초안 · 2026년 10월 31일 업데이트

Methodology

How the 250 trends were chosen, how the two weekly indices are built, why both are four-week rolling averages, and how to read change over a week, a month, a quarter or a year.

이 페이지는 영어로 제공됩니다. 번역본은 추후 제공될 예정입니다.

What the site measures

GINC Trends follows 250 trends: slow, structural forces that shape national power, prosperity and resilience. For each one the site publishes two weekly indices, kept apart:

  • The Trend Attention Index looks back. It measures how much people read and search about a trend, compared with the other trends and with its own past.
  • The Trend Expectations Index looks forward. It reads prediction markets for what traders expect to happen to the trend.

Attention is not strength. A trend can deepen for years with little notice, and one news story can lift attention for weeks. The indices say where interest and expectations are moving, not how much a trend matters.

How the list was chosen

The list is reviewed once a year and then held steady, so that trends can be followed over time. The list for 2027 was built in October 2026.

  1. Candidates. Four independent AI panels, each using a different model (Anthropic, DeepSeek, OpenAI and Mistral), proposed 300 candidate trends. The candidates were balanced across the nine domains of the GINC National Capability Framework.
  2. Cross-check. The candidates were tested against 27 trend and risk reports published in 2025 and 2026, listed below.
  3. Expert review. People rated each candidate one by one and cut the list to 275. A final pass reached 250, removing overlap, sharpening definitions and keeping only trends with a sustained, measurable signal.

Of the 250, 199 carry over from the previous list, 150 of them under new names, and 51 are new. Of the 32 trends dropped, the 25 closest to the cut are kept on a private watchlist and measured each week, so a sharp rise is noticed. A renamed trend keeps its page and address.

Each trend is also graded against the 20 GINC Scenarios, and linked to the trends most like it. The links make the trend network, whose clusters are the themes.

The Trend Attention Index

Sources.

  • Wikipedia page views in 28 languages.
  • Google search interest worldwide and in 48 countries, with Google search volumes behind the share of attention.

Each trend has its own set of search terms and pages, chosen to capture it broadly while staying specific to it. Terms driven by a single news event or by another meaning of the words are reviewed and replaced. Sources for China and Russia are being added, where Google and Wikipedia reach few people.

From sources to a score. Each source is set against the typical trend that week. This removes what moves every trend at once, such as Wikipedia’s long decline in readers. The sources are then combined into one score. An index of 100 is the typical trend, 200 is twice its attention and 50 is half.

Places. Each country is read from its own searching and the languages mostly read there. A region or bloc is the average of its member countries, each counting equally, so no group stands in for its largest member.

History. Every trend has three years of weekly history, recalculated with the current method at every run, so a trend can always be compared with its own past.

The Trend Expectations Index

The Expectations Index reads three prediction markets: Polymarket, Kalshi and Manifold, with Manifold at half weight.

  1. Mapping. Each market question is matched to the trends it might bear on. Three AI judges then work blind. A question counts towards a trend only when at least two of them agree it does, and in which direction.
  2. Reading. A trend’s expectation is the market-implied chance, from 0 to 100%, that the trend strengthens. It is a weighted average of the prices of its live questions.
  3. Coverage. Every reading is published with the number of live questions behind it. A trend with no live questions has no reading, rather than a reading of nought.

The index is a prototype.

The four-week rolling average

Both indices are published as four-week rolling averages. Each week’s figure is the average of that week and the three weeks before it. This is the index itself, not a smoothing added to the charts: every figure, ranking, chart and download on the site is worked out from it.

Why four weeks:

  • Attention moves on a cycle of weeks, not days. A major event, such as riots in Paris, a cyber attack on a hospital or a surprise election result, does not come and go in a week. It runs through the news cycle for several weeks: first reports, then analysis, follow-ups, hearings and reactions. People keep reading and searching about it for as long as it stays in their minds. A four-week window follows that whole cycle, not one slice of it.
  • Information spreads unevenly. Searching often comes first, reading follows, and markets reprice as new information arrives over days and weeks. The sources rarely move in step within a single week; over a month they tell a more consistent story.
  • A month suits slow forces. The trends change over years. A month is the shortest span over which a change in attention says something about the force, rather than about one week’s headlines.
  • The calendar adds noise of its own. Public holidays, school terms and an event that falls either side of the weekly cut-off can move a single week for reasons unconnected with the trend.
  • The latest week is a little short. We publish on the Saturday, before the week has quite ended. Averaged with the three weeks before, a short week cannot swing the figure.
  • Rankings should not reshuffle by chance. Ranked on single weeks, the list of fastest-rising trends changes largely at random from one week to the next. Ranked on four-week averages, it holds steady, while a genuine burst of attention still shows in the week it happens.

We tested longer windows too. Six or eight weeks are steadier still, but they show a burst a week or two late and blunt it.

Reading change

Every change on the site compares two four-week averages.

  • Week on week: this week’s four-week average against last week’s. It is not the latest four weeks against the four weeks before.
  • Month on month: this week’s four-week average against the one four weeks earlier.
  • Quarter on quarter: this week’s four-week average against the one 13 weeks earlier.
  • Year on year: this week’s four-week average against the one 52 weeks earlier.

For a week or a month, the level shown is this week’s four-week average. For a quarter or a year, it is the average across that period. The same rule applies to the Expectations Index, where change is shown in percentage points.

The week

Each week runs from 00:00 GMT on Sunday to 23:59 GMT on Saturday, and is named by its Saturday: “week ending 31 October”. We publish on the Saturday, so the latest week holds the data available when the run is made, usually a few hours short of the full week.

This does not make the latest week look weaker. Each trend is compared with the typical trend in the same short week, not with a full one.

Comparing over time

Figures published before 24 October 2026 used the earlier list and search terms. Compare them with later figures only for trends that continue, and with care. Figures published before 31 October 2026 were not four-week rolling averages, so week-on-week changes from before that date are not comparable with those after it.

Limitations

  • It leans to the connected. Wikipedia and Google are thin where other platforms dominate or access is restricted. China cannot yet be read, and Russia rests on Russian-language reading alone.
  • Search terms are a judgement. A term can be searched for reasons unconnected with the trend. The terms are reviewed, but not exhaustive.
  • Markets are thin on some trends. An expectation resting on two or three questions can move sharply when one of them settles.
  • Grades and estimates are provisional. The scenario grades and the estimates of likelihood and impact come from AI models working to a rubric, and will be replaced by expert judgement.

Reports consulted

These are the 27 trend and risk reports the candidate list was tested against.

  • Accenture Strategy. Macro Foresight Brief: 2026 Outlook and Top 10 Macro Trends for Companies. 2025.
  • BlackRock Investment Institute. Mega Forces. 2026.
  • Capgemini. TechnoVision: Top Tech Trends of 2026. 2026.
  • Control Risks. RiskMap 2026: Top Risks. 2025.
  • Control Risks. RiskMap 2027 (preview themes). 2026.
  • Deloitte. Tech Trends 2026. 2026.
  • The Economist. The World Ahead 2026. 2025.
  • Eurasia Group. Top Risks 2026. 2026.
  • European Commission. 2025 Strategic Foresight Report: Resilience 2.0. 2025.
  • EY. Futures Reimagined: EY Megatrends 2026 and Beyond. 2026.
  • Gartner. Top Strategic Technology Trends for 2026. 2025.
  • International Monetary Fund. World Economic Outlook, October 2026. 2026.
  • Ipsos. Ipsos Global Trends, 9th edition (2025) and 10th edition (2026).
  • McKinsey & Company. McKinsey Technology Trends Outlook 2026. 2026.
  • MIT Technology Review. 10 Breakthrough Technologies 2026. 2026.
  • Morgan Stanley Research. Key Investment Themes for 2026 and 10 Thematic Predictions. 2026.
  • Munich Security Conference. Munich Security Report 2026, with the Munich Security Index 2026. 2026.
  • OECD. OECD Economic Outlook, Volume 2026 Issue 1. 2026.
  • OECD. OECD Science, Technology and Innovation Outlook 2025. 2025.
  • Stockholm International Peace Research Institute. SIPRI Yearbook 2026. 2026.
  • Temasek. Temasek Review 2026. 2026.
  • UK Ministry of Defence, Development, Concepts and Doctrine Centre. Global Strategic Trends: Out to 2055. 2024.
  • UN Department of Economic and Social Affairs. World Economic Situation and Prospects 2026. 2026.
  • UN Economist Network. Shaping the Trends of Our Time. 2020.
  • US National Intelligence Council. Global Trends 2040: A More Contested World. 2021.
  • World Economic Forum, with Marsh McLennan and Zurich Insurance Group. Global Risks Report 2026. 2026.
  • World Economic Forum, with Frontiers. Top 10 Emerging Technologies of 2026. 2026.

Downloads

Everything on the site can be downloaded: the list, its scores against every scenario, the trend network and the weekly attention data for every place. The files are listed below.

Licence

Content and data are published under CC BY 4.0. You may share and adapt them for any purpose, with attribution. Code is published under the MIT licence. Wikipedia page view counts are released by the Wikimedia Foundation under CC0. Google Trends figures may be reused with attribution to Google Trends.

Citation

  • The list. GINC (2026). GINC Trends v2.0: 250 trends scored against 20 scenarios. Oxford: Global Institute for National Capability. trends.ginc.org
  • A trend. GINC (2026). Tariff barrier normalisation, GINC Trends v2.0. trends.ginc.org/trends/tariff-barrier-normalisation
  • Attention data. GINC (2026). Trend attention index v1.2 [dataset]. Oxford: Global Institute for National Capability. trends.ginc.org/methodology/#downloads

파일

모든 다운로드 파일과 그 내용. 주소는 릴리스가 바뀌어도 변하지 않는다.

트렌드

GINC 250 v3.0. 이 사이트는 이 파일들을 보여 주는 화면이며, 페이지의 모든 수치는 이들 파일 중 하나의 필드로 거슬러 올라간다.

파일내용형식
트렌드모든 트렌드와 그 역량 영역, 설명, 태그, 20개 시나리오 각각에 대한 점수JSON
트렌드 순위트렌드마다 한 행: 민감도와 네트워크 지표CSV
트렌드×시나리오 점수트렌드·시나리오마다 한 행, 7점 척도 기준CSV
트렌드 유사도유사도가 보통 이상인 트렌드 쌍마다 한 행CSV
트렌드 네트워크유사도 행렬, 테마, 지표와 방법JSON
발생 가능성과 영향트렌드마다 한 행: 2035년까지 실현될 가능성과 국가 역량을 얼마나 바꿀지에 대한 100점 만점의 잠정 추정치와 한 줄 근거. 두 AI 모델의 평균과 각 모델의 판단CSV
2차 검토 기록모델 패널이 목록을 어떻게 수정했는지: 패널 구성, 변경 사항, 점수에 대한 패널의 합의 정도JSON

관심도

세계 전체와 지역별로 본, 3년간 각 트렌드에 대한 주간 대중 관심도.

파일내용형식
GINC 관심도 지수, 전체 지역마스터 파일(gzip 압축). 트렌드·지역·주마다 한 행이며, 열은 trend, geo(world, 코드로 표시한 지역·하위 지역·블록, 또는 ISO 3166 alpha-3 코드로 표시한 국가), week_ending(해당 주가 끝나는 토요일), attention_index(100이 일반적인 트렌드이며, 그룹은 회원국의 평균)이다. 매주 갱신CSV, GZIP
GINC 관심도 지수, 단일 지역한 지역에 대한 같은 열: world.csv 또는 국가 코드 파일(예: USA.csv). 지수 페이지에서는 그곳에서 선택한 지역의 파일을 내려받는다CSV
트렌드 관심도 지수트렌드별 주간 점수와 설정값, 각 트렌드에 대해 집계한 Wikipedia 문서와 검색어JSON
트렌드 관심도, 주간트렌드·주마다 한 행: 점수와 두 출처의 값CSV
GINC 관심도 강도, 주간트렌드마다 한 행, 주마다 한 열: 각 트렌드의 원 관심도 점수를 기준값(최근 52주 동안 모든 트렌드 점수의 중앙값)의 배수로 나타낸 값. 1이 일반적인 트렌드CSV
GINC 관심도 지수, 주간같은 값을 지수로 나타낸 것: 강도 × 100, 즉 100이 일반적인 트렌드CSV
GINC 기간별 관심도 강도트렌드마다 한 행: 최근 1주·1개월·1분기·1년의 강도와 지수, 그리고 직전 기간 대비 지수 변화CSV
관심도 기준값, 주간주마다 한 행: 강도 측정의 기준이 되는 모든 트렌드 원점수의 이동 중앙값과, 같은 기간의 평균CSV
GINC 관심도 점유율, 주간트렌드마다 한 행, 주마다 한 열: 목록 전체가 받는 관심 가운데 각 트렌드가 차지하는 비중(%). 매주 합계는 100이다. 같은 원점수에서 산출하며, 사이트에서는 지역과 국가에 대해서만 점유율을 보여 준다CSV
GINC 지역별 관심도 점유율, 주간트렌드·지역마다 한 행: 그곳에서 주로 읽히는 언어의 독자 사이에서 차지하는 관심도 점유율(%), 주별CSV
트렌드 관심도, 요약트렌드마다 한 행: 현재 수준, 1년·3년간 변화, 최고치, 출처 간 일치 정도CSV
지역·G20 회원국별 트렌드 관심도트렌드·지역마다 한 행: 현재 수준과 90일, 180일, 1년, 2년, 3년간의 변화CSV
지역별 트렌드 관심도, 전체각 지역의 측정 기준과 모든 지역에서 모든 트렌드의 주간 지수JSON
언어별 트렌드 관심도Wikipedia 열람: 트렌드·언어마다 한 행, 주마다 한 열CSV
국가별 트렌드 관심도3년간 국가별 Google 검색 관심도: 트렌드·국가마다 한 행CSV
트렌드별 Wikipedia 열람모든 언어의 Wikipedia 지수, 언어별 독자, 집계한 문서JSON
트렌드별 Google 검색 관심도전 세계 주간 시계열과 국가 간 분포JSON
트렌드·국가별 Google 검색 관심도대상 국가 각각에서의 트렌드별 주간 시계열과 사용한 검색어JSON

트렌드 평가에 쓰인 시나리오는 그 기록, 검증, 결과와 함께 scenarios.ginc.org/data에 공개되어 있다.