Journey of your life: Demography for the demos

Nov 5, 2014 | Demography

By Samir K.C., IIASA World Population Program

How old are you? This is the most basic demographic question about an individual, and an easy one to answer. What is the population of the world or your country? Well, many who read the news roughly know the number, about seven billion for the world and more than a billion in China and India. But when asked more detailed questions about demography, “What percentage of people are younger than you in the world or your country?” or “What’s the remaining life expectancy for you in your country and the world?” the eyes start rolling. Such questions are important because they lead to better knowledge and awareness about the population, especially the question of life expectancy.

(Photo: UN Photo/Sebastiao Barbosa)

(Photo: UN Photo/Sebastiao Barbosa)

This is why I, with my colleagues Wolfgang Fengler (World Bank), Benedikt Gross (data visualization designer), and many others, have developed a website where people can find out their respective place in the world population or the country population: population.io.  The website was launched last Saturday at the TEDxVienna.

How long will we live? Most of us in the general public do not know the answer.  But demographers and actuaries can actually project the expected date of death for populations, based on factors such as place of residence, age, and sex. Demographers use data on deaths occurring during a period and the population structure to estimate death rates. These death rates are then included in the life table calculations that show, among other details, expected number of years of remaining life given one’s place of residence, age, and sex.

On population.io, you can find your own expected death date, based on population projections and details such as where you were born, where you live, and your sex. Of course, this date is just an average with a distribution. If the remaining life expectancy for a 40-year-old is 30 more years,  that does not mean that all today’s 40-year-olds will die in 2044: roughly half will die earlier and half later. But we hope that exploring this tool will give people some insight into the world and their country’s population and their place within it.

How do we know how long you will live?
To answer this question, we use population projections. To make good population projections, demographers need information about the demographic structure, including current age and sex structure and assumptions about the future scenarios of mortality,  fertility, and migration. A “cohort component” method is then applied to calculate the future population size and structure and to obtain number of births, deaths, and migration. This method projects each cohort born in the same one- or five-year period forward in time, to replace the older cohort occupying the age. In the process some die or migrate out (population decreases) and some migrate in (population increases), while women in reproductive age groups might give birth to children, who will then enter the population as a new cohort. All of these numbers and assumptions are needed for many purposes within and outside the discipline of population studies including for a proper answer to our question, “How long will I live?”

Here’s how the calculations behind population.io work. As an example, I’ll take myself: For a male of my age,  40 years old, on average according to the current global mortality rates, my remaining life expectancy would be about 37 years. This is bit scary for me – that means as an average “global citizen, I would die at age 77. In Nepal, where I am from, my life expectancy would be a little more than one year less. However, since I will most likely live in Austria, my remaining life expectancy increases to 43 years, an increase of 7.4 years due to migration.

nepalvsaustria

On population.io, you can explore–among lots of other population data–how living in a different country would affect your life expectancy. Click to try it yourself!

Now, if I add that I belong to the highest category in terms of education, what will happen to my life expectancy? Though education is not yet included in the population.io, it turns out that that also depends to a large degree on where I live. In Portugal or Italy, a person with a university degree would have lesser advantage compared to those with lower secondary education or below (2.5 and 2.6 years more respectively) than someone living in Estonia (13.8 years more) or the Czech Republic (12.5 years), Hungary and Bulgaria (12.1 years).

What if I am a smoker? Do not exercise? These factors too play an important role in future life expectancy, and we plan to add them soon to the population.io Web site.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Modeling terrorism

Jan 7, 2014 | IIASA Network, Systems Analysis

On October 15, 2012, a young man from Bangladesh named Quazi Mohammad Rezwanul Ahsan Nafis parked next  to New York Federal Reserve Bank in a van with what he believed was a 1000-pound bomb, walked a few blocks away, and then attempted to detonate the bomb by mobile phone.

In fact, the bomb was a fake, supplied by undercover agents for the United States FBI. The agents, posing as radical jihadists, had led Nafis along for months, allowing him to believe they were fellow terrorists and gathering information about his plot. The cover was maintained until the moment when his bomb failed to detonate, and Nafis was arrested. Disaster averted.

Researchers at IIASA study many risks to society, from floods, hurricanes, and natural disasters, to the impacts of climate change on future generations. They use models that can help disentangle the costs and benefits of different policies that could help prevent damage or deaths, or mitigate the impacts of global problems like climate change and air pollution. Could the same techniques apply to the dangers of terrorism and jihadists attacks? Could systems analysis help inform intelligence agencies in order to stop more terrorist attacks?

Boston Bombing

Could systems analysis techniques help guide policies to prevent terrorist attacks? Image Credit: Vjeran Pavic

Yale University Professor Ed Kaplan has done just that in work that he presented at IIASA in late December 2013. His research, which has intersected with IIASA in the past through collaborations with former IIASA Directors Howard Raiffa and Detlof von Winterfeldt, uses operations research to find ways to improve intelligence operations so as to catch more terrorists, before an attack can take place.

Kaplan, an expert on counterterrorism research, refined a simple economic model of customer service, known as a “queuing model” to instead represent the evolution of terror plots by terrorists, and interaction between the terrorists and the undercover agents who are working to uncover those plots.

“The best way to stop an attack is to know it’s about to happen beforehand,” says Kaplan. That means, in large part, having enough agents in the right places to detect attacks. But how many agents is the right number?

At IIASA, Kaplan described his terrorist “queuing model,” which can be applied to show how much a given number of agents would be likely to decrease attacks. Queuing models are an operations research method used to understand waiting times in lines, such as what happens at restaurants, offices, telephone queues or even internet servers.

But in the standard model, customers want to be served, and the servers know who the customers are.  In Kaplan’s terrorist model, the terrorists – customers –don’t want to be served, and the servers—the agents—don’t know where their customers are. By modifying the model to account for those differences, Kaplan can answer some tricky questions about the best way for intelligence agencies to fight terrorism.

“Even if you don’t know how many terrorists there are or where they are, you can make it more likely that they will show themselves, you can make it more difficult for them to carry out an attack,” says Kaplan.

Kaplan's method provides estimates of the numbers of undetected terrorist plots, as well as what it would take to increase detection rates.

Kaplan’s method provides estimates of the numbers of undetected terrorist plots, as well as what it would take to increase detection rates.

Using data from court records of terrorism cases, Kaplan refined his models to include the average time that a terror plot is active – that is, the time from when a terrorist group first starts a plot, to the time that they are either caught, or the attack takes place. Based on the data, he could then calculate how many terror plots were likely to be in progress at any one time. He could also estimate the probability of detecting those plots, and how much that probability could be increased by employing more agents. For example, the model calculates that by increasing FBI agents by a factor of two would increase the detection rate from 80% to 89%.

But the data also point to one disturbing conclusion: A 100% detection rate is impossible. As the number of agents increases, the detection rate increases in ever smaller increments. Kaplan says, “We have to decide how safe is safe enough. When should we stop putting money into Homeland Security, and start putting more back into education and health?”

Download Kaplan’s IIASA presentation (PDF, 2.8 KB)

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.