years to come.

Mikkel Stein Knudsen
Project Researcher (M.Sc., Pol. Science), Finland Futures Research Centre, Turku School of Economics, University of Turku    

Jari Kaivo-oja
Research Director, Finland Futures Research Centre, Turku School of Economics, University of Turku.

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The project ‘Manufacturing 4.0’ has received funding from the Finnish Strategic Research Council [grant number 313395]. The project “Platforms of Big Data Foresight (PLATBIDAFO)” has received funding from the European Regional Development Fund (project No 01.2.2-LMT-K-718-02-0019) under a grant agreement with the Research Council of Lithuania (LMTLT).

 

References and additional information:

Cimino, Chiara et al. (2019). “Review of digital twin applications in manufacturing”. Computers in Industry, 113. DOI.

Grieves, Michael & Vickers, John (2017). “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems”. In Kahlen, FJ. – Flumerfelt, S. & Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems. Springer, Cham. DOI.

Kaivo-oja, Jari et al. (2019). ”Digital Twins Approach and Future Knowledge Management Challenges: Where We Shall Need System Integration, Synergy Analyses and Synergy Measurements?”. In Uden, L. – Tinh, IH. & Corchado, J. (eds.) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, 1027. Springer, Cham. DOI.

Kritzinger, W. et al. (2018). ”Digital Twin in manufacturing: A categorical literature review and classification.”. IFAC-PapersOnLine, 51(11), 1016–1022. DOI.

Lu, Y. et al. (2020). “Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues.” Robotics and Computer-Integrated Manufacturing, 61. DOI.

Qi, Q. et al. (2019). “Enabling technologies and tools for digital twin”. Journal of Manufacturing Systems, in press. DOI.

Saracco, Roberto (2019). “Digital Twins: Bridging Physical Space and Cyberspace”. Computer, 52(12), 58–64. DOI.

Talkhestani, B.A. et al. (2019). ”An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System.” at – Automatisierungstechnik, 67(9), 762-782. DOI.

Tao, Fei & Qi, Qinglin (2019). ”Make more digital twins”. Nature. DOI.

Zhidchenko, V. et al. (2018). ”Faster than real-time simulation of mobile crane dynamics using digital twin concept”. Journal of Physics: Conference Series, 1096.

Warnke, Philine – Cuhls, Kerstin – Schmoch, Ultich – Daniel, Lea – Andreescu, Liviu – Dragomir, Bianca – Gheorghiu, Radu – Baboschi, Catalina – Curaj, Adrian – Parkkinen, Marjukka & Kuusi, Osmo (2019). 100 Radical Innovation Breakthroughs for the Future – The Radical Innovation Breakthrough Inquirer. Foresight-report. European Commission. Brussels. DOI.

Cover picture: Pixabay.com