Algorithmic and human prediction of success in human collaboration from visual features

Abstract As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a...

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Autores principales:	Martin Saveski, Edmond Awad, Iyad Rahwan, Manuel Cebrian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/90740de6a5a4461b940d63f1d79ae8e0
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spelling	oai:doaj.org-article:90740de6a5a4461b940d63f1d79ae8e02021-12-02T14:06:11ZAlgorithmic and human prediction of success in human collaboration from visual features10.1038/s41598-021-81145-32045-2322https://doaj.org/article/90740de6a5a4461b940d63f1d79ae8e02021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81145-3https://doaj.org/toc/2045-2322Abstract As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.Martin SaveskiEdmond AwadIyad RahwanManuel CebrianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Martin Saveski Edmond Awad Iyad Rahwan Manuel Cebrian Algorithmic and human prediction of success in human collaboration from visual features
description	Abstract As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.
format	article
author	Martin Saveski Edmond Awad Iyad Rahwan Manuel Cebrian
author_facet	Martin Saveski Edmond Awad Iyad Rahwan Manuel Cebrian
author_sort	Martin Saveski
title	Algorithmic and human prediction of success in human collaboration from visual features
title_short	Algorithmic and human prediction of success in human collaboration from visual features
title_full	Algorithmic and human prediction of success in human collaboration from visual features
title_fullStr	Algorithmic and human prediction of success in human collaboration from visual features
title_full_unstemmed	Algorithmic and human prediction of success in human collaboration from visual features
title_sort	algorithmic and human prediction of success in human collaboration from visual features
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/90740de6a5a4461b940d63f1d79ae8e0
work_keys_str_mv	AT martinsaveski algorithmicandhumanpredictionofsuccessinhumancollaborationfromvisualfeatures AT edmondawad algorithmicandhumanpredictionofsuccessinhumancollaborationfromvisualfeatures AT iyadrahwan algorithmicandhumanpredictionofsuccessinhumancollaborationfromvisualfeatures AT manuelcebrian algorithmicandhumanpredictionofsuccessinhumancollaborationfromvisualfeatures
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Algorithmic and human prediction of success in human collaboration from visual features

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