ARDEMIS visualizes the impact of assumptions on religious demographic projections. Traditional methods used do not support sensitivity analyses or estimates of the effect of measurement assumptions on projections. Computer simulation is more flexible and can help estimate the impact of assumptions.
Option toggle descriptions
Agent Count: This is how many artificial agents are present in the simulation model at the earliest modeled time point. Traditional demographic projections apply fertility, mortality, and migration rates to aggregates of the population. Simulations apply the rates as probabilities to individual agents. Starting simulations with fewer agents can introduce rounding errors (agents can’t have half a baby!), so it is necessary to maximize the agent count for demographic simulations. Learn more about these options here.
Model Step: This indicates whether the simulation model operates in one-year increments or five-year increments. Most traditional demographic projections operate in five-year steps, but simulations are often designed in one-year steps to improve their realism. Converting between time steps requires a careful re-designing of the simulation to avoid introducing error. Learn more about these options here.
Layout: This specifies whether the simulation is more “top-down” or “bottom-up” in its design. Top-down simulation models are deterministic. This means the exact number of agents experiencing an event (e.g. giving birth, migrating, or dying) is calculated at each time step based on how many agents are at risk of experiencing the event at that moment. A bottom-up simulation model, however, introduces more chance into the world. For each event, individual agents have a given probability of experiencing it, and each model will have slightly more or fewer agents experience the event. This approach is typical in simulations, and you can see a cone of uncertainty appear when the option is selected. Learn more about these options here.
Split/Intuitive: This indicates whether demographic events in the simulation model operate in an order that best mimics traditional demographic projection methods (called “Split Fertility”), or in a more intuitive order. The “Split Fertility” option sets the order of events as: round one fertility, mortality, aging, migration, round two fertility. The intuitive order is: fertility, mortality, migration, aging. Learn more about these options here.
In order to build realistic models, our data team has collected and collated data from each of the countries we are studying. Change in population size is determined by three factors: how many babies are born (fertility), how many people die (mortality), and how many people enter or exit that geographic region (migration). This data is available from the United Nations, Pew Research Center, and the World Religion Database. We also rely on our dimensions of religiosity to harmonize measures from different cultures and time periods. By including multiple dimensions of religiosity in our databases, we improve our ability to identify varying levels of religiosity, even among the non-religious
UN: These data are from the 2019 World Projection Report (WPP) released by the United Nations. The UN estimates past total population, births, deaths, and migration from survey and government sources, and uses demographic methods to project the population into the year 2100 in all countries. The UN revises its estimates and projections every few years and creates multiple scenarios with different assumptions about how births, deaths, and migration will change in the future. The “medium variant” scenario shown in ARDEMIS is among the most popular. The UN does not project religious affiliations. Learn more about the UN WPP here. CORE ASSUMPTIONS: birth rates will converge to replacement levels (2.1 births per woman), life expectancy will increase, and migration will continue at the same count as last observed in each country.
WRD: The World Religion Database includes estimates of past and future affiliation totals for # world religions for as early as 1900 in some countries through 2100 in all countries. It combines census, survey, and ethnographic sources, as well as informants from the religious communities themselves, with the medium variant of the UN’s 2019 WPP to estimate religion-specific growth rates in every country. The WRD estimates use qualitative methods to adjust quantitative data sources, which makes them both unique and difficult to replicate. Learn more about WRD here.
CORE ASSUMPTIONS: birth rates will converge to replacement levels, life expectancy will increase, and migration will continue at the same count estimated in 2019.
Pew: Pew Research Center’s World Religions Report estimates affiliation totals for seven religion groups across all countries in 2010 and uses traditional demographic methods to project the religious populations forward through 2050. To estimate the 2010 population by affiliation, age, and sex, Pew compiled over 2,500 survey and census data sources. For the projections, Pew used mortality rates from the UN’s 2010 WPP Projection Report and used survey/census data to adjust the UN’s fertility rates into affiliation-specific fertility rates. Pew calculates migration rates by affiliation within a global system, using the religious composition of sending-countries. Pew calculates rates of switching between affiliations for people aged 15-30 using survey data about the religion they grew up in. Switching rates were only available for 70 countries, so many countries (like India) do not include switching. Learn more about Pew’s World Religions Projection Report here, explore Pew’s more recent projections for the United States here, and learn more about the global religion migration database here. CORE ASSUMPTIONS: birth rates across religions will slowly converge to the UN’s overall fertility rate (which is itself converging to 2.1 births per woman), life expectancy will increase, and both migration and switching rates will continue at the level estimated for 2010.
CMAC: The Center for Mind and Culture models combine traditional demographic methods with simulation approaches to projection. Unlike traditional demographic projections, which aggregate the population according to their characteristics (e.g. age, sex, religion), simulation approaches operate at the individual level. For the Population dimension, CMAC adapts the United Nations’ projections to a microsimulation. For the Affiliation Counts dimension, CMAC adapts Pew Research Center’s religion projections to a microsimulation. The next step is CMAC models that include individual-level interactions, which allow for individual agents to inherit and switch religions over their lifetime. Learn more about CMAC’s models here.
DIM-R: The Dataset of Integrated Measures of Religiosity harmonizes similar religion measures that are available in four large cross-national surveys: the European Social Survey, the International Social Survey Programme, the World Values Survey, and the European Values Survey. Measures on religious service attendance, prayer, and self-described religiosity are available as early as 1980 in some countries. DIM-R was developed by researchers at the University of Bialystok in Poland, led by Konrad Talmont-Kaminski, Lukasz Kiszkiel, and Piotr Laskowski.
RICH: The Religious Identity and CHange datasets harmonize similar religion measures available in the United States, Norway, and India from survey and other sources. Measures on public participation in religion, private practices, the personal importance of religion, and supernatural worldviews are included in the harmonization effort. The RICH datasets include meta-characteristics of each measure, so that you can explore how differences between data sources can affect estimates.