Many of the challenges ahead of us are human in nature and many of the technological solutions to these challenges will be met with human resistance. Consider climate change. A majority of people now believe that climate change is a serious threat, that it is already happening, and that more should be done to curb CO2 emissions. Yet, despite increasing levels of climate change awareness and the availability of carbon free technologies, mitigation efforts remain disappointing. Here, the barrier to action is not technological, it is human. A similar observation could be made for the COVID crisis and vaccine hesitancy. The problem is not technological – we have vaccines – it is human.
In the future, technological change will create new opportunities for people, new ways of living and interacting. But these changes will need to coincide with profound adjustments in the way humans think and behave. The difficulty here is that people’s minds are not infinitely plastic, they simply cannot bend at will with technological and cultural changes. There are important constraints imposed by our biological and cognitive inner workings. Knowledge of these constraints makes it possible to predict that people will resist future game-changing technologies in very particular ways. Building a desirable future will therefore require a deeper understanding of our human nature to ensure that technologies and policies are human-friendly.
How can we leverage human sciences to build a human-proof future? In all likelihood, the answer lies in evolution. Human beings, like all other living beings, are the product of biological evolution. Just as the theory of evolution provides a unifying framework in biology, evolution can work as a common thread that weaves together disparate human sciences (Tooby & Cosmides, 1992, Boyer, 2018). This general framework relies on concepts such as optimisation, cost-benefit analyses, and trade-offs, which are at the core of economics, business and the effective altruism movement. This evolutionary logic provides a principled way to think about individual interactions and social organizations, as well as emotions, altruism, personal goals and the meaning of life (Pinker, 2003, Diamond, 2014).
Going back to climate change mitigation for instance, evolutionary biology has demonstrated that cooperation can only emerge under specific conditions. Humans have evolved cognitive mechanisms that regulate cooperation (Baumard et al., 2013). These mechanisms—norm detection, reputation management, and fairness computations—can stand in the way of pro-environmental behaviors and limit the impact of environmental policies. At the same time, the very same mechanisms can be leveraged as powerful solutions for effective climate change mitigation (Boon-Falleur et al., 2022). Importantly, the existence of these mechanisms is currently not sufficiently taken into account by policy makers (Boon-Falleur et al., 2022, Chevallier et al., 2021).
Evolutionary human sciences matter to understand the consequences of economic growth and predict the dynamics of human values
Anticipating the future also requires understanding human values. There are many technological predictions (about AI, molecular machines and the like), but far fewer human predictions: what will human preferences, human decisions, and human societies look like in 20, 50, or 100 years? Yet as technology improves and as individual freedom increases, everything about the world is somehow becoming the product of human decisions. The climate in which future generations will live, the species that will survive or go extinct, the pathogens we fight or craft, the technologies we develop or abandon, are all linked to human decisions, human priorities and human values. All existential risks are affected by human decisions in one way or another. Human preferences will therefore greatly determine the impact of future technologies and anticipating the future is impossible without a deeper understanding of our human nature.
Predicting human behavior might appear beyond our reach, as we lack a principled way of analyzing human phenomena and explain human behavioral diversity. Yet, large-scale initiatives in social sciences such as the World Values Survey have revealed that human values change in a predictable way in response to economic growth (Inglehart, 2018, Friedman, 2005) and that this is best explained in an evolutionary framework (Baumard, 2017). Humans have indeed evolved to adaptively adjust their priorities to their level of resources. With economic growth and technological progress, they move away from the most urgent needs (food, safety) to more future-oriented, high-risk/high reward goals (‘venture behaviors’, self-development, expanding the moral circle) (Baumard, 2019, Boon-Falleur et al., 2021). This adaptive plasticity of behavior can explain long-term changes in human history, from the rise of romantic love to the triggering of the industrial revolution and the increase in democratic regimes with time (Baumard, 2019, Baumard & Chevallier, 2015, Safra et al., 2020). A similar reasoning could shed light on the on-going societal changes such as the rise of veganism, sexual fluidity, imaginary worlds in fiction, and decentralized organisations, as well as the acceleration of innovations (André & Baumard, 2020).
Evolutionary human sciences matter to design sound epistemic institutions
For most of their history, human societies have been dominated by unreliable epistemic institutions, as people consulted oracles to know whether to wage wars, or plunge women in rivers to ascertain their guilt. Recent work has shown that evolutionary human sciences allow us to understand why institutions such as oaths, ordeals, or divination can be found in most human societies (Boyer, 2020).
If we still find in modern societies remnants of these institutions—from astrologers to tarot dealers—we have also seen the emergence of better functioning epistemic institutions, as science replaces divination, and the adversarial system replaces oaths and ordeals. However efficient, these epistemic institutions still emerged in a haphazard fashion, guided more by intuitions and power struggles than by a sound understanding of their epistemic properties.
It is only very recently—in the second half of the 20th century—that conscious attempts have been made to design better epistemic institutions and systematically test them. These efforts were first guided by RAND, with the development of the Delphi technique for instance (Dalkey & Helmer, 1963), and then taken up by researchers such as Phil Tetlock and his colleagues, funded in part by DARPA (Dalkey & Helmer, 1963).
These experimentations point to the wisdom of crowds as providing a sound basis for epistemic institutions. For instance, forecasters make better predictions if they can discuss the predictions together. This work converges with research in many different areas showing the power of combining opinions—to improve medical diagnoses, judicial opinions, educational achievement, etc. (Mercier, 2016). However, the wisdom of the crowd can be organized in many different ways: Are larger groups always better? How much diversity should they encompass? Is it better to let people talk to each other, or to gather independent opinions?
If experiments are crucial to test epistemic institutions, it is not practical to test every possible combination of features—group size, diversity, mode of communication, etc. Instead, we must explain why people make better or worse predictions in different contexts, and why. This is what the interactionist theory of reasoning attempts to do, focusing on the role of reasoning. Grounded in evolutionary thinking, this theory suggests that the function of human reason isn’t solitary ratiocination, but the public exchange of arguments. It can explain when people tend to reason poorly—when they are on their own or surrounded by like-minded peers—and when they tend to reason well—when they engage in good-faith arguments in a small group of people who disagree on at least some points. This theory has received much empirical support (Mercier, 2016), it has been used to guide practical interventions (designing chatbots to fight COVID-19 hesitancy), and it can inform the design of epistemic institutions (e.g., by highlighting the importance of discussion in the wisdom of crowds).
Unreliable epistemic institutions such as divination have persisted for millennia. By contrast, reliable epistemic institutions that result from conscious, systematic testing are barely starting to emerge, and their uptake is depressingly slow. An evolutionarily grounded understanding of human psychology isn’t only crucial to design epistemic institutions that should yield optimal results, but also to design epistemic institutions that will be culturally successful, spreading and hopefully persisting for many millennia in turn.
Conclusion
The future needs evolution-based thinking. But this requires more research, more communication toward the general public, and better training for academics, policy makers and the business world. None of this is possible if individual scientists and students are isolated from each other and if interactions between SSH and evolutionary sciences are not encouraged.