The formations of central places in human societies involved the development of multi-scalar institutions, for which central places played key roles in the economy, politics, social stratification, and religion. With the development of cities, we see a clear linkage to a multiplicity of hierarchical relationships that increasingly dominated ancient and modern societies. The term city has been applied variously to large, populous settlements, depending on the theoretical orientation of scholars, different cultural and geographical areas where they occur, and phases of urbanization through which they pass (Marcus and Sabloff 2008). As seen from cases considered by our group, not all societies had large cities. Pueblo IV in the American Southwest, the Nordic Bronze Age (BA) chiefdoms, and the South Pare people of East Africa lived in settlements without having anything approaching a city. Cities were dynamic and diversified communities that changed according to the social, environmental, and political conditions that shaped their political and economic roles within their territories. They arose for different reasons and their formation requires understanding the economies and environmental conditions that supported them. But what is a city and what is urban? Those are important distinctions to make before comparing the economies of early urban societies.

Chapter 9 examines how mental models shape attitudes toward artificial intelligence (AI), a rapidly emerging yet relatively unpoliticized technology. Initially, individuals with and without an Economist Mental Model (EMM) show no strong divergence in their baseline assessments of AI’s risks and benefits, likely reflecting the technology’s novelty. However, two survey experiments reveal that EMM-oriented respondents adapt their views more readily when presented with economic information about AI’s impacts on productivity, wages, or inequality. By contrast, those with Alternative Mental Models (AMMs) remain largely unmoved by the same data. In a second experiment using a conjoint design, EMM-oriented participants systematically adjust their support for AI adoption in hypothetical firms, raising support when gains outweigh losses and reducing it when the scenario shows net harms. Conversely, individuals with AMMs maintain fixed views. This responsiveness underscores the role of economic thinking in evaluating AI’s trade-offs and shaping policy preferences.

The effectiveness of comparative studies resides in the breadth and suitability of the cases used in pursuit of a research question. We have selected thirteen societies to develop a comparative understanding of how premodern economies were organized and operated. These span a broad range of societies in terms of organization, complexity, and their place in time and space. They include societies from around the world: six from the Americas and seven from Eurasia and Africa (Figure 2.1). They are diverse in adaptation and scale, and include horticultural, foraging, pastoral, mixed economy, and sedentary agricultural groups. Examples include tribal, chiefdom, and ancient state-level societies. Despite this diversity and the historical independence of the Americas and Eurasian/African examples, commonalities exist in economic structures because of the cumulative and shared nature of economic behaviors that we hope to capture.

Social scientists need to employ a comparative approach if they want to explain cultural variation from a cross-cultural perspective (Smith et al. 2012). The fundamental analytical problem is that the modern era simply does not encapsulate enough of the variation for how humans have lived or in fact do live. Although a few economists have attempted to include premodern economies into formal modeling of economic systems (Dow and Reed 2022), the collection of evidence on premodern economies and its interpretation primarily is the job that anthropologists and historians must undertake. This volume undertakes the challenge of developing a comparative understanding of premodern economies. We feel that economists often misrepresent modern economies by oversimplifying processes by not considering many earlier economic relationships of labor and exchange that continue into the present day. We envision economies as historically developed, adding new processes related to scale and changing objectives over time. As a first step, we should clarify what we mean when we discuss premodern economies.

Chapter 6 examines zero-sum thinking (ZST) – the idea that one party’s gain must be another’s loss – and contrasts it with the Economist Mental Model (EMM), which recognizes that economic interactions can be positive-sum. Historically adaptive in static, resource-scarce settings, ZST becomes counterproductive in modern, dynamic economies built on cooperation and specialization. Using survey data from the US, the chapter differentiates between generic ZST (a broad tendency to see life as win-lose) and policy-specific ZST, which can reveal partisan divides. Democrats often display zero-sum views about redistribution, whereas Republicans do so regarding immigration or trade. Crucially, people with higher economic knowledge – those more aligned with the EMM – show markedly lower generic ZST and are less inclined toward protectionist policies than those with lower economic knowledge – aligned with Alternative Mental Models (AMMs).

In 2011, Italy narrowly avoided financial collapse, prompting the formation of a technocratic government tasked with enacting sweeping reforms. Although these measures stabilized public finances, they drew fierce opposition and highlighted a broader pattern of public skepticism toward expert advice. Similar dynamics unfolded during the 2016 Brexit referendum, when voters dismissed economic forecasts, and in Donald Trump’s election, where nationalist rhetoric overshadowed warnings on protectionism. These events highlight a persistent gap between how economists and the general public evaluate policy trade-offs, often producing outcomes economists view as welfare-reducing. This book explores how mental models, particularly the Economist Mental Model (EMM), shape individual political decisions and explains why populist solutions gain traction despite longer-term harms. Through diverse cases – from Brexit and US protectionism to price controls – it argues that wider adoption of the EMM could enhance support for welfare-enhancing policies, a crucial insight in an era of heightened populist sentiment.

Chapter 8 investigates how mental models shape individuals’ responses to policy information and partisan cues. Through a survey experiment in Italy involving hypothetical price controls on olive oil, the chapter shows that people with high economic knowledge – the Economist Mental Model – are far more influenced by cost–benefit data, reducing their support for price controls by 20 percentage points once presented with evidence of net societal losses. Meanwhile, those with lower economic knowledge respond more to partisan cues, increasing their support by about 6 points when party leaders endorse the policy. Notably, both groups share similar baseline preferences in the absence of new information, indicating that economic knowledge – not preexisting ideology – drives these contrasting reactions. The chapter further reveals that high-knowledge participants are 25 percentage points more likely to calculate the policy’s true societal impact, illustrating the distinctive role of economic reasoning in integrating and acting on new policy information.