Peter A. Gloor

Bio: Peter A. Gloor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Social network. The author has an hindex of 37, co-authored 211 publications receiving 4918 citations. Previous affiliations of Peter A. Gloor include University of Cologne & Union Bank of Switzerland.

“No Pain No Gain”: Predicting Creativity Through Body Signals

Abstract: Creative people are highly valued in all parts of the society, be it companies, government, or private life. However, organizations struggle to identify their most creative members. Is there a “magic ingredient” that sets the most creative individuals of an organization apart from the rest of the population? This paper aims to shed light on a part of this puzzle by introducing a novel method based on analyzing body language measured with sensors. We assess an individual’s creativity with the Torrance Tests of Creative Thinking, while their body signals are tracked with the sensors of a smartwatch measuring heart rate, acceleration, vector magnitude count, and loudness. These variables are complemented with external environmental features such as light level measured by the smartwatch. In addition, the smartwatch includes a custom-built app, the Happimeter, that allows users to do mood input in a two-dimensional framework consisting of pleasance and activation. Using multilevel regression, we find that people’s creativity is predictable by their body sensor readings. We thus provide preliminary evidence that the body movement as well as environmental variables have a relationship with an individual’s creativity. The results also highlight the influence of affective states on an individual’s creativity.

E-Mail Network Patterns and Body Language Predict Risk-Taking Attitude

Abstract: As the Enron scandal and Bernie Madoff’s pyramid scheme have shown, individuals’ attitude towards ethical risks can have a huge impact on society at large. In this paper, we compare risk-taking attitudes assessed with the Domain-Specific Risk-Taking (DOSPERT) survey with individual e-mail networking patterns and body language measured with smartwatches. We find that e-mail communication signals such as network structure and dynamics, and content features as well as real-world behavioral signals measured through a smartwatch such as heart rate, acceleration, and mood state demonstrate a strong correlation with the individuals’ risk-preference in the different domains of the DOSPERT survey. For instance, we found that people with higher degree centrality in the e-mail network show higher likelihood to take social risks, while using language expressing a “you live only once” attitude indicates lower willingness to take risks in some domains. Our results show that analyzing the human interaction in organizational networks provides valuable information for decision makers and managers to support an increase in ethical behavior of the organization’s members.

Capturing "honest signals" of communication between infants and parents

Abstract: We describe a preliminary project studying interaction patterns between parents and infants age 0 to 3, using sociometric badges worn by the children and parents.

Show Me Your Moves: Analyzing Body Signals to Predict Creativity of Knowledge Workers

Abstract: We propose a novel approach to measuring the collaboration of knowledge workers, using body sensing smartwatches to capture psychometric data about individuals in a team. In a proof of concept study, we collected 2653 samples of body signals by equipping 15 people with our body sensing smartwatch over the course of 3 days during a design workshop. Additionally, we polled the users about their self-perceived team creativity at the end of each day. By employing multiple linear regression models, we found that body signals tracked by the smartwatch correlate significantly with the perceived team creativity reported by the individuals. Comparing those correlations with known predictors of creativity such as mood states and personality traits, we found that movement-related body signals predict creativity on the same accuracy level as mood states and personality traits do.

Introduction to Happimetrics

Abstract: This chapter introduces the key parts of happimetrics, the AI-based science to measure human emotions for better teamwork and higher organizational performance. Happimetrics consists of three parts: I – How do emotional reactions reflecting individual morals create entangled tribes? II – How can measuring emotions and morals create happy and successful teams? - III – How can emotions and morals be measured with AI?

Using Multivariate Statistics

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Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.