Social norms are unwritten behavioral codes. They direct individual behaviors, facilitate interpersonal coordination and cooperation, and lead to variation among human populations. Understanding how norms are maintained and how they change is critical for understanding human evolutionary psychology, social organization, and cultural change. This dissertation uses a mathematical model and a field study to answer two questions: First, what factors determine the content and dynamics of a social norm? Second, how do people make decisions in a normative context? The mathematical model finds that contrary to the popular belief that even arbitrary or deleterious social norms can be maintained once established because deviants suffer coordination failures and social sanctions, norms with continuously varying options cannot be maintained by the pressure to do what others do. Instead, continuous norms evolve to the optimum determined by environmental pressure, individual preferences, or cognitive processes. Therefore, the content of norms across human societies may be less historically constrained than previously assumed. The field study shows that unlike what rational choice theory predicts, people in a small-scale subsistence society do not calculate the ecological and social payoffs of different behaviors in a normative context, even when they have the information to do so. Instead, they rely heavily on social information about what others do. This decision-making algorithm, together with mental categorization that ignores small deviations, and cognitive biases that favor the division prescribed by the norm, maintain an ecologically inefficient and widely disliked cooperative surplus division norm in a Derung village, Dizhengdang, in Yunnan, China.

For most of human history hunting has been the primary economic activity of men. Hunted animals are valued for their food energy and nutrients, however, hunting is associated with a high risk of failure. Additionally, large animals cannot be consumed entirely by the nuclear family, so much of the harvest may be shared to others. This has led some researchers to ask why men hunt large and difficult game. The “costly signaling” and “show-off” hypotheses propose that large prey are hunted because the difficulty of finding and killing them is a reliable costly signal of the phenotypic quality of the hunter.

These hypotheses were tested using original interview data from Aché (hunter gatherer; n=52, age range 50-76, 46% female) and Tsimané (horticulturalist; n=40, age range 15-77, 45% female) informants. Ranking tasks and paired comparison tasks were used to determine the association between the costs of killing an animal and its value as a signal of hunter phenotypic quality for attracting mates and allies. Additional tasks compared individual large animals to groups of smaller animals to determine whether assessments of hunters’ phenotypes and preferred status were more impacted by the signal value of the species or by the weight and number of animals killed.

Aché informants perceived hunters who killed larger or harder to kill animals as having greater provisioning ability, strength, fighting ability, and disease susceptibility, and preferred them as mates and allies. Tsimané informants held a similar preference for hunters who killed large game, but not for hunters targeting hard to kill species. When total biomass harvested was controlled, both populations considered harvesting more animals in a given time period to be a better signal of preferred phenotypes than killing a single large and impressive species. Male and female informants both preferred hunters who consistently brought back small game over hunters who sometimes killed large animals and sometimes killed nothing. No evidence was found that hunters should forgo overall food return rates in order to signal phenotypic qualities by specializing on large game. Nutrient provisioning rather than costly phenotypic signaling was the strategy preferred by potential mates and allies.

Two of the defining behaviors associated with the hominin lineage are an increased reliance on tool use and the routine incorporation of animal tissue in the diet. These adaptations have been linked to numerous downstream consequences including key physiological adaptations as well as social and cognitive effects associated with modern humans. Thus, a critical issue in human evolution is how to determine when hominins began incorporating significant amounts of meat into their diets. Bone surface modifications (BSM) have long been recognized as a powerful inferential tool in identifying the differential involvement of actors responsible for altering assemblages of bone recovered from both archaeological and paleontological contexts and remain a primary source of direct evidence for butchery activities. Thus, determining the spatiotemporal context of increased carnivory in the hominin lineage relies on the accurate identification of fossil BSM.

Multidecade-long debates over the agents responsible for individual BSM indicate systemic flaws in historical approaches to identification. These debates are in part due to the extreme morphological overlap between BSM produced by certain agents of modification. The primary goal of this dissertation project therefore, is to construct probability models of BSM capable of identifying individual marks with an associated probability of assignment. Using a multivariate Bayesian approach to analyze experimentally-generated BSM data, this dissertation uses two different models, one incorporating both two and three-dimensional (3D) metric and attribute data associated with individual BSM and a second model comparing 3D geometric morphometric (GM) shape data associated with BSM.

The 2D/3D attribute model of BSM is used evaluate an assemblage of fossil BSM recovered from the Ledi-Geraru research area, Ethiopia (2.82 Ma) in spatiotemporal association with early Homo. The results of the analysis reveal compelling evidence for early butchery activities, suggesting hominins may have been using both modified and unmodified stone implements to process carcasses.

The second model, based upon 3D GM data, was used to evaluate the earliest purported evidence for stone-mediated butchery at Dikika, Ethiopia (3.39 Ma). The Dikika marks have been argued to be the result of crocodile feeding, trampling, and butchery by three different research groups. The 3D GM model evaluates the likelihood of each of these actors in the production of the controversial Dikika marks.

Science is a formalized method for acquiring information about the world. In

recent years, the ability of science to do so has been scrutinized. Attempts to reproduce

findings in diverse fields demonstrate that many results are unreliable and do not

generalize across contexts. In response to these concerns, many proposals for reform have

emerged. Although promising, such reforms have not addressed all aspects of scientific

practice. In the social sciences, two such aspects are the diversity of study participants

and incentive structures. Most efforts to improve scientific practice focus on replicability,

but sidestep issues of generalizability. And while researchers have speculated about the

effects of incentive structures, there is little systematic study of these hypotheses. This

dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural

study of social discounting – the purportedly fundamental human tendency to sacrifice

more for socially-close individuals – conducted among three diverse populations (U.S.,

rural Indonesia, rural Bangladesh). This study finds no independent effect of social

distance on generosity among Indonesian and Bangladeshi participants, providing

evidence against the hypothesis that social discounting is universal. It also illustrates the

importance of studying diverse human populations for developing generalizable theories

of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test

the effect of incentive structures on research accuracy, in an instantiation of the scientific

process where the key decision is how much data to collect before submitting one’s

findings. The results demonstrate that rewarding novel findings causes respondents to

make guesses with less information, thereby reducing their accuracy. Chapter 3 presents

an evolutionary agent-based model that tests the effect of competition for novel findings

on the sample size of studies that researchers conduct. This model demonstrates that

competition for novelty causes the cultural evolution of research with smaller sample

sizes and lower statistical power. However, increasing the startup costs to conducting

single studies can reduce the negative effects of competition, as can rewarding

publication of secondary findings. These combined chapters provide evidence that

aspects of current scientific practice may be detrimental to the reliability and

generalizability of research and point to potential solutions.