Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework

Cedeno-Mieles, Vanessa; Hu, Zhihao; Ren, Yihui; Deng, Xinwei; Adiga, Abhijin; Barrett, Christopher L.; Contractor, Noshir; Ekanayake, Saliya; Epstein, Joshua M.; Goode, Brian J.; Korkmaz, Gizem; Kuhlman, Chris J.; Machi, Dustin; Macy, Michael W.; Marathe, Madhav V.; Ramakrishnan, Naren; Ravi, S. S.; Saraf, Parang; Self, Nathan

doi:10.1007/s13278-019-0620-8

Cited by 11 publications

(4 citation statements)

References 132 publications

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“…Empirical context for our pipelines. The works of [7,22,45] demonstrate the usefulness of our pipeline system, where collective identity was studied via online experiments and modeling of them. That is, these provide empirical context where our software tools are important.…”

Section: Plos Onementioning

confidence: 99%

“…The work [45] produced additional models for the individual actions of players (word formation, letter requests of game neighbors, and replies of letters to neighbors' letter requests) in the anagram game. Although all three of the works [7,22,45] used the software pipelines of this work, there is no mention nor description of the software pipelines in them. The purpose of our work is to describe the software pipeline system for general NESS experiments.…”

Section: Plos Onementioning

confidence: 99%

“…Analogous works that also provide context are experiments in [ 1 – 6 ]. Returning to [ 7 , 22 , 45 ], these works demonstrated that CI could be formed among players in a group anagram game, where multiple players interact with their assigned neighbors to form words from collections of letters. Devised and implemented in the software, games were played online, through players’ web browsers.…”

Section: Solution Overview and Contributionsmentioning

confidence: 99%

“…In abductive looping , experiments are performed first, patterns are searched for in the experimental data, and this information is used to construct and modify models. Detailed abductive looping examples for the study of collective identity in the social sciences are provided in [ 7 , 22 ]. Fig 1 provides one representation of the steps in abductive looping.…”

Section: Introductionmentioning

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

Self Cite

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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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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Solution Overview and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data analysis and modeling pipelines for controlled networked social science experiments

Cedeno-Mieles

Ren

et al. 2020

PLoS ONE

Self Cite

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Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games

Deng

Kuhlman

2022

Complex Networks &Amp; Their Applications X

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Trust as a determinant of social welfare in the digital economy

Shah,

Shah

2024

Soc. Netw. Anal. Min.

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Trust plays a critical role in determining social welfare in the digital economy and has significant socioeconomic implications. To investigate the relationship between trust and social welfare in this context, we developed a social welfare function that accurately quantifies the positive impact of the digital economy. Employing a Markov analysis framework, we modelled trust as a variable that evolves based on the agent's interactions with the system, characterizing the system's dynamic behaviour as a series of probabilistic transitions between states. We ran multiple simulations to gather a significant sample of possible outcomes and analyzed the statistical correlation between trust and social welfare. We used the time it takes for the digital economy to converge towards the non-digital economy as a metric to compare the performance of the separate economies. We controlled for other factors influencing social welfare, such as the number of transactions and market competition levels. We compared the actual outcomes of the simulation study with our theoretical predictions. Results indicate a significant impact of trust in maximising social welfare by achieving a steady state in fewer iterations for a developed digital economy. The socioeconomic benefits of trust are more visible in a digital than in a non-digital economy. Technological progress or innovation is crucial to attaining responsible consumption and production patterns. The digital economy's sustainability hinges on trust, which policymakers must prioritize in light of trust erosion. An inclusive approach considering cultural and socioeconomic factors is required to increase public trust in institutions and government. Policymakers may consider measures such as enhancing data privacy, promoting digital literacy, and developing effective legal frameworks that support trust-building. By prioritizing these areas, policymakers can build a more sustainable and equitable digital economy that maximizes social welfare.

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Networked experiments and modeling for producing collective identity in a group of human subjects using an iterative abduction framework

Cited by 11 publications

References 132 publications

Data analysis and modeling pipelines for controlled networked social science experiments

Data analysis and modeling pipelines for controlled networked social science experiments

Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games

Trust as a determinant of social welfare in the digital economy

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