How network theory predicts the value of Bitcoin

A recent research by Spencer Wheatley at ETH Zurich in Switzerland and a few colleagues shows that the key measure of value for cryptocurrencies is the network of people who use them. What’s more, they say, once Bitcoin is valued in this way it becomes possible to see when it is overvalued and perhaps even to spot the telltale signs that a market crash is imminent.

Read the complete article here:

And the article published by MIT Technology Review here:

Computational Social Science ≠ Computer Science + Social Data

Hanna Wallach published a thought piece of what computational social science is, especially from her computer science point of view. Given computational social science in mind, She made points of differences between computer science and social science in terms of goals, models, data, and challenges:

  •  Goals: Prediction vs. explanation — “[C]omputer scientists may be interested in finding the needle in the haystack—such as […] the right Web page to display from a search—but social scientists are more commonly interested in characterizing the haystack.”
  • Models: “Models for prediction are often intended to replace human interpretation or reasoning, whereas models for explanation are intended to inform or guide human reasoning.”
  • Data: “Computer scientists usually work with large-scale, digitized datasets, often collected and made available for no particular purpose other than “machine learning research.” In contrast, social scientists often use data collected or curated in order to answer specific questions.”
  • Challenges: Datasets consisting of social phenomena raised ethical concerns regarding privacy, fairness, and accountability — “they may be new to most computer scientists, but they are not new to social scientists.”


She concludes her article saying that “we need to work with social scientists in order to understand the ethical implications and consequences of our modeling decisions.”

The article is available here.



Cooperation, clustering, and assortative mixing in dynamic networks

A recent study by David Melamed and his colleagues examined whether the emergent structures that promote cooperation are driven by reputation or can emerge purely via dynamics. To answer the research question, they recruited 1,979 Amazon Mechanical Turkers and asked them to play an iterated prisoner’s dilemma game. Further, these participants were randomly assigned one of 16 experimental conditions. Results of the experiments show that dynamic networks yield high rates of cooperation even without reputational knowledge. Additionally, the study found that the targeted choice condition in static networks yields cooperation rates as high as those in dynamic networks.

The original article is available here.

Students’ social interactions and daily routines to make predictions about freshman retention

Sudha Ram’s Smart Campus research tracks students’ social interactions and daily routines via their CatCard usage — and leverages that information to make predictions about freshman retention. The goal of Ram’s Smart Campus research is to help educational institutions repurpose the data already being captured from student ID cards to identify those most at risk for not returning after their first year of college. Ram found that social integration and routine were stronger predictors than end-of-term grades, which is one of the more traditionally used predictors of freshman retention in higher education.
Read the article here.

Similar neural responses predict friendship

By Carolyn Parkinson, Adam M. Kleinbaum, & Thalia Wheatley

Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects’ brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction.

Read the article here.

Social influence and discourse similarity networks in workgroups

by Johanne Saint-Charles and Pierre Mongeau

Adopting a socio-semantic perspective, this study aims to verify the relation between social influence and discourse similarity networks in workgroups and explore its modification over time. Data consist of video transcripts of 45, 3-h group meetings and weekly sociometric questionnaires. Relation between tie strength, actor centrality within the influence network, and shared elements of discourse between group members are examined over time. Observed correlations support the hypothesis of a relation between social influence and discourse similarity. Changes over time suggest a similarity threshold above which the relation between similarity and influence is reversed.

Read the full article here.

Population structured by witchcraft beliefs

Anthropologists have long argued that fear of victimization through witchcraft accusations promotes cooperation in small-scale societies. Others have argued that witchcraft beliefs undermine trust and therefore reduce social cohesion. However, there are very few, if any, quantified empirical examples demonstrating how witchcraft labels can structure cooperation in real human communities. Here we show a case from a farming community in China where people labelled zhu were thought capable of supernatural activity, particularly poisoning food. The label was usually applied to adult women heads of household and often inherited down the female line. We found that those in zhuhouseholds were less likely to give or receive gifts or farm help to or from non-zhu households; nor did they have sexual partnerships or children with those in non-zhu households. However, those in zhuhouseholds did preferentially help and reproduce with each other. Although the tag is common knowledge to other villagers and used in cooperative and reproductive partner choice, we found no evidence that this assortment was based on cooperativeness or quality. We favour the explanation that stigmatization originally arose as a mechanism to harm female competitors. Once established, fear that the trait is transmissible may help explain the persistence of this deep-rooted cultural belief.

Source: Nature