The evolution of life is the result of a process whereby variant forms compete to harvest energy from the environment and convert it into replicates of those forms. Individuals "capture" energy from the environment-for example through foraging, hunting, or cultivating-and "allocate" it to reproduction and survival-enhancing activities. Selection favors individuals who efficiently capture energy and effectively allocate it to enhance fitness within their ecological niche.

Abstract

The development of individual differences has always been a primary focus of psychological research, and it continues to be an intensely debated topic to this day. Three issues in particular stand out in contemporary debate. The fi rst pertains to the sources of individual variation, with the pressing task of understanding the interplay between genetic and environmental factors. Second, there is the issue of early experience (especially within the family) and its role in shaping later development, a role which some question (e.g., Breur, 1999 ; Harris, 2005) and for which there exists no comprehensive theory capable of accounting for many confl icting fi ndings. Finally comes the issue of continuity versus discontinuity in individual differences across the life span; this subject is rendered diffi cult by the compartmentalized way in which development is often studied and by the lack of organizing principles for linking diverse behavioral phenomena, manifested at different points in time, into meaningful clusters. In this chapter, we illustrate how an evolutionary approach can advance understanding of all three of these issues, and how a developmental perspective can provide fascinating insights to the study of individual differences.

This book presents a unified approach to evolutionary psychopathology, and advances an integrative framework for the analysis and classification of mental disorders based on the concepts of life history theory. The framework does not aim to replace existing evolutionary models of specific disorders—which are reviewed and critically discussed in the book—but to connect them in a broader perspective and explain the large-scale patterns of risk and comorbidity that characterize psychopathology. The life history framework permits a seamless integration of mental disorders with normative individual differences in personality and cognition, and offers new conceptual tools for the analysis of developmental, genetic, and neurobiological data. The concepts synthesized in the book are used to derive a new taxonomy of mental disorders, the fast-slow-defense (FSD) model. The FSD model is the first classification system explicitly based on evolutionary concepts, a biologically grounded alternative to transdiagnostic models based on empirical correlations between symptoms. The book reviews a wide range of common mental disorders, discusses their classification in the FSD model, and identifies functional subtypes within existing diagnostic categories.

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.