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cil and Institute of Medicine, 2000); demographic behavior (National Research Council and Institute of Medicine, 2000; National Research Council, 2003; Hobcraft, 2006; Seltzer et al., 2005); social neuroscience (Cacioppo, Berntson, and Adolphs, 2002; Cacioppo, Berntson, Sheridan, and McLintock, 2000; see also Chapter 17 in this volume); health (Johnson and Crow, 2005; National Research Council, 2001b); human bonding (Miller and Rodgers, 2001); resilience (Curtis and Cicchetti, 2003); well-being (Davidson, 2004; Huppert and Bayliss, 2004); and economics (Chapter 15 in this volume).

Thus far, most real progress toward implementing integrative approaches has occurred for health, as reviewed in several chapters of this volume (see also National Research Council, 2001b; Johnson and Crow, 2005), and in psychology, especially psychopathology (see Plomin, DeFries, Craig, and McGuffin, 2003; Rutter, 2006). Bridging the gap between the biological and the social sciences has taken more time, although important developments have occurred at the intersections of economics and psychology in behavioral economics (Brocas and Carillo, 2003, 2004; Camerer, Loewenstein, and Rabin, 2004). However, an increasing number of important prospective national population surveys are collecting (or considering) DNA samples, with the intention of enabling social science researchers, subject to suitable disclosure controls, to access information on a significant range of genetic markers (e.g., in the United States, the National Longitudinal Study of Adolescent Health and the Fragile Families Survey). Although the importance of such large and often nationally representative samples for genetic research is recognized, the longer term potential lies in the ability to explore the interplays between genes and behavior over the life course in response to experiences.

One of the great challenges of an integrative biosocial life-course approach to the study of behavior is the very complexity involved and to find the means of avoiding drowning in the multiple levels and interplays. One of my favorite relevant aphorisms, from the different context of population projections, is John Hajnal’s (1955, p. 321) plea for “less computation and more cogitation.” At the meeting during the preparation of this volume, Jim Vaupel summed this up differently as “model simple; think complex.” My own preference would be to extend this somewhat to “model sufficiently complex, not simplistically; think more, both to complexify and to simplify.” The key here is that we need to have more and better theory and conceptualization, including attention to mechanisms and pathways or processes (a theme developed at greater length in Hobcraft, 2006). The problems of data dredging or gene hunting are well recognized, and the need for replication and ever larger samples to avoid false positives is often stressed (e.g., Plomin, 2005).

Yet some of the most influential findings on gene-environment inter-



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