Scientific disciplines are characterized by cultures of practice that shape how research is conducted. The conventional research designs of studies by human behavioral ecologists entail both pros and cons. This chapter considers alternatives that would allow human behavioral ecologists to marshal the empirical evidence that is needed for convincing answers to long-standing debates. In particular, the chapter advocates for greater emphasis on long-term, individual-based field research. Data acquired via prospective panel studies can be used to examine the dynamic processes that unfold over long periods of time, including life span and intergenerational processes. Remedies are needed to the structural obstacles that limit the implementation of prospective panel studies, including logistical and funding constraints. The chapter also addresses the disadvantageous academic research culture that incentivizes scientists to pursue status and prestige instead of research objectives with greater long-term value. Methods to support longitudinal research are discussed, including approaches to data management and data analysis. The chapter concludes by highlighting opportunities for rising generations of human behavioral ecologists to reshape the culture of research practice in order to advance principled, ethical, and compelling approaches to the comparative study of human behavior.

Chapter 8 is concerned with why research funds are concentrated among a select few researchers and disease groups, as well as how the concentration of funds influences the rate and direction of research. This concentration is also known as the Matthew Effect or rich-get-richer, and is often a result of biases in funding. The groups that are under-represented will tend to continue to be biased against, resulting in a systematic dearth in their participation and an accumulation of resources among a select few. The benefit of a greater dispersal of funds is that it increases the number of different perspectives actively participating in the creation of scientific knowledge, including the ability of younger researchers to participate. The latter part of the chapter investigates how and why certain disease groups such as diabetes and cancer receive more resources than others, and how this affects the trajectory. The chapter concludes with potential solutions to counteract this clustering of resources on both the individual and disease level.

Chapter 22 reviews how market-based thinking has come to dominate our value system, and how the overconcentration of power may negatively influence the rate at which we can make progress. For example, as neoliberalism became embedded in modern economic theory, pro-market policies such as reduced regulation, the privatization of public services, and lower taxes became more commonplace. The growing market-based norms in science stem from the fact that this approach to economics began diffusing into other spheres of public life. The way we approached economics also became the way we approached science. Suddenly, scientific discoveries became linked with economic profit maximization and innovation policy, a by-product of neoliberalism’s grip on public affairs. The second part of the chapter highlights how there is an increasing concentration of power and resources in medicine among few actors and disease groups, reflecting on the findings from previous chapters. Chapter 22 also explores how this overconcentration of power may have negative consequences for socially-relevant research, and concludes by providing solutions to counteract this concentration.

Chapter 3 is concerned with the following question: can scientific prizes affect the trajectory of discovery? Prizes, not to be confused with grants, work well in a couple of ways. Firstly, there is often an economic incentive to winning them. Sometimes the funds can be used in a personal capacity, other times the prize money is earmarked for future research. The other benefit is the prestige they bring. This prestige can increase the likelihood that their future papers and research will be highly cited, particularly immediately after publishing new research - much like the added hype that a famous artist or musician might be able to generate prior to the unveiling of new work. Prizes also seem to celebrate existing pursuits and create positive exposure, resulting in an influx of scientists from other fields. But how effective are prizes in steering research in new directions? Prizes seem to amplify achievements, probably reward those pursuing riskier hypotheses, and often identify scientific areas likely to grow in the future, but their ability to shift the trajectory is less likely. The reasons for this are elaborated upon in the rest of the chapter.

To describe coauthorship networks, we begin with the Erdös number, which links mathematicians to their famously prolific colleague through the papers they have collaborated on. Coauthorship networks help us capture collaborative patterns and identify important features that characterize them. We can also use them to predict how many collaborators a scientist will have in the future based on her coauthorship history. We find that collaboration networks are scale-free, following a power-law distribution. As a consequence of the Matthew effect, frequent collaborators are more likely to collaborate, becoming hubs in their networks. We then explore the small-world phenomenon evidenced in coauthorship networks, which is sometimes referred to as “six degrees of separation.” To understand how a network’s small-worldliness impacts creativity and success, we look to teams of artists collaborating on Broadway musicals, finding that teams perform best when the network they inhabit is neither too big or too small. We end by discussing how connected components within networks provide evidence for the “invisible college.”