Group-level traits emerge

The emergence of group fitness

Whether and how selection can act on collectives rather than single entities has been a tumultuous issue in evolutionary biology for decades. Despite examples of multilevel selection, a simple framework is needed that makes explicit the constraints that lead to the emergence of a "group fitness function." We use evolutionary game theory to show that two constraints are sufficient for the emergence of a well-defined group fitness, which could even apply to multispecies groups. First, different parts of the group contribute to one another's growth via resources produced proportionally to the density of each resource producer (not the density of the population receiving benefits). Second, invading groups do not share these resources with resident groups. Jointly, these two constraints lead to the "entanglement" of invading individuals' outcomes such that individual fitness can no longer be defined and group fitness predicts evolutionary dynamics through the emergence of a higher level evolutionary individual. Group fitness is an emergent property, irreducible to the fitness of the group's parts and exhibiting downward causality on the parts. By formalizing group fitness as a model for evolutionary transitions in individuality, these results open up a broad class of models under the multilevel-selection framework.

Collective Memory from a Psychological Perspective

Social scientists have studied collective memory for almost a century, but psychological analyses have only recently emerged. Although no singular approach to the psychological study of collective memory exists, research has largely: (i) explored the social representations of history, including generational differences; (ii) probed for the underlying cognitive processes leading to the formation of collective memories, adopting either a top-down or bottom-up approach; and (iii) explored how people live in history and transmit personal memories of historical importance across generations. Here, we discuss these different approaches and highlight commonalities and connections between them.

Agent-Based Modeling

Agent-based modeling is a long-standing but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes and can uniquely reveal nonlinear dynamics and emergence—the process whereby local interactions aggregate into often-surprising collective phenomena such as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a seven-step guide to creating your own model.

Solved paradoxes and old hats? The research needed on differentiated selves

The idea that differentiated selves almost always improve group outcomes is overly simplistic. We argue that it is essential to distinguish between two distinct elements of differentiated selves - identifiability and specialization - and to identify conditions under which they influence group outcomes. Adopting a group-by-situation perspective, in which group and situation variables are considered jointly, is recommended to generate novel hypotheses.

Theory development with agent-based models

Many social phenomena do not result solely from intentional actions by isolated individuals, but rather emerge as the result of repeated interactions among multiple individuals over time. However, such phenomena are often poorly captured by traditional empirical techniques. Moreover, complex adaptive systems are insufficiently described by verbal models. In this paper, we discuss how organizational psychologists and group dynamics researchers may benefit from the adoption of formal modeling, particularly agent-based modeling, for developing and testing richer theories. Agent-based modeling is well suited to capture multilevel dynamic processes and offers superior precision to verbal models. As an example, we present a model of social identity dynamics used to test the predictions of Brewer’s (1991) optimal distinctiveness theory, and discuss how the model extends the theory and produces novel research questions. We close with a general discussion on theory development using agent-based models.