Quantifying the effects of social influence

How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to repeatedly guess the correct answer to factual questions, while having only aggregated information about the answers of others. While the response of humans to aggregated information is a widely observed phenomenon, it has not been investigated quantitatively, in a controlled setting. We found that the adjustment of individual guesses depends linearly on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple, statistical regularity. It holds across all questions analysed, even though the correct answers differ in several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. It also complements previous results on the nonlinear response in information-rich scenarios. We argue that the nature of the response to social influence crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence.Comment: 3 figure

SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Mitigating Overexposure in Viral Marketing

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselinesComment: In AAAI-1

Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels

Organisms can affect one another’s phenotypes when they socially interact. Indirect genetic effects occur when an individual’s phenotype is affected by genes expressed in another individual. These heritable effects can enhance or reduce adaptive potential, thereby accelerating or reversing evolutionary change. Quantifying these social effects is therefore crucial for our understanding of evolution, yet estimates of indirect genetic effects in wild animals are limited to dyadic interactions. We estimated indirect phenotypic and genetic effects, and their covariance with direct effects, for the date of spring breeding in North American red squirrels (Tamiasciurus hudsonicus) living in an array of territories of varying spatial proximity. Additionally, we estimated indirect effects and the strength of selection at low and high population densities. Social effects of neighbours on the date of spring breeding were different from zero at high population densities but not at low population densities. Indirect phenotypic effects accounted for a larger amount of variation in the date of breeding than differences attributable to the among‐individual variance, suggesting social interactions are important for determining breeding dates. The genetic component to these indirect effects was however not statistically significant. We therefore showcase a powerful and flexible method that will allow researchers working in organisms with a range of social systems to estimate indirect phenotypic and genetic effects, and demonstrate the degree to which social interactions can influence phenotypes, even in a solitary species.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149549/1/jeb13437_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149549/2/jeb13437.pd

Cities and Satellites: Spatial Effects and Unobserved Heterogeneity in the Modeling of Urban Growth

The confluence of factors driving urban growth is highly complex, resulting from a combination of ecological and social determinants that co-evolve over time and space. Identifying these factors and quantifying their impact necessitates models that capture both why urbanization happens as well as where and when it happens. Using a database that links five satellite images spanning 1976–2001 to a suite of socioeconomic, ecological and GIS created explanatory variables, this study develops a spatial-temporal model of the determinants of built-up area across a 25,900 square kilometer swath across central North Carolina. Extensive conversion of forest and agricultural land over the last decades is modeled using the complementary log-log derivation of the proportional hazards model, thereby affording a means for modeling continuous- time landscape change using discrete-time satellite data. To control for unobserved heterogeneity, the model specification includes an error component that is Gamma distributed. Results confirm the hypothesis that the landscape pattern surrounding a pixel has a major influence on the likelihood of its conversion and, moreover, that the omission of external spatial effects can lead to biased inferences regarding the influence of other covariates, such as proximity to road. Cartographic and nonparametric validation exercises illustrate the utility of the model for policy simulation.Urban growth, landscape pattern, satellite imagery, hazard model,North Carolina

Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordOrganisms can affect one another’s phenotypes when they socially interact. Indirect genetic effects occur when an individual’s phenotype is affected by genes expressed in another individual. These heritable effects can enhance or reduce adaptive potential, thereby accelerating or reversing evolutionary change. Quantifying these social effects is therefore crucial for our understanding of evolution, yet estimates of indirect genetic effects in wild animals are limited to dyadic interactions. We estimated indirect phenotypic and genetic effects, and their covariance with direct effects, for the date of spring breeding in North American red squirrels (Tamiasciurus hudsonicus) living in an array of territories of varying spatial proximity. Additionally, we estimated indirect effects and the strength of selection at low and high population densities. Social effects of neighbours on the date of spring breeding were different from zero at high population densities but not at low population densities. Indirect phenotypic effects accounted for a larger amount of variation in the date of breeding than differences attributable to the among-individual variance, suggesting social interactions are important for determining breeding dates. The genetic component to these indirect effects was however not statistically significant. We therefore showcase a powerful and flexible method that will allow researchers working in organisms with a range of social systems to estimate indirect phenotypic and genetic effects, and demonstrate the degree to which social interactions can influence phenotypes, even in a solitary species

Co-dynamics of climate policy stringency and public support

Unidad de excelencia María de Maeztu CEX2019-000940-MAcord transformatiu CRUE-CSICPublic support for stringent climate policies is currently weak. We develop a model to study the dynamics of public support for climate policies. It comprises three interconnected modules: one calculates policy impacts; a second translates these into policy support mediated by social influence; and a third represents the regulator adapting policy stringency depending on public support. The model combines general-equilibrium and agent-based elements and is empirically grounded in a household survey, which allows quantifying policy support as a function of effectiveness, personal wellbeing and distributional effects. We apply our approach to compare two policy instruments, namely carbon taxation and performance standards, and identify intertemporal trajectories that meet the climate target and count on sufficient public support. Our results highlight the importance of social influence, opinion stability and income inequality for public support of climate policies. Our model predicts that carbon taxation consistently generates more public support than standards. Finally, we show that under moderate social influence and income inequality, an increasing carbon tax trajectory combined with progressive revenue redistribution receives the highest average public support over time

The Effect of Content on Global Internet Adoption and the Global “Digital Divide”

A country’s human capital and economic productivity increasingly depend on the Internet due to its expanding role in providing information and communications. This has prompted a search for ways to increase Internet adoption and narrow its disparity across countries – the global “digital divide.” Previous work has focused on demographic, economic, and infrastructure determinants of Internet access difficult to change in the short run. Internet content increases adoption and can be changed more quickly; however, the magnitude of its impact and therefore its effectiveness as a policy and strategy tool is previously unknown. Quantifying content’s role is challenging because of feedback (network effects) between content and adoption: more content stimulates adoption which in turn increases the incentive to create content. We develop a methodology to overcome this endogeneity problem. We find a statistically and economically significant effect, implying that policies promoting content creation can substantially increase adoption. Because it is ubiquitous, Internet content is also useful to affect social change across countries. Content has a greater effect on adoption in countries with more disparate languages, making it a useful tool to overcome linguistic isolation. Our results offer guidance for policy makers on country characteristics that influence adoption’s responsiveness to content and for Internet firms on where to expand internationally and how to quantify content investments.Internet, technology adoption, economic development, two-sided markets, network effects, technology diffusion, language, content

Selection, inheritance, and the evolution of parent-offspring interactions

Very few studies have examined parent-offspring interactions from a quantitative genetic perspective. We used a cross-fostering design and measured genetic correlations and components of social selection arising from two parental and two offspring behaviors in the burying beetle Nicrophorus vespilloides. Genetic correlations were assessed by examining behavior of relatives independent of common social influences. We found positive genetic correlations between all pairs of behaviors, including between parent and offspring behaviors. Patterns of selection were assessed by standardized performance and selection gradients. Parental provisioning had positive effects on offspring performance and fitness, while remaining near the larvae without feeding them had negative effects. Begging had positive effects on offspring performance and fitness, while increased competition among siblings had negative effects. Coadaptations between parenting and offspring behavior appear to be maintained by genetic correlations and functional trade-offs; parents that feed their offspring more also spend more time in the area where they can forage for themselves. Families with high levels of begging have high levels of sibling competition. Integrating information from genetics and selection thus provides a general explanation for why variation persists in seemingly beneficial traits expressed in parent-offspring interactions and illustrates why it is important to measure functionally related suites of behaviors