The idea of futures studies: visiting the future to learn what is worth doing now

Futures studies as a field

The future has interested people in every age, but conceptions of time have varied. The time of natural rhythms is cyclical, and that is how humans have experienced time for most of their history. Antiquity recognised another kind of time too, but there was dispute over what in time is essential. According to Heraclitus, everything flows, and one cannot step into the same river twice. Parmenides held the opposite view: what is essential is permanent. The great world religions regarded heavenly time as more essential than earthly time, eternal and unchanging. The modern era brought with it a time of progress, rushing forward in social rhythms that have displaced the rhythms of nature. The sociologist Manuel Castells speaks of the timeless time of the network society, in which traditional temporal structures and rhythms have broken down.

The history of futures studies is short, of the same generation as the Delphi method. Academic futures studies emerged in the United States after the Second World War, under the conditions of the Cold War. At first the focus was on military and strategic questions, but later it broadened to social and environmental issues, especially with the oil crisis of the 1970s and the rise of the environmental movement.

Finnish futures studies has its roots largely in environmental questions and in the Club of Rome, to which Professor Pentti Malaska was invited as a member. The motive of his futures thinking was concern for the future of humanity, nature and the planet. On Malaska's initiative, the Finland Futures Research Centre was founded at the Turku School of Economics in 1992. The Finnish Society for Futures Studies, which publishes the journal Futura, had already begun its work in 1980. Other important national actors are Sitra, the Finnish innovation fund, and the Committee for the Future of the Finnish Parliament, whose model has gradually spread to the parliaments of other countries. A distinctive place of learning is the Finland Futures Academy, a network of several universities.

The sociologist Wendell Bell was among the first to set out systematically the tasks and significance of futures studies. In his view, the main task of the field is to provide knowledge and analysis of the future so that people can make better decisions now. Its tasks can be divided into four: describing, explaining, evaluating and making futures. Futures studies describes possible, probable and desirable futures, often as scenarios in which different future scripts are built by varying the variables. It also explains: why are some futures more probable than others, and which factors influence the direction of development? Bell emphasised the ethical dimension. Researchers should assess which futures are better for people and communities, and encourage action that leads to desired futures.

Permanence and change

One of the hardest questions in futures studies is what in reality changes and what stays the same. Understanding change requires examining what does not change as well. All learning builds on what has already been absorbed: to understand a data network, we must have a notion of what a network and data are.

Many driving forces shape society and the environment, because they influence, often in hidden ways, people's decisions and choices. Much is also predetermined, such as the laws of nature, the direction of time and death. Some driving forces are highly probable, even though they are not laws of nature. The age structure of the population is an example: today's schoolchildren are tomorrow's workers, and today's workers will in future be pensioners, whom those then at work must support.

Futures studies speaks of rising, stable and declining trends, whose course can be measured and anticipated. Behind them lie basic beliefs that are taken as given and not questioned. Cultures contain patterns of thought that guide how the world is seen. People speak of the spirit of the age, which is often generational. Anita Rubin used smoking as an example: in the 1950s, advertisements even presented it as healthy, which now seems absurd but was then in keeping with the spirit of the time.

Megatrends are bundles of trends that point in a particular direction and affect both worldviews and change itself. At one time it was customary to draw up lists naming megatrends such as globalisation, networking, growing ecological awareness and population ageing. On closer inspection, these seemingly separate phenomena are different sides of the same whole.

Many factors bring stability to life. The image of the age stiffens basic beliefs, which is on the other hand necessary so that communities have enough cohesion for peaceful development. It is not a matter of indifference, however, what kind of belief system people carry in their heads. Research on social capital shows that communities whose members trust one another do better than others. Sustainable development nevertheless also requires a capacity for renewal, which seems to call for a dynamic society oriented towards new possibilities.

Reality also contains discontinuous phenomena: one-off events, and long-running trends that break off or turn into something different. Economic cycles are known, yet they still surprise. Epidemics overturn everyday life from time to time, most recently the COVID-19 pandemic. In theory humans could fully control the flow of cultural information, yet we repeatedly drift into self-inflicted catastrophes such as wars and environmental destruction.

A weak signal is the first symptom of a change, already latent but hiding in the margins or so rare that it goes unnoticed. A weak signal is finally revealed only when it becomes strong, and then everyone recognises it. Wild cards are even harder: they are completely surprising changes that suddenly turn development from certain to uncertain. Neither has a time series behind it from which it could be inferred.

Futures studies is close to the making of futures, which is the business of every human being. Research shows that a person without a future does not fare well. When nothing good seems to lie ahead, choices and feelings pile up in the present, which is marinated in past experiences. All action is helped if one's thinking includes a view of the big picture of development. So let us set off on a journey through time with Toffler's waves and land at last in Kurzweil's singularity.

Toffler's waves

Alvin Toffler (1928–2016) was a futurist and writer best known for his book Future Shock (1970). In The Third Wave (1980) he describes our own time as a transition in which upheaval in the operating environment overturns the conditions of life and our beliefs. Toffler divides history into three waves: the agricultural, industrial and information ages. The tens of thousands of years of hunter-gatherer life that preceded them received little attention from him.

The agricultural age began a little over 10,000 years ago, when people settled down to cultivate the land. Villages and towns arose, along with larger communities and more complex social structures. The central structural meme of the era was hierarchy. Producing a surplus allowed the nobility and clergy to rise above the rest of the population, and this happened in the same way in all agricultural cultures without their being in contact with one another.

The industrial age began in the eighteenth century, accelerated by technical inventions. Cities grew as factories were founded and labour flowed in from the countryside. Specialisation became the new meme of success, and authority moved from parsonages and castles to universities, factories and stock exchanges. Toffler also describes the drawbacks of the industrial age: environmental problems, social tensions and economic inequality.

According to Toffler, we already have one foot in the information age, saturated by the explosive growth of technology, knowledge and information. It has enabled global communication and the sharing of knowledge, but has also brought new problems, such as security threats, loss of privacy and the digital divide. The dead ends of hierarchy and specialisation are now being opened up by networks, which Toffler too regarded as the winning meme of the new age.

The Tofflers, Alvin and his wife Heidi, were pioneers in many respects, although some of their guesses now seem amusing: few people wear disposable paper clothes. They did, however, foresee the importance of data networks, the spread of remote work and a sharing economy that dilutes the importance of ownership. Toffler's most lasting contribution is his description of the rhythm of eras and upheavals. Many futurists today, however, see the information age rather as a transition between two eras than as an era of its own: the next age has not yet revealed its whole nature.

Castells's network society

The Spanish-born sociologist Manuel Castells continues where Toffler leaves off. His three-volume work The Information Age appeared in 1996–1998, at about the same time as the Academy of Finland's futures research programme Futu, whose researchers took up Castells's ideas. The philosopher Pekka Himanen wrote with Castells about the Finnish model of the information society and later about a model of sustainable growth. At that time many regarded Finland as a pioneering country of the new era.

Castells's central concept is the network society: a society built around technical networks and social networks. The networks are global and extend to all areas of life. According to Castells, the economic and social order rests increasingly on networks and less and less on hierarchies. In this way he extends Toffler's intuition about the development of companies to the whole of society.

Information has been valuable in every age, but now the difference is both quantitative and qualitative. In recent decades a gigantic project has been under way in which almost all human and natural information is being converted into digital form and brought within the reach of new kinds of information processing. According to Castells, digitised information is the basis of the new economy and society. The pace of change is shown by the fact that Castells's trilogy says surprisingly little about artificial intelligence, even though a quarter of a century later many regard it as the most powerful driver of change.

Technology also overturns the conception of time. Timeless time is a continuous present in which past, present and future mingle, as in real-time monitoring of share prices or the endless stream of social media. The boundaries between work and leisure blur, and global events affect local conditions in real time.

Castells also examines how the network society affects identity. He distinguishes three ways in which identities are formed. Legitimising identities arise from the dominant institutions of society. Resistance identities arise in groups that feel marginalised. Project identities arise when people build their own lives and seek to change society. A quarter of a century later the distinction is easy to recognise. Most striking are the resistance identities spreading worldwide, because of which Castells anticipated long-lasting unrest. He emphasised the tension between the global and the local and predicted powerful new social movements in the network society. The prediction has come true. What makes the world volatile is that identity movements pulling in different directions grow stronger while losing their ability to talk to one another.

Castells also describes informational capitalism, in which value is created from information rather than from traditional means of production such as natural resources or machines. This too has largely come true: the top of the world's most valuable companies consists of technology firms whose turnover and influence exceed those of many states. Castells nevertheless reminds us that technology alone does not determine the development of society. Social forces and actors decide how technology is used and what kind of society is built.

Kondratieff's cycles

The Russian economist Nikolai Kondratieff became known for his theory of long economic cycles. According to it, the modern capitalist economy shows waves of roughly 40–60 years, with periods of rise and decline. Each wave begins with a cluster of technical innovations that has a revolutionary effect on both the economy and the whole of society.

The first wave began in the 1780s with the steam engine, which multiplied productivity. The second began in the 1830s with railways and the steel industry, as goods and people began to move faster than before. The third was accelerated by electrification and the chemical industry, and it ran into the Great Depression of the 1930s. The fourth wave rose on petrochemicals and the car industry until the oil crisis broke it. The fifth rose with information technology, and its momentum faltered for a time in the financial crisis of 2008. According to Markku Wilenius and Sofi Kurki, the sixth wave is being shaped by intelligent technologies.

Each wave has four phases: rise, peak, decline and recession. The waves have produced enormous quantitative growth, but also qualitative changes visible in living conditions, mental models and institutions. The wave of information technology has been followed by growing complexity, as the amount of information and the interdependence of actors have multiplied. The balance is upset by problems on a planetary scale, such as environmental crises, population development and growing inequality.

An interesting addition to the waves is the hype cycle of the research firm Gartner. A new technology is accompanied by a rapid rise in expectations, followed by an almost equally rapid collapse when the business does not materialise as hoped. Lasting change comes only after the hype, and it may settle at a lower level than the original expectations.

Kondratieff stressed that long waves are not merely economic cycles but are linked to broad social, political and cultural changes. The theory has proved paradoxical: the more variables are added to the model, the worse it predicts. It has been criticised for explaining the past better than the future. Despite its shortcomings, the theory is inspiring when trying to understand the long-term relationship between economy and technology. It leads us astray if used deterministically. The future cannot be derived from it directly, or even indirectly.

Goonatilake's information flows

Kondratieff's thinking is rooted in economics, which seeks to use the methods and models of the natural sciences. In the natural sciences the starting point is that modelling what has already happened makes it possible to predict what will happen. This deterministic idea lives on latently in the research community, even though everyday experience testifies otherwise and Popper showed its problems: the future cannot be known, if only because we cannot know future inventions.

The scientific background of the Sri Lankan scholar Susantha Goonatilake lies in sociology and anthropology. Like Kondratieff, he builds his views on developments that have already taken place, but he does not stop at observations; he assesses the mechanisms behind them. Like Toffler, he distinguishes three information flows that developed one after another, through which all life has evolved and continues to evolve.

Biological information is encoded in genes. Natural selection takes place in two stages: first variations arise, and then the environment tests their viability. Evolution changes the environment, and the environment affects natural selection. The shortest time unit of biological change is a generation. According to Richard Dawkins, the real agent of evolution is the selfish gene, the carrier of information, rather than the organism that happens to carry it.

Cultural information begins to operate when the limits of genetic adaptation are reached. Psychobiologically, the human being is a learning machine that learns to adapt to the most varied environments. Cultural information is transmitted in language and recordings, and the time unit of its changes is much shorter than that of biological information. In culture, the gene's counterpart is the meme, a guiding thought or idea that competes with others as genes do. The thoughts best suited to their environment win, and when the environment changes, the thoughts may change too.

Exosomatic information, information outside the human body, is organised as digital code outside living organisms. It has two properties, of which only the second makes it an independent information flow: storing information and processing it. As early as the 1990s, Goonatilake anticipated that information stored outside humans would soon also be processed without human involvement. Artificial intelligence is now reaching exactly this point. A learning system can use feedback from its environment as input for its own activity and can thus improve its performance and become more autonomous.

Goonatilake also describes how a later information flow begins to modify an earlier one, and the flows begin to mix. Humans cut and paste biological information with gene scissors, and in the same way information processing outside the human being reaches out to modify cultural information. Cultural power still lies with humans, but change is in the air.

Kurzweil and the singularity

Whereas Goonatilake draws cool conclusions from his observations, Raymond Kurzweil approaches the future with passion. According to him, artificial intelligence will overtake human intelligence in the 2030s as a result of a chain of technological upheavals, and by 2045 development will lead to the singularity. The technological singularity refers to a threshold beyond which superhuman artificial intelligence accelerates technical and social change so much that the people who came before it can no longer understand it. Kurzweil updated his views in The Singularity Is Nearer (2024).

Kurzweil believes in the exponential nature of technological development. Development does not proceed linearly, 1-2-3-4-5, but by doubling, 1-2-4-8-16. The claim extends Moore's law from information technology to other fields of technology. Kurzweil divides evolution into six epochs: physics and chemistry, biology, brains, technology, the merger of technology and human intelligence, and finally the awakening of the universe, when matter and energy become saturated with intelligence. In the fifth epoch, human and machine merge, and the union can no longer be undone. In evolutionary terms this would be a new species, arising as the biological, cultural and exosomatic information flows described by Goonatilake intermingle. According to the philosopher Daniel Dennett, there is no reason to assume that evolution ends with humans.

Kurzweil's predictions are controversial, but some earlier ones have come close. In 1990 he anticipated that a computer would beat the world chess champion by 1998. Deep Blue defeated Garry Kasparov in 1997. Theses about the singularity can also be considered through the four paths that Vernor Vinge presented as early as 1993: computer networks and their users form a superhuman collective; the interface between human and computer becomes so subtle that the two can no longer be told apart; biological innovations develop humans beyond their natural capacity; or computers become self-aware.

Kurzweil is a technology optimist. He recognises the risks but believes problem-solving capacity grows at the same pace. One can think otherwise. Finland's best-known philosopher, Georg Henrik von Wright, criticised technical development long before today's age of AI and questioned the belief in progress. The deep stories of humanity repeat the same plot. In the myth of paradise, humans lost their innocence by eating from the tree of knowledge. Prometheus stole fire from the gods and received eternal punishment. Faust sold his soul for unlimited knowledge. Whenever humans reach to become gods over nature, hubris follows, and with it the punishment of the goddess of retribution, Nemesis. Every conquest of knowledge and technology brings the possibility of both good and evil, and in the light of history humans tend to realise both. In the case of AI, one may ask whether the same situation is at hand. An even more unsettling question is what the machine's own ethics will become.

The Delphi method among the methods

Futures studies has a methodology of its own that departs from the mainstream of science. This stems from the special nature of knowledge of the future, discussed in the essay The idea of the future. Since such knowledge can never be certain, it cannot simply be collected either. People's views of the future can of course be collected by survey, but the result then amounts to an opinion poll. The idea of the Delphi method, and of futures studies more broadly, is to put views and those who hold them to the test with one another so that the quality of uncertain knowledge improves in the process. In Bakhtin's terms, existential knowledge grows and deepens when it is formed in many voices within the method process itself. This carries a risk: the researcher has great freedom in facilitating the process, and research ethics forbids tampering with the data. Careful reporting of methods and reflection on one's own role as a researcher are especially important when significant interests are attached to the futures in question.

Futures research methods are developed worldwide by the Millennium Project, founded in 1996, which involves thousands of experts in dozens of countries. It publishes the State of the Future review on the condition of the planet and compiles method descriptions in Futures Research Methodology, edited by Jerome Glenn and Theodore Gordon, which presents nearly 40 methods. Many methods deal with historical and present-day information, such as the various forms of trend analysis. A discontinuous future can be approached through weak signals and wild cards. There are several scenario methods, and participatory methods and systems-thinking methods form families of their own.

Among the systems-thinking methods, Peter Checkland's Soft Systems Methodology (SSM) deserves an introduction. It is an approach akin to action research, in which an organisation or community is developed through the tension between its present state and a desired future. SSM does not look for one right solution, but examines different views side by side and builds shared understanding among the actors. Checkland shows that systems are created by people and can therefore be changed by people. Antti Kauppi and Hannu Linturi combined Soft Systems Methodology with the Delphi method in the study Kansalaisfoorumin viisi tulevaisuutta (Five futures of the Citizens' Forum) in 2018.

Causal Layered Analysis (CLA), developed by Sohail Inayatullah, ploughs deepest into the future, examining phenomena on four levels. The litany is the surface level of everyday talk and the media. The systemic level examines structural, economic and political factors, and expert talk often moves on this level. The worldview level focuses on cultural and ideological views and the lasting values behind them. The level of myth and metaphor reaches the deep-swimming stories that steer worldviews, mostly unseen. CLA can increase understanding of deep cause-and-effect relationships and help build rich scenarios.

One method stands out from all the others in that it can be used to carry out many other methods, or it can be linked into a chain of methods. That method is the Delphi method. Rafael Popper has placed 33 foresight methods in a diamond-shaped four-field model whose corners represent four qualities of foresight: expertise, creativity, interaction and evidence. The Delphi method sits in the middle of the diamond, which reflects that all qualities of foresight can be present in it with different emphases. Its flexibility in combination makes the Delphi method the Swiss army knife of methods, from which variants can be shaped by changing the emphases. It is used in both quantitative and qualitative research. At the creative end it captures weak signals; at the evidence end it tracks the development of expectations about the future, barometer-style. In technology foresight, panelists are chosen by strict expertise criteria; in panels for deliberative democracy, broad participation and the recognition of different interests matter most.

What was anticipated and where we ended up

The series began with Socrates' insight that a question opens up more thinking than an answer. That view has not changed, but at the end we give way enough to try to answer the question of how the Delphi method and futures research methods will develop.

In 2023 I based my anticipations on two panels that discussed the future of the Delphi method: the national Aavistus panel (2020) and the international Hunch panel (2021). The experts argued for hybrid method development. The use of the Delphi method was expected to spread to new user groups and purposes, in particular organisations' strategy work, decision-making and participation, as well as the challenges of deliberative democracy. This series also sought to advance the panel's anticipation that the pedagogical use of the Delphi method in educational institutions would increase.

The panels' strongest message, however, was the assumption that AI would become integrated into the method. In his experiments with an AI panelist, Kari A. Hintikka drew on Asbjørn Følstad's typology of chatbots. It examines chatbots along two axes: whether the interaction is short-term or long-term, and whether it is driven by the user or by the chatbot itself. The experts assumed that development would move from the user's occasional customer support towards a personal assistant and coach, with the facilitator and AI forming a working pair.

On this basis, the 2023 text sketched a roadmap. Metodix and Mindhive planned to build an ecosystem of futures research methods around Delphi software in three phases by 2032. First the core software would be strengthened, then method variants using the Delphi technique, such as scenario work and the futures wheel, would be conceived within it, and finally they would be detached as independent applications. I also predicted that it would not be long before an AI assistant worked alongside the facilitator in every phase of the method, like a human colleague.

The anticipation was right, but the route was different and the pace faster. The method ecosystem now exists as three interlinked phases. In planning (Delphi Planning Ecosystem, DPE), the facilitator builds the study from the description of the phenomenon, the panel and the theses, supported by pre-analysis methods such as the futures wheel and the relevance tree. Data collection takes place on the Delphi Pronoia platform, where the AI assistant Kastalia helps both with planning and in panelists' preparatory conversations. In analysis (Delphi Analysis Ecosystem, DAE), the AI assistant Kassotis opens up the material layer by layer, from argument analysis to systems methods such as Causal Layered Analysis, and on to weak signals and scenarios. The ecosystem also includes a model for a facilitated foresight journey that does not require panel data.

AI has played an absolutely central part in building the ecosystem. It is not only part of the method but also the tool with which the method has been built. Cooperation with Mindhive continues: Mindhive is responsible for the technical services of Delphi Pronoia, and together we are developing new MCP (Model Context Protocol) solutions through which AI assistants can use method tools directly.

One principle has remained, however, and it is perhaps more important than the anticipation. AI has not come to replace the facilitator but to work alongside them. AI carries the water, but seeing and interpreting are human work. Interpretations, conclusions and responsibility belong to the researcher, as in ancient Delphi: the god gave the signs, but people made the meaning.

RAND's method concept from the 1950s is still basically sound. A multidisciplinary anonymous panel is a good way to ensure sensible decision-making that is fast compared with many other methods, in which different perspectives and justifications are taken into account without social hierarchies distorting them. Mechanical implementations led to a method crisis in the 1970s, after which the quality of quantitative implementations improved and qualitative variants multiplied even more. These are characterised by allowing plurality: instead of agreement, they favour disagreement, in which attention turns to the reasoning behind different futures. The natural continuation of such studies is often scenario work.

The current state of Delphi practice is still not satisfactory. The Delphi method holds the promise of interaction, participation and learning, which the educational scientist Kai Hakkarainen calls the third level of learning, knowledge creation. In reality the idea is realised only partly in most panels. Panelists' everyday rush overwhelms them, and many fulfil their obligation with a single round of answers. They then miss hearing others' views, leave their own understanding undeepened and fail to support others' learning. Technology does not solve the problem of haste, but it can help. That is precisely why in Pronoia panelists are not left alone: Kastalia talks with them before the round, and the panel discussion is built so that others' reasoning becomes visible and can be taken up.

Closing the series

The seven ideas of the Delphi method are the question, dialogue, argument, open speech, expertise, the future and futures studies. Each lives in a house of its own, but they deal with all their neighbours. Apart, the ideas do not gain strength. Together they produce the very reason the Delphi method is worth doing at all: the future is not only studied and anticipated, it is also made every day.

The village is not finished. Since 2023 new houses have risen around it, tools for planning, data collection and analysis, and more are on the way. In a good village, neighbours deal with one another, and that is how the village grows.

Sources

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