Bridging developmental systems theory and evolutionary psychology using dynamic optimization

Abstract

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach.

What Can Cross-Cultural Correlations Teach Us about Human Nature?

“…Many of the issues we discussed apply to a broad range of research areas where cross-cultural correlations are presented as evidence for individual-level hypotheses-for instance, research on sex ratios (Dama 2011), parent-offspring conflict (Apostolou 2010), or intelligence (Kanazawa 2006). We hope that novel hypotheses derived from evolutionary theory continue to be tested with increasingly sophisticated and robust methods, for example by comparing individuals from different cultures (e.g., Stephen et al 2012), by combining many individual-level samples from small-scale societies (e.g., Borgerhoff Mulder et al 2009), or by modeling techniques (e.g., Frankenhuis et al 2013). Such approaches could allow researchers to make better inferences on human nature than those based on cross-cultural correlations.…”

Open Science is Liberating and Can Foster Creativity

“…In evolutionary psychology, for instance, researchers might disagree about the natural selection pressures that have shaped aspects of human cognition and behavior. A rigorous method for studying the logic and plausibility of evolutionary explanations is to build a mathematical model, which formalizes assumptions about the environment (its statistical properties) and organisms (their initial attributes), and computes the expected outcomes of evolution (Frankenhuis, Panchanathan, & Barrett, 2013;Frankenhuis & Tiokhin, 2018). Such modeling can benefit from transparency in several ways, some of which are obvious, but others not.…”

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