The role of decision confidence in advice-taking and trust formation

In a world where ideas flow freely between people across multiple platforms, we often find ourselves relying on others' information without an objective standard to judge whether those opinions are accurate. The present study tests an agreement-in-confidence hypothesis of advice perception, which holds that internal metacognitive evaluations of decision confidence play an important functional role in the perception and use of social information, such as peers' advice. We propose that confidence can be used, computationally, to estimate advisors' trustworthiness and advice reliability. Specifically, these processes are hypothesized to be particularly important in situations where objective feedback is absent or difficult to acquire. Here, we use a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors whose opinions are provided to participants performing a perceptual decision making task. We find that when advisors' and participants' judgments are independent, people are able to discriminate subtle advice features, like confidence calibration, whether or not objective feedback is available. However, when observers' judgments (and judgment errors) are correlated - as is the case in many social contexts - predictable distortions can be observed between feedback and feedback-free scenarios. A simple model of advice reliability estimation, endowed with metacognitive insight, is able to explain key patterns of results observed in the human data. We use agent-based modeling to explore implications of these individual-level decision strategies for network-level patterns of trust and belief formation

Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Co-evolution of Selection and Influence in Social Networks

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are influenced by the changing network structure. We derive an efficient variational inference procedure for our model, and validate the model on both synthetic and real-world data.Comment: In Proc. of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11

Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability

Notwithstanding the usefulness of system dynamics in analyzing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging field in network science, to facilitate the detection of effective leverage points in system dynamics models and thus to support the design of influential policies. We illustrate our approach by analyzing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary final state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a significantly larger impact on the system behavior compared to low ranked variables

Forecasting audience increase on YouTube

User profiles constructed on Social Web platforms are often motivated by the need to maximise user reputation within a community. Subscriber, or follower, counts are an indicator of the influence and standing that the user has, where greater values indicate a greater perception or regard for what the user has to say or share. However, at present there lacks an understanding of the factors that lead to an increase in such audience levels, and how a user’s behaviour can a!ect their reputation. In this paper we attempt to fill this gap, by examining data collected from YouTube over regular time intervals. We explore the correlation between the subscriber counts and several behaviour features - extracted from both the user’s profile and the content they have shared. Through the use of a Multiple Linear Regression model we are able to forecast the audience levels that users will yield based on observed behaviour. Combining such a model with an exhaustive feature selection process, we yield statistically significant performance over a baseline model containing all features