People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning.

The Late Formative period immediately precedes the emergence of Tiwanaku, one of the earliest South American states, yet it is one of the most poorly understood periods in the southern Lake Titicaca Basin (Bolivia). In this article, we refine the ceramic chronology of this period with large sets of dates from eight sites, focusing on temporal inflection points in decorated ceramic styles. These points, estimated here by Bayesian models, index specific moments of change: (1) cal AD 120 (60–170, 95% probability): the first deposition of Kalasasaya red-rimmed and zonally incised styles; (2) cal AD 240 (190–340, 95% probability): a tentative estimate of the final deposition of Kalasasaya zonally incised vessels; (3) cal AD 420 (380–470, 95% probability): the final deposition of Kalasasaya red-rimmed vessels; and (4) cal AD 590 (500–660, 95% probability): the first deposition of Tiwanaku Redwares. These four modeled boundaries anchor an updated Late Formative chronology, which includes the Initial Late Formative phase, a newly identified decorative hiatus between the Middle and Late Formative periods. The models place Qeya and transitional vessels between inflection points 3 and 4 based on regionally consistent stratigraphic sequences. This more precise chronology will enable researchers to explore the trajectories of other contemporary shifts during this crucial period in Lake Titicaca Basin's prehistory.

Faunal assemblages offer rich data for exploring domestication, subsistence, ritual practice, and political economy. Issues of equifinality, however, frequently complicate interpretations because different agents and processes may create similar archaeological signatures. Analysts are often forced to make interpretations based on qualitative observations, which can be difficult to justify or replicate. I present an alternative method for classifying Andean assemblages by using ethnographic, ethnohistoric, and archaeological data to construct a Bayesian network model. The model is assessed using specifically constructed test datasets and archaeological case studies. Bayesian models can lead to explicit and quantifiable probabilistic interpretations of faunal assemblages.

Social scientists are frequently interested in how populations evolve over time. Creating poststratification weights for surveys, for example, requires information on the weighting variables’ joint distribution in the target population. Typically, however, population data are sparsely available across time periods. Even when population data are observed, the content and structure of the data—which variables are observed and whether their marginal or joint distributions are known—differ across time, in ways that preclude straightforward interpolation. As a consequence, survey weights are often based only on the small subset of auxiliary variables whose joint population distribution is observed regularly over time, and thus fail to take full advantage of auxiliary information. To address this problem, we develop a dynamic Bayesian ecological inference model for estimating multivariate categorical distributions from sparse, irregular, and noisy data on their marginal (or partially joint) distributions. Our approach combines (1) a Dirichlet sampling model for the observed margins conditional on the unobserved cell proportions; (2) a set of equations encoding the logical relationships among different population quantities; and (3) a Dirichlet transition model for the period-specific proportions that pools information across time periods. We illustrate this method by estimating annual U.S. phone-ownership rates by race and region based on population data irregularly available between 1930 and 1960. This approach may be useful in a wide variety of contexts where scholars wish to make dynamic ecological inferences about interior cells from marginal data. A new R package estsubpop implements the method.

Most of the methods of mortality forecasting have been assessed using performance on overall mortality, and few studies address the issue of identifying the appropriate forecasting models for specific causes of deaths. This study analyses trends and forecasts mortality rates for three major causes of death — lung cancer, influenza-pneumonia-bronchitis, and motor vehicle accidents — using Lee–Carter, Booth–Maindonald–Smith, Age-Period-Cohort, and Bayesian models, to assess how far different causes of death need different forecasting methods. Using data from the Twentieth and Twenty-First Century Mortality databases for England and Wales, results show major differences among the different forecasting techniques. In particular, when linearity is the main driver of past trends, Lee–Carter-based approaches are preferred due to their straightforward assumptions and limited need for subjective judgment. When a clear cohort pattern is detectable, such as with lung cancer, the Age-Period-Cohort model shows the best outcome. When complete and reliable historical trends are available the Bayesian model does not produce better results than the other models.

A Bayesian geostatistical model was developed to predict the intensity of infection with Schistosoma mansoni in East Africa. Epidemiological data from purpose-designed and standardized surveys were available for 31458 schoolchildren (90% aged between 6 and 16 years) from 459 locations across the region and used in combination with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. The geostatistical model explicitly takes into account the highly aggregated distribution of parasite distributions by fitting a negative binomial distribution to the data and accounts for spatial correlation. Results identify the role of environmental risk factors in explaining geographical heterogeneity in infection intensity and show how these factors can be used to develop a predictive map. Such a map has important implications for schisosomiasis control programmes in the region.