A generic model of dyadic social relationships

We introduce a model of dyadic social interactions and establish its correspondence with relational models theory (RMT), a theory of human social relationships. RMT posits four elementary models of relationships governing human interactions, singly or in combination: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing. To these are added the limiting cases of asocial and null interactions, whereby people do not coordinate with reference to any shared principle. Our model is rooted in the observation that each individual in a dyadic interaction can do either the same thing as the other individual, a different thing or nothing at all. To represent these three possibilities, we consider two individuals that can each act in one out of three ways toward the other: perform a social action X or Y, or alternatively do nothing. We demonstrate that the relationships generated by this model aggregate into six exhaustive and disjoint categories. We propose that four of these categories match the four relational models, while the remaining two correspond to the asocial and null interactions defined in RMT. We generalize our results to the presence of N social actions. We infer that the four relational models form an exhaustive set of all possible dyadic relationships based on social coordination. Hence, we contribute to RMT by offering an answer to the question of why there could exist just four relational models. In addition, we discuss how to use our representation to analyze data sets of dyadic social interactions, and how social actions may be valued and matched by the agents

Strong gender differences in reproductive success variance, and the times to the most recent common ancestors

The Time To the Most Recent Common Ancestor (TMRCA) based on human mitochondrial DNA (mtDNA) is estimated to be twice that based on the non-recombining part of the Y chromosome (NRY). These TMRCAs have special demographic implications because mtDNA is transmitted only from mother to child, and NRY from father to son. Therefore, mtDNA reflects female history, and NRY, male history. To investigate what caused the two-to-one female-male TMRCA ratio in humans, we develop a forward-looking agent-based model (ABM) with overlapping generations and individual life cycles. We implement two main mating systems: polygynandry and polygyny with different degrees in between. In each mating system, the male population can be either homogeneous or heterogeneous. In the latter case, some males are `alphas' and others are `betas', which reflects the extent to which they are favored by female mates. A heterogeneous male population implies a competition among males with the purpose of signaling as alphas. The introduction of a heterogeneous male population is found to reduce by a factor 2 the probability of finding equal female and male TMRCAs and shifts the distribution of the TMRCA ratio to higher values. We find that high male-male competition is necessary to reproduce a TMRCA ratio of 2: less than half the males can be alphas and betas can have at most half the fitness of alphas. In addition, in the modes that maximize the probability of having a TMRCA ratio between 1.5 and 2.5, the present generation has 1.4 times as many female as male ancestors. We also tested the effect of sex-biased migration and sex-specific death rates and found that these are unlikely to explain alone the sex-biased TMRCA ratio observed in humans. Our results support the view that we are descended from males who were successful in a highly competitive context, while females were facing a much smaller female-female competition

Island networks: Transformations of inter-community social relationships in the Lesser Antilles at the advent of European colonialism

The Caribbean Sea was a conduit for human mobility and the exchange of goods and ideas during the whole of its pre-colonial history. The period cal. AD 1000-1800, covering the Late Ceramic Age and early colonial era, represents an archaeologically understudied time during which the Lesser Antilles came under increasing influence from the Greater Antilles and coastal South America and participated in the last phase of indigenous resistance to colonial powers. This article summarizes the results of the Island Network project, supported by the Netherlands Organisation for Scientific Research (NWO) in which a multi-disciplinary set of archaeological, archaeometric, geochemical, GIS, and network science methods and techniques have been employed to disentangle this turbulent era in regional and global history. These diverse approaches reveal and then explore multi-layered networks of objects and people and uncover how Lesser Antillean communities were created and transformed through teaching, trade, migration, movement, and exchange of goods and knowledge

Debunking mathematically the logical fallacy that cancer risk is just “bad luck”

Tomasetti and Vogelstein recently proposed that the majority of variation in cancer risk among tissues is due to “bad luck,” that is, random mutations arising during DNA replication in normal noncancerous stem cells. They generalize this finding to cancer overall, claiming that “the stochastic effects of DNA replication appear to be the major contributor to cancer in humans.” We show that this conclusion results from a logical fallacy based on ignoring the influence of population heterogeneity in correlations exhibited at the level of the whole population. Because environmental and genetic factors cannot explain the huge differences in cancer rates between different organs, it is wrong to conclude that these factors play a minor role in cancer rates. In contrast, we show that one can indeed measure huge differences in cancer rates between different organs and, at the same time, observe a strong effect of environmental and genetic factors in cancer rates.publishe

Detection of categories of action fluxes in data sets of dyadic interactions.

<p>Patterns of action fluxes expected to be observed in each category. X and Y are social actions belonging to a set of size N. A and B are agents.</p><p>Detection of categories of action fluxes in data sets of dyadic interactions.</p

Six categories of action fluxes.

<p>Exhaustive categorization of relationships in the model of two agents A and B that can each do X, Y or nothing (∅). In elementary interactions, agents can do the same thing or not (i.e. actions can be identical or different) and actions can be null (∅) or not (X or Y). Within the relationship, agents can be able to exchange roles or not.</p><p>Six categories of action fluxes.</p

Nine elementary interactions, simplified.

<p>Same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120882#pone.0120882.t001" target="_blank">Table 1</a>, with simplified notations for the interactions involving one empty flux.</p><p>Nine elementary interactions, simplified.</p