No Arabic abstract
We perform laboratory experiments to elucidate the role of historical information in games involving human coordination. Our approach follows prior work studying human network coordination using the task of graph coloring. We first motivate this research by showing empirical evidence that the resolution of coloring conflicts is dependent upon the recent local history of that conflict. We also conduct two tailored experiments to manipulate the game history that can be used by humans in order to determine (i) whether humans use historical information, and (ii) whether they use it effectively. In the first variant, during the course of each coloring task, the network positions of the subjects were periodically swapped while maintaining the global coloring state of the network. In the second variant, participants completed a series of 2-coloring tasks, some of which were restarts from checkpoints of previous tasks. Thus, the participants restarted the coloring task from a point in the middle of a previous task without knowledge of the history that led to that point. We report on the game dynamics and average completion times for the diverse graph topologies used in the swap and restart experiments.
Environments for decentralized on-line collaboration are now widespread on the Web, underpinning open-source efforts, knowledge creation sites including Wikipedia, and other experiments in joint production. When a distributed group works together in such a setting, the mechanisms they use for coordination can play an important role in the effectiveness of the groups performance. Here we consider the trade-offs inherent in coordination in these on-line settings, balancing the benefits to collaboration with the cost in effort that could be spent in other ways. We consider two diverse domains that each contain a wide range of collaborations taking place simultaneously -- Wikipedia and GitHub -- allowing us to study how coordination varies across different projects. We analyze trade-offs in coordination along two main dimensions, finding similar effects in both our domains of study: first we show that, in aggregate, high-status projects on these sites manage the coordination trade-off at a different level than typical projects; and second, we show that projects use a different balance of coordination when they are crowded, with relatively small size but many participants. We also develop a stylized theoretical model for the cost-benefit trade-off inherent in coordination and show that it qualitatively matches the trade-offs we observe between crowdedness and coordination.
This paper presents an experimental study to investigate the learning and decision making behavior of individuals in a human society. Social learning is used as the mathematical basis for modelling interaction of individuals that aim to perform a perceptual task interactively. A psychology experiment was conducted on a group of undergraduate students at the University of British Columbia to examine whether the decision (action) of one individual affects the decision of the subsequent individuals. The major experimental observation that stands out here is that the participants of the experiment (agents) were affected by decisions of their partners in a relatively large fraction (60%) of trials. We fit a social learning model that mimics the interactions between participants of the psychology experiment. Misinformation propagation (also known as data incest) within the society under study is further investigated in this paper.
In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users local network structure. These bursts transform users networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.
We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the networks topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.
Public stakeholders implement several policies and regulations to tackle gender gaps, fostering the change in the cultural constructs associated with gender. One way to quantify if such changes elicit gender equality is by studying mobility. In this work, we study the daily mobility patterns of women and men occurring in Medellin (Colombia) in two years: 2005 and 2017. Specifically, we focus on the spatiotemporal differences in the travels and find that purpose of travel and occupation characterise each gender differently. We show that women tend to make shorter trips, corroborating Ravensteins Laws of Migration. Our results indicate that urban mobility in Colombia seems to behave in agreement with the archetypal case studied by Ravenstein.