A structurational group decision-making perspective on the commons dilemma: results from an online public goods game

Pilny, Andrew; Poole, Marshall Scott; Reichelmann, Ashley V.; Klein, Brennan

doi:10.1080/00909882.2017.1355559

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…In order to understand human behavior, there has been significant interest in using online systems to carry out social science experiments. These experiments analyze a variety of phenomena, like collective identity [ 17 , 60 , 61 ], and cooperation and contagion [ 62 ], to name a few. The methodological and practical challenges of online interactive experimentation, and the value of an online labor market has been discussed in different studies [ 63 , 64 ].…”

Section: Related Workmentioning

confidence: 99%

“…Research teams use different options to design and deploy their online experiments. While some teams, create web-based programs especially designed for their research [ 17 , 61 , 62 ], others use web-based experimental platforms that provide this service [ 60 , 63 ]. In [ 60 ] the online platform Volunteer Science [ 35 ] was used to implement a web-based public goods experiment, and to recruit participants around the world.…”

Section: Related Workmentioning

confidence: 99%

“…While some teams, create web-based programs especially designed for their research [ 17 , 61 , 62 ], others use web-based experimental platforms that provide this service [ 60 , 63 ]. In [ 60 ] the online platform Volunteer Science [ 35 ] was used to implement a web-based public goods experiment, and to recruit participants around the world. In [ 63 ], a repeated public goods experiment was implemented in the free web-based platform for interactive online experiments, LIONESS [ 36 ], and participants were recruited via Amazon Mechanical Turk (MTurk).…”

Section: Related Workmentioning

confidence: 99%

“…Experiments with interacting participants can be represented as networks, where edges represent interaction channels. There are several online and in-person experiments with individuals [ 60 , 61 , 65 – 69 ] and groups [ 1 , 3 – 6 , 9 ]. Some include modeling of the experiment [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

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Data analysis and modeling pipelines for controlled networked social science experiments

Cedeno-Mieles

Ren

et al. 2020

PLoS ONE

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There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

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