Structural balance emerges and explains performance in risky decision-making.

Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize

Dynamics of collective performance in collaboration networks

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members' task-related capabilities constrain the potential for the team's success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion. In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks-as indicated by the topological and spectral properties of the latter-which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions-with a prediction error of 15-25%-on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.National Science Foundation (U.S.) (Grant 1322254

Explainable Models of Performance on Networks

Networks model complex systems in myriad applications, including social media, finance, and political systems. In such settings, nodes often represent people, artificial agents, or political parties while edges portray their relationships. Interpersonal relationships change due to a person’s cognitive biases, societal roles, and what their in-group perceptions are. These relationships impact one's task performance. Often times, there is a need to estimate the underlying relationships and forecast their changes. This dissertation is at the intersection of machine learning, network science, and social science. In our studies, we use graph theory, natural language processing, and convex optimization to extract information on how to improve the performance of individuals in financial and social systems.First, by leveraging data, we study how the patterns of change in positive and negative relationships may impact the performance of stock traders. We build upon theories from sociology, namely structural balance theory—which describes the dynamics that govern the sentiment of interpersonal relationships—and assess the impact on stock traders' profitability. Our studies show traders trade best when their social network at their workplace is structurally balanced.Second, we show a generalization of structural balance theory that describes the dynamics of relationships among countries over more than two decades. We capture their dynamics using a time-varying Markov model, pinpoint the international shocks, and international conflicts. We also present rigorous proof for the convergence rate of the proposed model.Third, we collect data from human subjects answering trivia questions in teams of four. After individually answering a question, subjects collaborate on a final answer through a chat system. The participants are periodically asked to assess their appraisals of each other. We seek to find underlying factors that contribute to the awarded appraisals. We report that expertise and social confidence are the two most salient factors in determining the amount of influence one may receive. Furthermore, we build a model using message content, message times, and individual task performance to estimate the interpersonal influence matrix. Our experimental results demonstrate that the proposed neural network model surpasses baseline algorithms

Learning Deep Graph Representations via Convolutional Neural Networks

Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. Graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them. One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensional feature space. In addition, graph kernels cannot capture the high-order complex interactions between vertices. To mitigate these two problems, we propose a framework called DeepMap to learn deep representations for graph feature maps. The learnt deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood. DeepMap extends Convolutional Neural Networks (CNNs) to arbitrary graphs by aligning vertices across graphs and building the receptive field for each vertex. We empirically validate DeepMap on various graph classification benchmarks and demonstrate that it achieves state-of-the-art performance.Comment: arXiv admin note: text overlap with arXiv:2002.0984

The 1995-2018 global evolution of the network of amicable and hostile relations among nation-states

AbstractThere has been longstanding interest in the evolution of positive and negative relationships among countries. An interdisciplinary field of study, Structural Balance Theory, has developed on the dynamics of such appraisal systems. However, the advancement of research in the field has been impeded by the lack of longitudinal empirical data on large-scale networks. We construct the networks of international amicable and hostile relations occurring in specific time-periods in order to study the global evolution of the network of such international appraisals. Here we present an empirical evidence on the alignment of Structural Balance Theory with the evolution of the structure of this network, and a model of the probabilistic micro-dynamics of the alterations of international appraisals during the period 1995-2018. Also remarkably, we find that the trajectory of the Frobenius norm of sequential transition probabilities, which govern the evolution of international appraisals among nations, dramatically stabilizes

Predictive models for human–AI nexus in group decision making

Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human-AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human-AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human-AI decision making in a group setting