This chapter explores the diverse ways in which coins serve as ‘monuments in miniature’, commemorating a wide variety of aspects of Roman public life.The first section uses two case studies to exemplify the different types of interactions of individuals, families, and the state seen through the coins.The first looks at the coinage produced over three generations by the Marcii Philippi; the second looks at the diversity of commemorative strategies used within the divisive years 56-55 BCE.The second section looks at how the Romans conceived of their empire as proof of divine favor.This type of ideology is evident in their foundation legends, how Rome is personified, the importance of priesthoods to individual and family status, and how military victories are themselves the subject of religious thanksgiving.

You want to predict rental prices of apartments in a big city using their location, size, amenities, and other features. You have access to data on many apartments with many variables. You know how to select the best regression model for prediction from several candidate models. But how should you specify those candidate models to begin with? In particular, which of the many variables should they include, in what functional forms, and in what interactions? More generally, how can you make sure that the candidates include the truly good predictive models?

High-quality behavioural data can be recorded using cheap and simple technologies such as checks sheets and sound recorders. Advances in technologies for data recording have made big data available to behavioural scientists, which in turn has stimulated the development of AI technologies for automated data processing. A data pipeline describes the workflow of data recording, processing and analysis, including details of the technologies used in each step. The choice of technology for capturing behavioural data will depend on the research question and the resources available, the quantity of data required, where the data is to be collected, the amount of interaction with subjects and the likely impact of the technology on the subjects and their environment. Data that are initially recorded in a relatively rich form will require subsequent processing to code behavioural metrics. Coding of data can be either manual or automated using rules-based approaches and machine learning.

You have a car that you want to sell in the near future. You want to know what price you can expect if you were to sell it. You may also want to know what you could expect if you were to wait one more year and sell your car then. You have data on used cars with their age and other features, and you can predict price with several kinds of regression models with different right-hand-side variables in different functional forms. How should you select the regression model that would give the best prediction?

Behavioural studies aim to discover scientific truths. True facts should be replicable, meaning that the same conclusions are reached if the same data are analysed, if the same methods are applied to collect a new dataset and if different methodological approaches are used to address the same general hypothesis. The replication crisis refers to a widespread failure to replicate published findings in the biological and social sciences. The causes of the replication crisis include the presence of uncontrolled moderators of behaviour, low statistical power and dubious research practices. Various sources of information can help to distinguish good research from bad. An evidence pyramid ranks different study types according to the quality of evidence produced. The Open Science movement encourages replication, preregistration and transparency over materials, methods and data, all of which should improve the quality of science and the likelihood that findings will be replicated.

This chapter provides answers to some basic questions: when did Rome start making coins, and why did they make them?What caused the coinage to change?And what are the limits of our quantification of the coin evidence that survives? Answering these questions gives new insights into Rome’s relationship with her regional neighbors in the third century, especially the Pyrrhic War and the wars with Carthage. Attention is given to legends and designs that advertise the purpose of a specific issue, as well as changing weight standards, denomination systems, and retariffing of the denarius.The final section reviews the application of statistics to estimate the size of issues and to compare hoards, and interpret coin weights and metallurgical tests.

Behaviour is the actions and reactions of an organism or group of organisms. Living organisms, robots and virtual agents all exhibit measurable forms of behaviour. Measuring behaviour involves assigning numbers to direct observations of behaviour using specified rules. Direct observation means collecting data that relates directly to the performance of the behaviour pattern in question. Measuring behaviour accurately and reliably is important because behaviour is central to answering many questions in the biological and social sciences. Measuring behaviour is challenging because behaviour has a temporal component, does not always occur in discrete bouts, is generally complicated, can be influenced by stimuli undetectable to humans and varies both within and between individuals. Studying behaviour can be broken down into a series of steps that starts with asking a question and ends with communicating findings.

A country experienced a major natural disaster in the recent past. You want to estimate the effect on total GDP in the year of the disaster, and the following few years. You have data on GDP and other macro variables for the country and several other countries for several years before and after the disaster. It’s straightforward to show how total GDP changed after the disaster. But how should you use this data to estimate the counterfactual: how total GDP would have changed in the country without the natural disaster?

This chapter documents how the Roman elite attempted to speak to ‘popular’ concerns: Will there be enough to eat? Can we keep the favor of the gods? How will our rights to land and our own bodies be protected? What can preserve the anonymity of our votes? It starts with coins celebrating concord in the aftermath of the Catilinarian conspiracy.It then looks at the representation of religious festivals and the city's grain supply on the coinage.The next section examines numismatic evidence related to Roman agrarian policies and colonization, with particular attention to Paestum.The last section considers how the coinage reflects constitutional issues, especially the secret ballot and political rhetoric in reaction to the Sullan Constitution.

Social behaviour can be measured at different levels, from the behaviour of individuals to the behaviour of very large groups. The group is the basic unit of social organisation and must be clearly defined. It will often be important to measure group size. Crowding describes the average group size experienced by an individual. Individual identification is essential in many studies and can be accomplished either by artificially marking or tagging individuals, or by using natural variation. Marking and tagging have ethical and scientific implications. Social network analysis is the set of methods for describing and analysing how individuals interact within a group. Social network analysis yields metrics that describe properties of social interactions at both the individual and group levels. Dominance hierarchies rank the individuals within a group relative to one another and can be characterised in terms of their linearity, steepness and temporal stability.

Study design is fundamental to good science. A poorly designed study will waste time and resources and could produce misleading or uninterpretable results. A good study design aims to minimise random variation and eliminate confounding variables. Correlational studies make use of natural variation in the variables of interest, whereas experimental studies manipulate variables to understand their causal effects on behaviour. There are advantages and disadvantages to both types of study, but experiments uniquely allow inferences about causation. A good experimental design requires subjects to be randomly allocated to experimental groups. Randomisation ensures the generalisability of results and eliminates confounds in experimental studies. Measurements should ideally be made blind to group membership. Blinding minimises biases caused by the conscious or unconscious expectations of the experimenter or subjects. Careful consideration should be given to when behaviour is measured, as time can affect behaviour. Power calculations can be used to determine the appropriate sample size.

Behaviour can be recorded in either the laboratory or the field. In either setting, it can be recorded using standardised behavioural tests that elicit specific behaviour, or by observing freely-behaving subjects. Observation requires decisions about which subjects to observe (sampling rules) and how to record their behaviour (recording rules). There are four sampling rules: ad libitum sampling, focal sampling, scan sampling and behaviour sampling. There are two basic types of recording rule: continuous recording and time sampling; the latter can be further divided into instantaneous sampling and one–zero sampling. Continuous recording is more demanding for the observer but is the only recording method that produces true frequencies and durations. Estimates of frequencies and durations derived from time sampling will be more accurate if the sample interval is short relative to the mean duration of the behaviour. One–zero sampling is likely to yield biased estimates of frequency and duration.

Many firms are owned by their founder or family members of their founder. You want to uncover whether such founder/family-owned firms are as well managed as other kinds of firms and, if there is a difference, how much of that is due to their ownership as opposed to something else. You have cross-sectional observational data on firms and their management practices, and you estimate a difference using simple regression. But is that difference due to founder/family ownership? In particular, can you use multiple regression to get a good estimate of the effect of founder/family ownership? If not, can you tell whether your estimate is larger or smaller than the true effect?