A Game-Theoretical Winner and Loser Model of Dominance Hierarchy Formation

Postsynaptic lncRNA Sera/Pkm2 pathway orchestrates the transition from social competition to rank by remodeling the neural ensemble in mPFC

Individuals' continuous success in competitive interactions with conspecifics strongly affects their social hierarchy. Medial prefrontal cortex (mPFC) is the key brain region mediating both social competition and hierarchy. However, the molecular regulatory mechanisms underlying the neural ensemble in the mPFC remains unclear. Here, we demonstrate that in excitatory neurons of prelimbic cortex (PL), lncRNA Sera remodels the utilization of Pkm Exon9 and Exon10, resulting in a decrease in the Pkm1/2 ratio in highly competitive mice. By employing a tet-on/off system, we disrupt or rebuild the normal Pkm1/2 ratio by controlling the expression of Pkm2 in PL excitatory neurons. We find that long-term Pkm2 modulation induces timely competition alteration and hysteretic rank change, through phosphorylating the Ser845 site of GluA1. Together, this study uncovers a crucial role of lncRNA Sera/Pkm2 pathway in the transition of social competition to rank by remodeling neural ensemble in mPFC.

Winner-loser effects improve social network efficiency between competitors with equal resource holding power

Animal societies are structured of dominance hierarchy (DH). DH can be viewed as networks and analyzed by graph theory. We study the impact of state-dependent feedback (winner-loser effect) on the emergence of local dominance structures after pairwise contests between initially equal-ranking members (equal resource-holding-power, RHP) of small and large social groups. We simulated pairwise agonistic contests between individuals with and without a priori higher RHP by Monte-Carlo-method. Random pairwise contests between equal-ranking competitors result in random dominance structures ('Null variant') that are low in transitive triads and high in pass along triads; whereas state-dependent feedback ('Winner-loser variant') yields centralized 'star' structured DH that evolve from competitors with initially equal RHP and correspond to hierarchies that evolve from keystone individuals. Monte-Carlo simulated DH following state-dependent feedback show motif patterns very similar to those of a variety of natural DH, suggesting that state-dependent feedback plays a pivotal role in robust self-organizing phenomena that transcend the specifics of the individual. Self-organization based on state-dependent feedback leads to social structures that correspond to those resulting from pre-existing keystone individuals. As the efficiency of centralized social networks benefits both, the individual and the group, centralization of social networks appears to be an important evolutionary goal.

Limited Cognitive Abilities and Dominance Hierarchies

We propose a novel model to explain the mechanisms underlying dominance hierarchical structures. Guided by a predetermined social convention, agents with limited cognitive abilities optimize their strategies in a Hawk-Dove game. We find that several commonly observed hierarchical structures in nature such as linear hierarchy and despotism, emerge as the total fitness-maximizing social structures given different levels of cognitive abilities.

How feedback and feed-forward mechanisms link determinants of social dominance

In many animal societies, individuals differ consistently in their ability to win agonistic interactions, resulting in dominance hierarchies. These differences arise due to a range of factors that can influence individuals' abilities to win agonistic interactions, spanning from genetically driven traits through to individuals' recent interaction history. Yet, despite a century of study since Schjelderup-Ebbe's seminal paper on social dominance, we still lack a general understanding of how these different factors work together to determine individuals' positions in hierarchies. Here, we first outline five widely studied factors that can influence interaction outcomes: intrinsic attributes, resource value asymmetry, winner-loser effects, dyadic interaction-outcome history and third-party support. A review of the evidence shows that a variety of factors are likely important to interaction outcomes, and thereby individuals' positions in dominance hierarchies, in diverse species. We propose that such factors are unlikely to determine dominance outcomes independently, but rather form part of feedback loops whereby the outcomes of previous agonistic interactions (e.g. access to food) impact factors that might be important in subsequent interactions (e.g. body condition). We provide a conceptual framework that illustrates the multitude potential routes through which such feedbacks can occur, and how the factors that determine the outcomes of dominance interactions are highly intertwined and thus rarely act independently of one another. Further, we generalise our framework to include multi-generational feed-forward mechanisms: how interaction outcomes in one generation can influence the factors determining interaction outcomes in the next generation via a range of parental effects. This general framework describes how interaction outcomes and the factors determining them are linked within generations via feedback loops, and between generations via feed-forward mechanisms. We then highlight methodological approaches that will facilitate the study of feedback loops and dominance dynamics. Lastly, we discuss how our framework could shape future research, including: how feedbacks generate variation in the factors discussed, and how this might be studied experimentally; how the relative importance of different feedback mechanisms varies across timescales; the role of social structure in modulating the effect of feedbacks on hierarchy structure and stability; and the routes of parental influence on the dominance status of offspring. Ultimately, by considering dominance interactions as part of a dynamic feedback system that also feeds forward into subsequent generations, we will understand better the factors that structure dominance hierarchies in animal groups.

The Evolution of Social Dominance through Reinforcement Learning

AbstractGroups of social animals are often organized into dominance hierarchies that are formed through pairwise interactions. There is much experimental data on hierarchies, examining such things as winner, loser, and bystander effects, as well as the linearity and replicability of hierarchies, but there is a lack evolutionary analyses of these basic observations. Here I present a game theory model of hierarchy formation in which individuals adjust their aggressive behavior toward other group members through reinforcement learning. Individual traits such as the tendency to generalize learning between interactions with different individuals, the rate of learning, and the initial tendency to be aggressive are genetically determined and can be tuned by evolution. I find that evolution favors individuals with high social competence, making use of individual recognition, bystander observational learning, and, to a limited extent, generalizing learned behavior between opponents when adjusting their behavior toward other group members. The results are in qualitative agreement with experimental data, for instance, in finding weaker winner effects compared to loser effects.

Dominance, sharing, and assessment in an iterated Hawk-Dove game

Animals use a wide variety of strategies to reduce or avoid aggression in conflicts over resources. These strategies range from sharing resources without outward signs of conflict to the development of dominance hierarchies, in which initial fighting is followed by the submission of subordinates. Although models have been developed to analyse specific strategies for resolving conflicts over resources, little work has focused on trying to understand why particular strategies are more likely to arise in certain situations. In this paper, we use a model based on an iterated Hawk-Dove game to analyse how resource holding potentials (RHPs) and other factors affect whether sharing, dominance relationships, or other behaviours are evolutionarily stable. We find through extensive numerical simulations that sharing is stable only when the cost of fighting is low and the animals in a contest have similar RHPs, whereas dominance relationships are stable in most other situations. We also explore what happens when animals are unable to assess each other's RHPs without fighting, and we compare a range of strategies for contestants using simulations. We find (1) that the most successful strategies involve a limited period of assessment followed by a stable relationship in which fights are avoided and (2) that the duration of assessment depends both on the costliness of fighting and on the difference between the animals' RHPs. Along with our direct work on modelling and simulations, we develop extensive software to facilitate further testing. It is available at https://bitbucket.org/CameronLHall/dominancesharingassessmentmatlab/.