https://centre.santafe.edu/complextime/w/api.php?action=feedcontributions&user=10.10.5.171&feedformat=atomComplex Time - User contributions [en]2024-03-28T10:11:17ZUser contributionsMediaWiki 1.35.6https://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Household_decisions_and_their_consequences_-_fundamentals_of_the_demographic_transition&diff=2608Population and the Environment: Analytical Demography and Applied Population Ethics/Household decisions and their consequences - fundamentals of the demographic transition2018-10-14T18:23:18Z<p>10.10.5.171: </p>
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<div>{{Agenda item<br />
|Start time=October 13, 2018 11:15:00 AM<br />
|End time=October 13, 2018 11:30:00 AM<br />
|Presenter=MaryShenk<br />
|Pre-meeting notes=In this lecture/discussion we will define and discuss four primary causal models for the demographic transition and especially the remarkable changes in fertility that have accompanied it, aiming to understand how the transition to industrialization and associated changes in economic systems, technology, culture, and the marriage market have motivated people around the world to reduce their fertility. We will discuss research comparing causal models, examining both the contrasts and synergies between them. We will also briefly discuss the remaining gaps in our knowledge of the demographic transition and fertility decision-making. <br />
|Post-meeting notes=This has been a very productive meeting for me. At first I thought "I don't do environmental work, so what do I have to contribute to this course?" But I was interested in the topic so I decided to participate, and it turns out that there are many interesting intersections between my work and that of other participants who are more directly focused on the environment. I have also found an environmental perspective embedded in my own work that I have been able to make more explicit as part of my presentation for this workshop. In terms of professional outcomes, I have already developed one new potential collaboration relevant to human population and demographic transitions in the past and an idea for a future workshop.<br />
|Reference material notes=Gurven & Kaplan 2007 discuss longevity among hunter-gatherers, giving us a framework for understanding what human demography may have looked like in our evolutionary past.<br />
<br />
Lam 2011 gives important context for concerns about overpopulation in the past, and how many of these concerns were not realized though some were. <br />
<br />
Page et al. 2016 gives an empirical test in the modern world of the mechanism by which the Neolithic Demographic Transition may have occurred thousands of years ago.<br />
<br />
Shenk et al. 2013 gives a brief review of different causal models of the demographic transition and a comparison among them using model selection methods on detailed data.<br />
}}</div>10.10.5.171https://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Household_decisions_and_their_consequences_-_fundamentals_of_the_demographic_transition&diff=2607Population and the Environment: Analytical Demography and Applied Population Ethics/Household decisions and their consequences - fundamentals of the demographic transition2018-10-14T18:19:04Z<p>10.10.5.171: </p>
<hr />
<div>{{Agenda item<br />
|Start time=October 13, 2018 11:15:00 AM<br />
|End time=October 13, 2018 11:30:00 AM<br />
|Presenter=MaryShenk<br />
|Description=This is a Mike Price test<br />
|Pre-meeting notes=In this lecture/discussion we will define and discuss four primary causal models for the demographic transition and especially the remarkable changes in fertility that have accompanied it, aiming to understand how the transition to industrialization and associated changes in economic systems, technology, culture, and the marriage market have motivated people around the world to reduce their fertility. We will discuss research comparing causal models, examining both the contrasts and synergies between them. We will also briefly discuss the remaining gaps in our knowledge of the demographic transition and fertility decision-making. <br />
|Post-meeting notes=This has been a very productive meeting for me. At first I thought "I don't do environmental work, so what do I have to contribute to this course?" But I was interested in the topic so I decided to participate, and it turns out that there are many interesting intersections between my work and that of other participants who are more directly focused on the environment. I have also found an environmental perspective embedded in my own work that I have been able to make more explicit as part of my presentation for this workshop. In terms of professional outcomes, I have already developed one new potential collaboration relevant to human population and demographic transitions in the past and an idea for a future workshop.<br />
|Reference material notes=Gurven & Kaplan 2007 discuss longevity among hunter-gatherers, giving us a framework for understanding what human demography may have looked like in our evolutionary past.<br />
<br />
Lam 2011 gives important context for concerns about overpopulation in the past, and how many of these concerns were not realized though some were. <br />
<br />
Page et al. 2016 gives an empirical test in the modern world of the mechanism by which the Neolithic Demographic Transition may have occurred thousands of years ago.<br />
<br />
Shenk et al. 2013 gives a brief review of different causal models of the demographic transition and a comparison among them using model selection methods on detailed data.<br />
}}</div>10.10.5.171https://centre.santafe.edu/complextime/w/index.php?title=Population_and_the_Environment:_Analytical_Demography_and_Applied_Population_Ethics/Modeling_complex_populations_-_statistical_inference_from_demographic_data&diff=2602Population and the Environment: Analytical Demography and Applied Population Ethics/Modeling complex populations - statistical inference from demographic data2018-10-14T18:14:21Z<p>10.10.5.171: </p>
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<div>{{Agenda item<br />
|Start time=October 13, 2018 02:15:00 PM<br />
|End time=October 13, 2018 02:30:00 PM<br />
|Presenter=PaulHooper<br />
|Description=Test2<br />
|Pre-meeting notes=Summary statistics such as the mortality rate, birth rate, or Total Fertility Rate can be useful for understanding some basic characteristics of a population. Often, however, we’re interested in having a more detailed understanding of how demographic events—fertility, mortality, or morbidity—relate to individual characteristics or environmental conditions. One may want to ask questions such as: Does education predict fertility, controlling for wealth? Does infant mortality vary with proximity to clean water, accounting for household-level differences? Does the interval between births depend on a parent’s age, economic strategy, or social network? This session will introduce a number of statistical models that are useful for answering these kinds of questions. We will discuss generalized regression models (customizable to model yes/no outcomes, count data, or continuous variables) as well as survival analysis (also called event history or duration analysis). These models allow us to estimate demographic rates as a function of multiple predictor variables, control for confounding variables, and take into account individual- or group-level heterogeneity. <br />
|Post-meeting notes=It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here.<br />
|Reference material notes=The optional reading Kaplan, Hooper, Stieglitz & Gurven (2015) [[The Causal Relationship between Fertility and Infant Mortality: Prospective analyses of a population in transition]] provides worked examples of analyzing fertility data (using Cox proportional hazards to model time to next birth) and infant mortality data (using logistic regression).<br />
}}</div>10.10.5.171