Monthly Archives: April 2023

Adapting Cohort-Component Methods to a Microsimulation: A case study

A graph showing the close match between the UN’s estimated (or, for years after 2020, projected) values for Norway’s population size, number of births, and number of deaths and the values for the same demographic variables as projected by the microsimulation we built.

Accurate projections of population growth or decline are incredibly important for policymakers to plan for the future, making decisions about likely education, healthcare, infrastructure, and environmental needs. At present, most demographic projections such as those produced by the United Nations, rely on the cohort-component method (CCM). CCM is based on a deceptively simple equation specifying that the population at time t + 1 is equal to the population at time t, minus deaths, plus births, plus net migration (i.e. immigration minus emigration). This simple equation grows more complex but also more accurate when the population is split into cohorts, usually of 5 year periods, because then one needs to determine the probabilities of dying, giving birth, or immigrating to a new country for each 5-year cohort. The complexity increases exponentially as one tracks additional demographic factors beyond age and sex (e.g. religious or political affiliation). This paper reports on a microsimulation we created to replicate the United Nation’s CCM projections for the country of Norway. Though they require more raw computing power to run, microsimulations permit greater implementation flexibility and they also force one to specify assumptions that are often only half-conscious, yet have profound consequences on final results. As the graph above shows, our final microsimulation matched the UN CCM’s estimates and projections quite precisely, but this result required the painstaking work of surfacing these tacit assumptions and then determining how to best implement them in the microsimulation.

Here’s a link to the full article; the abstract follows below.

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Scholarly Values, Methods, and Evidence in the Academic Study of Religion

A bar graph showing the relative importance of methodological naturalism and methodological secularism (MNMS) as scholarly values among members in several academic societies dedicated to the study of religion. Scholars associated with the North American Society for the Study of Religion (NAASR) were the most naturalistic and secular in method, while scholars associated with a variety of theological organizations tended to oppose naturalism and secularism as methodological starting points.

Researchers and scholars are typically and rightly identified with the methods they employ: anthropologists with their immersive field observations, archaeologists with their digging tools and dating methods, astronomers with their telescopes, and nuclear physicists with their atom-smashing, matter-creating particle accelerators. Less obvious but arguably as important to each field of research are deeply ingrained values and norms that govern and guide research, often making possible otherwise unlikely forms of cooperation that are essential to fruitful and progressive research. This article provides an analysis of survey data we collected that explores the methods and values that guide research in academic societies dedicated to the study of human religion. While there was considerable convergence across academic societies regarding some values, there were stark differences with respect to whether methodological naturalism and methodological secularism are regarded as important scholarly values.

For other insights that can be gleaned from this survey data about the methods and values that guide the academic study of religion, check out the full article. The abstract follows below.

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The Academic Study of Religion in Bibliometric Perspective

A co-authorship network showing ten of the largest groups of co-authoring scholars in the academic study of religion.

By nature and by training, scholars tend to be specialists, and this is as true in the academic study of religion as in other fields. Scholars focus on particular religious traditions, in particular geographical and cultural contexts, during particular time periods. And they study these particular religious phenomena using diverse methods, including but far from limited to: textual translation and exegesis, philosophical analysis and argumentation, ethnographic and anthropological observation, sociological data collection and analysis, psychological experimentation, and neuroscientific imaging and analysis. Given this specialization, scholars of religion are often only deeply familiar with a few small niches within the broad and extremely diverse academic study of religion as a whole. However, using modern computing power and the tools of data science, it’s possible to map an entire academic field. This paper provides a bibliometric analysis – i.e. the quantitative analysis of publications – of the academic study of religion, including the relatively recent explosion of publications in the scientific study of religion. Using co-authorship and citations networks, we were able to demonstrate something we already suspected: that there is little cross-pollination occurring between the more traditional humanities and social sciences branches of the study of religion and this newer scientific branch. Such field-mapping exercises are important not only for helping scholars of religion appreciate the breadth and diversity of research about religion, but they can also provide critical insights about where the field is growing and shrinking so that institutions – from religion departments to private funders – can plan accordingly.

Here’s a link to the full article; the abstract follows below:

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A Neurocomputational Theory of Nightmares: The Role of Formal Properties of Nightmare Images

A diagram of the Disturbed Dreaming Model, showing different possible pathways taking during a night of sleep. Check out the full article for a detailed explanation.

This is an article I published in 2021 (I’m a just a liiittle behind with updates on WW.com, but I’ll be making an effort to catch up in the coming weeks!) with neuroscientist Patrick McNamara and two colleagues from The Center for Mind and Culture, George Hodulik and David Rohr. The article presents a computational simulation of a prior conceptual model of disturbed dreaming published in 2007 by Levin and Nielsen. This publication builds on a prior pilot study using ReScript virtual reality technology to help people suffering from frequent nightmares to gain a sense of control over the frightening images that populate their nightmares. And it leads into the work we’re currently doing examining nightmare disorder among people who are 65 years and older. Eventually, we hope to use ReScript technology to help these elderly nightmare sufferers. Since older people often don’t have the visualization capacities needed to benefit from imagery rehearsal therapy – at present, the most effective non-pharmaceutical treatment for nightmare disorder – ReScript might prove an especially promising therapy for this underserved demographic.

Here’s a link to the full article; the abstract is included below. Check it out and leave a comment if you’re curious about the project!

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