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How It Works

Methodology, data sources, and scientific foundation

This project does not predict the future. It uses official demographic life tables to approximate how many more times you might see someone important in person, if current conditions persist. It's a statistical mirror, not a sentence.

1. What we calculate

Expected in-person visits remaining between two people.

Based on the probability that both are alive in each future year, multiplied by the visit frequency you currently maintain.

2. Where the data comes from

UN World Population Prospects 2024 (WPP-2024)

Primary data source

What it provides:

  • Life tables by age, country, and sex
  • Residual life expectancy (ex)
  • Year-by-year survival probability
  • Coverage: 237 countries and areas

Organization: United Nations, Population Division

Update: 2024 (most recent revision)

View official source

Validation sources:

WHO Global Health Observatory - Cross-validation of mortality data
www.who.int/data/gho
Human Mortality Database - Additional precision for countries with good statistics
www.mortality.org

3. What assumptions we make

  • Visit frequency remains stable

    If you currently see the person 12 times a year, we assume that will continue. In reality it may increase or decrease.

  • We use population averages

    Life tables represent the average experience of a population, they don't consider individual health.

  • We don't incorporate individual factors

    We don't know specific illnesses, lifestyle habits, or particular conditions.

4. What this tool does NOT do

  • It does not predict when anyone will die
  • It should not be used for medical decisions
  • It does not replace personal judgment or professional advice
  • It does not incorporate unforeseen events (accidents, pandemics, etc.)

5. Why we believe showing this number is useful

Psychological foundation

Socioemotional Selectivity Theory (Carstensen, 1999, 2021) demonstrates that when people perceive their future time as limited, they prioritize close relationships and emotional goals over exploration or information accumulation.

People tend to underestimate how finite their time with others is. A concrete number helps make more conscious decisions about priorities.

Scientific reference:

Carstensen, L. L. (2021). Socioemotional Selectivity Theory: The Role of Perceived Endings in Human Motivation. The Gerontologist, 61(8), 1188–1196.

Read article (open access)

Theoretical context: Time use throughout life

Data from the American Time Use Survey (Bureau of Labor Statistics, USA) and analysis from Our World in Data show that:

  • Most time with parents is concentrated before age 20
  • After that age, frequency of encounters drops dramatically
  • The pattern is similar for grandparents, with even earlier concentration

This means that most of the time has already been spent, even when both are still alive.

Why don't we show this data? We decided not to include the "time already spent" percentage in the calculator because the goal of this tool is to motivate action toward the future, not generate guilt about the past. Showing that "you've already spent 85% of your time with your mother" can provoke hopelessness or resignation, contradicting the tool's purpose. Additionally, this data comes from American surveys that may not reflect the reality of other cultures where family coexistence patterns differ.

View time use data

Mathematical model

Notation (per Preston et al., 2001):

  • lx = number of survivors at exact age x in a hypothetical cohort of l0 = 100,000
  • ex = residual life expectancy at age x (expected remaining years of life)
  • f = visit frequency per year (assumed constant)
  • T = time horizon of the calculation

Step-by-step calculation

1. Survival function

The probability that a person of age a survives an additional t years is calculated using the life table survival function:

tpa = la+t / la

This is the standard conditional survival formula (Preston et al., Ch. 3).

2. Time horizon (T)

We determine the calculation horizon up to the maximum age in life tables (100 years), which allows us to capture all possible scenarios:

T = min(100 - a₁, 100 - a₂)

Where a₁ is your age and a₂ is the other person's age. This ensures we don't underestimate visits from people who live beyond their life expectancy.

3. Joint survival probability

Assuming independence between the deaths of both persons, the probability that both are alive in year t is:

P(both alive in t) = tpa₁ × tpa₂

This independence assumption is standard in formal demography.

4. Expected visits (main result)

The expected number of visits is the weighted sum of frequency by joint survival probability:

E[visits] = Σt=0T f × tpa₁ × tpa₂

Each year contributes proportionally to the probability that both are still alive.

5. Monte Carlo simulation

We run 10,000 simulations to obtain statistically valid confidence intervals. In each simulation, we sample the death year for each person using the qx probabilities:

Percentiles p25, p50, p75 from 10,000 simulations

This provides real confidence intervals based on the empirical distribution of possible outcomes.

Methodological reference: Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell Publishers. Chapters 2-3.

Complete references

Demographic data

  1. United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024. https://population.un.org/wpp/
  2. World Health Organization (2024). Global Health Observatory. https://www.who.int/data/gho
  3. Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). www.mortality.org

Time use

  1. Roser, M., Ritchie, H., & Spooner, F. (2023). Time Use. Our World in Data. https://ourworldindata.org/time-use
  2. U.S. Bureau of Labor Statistics (2024). American Time Use Survey. https://www.bls.gov/tus/

Theoretical framework

  1. Carstensen, L. L. (2021). Socioemotional Selectivity Theory: The Role of Perceived Endings in Human Motivation. The Gerontologist, 61(8), 1188–1196. https://doi.org/10.1093/geront/gnab116
  2. Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist, 54(3), 165–181.