The Predictive Anthropology Platform provides insights into the future of a specific trend or conversational theme by forecasting its maturity and population growth.
Our forecasting process involves two main steps:
- The AI anthropologist analyzes the language context related to the trend.
- It tracks how this context has evolved historically and
By looking at the pattern of change around a topic, the platform determines whether the meaning around the trend is converging (indicating consensus), staying static, or diverging (suggesting cultural volatility, where consensus breaks down).
In other words, we examine whether the language surrounding the topic shifts from diverse meanings to unified ones, ultimately assessing whether this shift follows a consistent pattern over time.
To accomplish this, our algorithm analyzes the evolution of topics within the topic universe by observing new topics entering the contextual universe and existing topics leaving it.
The AI anthropologist’s objective is to ascertain whether, over time, the topics entering the universe contribute to a reduction in the overall diversity of the culture. If this ongoing consolidation of meaning is consistent, it signals that the topic or trend is maturing.
However, if a consistent reduction is not observed, it implies there is likely volatility within the context. In such cases, we do not run a prediction.
Timeframe
The AI anthropologist conducts its analysis by leveraging a 1-year block of historical contextual data on a topic or trend at a time. This involves running thousands of regressions on the movement of thousands of topics.
Through this comprehensive approach, we can track the trajectory of topics and assess whether they are consistently converging over time. Such convergence signifies a reduction in diversity and a reliable evolution of the trend.
Predicted Time Frame
Our growth predictions provide anticipated growth rates and timelines, with timelines specified as occurring within 1-5+ years.
Two key factors contribute to the variations in time frames between different cultures.
Simulated Scenarios
Firstly, our predictions result from regression analysis, wherein we simulate multiple future development scenarios for the culture, across different time frames.
We then present the scenario that returned the highest predicted accuracy.
It’s worth noting that we strive for a minimum accuracy rate of 80.4% on our predictions!
Pace of Change & Velocity of Change
Secondly, the pace of cultural change influences the expected time frame for growth.
In mathematical terms, a higher velocity of change corresponds to a more immediate predicted time frame.
And a slower velocity of change leads to a more gradual predicted time frame.
In cultural terms, a high velocity of change indicates rapid solidification of ideas within the culture, while a low velocity of change suggests a slower spread of these ideas and meanings.
Each prediction comes with a few important metrics.
- The predicted future position of the maturity curve.
- The future predicted population size.
- The anticipated time frame.
- The level of volatility exhibited by the topic.
- If the topic is highly volatile, then the system may not deliver a prediction. You will see this from time to time.
- If the topic is somewhat volatile, you will still see a prediction, but there will be a warning that volatility has dropped the prediction below our standard threshold for accuracy.