Utilising Provenance to Enhance Social Computation

Postprint

SC-PROV: A Provenance Vocabulary for Social Computation

The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.

A Role for Provenance in Social Computation

Postprint

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

The genetical theory of social behaviour.

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

A Web Future of Computation

Crowdsourcing. Social Machines. Human computation. Co-construction Made Real

Self-organized sociopolitical interactions as the best way to achieve organized patterns in human social systems: going beyond the top-down control of classical political regimes

La monografía presenta la auto-organización sociopolítica como la mejor manera de lograr patrones organizados en los sistemas sociales humanos, dada su naturaleza compleja y la imposibilidad de las tareas computacionales de los regímenes políticos clásico, debido a que operan con control jerárquico, el cual ha demostrado no ser óptimo en la producción de orden en los sistemas sociales humanos. En la monografía se extrapola la teoría de la auto-organización en los sistemas biológicos a las dinámicas sociopolíticas humanas, buscando maneras óptimas de organizarlas, y se afirma que redes complejas anárquicas son la estructura emergente de la auto-organización sociopolítica.

Did farming arise from a misapplication of social intelligence?

The origins of farming is the defining event of human history-the one turning point that has resulted in modern humans having a quite different type of lifestyle and cognition to all other animals and past types of humans. With the economic basis provided by farming, human individuals and societies have developed types of material culture that greatly augment powers of memory and computation, extending the human mental capacity far beyond that which the brain alone can provide. Archaeologists have long debated and discussed why people began living in settled communities and became dependent on cultivated plants and animals, which soon evolved into domesticated forms. One of the most intriguing explanations was proposed more than 20 years ago not by an archaeologist but by a psychologist: Nicholas Humphrey suggested that farming arose from the 'misapplication of social intelligence'. I explore this idea in relation to recent discoveries and archaeological interpretations in the Near East, arguing that social intelligence has indeed played a key role in the origin of farming and hence the emergence of the modern world.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

Closed-loop feedback computation model of dynamical reputation based on the local trust evaluation in business-to-consumer e-commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.

Dynamical trust and reputation computation model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce.

SOCIAL, ATTITUDINAL AND BEHAVIORAL CORRELATES OF WEIGHT CHANGE AMONG MEXICAN AMERICAN WOMEN (OBESITY, DIET, TEXAS)

In industrialized countries the prevalence of obesity among women decreases with increasing socioeconomic status. While this relation has been amply documented, its explanation and implications for other causal factors of obesity has received much less attention. Differences in childbearing patterns, norms and attitudes about fatness, dietary behaviors and physical activity are some of the factors that have been proposed to explain the inverse relation.^ The objectives of this investigation were to (1) examine the associations among social characteristics and weight-related attitudes and behaviors, and (2) examine the relations of these factors to weight change and obesity. Information on social characteristics, weight-related attitudes, dietary behaviors, physical activity and childbearing were collected from 304 Mexican American women aged 19 to 50 living in Starr County, Texas, who were at high risk for developing diabetes. Their weights were recorded both at an initial physical examination and at a follow-up interview one to two and one-half years later, permitting the computation of current Body Mass Index (weight/height('2)) and weight change during the interval for each subject. Path analysis was used to examine direct and indirect relations among the variables.^ The major findings were: (1) After controlling for age, childbearing was not an independent predictor of weight change or Body Mass Index. (2) Neither planned exercise nor total daily physical activity were independent predictors of weight change. (3) Women with higher social characteristics scores reported less frequent meals and less use of calorically dense foods, factors associated with lower risk for weight gain. (4) Dietary intake measures were not significantly related to Body Mass Index. However, dietary behaviors (frequency of meals and snacks, use of high and low caloric density foods, eating restraint and disinhibition of restraint) did explain a significant portion (17.4 percent) of the variance in weight change, indicating the importance of using dynamic measures of weight status in studies of the development of obesity. This study highlights factors amenable to intervention to reverse or to prevent weight gain in this population, and thereby reduce the prevalence of diabetes and its sequelae. ^

Spatial perspective taking is an embodied process, but not for everyone in the same way:differences predicted by sex and social skills score

We re-analysed visuo-spatial perspective taking data from Kessler and Thomson (2010) plus a previously unpublished pilot with respect to individual- and sex differences in embodied processing (defined as body-posture congruence effects). We found that so-called 'systemisers' (males/low-social-skills) showed weaker embodiment than so-called 'embodiers' (females/high-social-skills). We conclude that 'systemisers' either have difficulties with embodied processing or, alternatively, they have a strategic advantage in selecting different mechanisms or the appropriate level of embodiment. In contrast, 'embodiers' have an advantageous strategy of "deep" embodied processing reflecting their urge to empathise or, alternatively, less flexibility in fine-tuning the involvement of bodily representations. © 2012 Copyright Taylor and Francis Group, LLC.