Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy

Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D. Hamilton to explain the emergence and maintenance of cooperation between individuals that allows the existence of society. Contemporary evolutionary ecology identified several factors that increase inclusive fitness, in addition to kin-selection, such as assortation or homophily, and social synergies triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory (EIFT) that includes in the fitness calculation all direct and indirect benefits an agent obtains by its own actions, and through interactions with kin and with genetically unrelated individuals. This formulation focuses on the sustainable cost/benefit threshold ratio of cooperation and on the probability of agents sharing mutually compatible memes or genes. This broader description of the nature of social dynamics allows to compare the evolution of cooperation among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the emergence of symbioses, of social synergies, and the emergence of division of labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing to describe the role for division of labor in the emergence of social synergies, providing an integrated framework for the study of both, biological evolution of social behavior and economic market dynamics.Comment: Bioeconomics, Synergy, Complexit

Steps towards operationalizing an evolutionary archaeological definition of culture

This paper will examine the definition of archaeological cultures/techno-complexes from an evolutionary perspective, in which culture is defined as a system of social information transmission. A formal methodology will be presented through which the concept of a culture can be operationalized, at least within this approach. It has already been argued that in order to study material culture evolution in a manner similar to how palaeontologists study biological change over time we need explicitly constructed ‘archaeological taxonomic units’ (ATUs). In palaeontology, the definition of such taxonomic units – most commonly species – is highly controversial, so no readily adoptable methodology exists. Here it is argued that ‘culture’, however defined, is a phenomenon that emerges through the actions of individuals. In order to identify ‘cultures’, we must therefore construct them from the bottom up, beginning with individual actions. Chaîne opèratoire research, combined with the formal and quantitative identification of variability in individual material culture behaviour allows those traits critical in the social transmission of cultural information to be identified. Once such traits are identified, quantitative, so-called phylogenetic methods can be used to track material culture change over time. Phylogenetic methods produce nested hierarchies of increasingly exclusive groupings, reflecting descent with modification within lineages of social information transmission. Once such nested hierarchies are constructed, it is possible to define an archaeological culture at any given point in this hierarchy, depending on the scale of analysis. A brief example from the Late Glacial in Southern Scandinavia is presented and it is shown that this approach can be used to operationalize an evolutionary definition of ‘culture’ and that it improves upon traditional, typologically defined technocomplexes. In closing, the benefits and limits of such an evolutionary and quantitative definition of ‘culture’ are discussed

Inferring Networks of Substitutable and Complementary Products

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.Comment: 12 pages, 6 figure

Permanent residents or temporary lodgers: characterizing intracellular bacterial communities in the siphonous green alga Bryopsis

The ecological success of giant celled, siphonous green algae in coastal habitats has repeatedly been linked to endophytic bacteria living within the cytoplasm of the hosts. Yet, very little is known about the relative importance of evolutionary and ecological factors controlling the intracellular bacterial flora of these seaweeds. Using the marine alga Bryopsis (Bryopsidales, Chlorophyta) as a model, we explore the diversity of the intracellular bacterial communities and investigate whether their composition is controlled by ecological and biogeographic factors rather than the evolutionary history of the host. Using a combination of 16S rDNA clone libraries and denaturing gradient gel electrophoresis analyses, we show that Bryopsis harbours a mixture of relatively few but phylogenetically diverse bacterial species. Variation partitioning analyses show a strong impact of local environmental factors on the presence of Rickettsia and Mycoplasma in their association with Bryopsis. The presence of Flavobacteriaceae and Bacteroidetes, on the other hand, reflects a predominant imprint of host evolutionary history, suggesting that these bacteria are more specialized in their association. The results highlight the importance of interpreting the presence of individual bacterial phylotypes in the light of ecological and evolutionary principles such as phylogenetic niche conservatism to understand complex endobiotic communities and the parameters shaping them

Historical contingency in species interactions: towards niche-based predictions.

The way species affect one another in ecological communities often depends on the order of species arrival. The magnitude of such historical contingency, known as priority effects, varies across species and environments, but this variation has proven difficult to predict, presenting a major challenge in understanding species interactions and consequences for community structure and function. Here, we argue that improved predictions can be achieved by decomposing species' niches into three components: overlap, impact and requirement. Based on classic theories of community assembly, three hypotheses that emphasise related, but distinct influences of the niche components are proposed: priority effects are stronger among species with higher resource use overlap; species that impact the environment to a greater extent exert stronger priority effects; and species whose growth rate is more sensitive to changes in the environment experience stronger priority effects. Using nectar-inhabiting microorganisms as a model system, we present evidence that these hypotheses complement the conventional hypothesis that focuses on the role of environmental harshness, and show that niches can be twice as predictive when separated into components. Taken together, our hypotheses provide a basis for developing a general framework within which the magnitude of historical contingency in species interactions can be predicted

FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds

Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.Comment: WSDM 201

Gamifying massive online courses: effects on the social networks and course completion rates

This paper analyzes the e ects of gamification in the social network of a massive online course. An educational social-networking platform gathered information about the contributions of participants and about the social networks that were formed during the course. A gamification layer with three game elements (points, badges, and leaderboard) was then implemented in the online learning platform. Social network analysis (SNA) and principal component analysis (PCA) were used to analyze the di erences between a treatment and a comparison group (N = 591 and N = 427), using a set of 20 variables for each participant which quantified contributions to the learning platform as well as position and influence in the social network. The results of SNA show that gamification influences the structure of the social network of the course. The results also suggest that the variables cluster similarly for each group and that the linear combination of variables called the first component (F1) is a good descriptor of students’ work and position in the network. F1 can be used to build predictive models of course completion. The models show that the probability of passing the course increases more rapidly in the treatment (gamified) group.Spanish Ministry of Economic A airs and Digital Transformation (Grant TIN2014-54874-R) and Government of Comunidad de Madrid (Grant CM/JIN/2019-037).info:eu-repo/semantics/publishedVersio

FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds

Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method