Noshir Contractor presented at Management Science Workshop 2019 in Santa Cruz, Chile
Professor Contractor presented, “Using optimization methods to benchmark performance in space teams” at Management Science Workshop 2019 in Santa Cruz, Chile.
The presentation was about providing a performance measurement method based on optimization techniques for Multi-Team Systems (MTS). The method is tested on an MTS for Project RED -a project funded by the National Aeronautics and Space Administration (NASA). The main goal of the MTS is planning the development of a water well to support a human colony on Mars.
This workshop is sponsored by the Department of Industrial Engineering, Universidad de Chile and Complex Engineering Systems Institute. The workshop was successful in bringing together researchers and innovators from many leading universities and business schools such as MIT, Management Sloan School, U.C Berkeley, Northwestern, Yale University, etc. to connect, meet and establish channels for collaboration.
Citation: Izadinia, N., DeChurch, L., Contractor, N., & Waechter, A. (2019, January). Introducing a measure for calculating the efficiency of work in space. Paper presented at 2019 Management Science Workshop, Santa Cruz, Chile.
Acknowledgment: This research is funded by the National Aeronautics and Space Administration (NASA), Award number NNX15AK73G
SONIC Speaker Series presents: Anirban Mukherjee
The SONIC Speaker Series presents
Anirban Mukherjee
Marketing Faculty at INSEAD
Investigating the Multiple-Source Effect in Product-Pitch Videos
SONIC Lab is proud to welcome Prof. Mukherjee of INSEAD. He will speak on Tuesday, April 2nd, 2019 at 11 aM in Frances Searle Building, Room 1-483. Please contact Brent Hoagland with any questions.
Abstract:
Conventional wisdom suggests that having multiple speakers (“sources”) deliver content enhances persuasion. Laboratory evidence confirms the folk knowledge and terms it the “multiple-source effect.” As the prior evidence was developed in the behavioral laboratory, it derives from relatively simple laboratory stimuli conveying information on far fewer topics than is typical in the real-world marketplace. Our study addresses this limitation. We investigate the multiple-source effect in all (more than 30,000) product-pitch videos in nine product-innovation-related categories on Kickstarter, an online crowdfunding portal, since its inception in April 2009 to mid-February 2017. We use deep-learning models to algorithmically measure the number of speakers, transcribe and analyze the spoken content, and measure other audial and visual control variables. We document a novel boundary condition of the multiple-source effect—the effect depends on the number of topics discussed in the video. In simpler videos discussing fewer topics, which is more similar to stimuli in prior laboratory studies, we corroborate prior findings that having more speakers leads to more funding. However, in more complex videos discussing more topics, we find that having more speakers does not affect funding. The latter is consistent with the literature on information overload. More broadly, our research demonstrates the potential of deep learning to enable the analysis of large-scale audio and video data in order to investigate human behavior in real-world settings.
Prof. Mukherjee is Visiting Assistant Professor of Marketing at INSEAD and Fellow of the Institute on Asian Consumer Insight at Nanyang Technological University. Prior to INSEAD, Prof. Mukherjee was Assistant Professor of Marketing at the Lee Kong Chian School of Business at Singapore Management University. He holds a B.Sc. in Electrical and Computer Engineering (2003), and a M.Sc. and Ph.D. in Marketing (2008, 2009), from Cornell University. He studied at The Doon School, Dehra Dun (353 KA, 1999).
Prof. Mukherjee is an expert in quantitative and computational marketing methodology. He develops and applies cutting-edge methods to managerially and substantively important marketing phenomena. His work has been published in prestigious journals (such as the Journal of Marketing Research, Journal of Retailing, and Management Science), featured in popular press outlets (including Forbes), and received several awards (including several best paper awards). He has been invited to give research talks at numerous prestigious universities and he consults and teaches for several major companies (such as IBM, LinkedIn, and Sony).
Prof. Mukherjee is an expert in quantitative and computational marketing methodology. He develops and applies cutting-edge methods to managerially and substantively important marketing phenomena. His work has been published in prestigious journals (such as the Journal of Marketing Research, Journal of Retailing, and Management Science), featured in popular press outlets (including Forbes), and received several awards (including several best paper awards). He has been invited to give research talks at numerous prestigious universities and he consults and teaches for several major companies (such as IBM, LinkedIn, and Sony).
Watch our most recent SONIC Speaker – @gvegayon
The SONIC Speaker Series presents
George G. Vega Yon
Department of Preventive Medicine at USC’s Keck School of Medicine
Big Problems for Small Networks: Statistical Analysis of Small Networks and Team Performance
SONIC Lab is proud to welcome George G. Vega Yon of USC’s Keck School of Medicine. George will speak on Wednesday, March 20th, 2019 at 2PM in Frances Searle Building, Room 1-483 with a workshop to follow. Please contact Brent Hoagland with any questions.
Abstract:
Small network data such as team, family, or personal networks, is common in many fields that study social networks. Although the analysis of small networks may appear simplistic relative to the difficulties posed by “big” datasets, there are at least two key challenges: (1) fitting statistical models to explain the network structure in small groups, and (2) testing if structural properties of small networks are associated with group-level outcomes; for example, team performance. In this presentation, we introduce two new statistical methods that use a revisited version of Exponential Random Graph Models (ERGMs) in the context of small networks. Using exhaustive enumeration of networks in the support, we are able to calculate exact likelihood functions for ERGMs, which allows us to obtain maximum likelihood estimates directly (without using simulations), avoiding common problems that arise from methods that rely on approximations instead. This is joint work with Prof. Kayla de la Haye (USC).
A workshop on the R packages ergmito and gnet for applying the methods introduced during the talk will be conducted.
George G. Vega Yon is a Biostatistics Ph.D student and Research Programmer in the Department of Preventive Medicine at USC’s Keck School of Medicine. His interests are in computational statistics and scientific software development. Most recently, his research has focused on the development of statistical methods for both phylogenetics and social network analysis. He holds a MS degree in Economics from Caltech, and a MA in Economics and Public Policy from Universidad Adolfo Ibáñez, Chile.
Large teams develop and small teams disrupt science and technology
A research article by Lingfei Wu, Dashun Wang, and James A. Evans.
One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology, or the complexity of modern problems that require interdisciplinary solutions. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast, contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future—if at all. Observed differences between small and large teams are magnified for higher-impact work, with small teams known for disruptive work and large teams for developing work. Differences in topic and research design account for a small part of the relationship between team size and disruption; most of the effect occurs at the level of the individual, as people move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, and suggest that, to achieve this, science policies should aim to support a diversity of team sizes.
Link: https://www.nature.com/articles/s41586-019-0941-9
SONIC Speaker Series presents: George G. Vega Yon
The SONIC Speaker Series presents
George G. Vega Yon
Department of Preventive Medicine at USC’s Keck School of Medicine
Big Problems for Small Networks: Statistical Analysis of Small Networks and Team Performance
SONIC Lab is proud to welcome George G. Vega Yon of USC’s Keck School of Medicine. George will speak on Wednesday, March 20th, 2019 at 2PM in Frances Searle Building, Room 1-483 with a workshop to follow. Please contact Brent Hoagland with any questions.
Abstract:
Small network data such as team, family, or personal networks, is common in many fields that study social networks. Although the analysis of small networks may appear simplistic relative to the difficulties posed by “big” datasets, there are at least two key challenges: (1) fitting statistical models to explain the network structure in small groups, and (2) testing if structural properties of small networks are associated with group-level outcomes; for example, team performance. In this presentation, we introduce two new statistical methods that use a revisited version of Exponential Random Graph Models (ERGMs) in the context of small networks. Using exhaustive enumeration of networks in the support, we are able to calculate exact likelihood functions for ERGMs, which allows us to obtain maximum likelihood estimates directly (without using simulations), avoiding common problems that arise from methods that rely on approximations instead. This is joint work with Prof. Kayla de la Haye (USC).
A workshop on the R packages ergmito and gnet for applying the methods introduced during the talk will be conducted.
George G. Vega Yon is a Biostatistics Ph.D student and Research Programmer in the Department of Preventive Medicine at USC’s Keck School of Medicine. His interests are in computational statistics and scientific software development. Most recently, his research has focused on the development of statistical methods for both phylogenetics and social network analysis. He holds a MS degree in Economics from Caltech, and a MA in Economics and Public Policy from Universidad Adolfo Ibáñez, Chile.
Networks in the News – The Determinants of Sharing Strategy in a Wi-Fi Sharing Game
A new study by the Human Nature Lab at Yale University explored how people allocate a limited, but personally usable, resource (e.g., unused Wi-Fi bandwidth) to their neighbors. Based on results from a Wi-Fi sharing game that the authors developed, the study found that (a) network density (i.e., the extent which people are connected with each other in the network) impacts the inequality of Wi-Fi sharing, and (b) those who benefit from Wi-Fi sharing at most tend to have many neighbors who in turn have few neighbors.
If you’re interested in the study, it is available: https://www.nature.com/articles/s41467-019-08935-2
SONIC Speaker – George G. Vega Yon March 20th at 2pm
Please join the SONIC Lab in Frances Searle Room 1-483 on March 20th at 2pm to welcome SONIC Speaker, George G. Vega Yon of USC’s Keck School of Medicine, who will present his talk “Big Problems for Small Networks: Statistical Analysis of Small Networks and Team Performance.” Followed by a workshop on R packages ‘ergmito’ and ‘gnet’ for applying the methods introduced during the talk.
Abstract:
Small network data such as team, family, or personal networks, is common in many fields that study social networks. Although the analysis of small networks may appear simplistic relative to the difficulties posed by “big” datasets, there are at least two key challenges: (1) fitting statistical models to explain the network structure in small groups, and (2) testing if structural properties of small networks are associated with group-level outcomes; for example, team performance. In this presentation, we introduce two new statistical methods that use a revisited version of Exponential Random Graph Models (ERGMs) in the context of small networks. Using exhaustive enumeration of networks in the support, we are able to calculate exact likelihood functions for ERGMs, which allows us to obtain maximum likelihood estimates directly (without using simulations), avoiding common problems that arise from methods that rely on approximations instead. This is joint work with Prof. Kayla de la Haye (USC).
Press Coverage: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium’
Publication: The Times of India
Headline: IIT-Ms web science symposium to help community exchange ideas
Headline: IIT-Ms web science symposium to help community exchange ideas
Publication: The New Indian Express (clip attached)
Headline: IIT–Madras hosts web science symposium
Headline: IIT–Madras hosts web science symposium
Publication: UNI
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium‘
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium‘
Publication: Dinamani
Headline: சென்னை ஐஐடி-யில் இணைய அறிவியல் கருத்தரங்கம் (IIT Madras hosts Web Sciencesymposium)
Publication: BW Education
Headline: IIT Madras Hosts The First RBCDSAI ‘Web Science Symposium’
Publication: DT Next
Headline: IIT-M hosts two-day Web Science Symposium
Headline: IIT-M hosts two-day Web Science Symposium
Publication: Infodea
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium’
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium’
Publication: Techi Expert
Headline: IIT Madras Hosts The First RBCDSAI ‘Web Science Symposium’
Headline: IIT Madras Hosts The First RBCDSAI ‘Web Science Symposium’
Publication: Penn News
Headline: IIT Madras hosts the first ‘Web Science Symposium’
Headline: IIT Madras hosts the first ‘Web Science Symposium’
Publication: The Hindu Tamil
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium’
Publication: India Education Diary
Headline: IIT Madras hosts the first RBCDSAI ‘Web Science Symposium’
Publication: The Statesman
Headline: Exchanging ideas
Welcoming our latest high school intern – Steven Ding
We are delighted to welcome Steven Ding to SONIC for the next couple of weeks. Steven is originally from Beijing and is currently a junior at St. Andrews High School in Delaware. He is interested in study behavioral economics and the psychology of decision making. While at SONIC, he hopes to learn more about research, handling data, and network analysis.