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This chapter explores the potential and the problems of Mediterranean regional survey for demographic modeling, drawing on case studies from Italy, Greece, and North Africa. Specifically, it addresses concerns about recovery rates, or the percentage of settlement sites and, indirectly, population identified by surface survey. Discussion is structured into four main sections. The first reviews the general literature on recovery rates, particularly their variability, and leads to a consideration of the situation in the ancient Mediterranean. The second section presents opposing models of recovery rates in the context of the early imperial population of Italy and explores the implications for economic organization. The third section tackles the issue of the Italian population from an alternative model-building approach using the results of the Liri Valley Survey. Finally, issues of recovery rates and demographic modelling in Greek and North African contexts are used to develop a comparative understanding of population and wider economic organization across the ancient Mediterranean.

This chapter outlines a series of plausible arguments intended to demonstrate that the key to economic development is to be found in the relationship between population and resources, labour and land, people and the environment, and the economic forces that they generate. Agriculture dominated the economy of medieval England. Farming technology changed but slowly, and most farms were small and utilised little capital. Hence the relationship between the numbers of people and the amount of land available to support them was significant to the economy. It is upon such fundamentals that the model variously named as the ‘population and resources’, ‘demographic’, or ‘neo-Malthusian’ model is founded. This model puts forward explanations of the operation of the medieval economy and society, and of long-term trends in economic growth and the distribution of incomes, which have at their core a set of simple economic relationships between land and labour.

The explosion of archaeological field survey projects in the late 20th century has contributed to a general sense that rural settlement expanded considerably in many areas of the Roman Empire, but has not yet had much impact on overall demographic models and estimates for Roman population of provinces or the empire as a whole. Chapter 5 raises many points of fundamental importance for the debate about how best to mobilize the archaeological data. This chapter responds to Chapter 5 and focuses on potential problems of interpretation and methodology: issues relating to modelling settlement from archaeological site ‘numbers’, to the application of random sampling methods in Mediterranean survey, to the recoverability of rare upper-echelon and potentially abundant but elusive lower-order settlements, to the use of interpretative labels for ploughzone sites, to distinguishing evidence of absence from absence of evidence.

This chapter examines Jack Goldstone's demographic-structural theory of state breakdown. It first considers the role of excessive population dynamics in state collapse before introducing a simple mathematical model of the interaction between population dynamics and the fiscal health of the state. This demographic-fiscal model predicts recurrent episodes of state building and population growth followed by state breakdown and population decline. Class structure is then added to the basic model to account for the dynamics of the state, elites, and commoners. The chapter also discusses empirical applications of the demographic-structural theory by focusing on the English Revolution of the seventeenth century and reviews David Hackett Fischer's theory and data dealing with four “great waves” of socioeconomic dynamics in western Europe from the twelfth to the twentieth century. Finally, it analyzes Stuart Borsch's study of Egypt after the Black Death and after the Antonine plagues.

This chapter introduces Kingman’s coalescent process, which describes the genealogical relationships within a sample of DNA sequences taken from a population, and forms the basis for likelihood-based inference methods using such data. The simple case of Bayesian estimation of the population size parameter theta using a DNA sample is discussed to illustrate the basic features of Bayesian Markov chain Monte Carlo (MCMC) inference algorithms. The chapter then discusses the use of parametric and nonparametric demographic models of population size change to infer the past demographic history of a species. The multispecies coalescent model, which extends the single-population coalescent to multiple populations, is introduced with examples. This is then used as the general framework for estimating parameters such as species divergence times and ancestral population sizes, for inferring the species tree from multiple genetic loci despite the existence of conflicting gene trees, for estimating migration rates between populations, and for delimiting species using multi-locus DNA sequence data.

The key to wise decision-making in disciplines such as conservation, wildlife management, and epidemiology is the ability to predict consequences of management actions on focal systems. Predicted consequences are evaluated relative to programme objectives in order to select the favoured action. Predictions are typically based on mathematical models developed to represent hypotheses about management effects on system dynamics. For populations ranging from large mammals to plant communities to bacterial pathogens, demographic modelling is often the approach favoured for model development. State variables of such models may be population abundance, density, occupancy, or species richness, with corresponding vital rates such as rates of reproduction, survival, local extinction, and local colonisation. A key source of uncertainty that characterises such modelling efforts is the nature of relationships between management actions and vital rates. Adaptive management is a form of structured decision-making developed for decision problems that are recurrent and characterised by such structural uncertainty. One approach to incorporating this uncertainty is to base decisions on multiple models, each of which makes different predictions according to its underlying hypothesis. An information state of model weights carries information about the relative predictive abilities of the models. Monitoring of system state variables provides information about system responses, and comparison of these responses with model-based predictions provides a basis for updating the information state. Decisions emphasise the better-predicting model(s), leading to better decisions as the process proceeds. Adaptive management can thus produce optimal decisions now, while simultaneously reducing uncertainty for even better management in the future.

This chapter exploits the previous chapter's demographic model to make impact assessment of fishing and calculate fisheries reference points for fish stocks with asymptotic sizes of 10 g, 333 g, and 10 kg. The three asymptotic sizes span the variation in fish life histories from small and short-lived forage fish species, such as sardine or sprat; to small pelagic fish, such as herring or mackerel; to large demersal species, such as cod or saithe. When fishing is added to the demographic model, the model has to be solved numerically. To complement the numerical results, the chapter first develops a very simplified analytical model. It then goes on to formulate a complete theoretical framework that can be applied to make ecological impact assessments of fishing a single stock.

This introductory chapter outlines the book’s theme and the main questions that will be addressed. It discusses the merits and drawbacks of the various theoretical approaches that have been applied to the analysis of land and natural resources in the Roman economy until now—market-models, substantivist analysis, and population-resources modelling. It concludes with an argument in favour of a New Institutional Economics (NIE) approach.

This chapter shows that Japanese government demographers failed in their 1991–92 population projection due to a fundamental flaw in their methodology. In fact, if the same methodology is applied to the 1997 projection, there is hardly any need to change their 1991–92 projection. Thus, their methodologically “correct” projection will continue to diverge from reality for another five years, when they are scheduled for another projection. The chapter is organized as follows. Section 12.2 describes the formal demographic model used in the 1991–92 projection and presents the author's own estimates using new data made available since 1992. Section 12.3 formulates a marriage/birth model and explores the possibility of misspecification as a source of the government model's sensitivity to truncation. Section 12.4 formulates the age distribution of marital fertility rates and reports estimation results. Section 12.5 looks at significant changes in the marital behavior of Japanese women that took place in the last twenty years. Section 12.6 analyzes the causes of the decline in the fertility rates observed among three different cohorts almost five years apart, while Section 12.7 provides concluding remarks.