Future studies, more commonly called futures studies, is the systematic and interdisciplinary exploration of possible, plausible, probable, and preferable futures. It does not claim to predict one fixed outcome. Instead, it uses evidence, expert judgment, scenarios, systems thinking, and participatory methods to help researchers, organizations, and communities make better decisions under uncertainty.
Future studies helps people investigate how society, technology, economies, environments, politics and values might change. It asks not only “What is likely to happen?” but also “What could happen?”, “What do we want to happen?” and “What should we do now?”
This article explains the meaning, purpose, methods, applications, advantages and limitations of future studies. It also provides a step-by-step process and a worked example for students and researchers.
Key Takeaways
- The established academic term is usually futures studies, with “futures” in the plural.
- The field explores alternatives rather than claiming that one future can be predicted with certainty.
- Common methods include horizon scanning, trend analysis, Delphi studies, scenarios, backcasting, roadmapping and causal layered analysis.
- Good futures research combines evidence, imagination, stakeholder participation and transparent assumptions.
- Scenarios are tools for learning and decision-making, not forecasts that must come true.
- Artificial intelligence can support scanning and analysis, but human judgment, source verification and ethical oversight remain essential.
What Are Future Studies?
Future studies is an interdisciplinary field that systematically investigates alternative futures and uses the resulting insights to improve present-day decisions.
People frequently search for future studies, but scholars and practitioners generally prefer the plural expression futures studies. Other names include futures research, futures thinking, foresight and, less commonly in current professional practice, futurology.
The field draws on the social sciences, humanities, economics, technology studies, environmental research, systems science, design and policy analysis. It may use numerical data, historical evidence, expert opinion, stakeholder workshops, models, narratives and creative techniques.
Futures studies is concerned with questions such as:
- How could artificial intelligence change university education by 2035?
- What different energy systems might exist in 2050?
- How could demographic change affect healthcare demand?
- Which unexpected developments could disrupt an industry?
- What policies would remain effective under several different futures?
- What steps would be needed to achieve a preferred future?
The purpose is not to produce a perfect image of what will happen. It is to enlarge the range of developments that researchers and decision-makers consider.
Why Is “Futures Studies” Written in the Plural?
The plural word “futures” emphasizes that the future is not a single, predetermined destination. Different decisions, events, values and uncertainties can produce multiple possible outcomes.
The World Futures Studies Federation explicitly supports the plural form because it challenges the assumption that there is only one inevitable future. A plural perspective also encourages researchers to examine who defines a desirable future and whose interests may be excluded.
Using the plural has several implications:
- More than one scenario should normally be considered.
- Current trends should not automatically be treated as permanent.
- Social choices and human agency can affect outcomes.
- Different communities may prefer different futures.
- Uncertainty should be represented rather than hidden.
The exact-match keyword future studies can still be used naturally in headings, metadata and introductory explanations. Within academic discussion, however, futures studies is usually the more precise term.
What Is the Purpose of Futures Studies?
The central purpose of futures studies is to improve present decisions by examining change, uncertainty, alternatives and desirable long-term outcomes.
Its main objectives include:
Anticipating change
Researchers identify emerging developments, weak signals, trends, uncertainties and potential disruptions before their effects become fully visible.
Challenging assumptions
Every forecast or strategy contains assumptions. Futures research makes those assumptions explicit and asks what would happen if they proved incorrect.
Creating alternative scenarios
Scenarios describe significantly different ways in which a system could develop. They help prevent dependence on one expected outcome.
Testing decisions
A policy or strategy can be tested against several scenarios. An option that performs reasonably well across different conditions may be more robust than one optimized for a single forecast.
Clarifying desirable outcomes
Normative futures research asks what should happen, according to specified values and criteria. Researchers can then work backward to identify pathways toward that outcome.
Expanding participation
Participatory foresight can bring affected communities, specialists, decision-makers and underrepresented groups into discussions about long-term change.
Is Futures Studies About Predicting the Future?
No. Although forecasting may be one component, modern futures studies does not assume that one complex social future can be predicted with certainty.
Forecasting estimates what is likely to happen under stated data, models and assumptions. Futures studies takes a wider view. It investigates probable developments, plausible alternatives, preferred outcomes and low-probability disruptions.
For example, a demographic forecast might estimate a country’s population in 2040. A broader futures study could examine several population scenarios and ask how migration, fertility, automation, housing policy, climate pressures and public attitudes might interact.
Forecasts can be useful, but they should not be confused with facts. Their value depends on data quality, model design, assumptions and the stability of the system being studied.
Main Characteristics of Futures Studies
A strong futures study is usually:
Systematic
The researcher follows a transparent process rather than presenting unsupported speculation.
Interdisciplinary
Complex changes rarely fit within one academic discipline. A study of the future of cities, for example, may require knowledge of transport, economics, public health, energy, governance, architecture and social inequality.
Long-term
Many projects examine developments over five, ten, twenty or more years. The appropriate horizon depends on the system and decision being studied.
Exploratory
The research considers alternatives, discontinuities and uncertainties rather than extending current trends mechanically.
Evidence-informed
Scenarios are not pure fiction. They should be connected to credible evidence about current conditions, past change, emerging developments and causal relationships.
Participatory
Many projects involve experts, stakeholders or communities whose experience can reveal assumptions and consequences that are absent from published data.
Reflexive
Researchers examine how their own concepts, values, cultural perspectives and institutional interests shape the futures they construct.
Action-oriented
The findings should help people make decisions, test strategies, design experiments, monitor change or debate desirable outcomes.
Types of Futures
Terminology varies across authors and frameworks, but the following categories are widely useful.
| Type of future | Main question | Meaning | Example |
|---|---|---|---|
| Possible | What could happen? | Any outcome that does not violate the study’s basic constraints | A largely virtual university system |
| Plausible | What could reasonably happen? | An outcome supported by credible causal reasoning and available knowledge | Universities combining AI tutors with campus instruction |
| Probable | What is most likely to happen? | An outcome judged comparatively likely under current evidence and assumptions | Continued growth of hybrid learning |
| Preferable | What do stakeholders want to happen? | A desirable future based on stated values and criteria | Affordable, accessible and human-centred education |
| Projected | What happens if a trend continues? | A conditional extension of an existing pattern or model | Enrollment declining at its current rate |
| Unexpected or disruptive | What might surprise us? | A low-probability or poorly anticipated development with major consequences | A sudden ban on a widely used educational technology |
These categories should not be treated as exact measurements unless probabilities have been estimated using an appropriate model. A scenario can be plausible without being probable, and a preferable future is not necessarily achievable without major changes.
Futures Studies Compared with Related Concepts
| Concept | Primary purpose | Typical output | Relationship to futures studies |
|---|---|---|---|
| Futures studies | Explore alternative and desirable futures | Scenarios, insights, pathways and strategic options | Broad academic and applied field |
| Strategic foresight | Apply futures thinking to decisions | Strategy options, risk tests and action plans | Applied organizational practice |
| Forecasting | Estimate a likely future value or event | Numerical or judgment-based forecast | One possible component |
| Scenario planning | Construct and examine alternative futures | Several internally coherent scenarios | A major futures method |
| Trend analysis | Examine the direction and development of change | Trend description or projection | Often used during scanning and analysis |
| Technology foresight | Explore technological change and its implications | Technology scenarios, priorities or roadmaps | Specialized application |
| Futurism | Can refer to future-oriented ideas, advocacy or an artistic movement | Visions, arguments or creative work | Broader and less methodologically precise |
| Future research recommendations | Identify questions that later studies should investigate | A section in a thesis or journal article | Different academic meaning |
Future studies versus future research recommendations
In a dissertation or journal article, “future studies should examine…” normally refers to recommendations for later researchers. That is not automatically a futures-studies project.
A recommendation such as “future studies should use a larger sample” concerns the continuation of an existing research program. A futures study asks structured questions about alternative future conditions, uncertainties, systems and decisions.
Major Approaches in Futures Studies
Predictive or empirical approaches
These approaches use data, models and observed patterns to estimate probable developments. Examples include demographic projections, technology-adoption models and time-series forecasts.
Their strength is analytical discipline. Their limitation is dependence on assumptions that may fail during structural change.
Exploratory approaches
Exploratory work begins in the present and asks how events could develop under different drivers and uncertainties. Scenario planning is a common example.
This approach is useful when the future is uncertain and several outcomes are credible.
Normative approaches
Normative research begins with a preferred future and investigates how it could be achieved. Visioning and backcasting are central methods.
Because “preferred” is a value judgment, the study must state who participated, which values were used and how conflicts were handled.
Interpretive approaches
Interpretive futures research examines how language, culture, stories, identities and images influence expectations about the future.
It may compare the future narratives of different societies, institutions or social groups.
Critical approaches
Critical futures studies asks who benefits from a dominant image of the future, whose knowledge is treated as authoritative and which alternatives are excluded.
It is particularly useful when claims about inevitability conceal political choices or unequal power.
Participatory approaches
Stakeholders collaboratively identify drivers, create scenarios, examine implications and design actions. Participation may improve relevance and legitimacy, although it does not automatically remove bias or power imbalances.
Methods Used in Futures Studies
No single method is appropriate for every project. Method choice should follow the research question, time horizon, uncertainty, available evidence, participants and intended use.
| Method | Main purpose | Typical output | Particularly useful when | Main limitation |
|---|---|---|---|---|
| Horizon scanning | Detect emerging developments | Database of signals, issues and evidence | The domain is changing quickly | Can collect noise without a clear framework |
| Trend analysis | Examine direction and momentum | Trend descriptions, charts or projections | Reliable longitudinal evidence exists | Trends may reverse or interact unexpectedly |
| STEEP/PESTLE analysis | Organize external drivers | Structured driver map | A broad system must be scanned | Categories can oversimplify relationships |
| Delphi method | Gather iterative expert judgments | Convergence, disagreement and rationales | Evidence is incomplete or dispersed | Expert selection and conformity can bias results |
| Cross-impact analysis | Examine interactions among developments | Interaction matrix or modeled effects | Events influence one another | Ratings can be subjective |
| Futures wheel | Trace direct and indirect consequences | Visual impact map | Early implications need exploration | Does not establish probability |
| Scenario planning | Explore alternative system-level futures | Several coherent scenarios | Critical uncertainties are high | Scenarios may become superficial stories |
| Backcasting | Identify pathways from a desired future | Milestones, policies and action pathways | A long-term goal is defined | Can underestimate political or practical barriers |
| Roadmapping | Connect objectives, capabilities and time | Sequenced development roadmap | Technology or institutional change must be coordinated | May imply more control than actually exists |
| Systems modeling | Analyze dynamic relationships | Simulations, sensitivity tests and trajectories | Relationships can be represented formally | Models depend heavily on structure and data |
| Causal layered analysis | Examine surface issues and deeper worldviews | Layered interpretation and alternative narratives | Hidden assumptions need to be challenged | Requires skilled facilitation and interpretation |
| Participatory workshops | Combine perspectives and co-create options | Shared scenarios and action priorities | Legitimacy and local knowledge matter | Powerful participants may dominate |
Methodological reviews emphasize that method selection should reflect the stage of the foresight process and the type of knowledge required rather than the facilitator’s preferred technique (Popper, 2008).
How to Conduct a Futures Study
A futures study can be organized into eight stages.
Step 1: Define the decision and domain
State what the research will examine and why.
A weak question is:
What will education be like in the future?
A stronger question is:
How might artificial intelligence, demographic change and funding pressures reshape undergraduate education in the United Kingdom by 2035, and which institutional strategies would remain effective across different scenarios?
Specify:
- Geographic boundary.
- Population or sector.
- Time horizon.
- Intended users.
- Decision to be informed.
- Topics inside and outside the scope.
Step 2: Review the present system and its history
A researcher cannot examine change without understanding the current system.
Collect evidence about:
- Existing structures.
- Stakeholders.
- Historical turning points.
- Current policies.
- Resources and constraints.
- Previous forecasts and scenarios.
- Known disagreements.
Historical analysis helps distinguish long-term forces from temporary attention cycles.
Step 3: Scan for signals, trends and emerging issues
Search academic databases, policy reports, patents, statistics, news archives, regulatory publications and stakeholder knowledge.
Separate:
- Signal: a specific observation that may indicate emerging change.
- Trend: a pattern developing over time.
- Driver: a force that contributes to change.
- Megatrend: a broad and persistent transformation across systems.
- Uncertainty: a development whose direction, magnitude or effect is unclear.
- Disruption: an event or shift capable of changing the system substantially.
Record the source, date, evidence strength and relevance of every important item.
Step 4: Analyze relationships and uncertainties
Drivers do not act independently. Examine:
- Reinforcing and balancing relationships.
- Delays and feedback loops.
- Stakeholder interests.
- Dependencies.
- Potential second- and third-order effects.
- Areas of strong disagreement.
- Assumptions that might fail.
A cross-impact matrix, systems map, futures wheel or causal-loop diagram can support this stage.
Step 5: Construct alternative scenarios
Select a scenario technique suited to the question.
A common approach uses two critical uncertainties as axes, producing four contrasting scenarios. Other studies use archetypes, morphological analysis, simulation, expert judgment or participatory storytelling.
Each scenario should include:
- A clear title.
- Initial conditions.
- Major drivers.
- Causal development.
- Stakeholder behavior.
- Social and ethical implications.
- Early indicators.
- Consequences for the decision under study.
Scenarios should be meaningfully different, internally coherent and relevant to the research question.
Step 6: Examine implications and test options
Ask how each policy, strategy or research priority performs in every scenario.
Classify actions as:
- Robust: useful across several futures.
- Contingent: useful only under specific conditions.
- Hedging: reduces exposure to a major risk.
- Shaping: attempts to influence which future develops.
- Monitoring: collects evidence before a commitment is made.
This stage converts scenarios into decision support.
Step 7: Develop a preferred future and pathways
When the study has a normative purpose, define the desired outcome using explicit criteria.
Backcasting then asks:
- What conditions must exist in the preferred future?
- What milestones would indicate progress?
- What capabilities and policies are required?
- What should happen immediately?
- What barriers could block the pathway?
- Who has responsibility and authority?
The preferred future should not be presented as universally desirable when stakeholders disagree.
Step 8: Monitor indicators and revise
Futures research should be treated as an ongoing learning process.
Create indicators for:
- Trends accelerating or weakening.
- Critical assumptions becoming invalid.
- New regulations or technologies.
- Changes in stakeholder behavior.
- Early signs associated with particular scenarios.
- Previously neglected issues.
Review the scenarios and strategic implications periodically.
Worked Example: The Future of University Learning in 2035
The following hypothetical example demonstrates the process.
Research question
How might artificial intelligence and changing student needs reshape university learning by 2035, and what actions should universities take now?
Present-system evidence
The research team examines:
- Current uses of generative AI.
- Enrollment and demographic patterns.
- Student costs and funding models.
- Employer demand for credentials and skills.
- Data-protection rules.
- Evidence about online, hybrid and campus learning.
- Faculty roles and assessment practices.
Major drivers
The team identifies:
- AI tutoring and content generation.
- Labor-market demand for continuous reskilling.
- Public funding constraints.
- Growth of short credentials.
- Student expectations for flexibility.
- Concern about privacy, academic integrity and automation.
- Unequal access to digital infrastructure.
Critical uncertainties
Two especially influential uncertainties are selected:
- Will society maintain high trust in universities as credentialing institutions?
- Will AI in education be governed mainly as a public learning resource or as a commercial platform?
Four illustrative scenarios
1. Augmented Public University
Universities retain public trust. Carefully governed AI assists students and staff, while human teaching, research communities and campus experiences remain central.
2. Platform Credentials
Large technology platforms provide personalized learning and widely accepted credentials. Traditional universities compete by specializing in research, regulated professions and high-value experiences.
3. Fragmented Access
Funding pressure and unequal infrastructure create a divided system. Some learners receive advanced AI-supported education, while others rely on lower-quality automated provision.
4. Human Learning Renewal
Public concern about automation creates demand for face-to-face inquiry, mentoring, practical work and verified human authorship. AI remains present but restricted in high-stakes learning.
Strategic implications
Potentially robust actions include:
- Teaching AI and information literacy.
- Redesigning assessment around reasoning and authentic performance.
- Protecting student data.
- Maintaining meaningful human feedback.
- Building flexible learning pathways.
- Evaluating unequal effects across student groups.
- Monitoring changes in credential recognition.
The example does not predict which scenario will occur. Its value lies in revealing different pressures and identifying decisions that remain useful across them.
Applications of Futures Studies
Academic research
Researchers use futures methods to:
- Define long-term research agendas.
- Explore emerging interdisciplinary questions.
- examine societal implications of new technologies.
- identify gaps in current evidence.
- design responsible innovation programs.
- test assumptions behind theories and policies.
- involve stakeholders in setting priorities.
A futures study can combine literature reviews, interviews, surveys, case studies, workshops, modeling and scenario analysis.
Public policy
Governments use strategic foresight to identify emerging issues, stress-test policies and improve institutional preparedness. Recent government and OECD toolkits organize foresight as a structured process connecting assumptions, scenarios, policy testing and present action.
Applications include:
- Climate adaptation.
- Public health.
- Migration.
- Infrastructure.
- National security.
- Education.
- Emerging-technology governance.
- Aging populations.
Business and organizational strategy
Organizations may use futures studies to explore:
- Market change.
- Competitor behavior.
- Regulation.
- supply-chain vulnerability.
- consumer expectations.
- workforce skills.
- technological disruption.
- new products and business models.
The objective should not be a dramatic prediction. It should be a stronger set of strategic choices.
Healthcare
Healthcare futures research may examine disease burdens, workforce availability, public expectations, medical technology, financing and health inequality.
Participatory approaches are particularly important because a technologically efficient future may not be ethically acceptable, accessible or preferred by patients.
Education
Futures methods support curriculum design, institutional planning, skills analysis and examination of alternative learning systems.
They can help educators avoid assuming that every technological development will be adopted uniformly or that current educational structures will remain unchanged.
Sustainability and cities
Backcasting is frequently used when researchers have a defined long-term sustainability objective. The process develops a desired future state and then identifies pathways connecting that future with present action (Bibri, 2020).
Technology and innovation
Technology foresight considers technical feasibility together with social, economic, regulatory, ethical and environmental consequences.
It should avoid technological determinism—the assumption that a technology automatically produces a particular social outcome.
Futures Studies in Modern Research Practice
Modern futures research increasingly combines several forms of inquiry.
Mixed-method designs
Quantitative evidence may identify trends, estimate ranges or test models. Qualitative methods can explain meanings, motivations, institutions and contested values. Participatory methods add experiential and local knowledge.
Combining methods can strengthen a study when each method has a defined role. Simply collecting many kinds of data does not guarantee quality.
Participatory and inclusive foresight
Researchers increasingly recognize that futures are political. Decisions about which scenario is desirable can distribute risks, opportunities and resources unequally.
Inclusive projects should consider:
- Who is affected.
- Who participates.
- Who controls the process.
- Which knowledge is recognized.
- Which groups cannot participate easily.
- How disagreements are documented.
- Whether the final scenarios reproduce existing power.
Adaptive foresight
A static report can become outdated quickly. Adaptive practice connects scanning, scenarios, experiments, indicators and periodic revision.
The aim is not to change direction after every new headline. It is to recognize when accumulated evidence alters a central assumption.
Responsible innovation
Futures thinking can be integrated into technology research before systems are widely deployed. Researchers can ask how an innovation might affect rights, labor, inequality, safety, sustainability and public trust.
This shifts evaluation from “Can it be built?” to “Under what conditions should it be developed and used?”
Digital Tools and Artificial Intelligence in Futures Studies
Digital tools can support nearly every stage of a futures project.
Evidence discovery
Academic databases, policy repositories, patent databases, statistical portals and news archives help researchers identify signals and trends.
Automated alerts can monitor new evidence, but search strategies and inclusion criteria should be documented.
Text analysis
Natural-language-processing tools can assist with:
- Topic modeling.
- Entity extraction.
- document clustering.
- sentiment analysis.
- identifying frequently associated concepts.
- comparing large bodies of reports.
These methods may reveal patterns that are difficult to detect manually. They can also reproduce biases in the source material.
Data visualization
Dashboards, network diagrams, timelines and systems maps can make relationships visible. A polished visualization is not evidence by itself; sources, transformations and assumptions must remain accessible.
Modeling and simulation
Researchers may use system-dynamics models, agent-based models, statistical forecasts or Monte Carlo simulation.
Models are most useful when their assumptions can be inspected and alternative parameter values are tested.
Generative AI
Generative AI can help researchers:
- Summarize large document collections.
- suggest search terms.
- group signals into provisional themes.
- generate questions for workshops.
- propose initial scenario variations.
- identify possible consequences for human review.
- rewrite technical scenarios for different audiences.
However, AI output can contain fabricated evidence, hidden bias, shallow causal reasoning and false consensus. It may overrepresent dominant English-language sources and underrepresent local, Indigenous or unpublished knowledge.
A responsible workflow should:
- Verify every factual claim against the original source.
- Preserve links between evidence and scenario statements.
- Treat generated scenarios as drafts, not findings.
- involve diverse human participants.
- document which tools and models were used.
- protect confidential or personally identifiable information.
- compare AI-assisted output with independent human analysis.
- retain disagreement instead of forcing artificial consensus.
AI can increase analytical capacity, but it cannot decide which future is ethically preferable on behalf of affected communities.
Advantages of Futures Studies
It improves preparedness
Considering alternative conditions reduces dependence on a single forecast.
It reveals hidden assumptions
Researchers can identify beliefs that would otherwise remain embedded in policies, strategies or models.
It encourages systems thinking
The field examines interactions, feedback loops and indirect consequences.
It supports innovation
Alternative futures can reveal new research questions, services, policies and organizational models.
It connects long-term goals with present action
Backcasting and roadmapping help translate broad visions into milestones and responsibilities.
It creates space for participation
Well-designed processes enable stakeholders to discuss values and consequences before decisions become difficult to reverse.
It helps identify robust strategies
Scenario testing can reveal actions that remain useful under several different conditions.
Limitations of Futures Studies
The future cannot be observed directly
Claims about future conditions cannot be validated in the same way as observations of current or past events.
Scenarios depend on assumptions
A coherent scenario may still rest on weak evidence or omit an important driver.
Participants can introduce bias
Experts may share similar backgrounds, incentives or professional assumptions. Group processes can also suppress minority views.
Powerful actors can shape the preferred future
Participatory language does not guarantee equal influence. The sponsor may control the question, participant list or acceptable outcomes.
Long time horizons increase uncertainty
Data and models usually become less reliable as the time horizon expands and structural change accumulates.
Creative methods may be mistaken for evidence
A memorable story or visual object can help people engage with a scenario, but its emotional impact does not prove plausibility.
Scenarios may not influence decisions
Organizations sometimes complete a foresight exercise without changing budgets, responsibilities, experiments or monitoring systems.
False precision can mislead readers
Assigning exact dates or probabilities without adequate evidence can create an unjustified sense of certainty.
Common Mistakes
Treating the most likely future as the only future
A probable scenario should not eliminate exploration of plausible alternatives.
Extending trends mechanically
Linear projection ignores feedback, policy change, saturation, disruption and social response.
Writing scenarios before completing research
Scenarios created without adequate scanning and system analysis often reproduce familiar assumptions.
Producing minor variations of one scenario
“High growth,” “medium growth” and “low growth” may be useful projections, but they do not necessarily represent genuinely alternative futures.
Confusing desirability with probability
A preferred future is a value-based choice, not evidence that the outcome is likely.
Ignoring who benefits and who bears the cost
Every scenario should consider distributional and ethical consequences.
Using too many methods without integration
A study becomes methodologically cluttered when interviews, surveys, modeling and workshops are included without explaining how their findings connect.
Failing to document evidence
Readers should be able to trace important claims, drivers and assumptions to their sources.
Ending with scenarios
The process should continue to implications, strategic options, responsibilities, experiments and indicators.
How to Evaluate the Quality of a Futures Study
A high-quality study should meet the following criteria.
Relevance
The scenarios and findings address a clearly defined decision or research problem.
Transparency
The authors explain the scope, methods, participants, evidence, assumptions and limitations.
Evidence traceability
Major claims and drivers can be traced to credible sources.
Internal coherence
Events within each scenario develop through understandable causal relationships.
Differentiation
The scenarios are sufficiently distinct to expose different decisions and vulnerabilities.
Plausibility
The scenarios are consistent with available knowledge while allowing uncertainty and structural change.
Diversity
The study considers different disciplines, stakeholders, values and forms of knowledge.
Reflexivity
Researchers examine how their language, institutional position and values influence the process.
Actionability
The study identifies implications, options, pathways or indicators that users can apply.
Revisability
The outputs can be updated when evidence or assumptions change.
Mini Futures-Study Template
| Component | Questions to answer |
|---|---|
| Topic | What system or decision is being studied? |
| Purpose | Why is the study needed, and who will use it? |
| Scope | Which geography, population and sectors are included? |
| Time horizon | What future year or period is appropriate? |
| Present system | How does the system currently work? |
| Evidence | Which primary, academic and institutional sources will be used? |
| Drivers | What forces are shaping change? |
| Signals | Which early observations may indicate emerging change? |
| Uncertainties | Which developments are important but difficult to predict? |
| Stakeholders | Who affects or is affected by the system? |
| Methods | Which combination of scanning, analysis, participation and modeling is appropriate? |
| Scenarios | What meaningfully different futures will be explored? |
| Implications | What risks, opportunities and ethical issues appear in each scenario? |
| Strategy | Which actions are robust, contingent, shaping or hedging? |
| Preferred future | What outcome is desired, by whom and according to which values? |
| Indicators | What evidence would show that conditions are changing? |
| Limitations | What could the study be missing or misrepresenting? |
Conclusion
Future studies provides a structured way to investigate uncertainty without pretending that the future is fixed or perfectly predictable. It combines evidence, analysis, imagination and participation to explore what could happen, what is likely under stated assumptions and what people may want to create.
Its value does not depend on selecting the one scenario that eventually proves correct. Its value lies in improving questions, revealing assumptions, testing decisions and connecting long-term possibilities with responsible action in the present.
References
- Bibri, S. E. (2020). A methodological framework for futures studies: Integrating normative backcasting approaches and descriptive case study design for strategic data-driven smart sustainable city planning. Energy Informatics, 3, Article 31. https://doi.org/10.1186/s42162-020-00133-5
- Börjeson, L., Höjer, M., Dreborg, K.-H., Ekvall, T., & Finnveden, G. (2006). Scenario types and techniques: Towards a user’s guide. Futures, 38(7), 723–739. https://doi.org/10.1016/j.futures.2005.12.002
- Government Office for Science. (2024). The Futures Toolkit (2nd ed.). UK Government.
- Inayatullah, S. (2008). Six pillars: Futures thinking for transforming. Foresight, 10(1), 4–21. https://doi.org/10.1108/14636680810855991
- Kristóf, T., & Nováky, E. (2023). The story of futures studies: An interdisciplinary field rooted in social sciences. Social Sciences, 12(3), Article 192. https://doi.org/10.3390/socsci12030192
- Organisation for Economic Co-operation and Development. (2025). Strategic foresight toolkit for resilient public policy: A comprehensive foresight methodology to support sustainable and future-ready public policy. OECD Publishing. https://doi.org/10.1787/bcdd9304-en
- Popper, R. (2008). How are foresight methods selected? Foresight, 10(6), 62–89. https://doi.org/10.1108/14636680810918586
- Voros, J. (2003). A generic foresight process framework. Foresight, 5(3), 10–21. https://doi.org/10.1108/14636680310698379
- World Economic Forum, & Organisation for Economic Co-operation and Development. (2025). AI in strategic foresight: Reshaping anticipatory governance. https://doi.org/10.1787/aa573076-en
