In contrast to Pietraszewski's account, latent structure learning neither requires conflict nor relies on observation of explicit coalitional behavior to support group inference. This alternative addresses how even non-conflict-based groups may be defined and is supported by experimental evidence in human behavior.

Pietraszewski's representation scheme is parsimonious and intuitive. However, internal mental representations may be subject to resource constraints that prefer more unusual systems such as sparse coding or compressed sensing. Pietraszewski's scheme may be most useful for understanding how agents communicate. Conflict may be driven in part by the complex interplay between parsimonious public representations and more resource-efficient internal ones.

Pietraszewski exemplifies the need for computational theory using group conflict; I complement this with an example of group cooperation. He criticizes past theories for having black boxes; I suggest his theory also has a black box – the concept of costs. He divides what mentally constitutes a group from mere ancillary attributes; I hazard that some of these attributes are essential.

Pietraszewski's model allows understanding group dynamics through the lens of evolved coalitionary psychology. This framework is particularly relevant to understanding group dynamics on social media platforms, where coalitions based on salience of group identity are prominent and generate unique frictions. We offer testable hypotheses derived from the model that may help to shed light on social media behavior.

Conflict is neither necessary nor sufficient for the existence of groups. First, the existence of mutually supporting, rather than antagonistic, interactants is sufficient to constitute a “social group.” Second, conflict does not necessarily mark group boundaries but can also exist within an ingroup. Third, psychological representations of social groups do not only trace, but also perpetuate the existence of groups.

Here, we revise Pietraszewski's model of groups by assigning participant pairs with two triplets, denoting: (1) the type of game that models the interaction, (2) its critical switching point between alternatives (i.e., the game's similarity threshold), and (3) the perception of strategic similarity with the opponent. These triplets provide a set of primitives that accounts for individuals' strategic motivations and observed behaviors.

A group in intergroup conflict needs to overcome the collective action problem in order to defend itself against an external enemy. This leads to increasing complexity that cannot be adequately covered by just scaling up the model of intragroup conflicts. Research on cultural evolution and evolutionary psychology shows that external conflict has profound effects on group organization.

Pietraszewski contends that group representations that rely on a “containment metaphor” fail to adequately capture phenomena of group dynamics such as shifts in allegiances. We argue, in contrast, that social categories allow for computationally efficient, richly structured, and flexible group representations that explain some of the most intriguing aspects of social group behaviour.

We suggest that variation, error, and bias will be essential to include in a complete computational theory of groups – particularly given that formation of group representations must often rely on inferences of intentions. We draw on the case study of paranoia to illustrate that intentions that do not correspond to group-constitutive roles may often be perceived as such.

Benevolent intersubjectivity developed in parent–infant interactions and compassion toward friend and foe alike are non-violent interventions to group behavior in conflict. Based on a dyadic active inference framework rooted in specific parental brain mechanisms, we suggest that interventions promoting compassion and intersubjectivity can reduce stress, and that compassionate mediation may resolve conflicts.

Humans are learning agents that acquire social group representations from experience. Here, we discuss how to construct artificial agents capable of this feat. One approach, based on deep reinforcement learning, allows the necessary representations to self-organize. This minimizes the need for hand-engineering, improving robustness and scalability. It also enables “virtual neuroscience” research on the learned representations.

Pietraszewski misrepresents both the nature of behaviour in conflict and the ability of psychology to theorise the relational properties of group designation. At the behavioural level, he focusses exclusively on “attack,” when consolation/care in conflict is equally present and important. At the theoretical level, he ignores existing psychological work on how group perception is shaped by the meta-contrast principle.

People live in distinct groups, notably territory-holding societies, whose boundaries aren't neatly defined by the traits that Pietraszewski describes for his socially aligned groups (or SAGs), as I propose calling them, which occur both within and between our societies. Although studying SAGs could prove enlightening, societies are essential human groups that likely existed long before the complex SAGs of today.

Pietraszewski's model – though promising in many respects – needs to be extended so that it can explain the multitude of rich inferences that people draw from group membership. In this commentary, we highlight some facets of group thinking, especially from the field of developmental psychology, that cannot be unambiguously accounted for by a model that is built solely on relational cues.

Pietraszewski provides a compelling case that representations of certain interaction-types are the “cognitive primitives” that allow all tokens of group-in-conflict to be represented within the mind. Here, I argue that the folk concept GROUP encodes shared intentions and goals as more central than these interaction-types, and that providing a computational theory of social groups will be more difficult than Pietraszewski envisages.

Central to Pietraszewski's theory is a set of group-constitutive roles within four triadic primitives. Although some data from the developmental and biological sciences support Pietraszewski's theory, other data raise questions about whether similar behavioral expectations hold across various ecological conditions and interactions. We discuss the potential for a broader set of conceptual primitives that support reasoning about groups.

Pietraszewski proposes four triadic “primitives” for representing social groups. We argue that, despite surface differences, these triads can all be reduced to similar underlying welfare trade-off ratios, which are a better candidate for social group primitives. Welfare trade-off ratios also have limitations, however, and we suggest there are multiple computational strategies by which people recognize and reason about social groups.

Group representations based on recursive utilities can be used to derive the same predictions as Pietraszewski in conflict situations. Additionally, these representations generalize to non-conflict situations, asymmetric relationships, and represent the stakes in a conflict. However, both proposals fail to represent asymmetries of power and responsibility and to account for generalizations from specific observed individuals to collections of non-observed individuals.

Pietraszewski asserts that social psychological research on groups is too vague, tautological, and dependent on intuitions to be theoretically useful. We disagree. Pietraszewski's contribution is thought-provoking but also incomplete and guilty of many of the faults he attributes to others. Instead of rototilling the existing knowledge landscape, we urge for more integration of new and old ideas.

Network theory is necessary for the realization of cognitive representations and resulting empirical observations of social groups. We propose that the triadic primitives denoting individual roles are multilayer, with positive and negative relations feeding into cost–benefit calculations. Through this, we advance a computational theory that generalizes to different scales and to contexts where conflict is not present.