Bayesian Networks for Social Modeling

Whitney, Paul D.; White, A. M.; Walsh, Stephen J.; Dalton, Angela C.

doi:10.1007/978-3-642-19656-0_33

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…While BNs have been used to model social-behavioural mechanisms in other contexts [17,19,57], this is the first attempt to use BNs to analyse the processes underlying labour migration. The results of our study confirm that BNs are a useful tool for modelling complex systems and assisting realist evaluations.…”

Section: Empirical and Methodological Contributionsmentioning

confidence: 99%

“…These approaches are being used to examine how causes work together to produce outcomes instead of focusing solely on specific causal effects of single risk factors [2]. Agent-based models, microsimulation, dynamic systems, data mining and Bayesian Networks (BNs) are examples of these applications [6,7,11,17].…”

Section: Realist Evaluation Of Complex Social Systemsmentioning

confidence: 99%

“…BNs can combine a priori knowledge with empirical data, cope well with missing values and predict the effects of different interventions on the outcome variables of interest. BNs can model non-linear interactions, multiple causal pathways, population heterogeneity and changes over time and across contexts [17,22]. They can be a powerful tool to inform intervention development and evaluation of complex interventions [54][55][56][57][58].…”

Section: Why Bayesian Network?mentioning

confidence: 99%

See 2 more Smart Citations

The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking

Kiss

Fotheringhame²,

Mak

et al. 2020

J Comput Soc Sc

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Complex systems and realist evaluation offer promising approaches for evaluating social interventions. These approaches take into account the complex interplay among factors to produce outcomes, instead of attempting to isolate single causes of observed effects. This paper explores the use of Bayesian networks (BNs) in realist evaluation of interventions to prevent complex social problems. It draws on the example of the theory-based evaluation of the Work in Freedom Programme (WIF), a large UK-funded anti-trafficking intervention by the International Labour Organisation in South Asia. We used BN to explore causal pathways to human trafficking using data from 519 Nepalese returnee migrants. The findings suggest that risks of trafficking are mostly determined by migrants’ destination country, how they are recruited and in which sector they work. These findings challenge widely held assumptions about individual-level vulnerability and emphasize that future investments will benefit from approaches that recognise the complexity of an intervention’s causal mechanisms in social contexts. BNs are a useful approach for the conceptualisation, design and evaluation of complex social interventions.

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Section: Empirical and Methodological Contributionsmentioning

confidence: 99%

Section: Realist Evaluation Of Complex Social Systemsmentioning

confidence: 99%

Section: Why Bayesian Network?mentioning

confidence: 99%

See 1 more Smart Citation

The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking

Kiss

Fotheringhame²,

Mak

et al. 2020

J Comput Soc Sc

View full text Add to dashboard Cite

show abstract

“…As a marriage of causality and probability theories, BNs convey knowledge of data-generating process and are capable of identifying and inferring causation in both experimental and observational data. In this regard, BNs received a great amount of attention from various scientific fields such as reverse engineering of gene regulatory network (Baldi and Long, 2001;Hartemink et al, 2001;Xiao et al, 2015) and explanations of social phenomena (Whitney et al, 2011;Farasat et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

bAIcis: A Novel Bayesian Network Structural Learning Algorithm and Its Comprehensive Performance Evaluation Against Open-Source Software

Zhang¹,

Rodrigues²,

Narain³

et al. 2020

Journal of Computational Biology

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Structural learning of Bayesian networks (BNs) from observational data has gained increasing applied use and attention from various scientific and industrial areas. The mathematical theory of BNs and their optimization is well developed. Although there are several open-source BN learners in the public domain, none of them are able to handle both small and large feature space data and recover network structures with acceptable accuracy. bAIcis Ò is a novel BN learning and simulation software from BERG. It was developed with the goal of learning BNs from ''Big Data'' in health care, often exceeding hundreds of thousands features when research is conducted in genomics or multi-omics. This article provides a comprehensive performance evaluation of bAIcis and its comparison with the open-source BN learners. The study investigated synthetic datasets of discrete, continuous, and mixed data in small and large feature space, respectively. The results demonstrated that bAIcis outperformed the publicly available algorithms in structure recovery precision in almost all of the evaluated settings, achieving the true positive rates of 0.9 and precision of 0.8. In addition, bAIcis supports all data types, including continuous, discrete, and mixed variables. It is effectively parallelized on a distributed system and can work with datasets of thousands of features that are infeasible for any of the publicly available tools with a desired level of recovery accuracy.

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“…Classes also can be represented in percentages or as absolute values, and features may be associated with more than one class to different degrees. Baynesian networks have been used to synthesize the findings from these separate studies of sociology, biology and economics [20], and can be used for representing and predicting social behaviors [21]. However, they assume parent/child relations between variables, while in our problem we are assuming independence between class-condition feature probabilities.…”

mentioning

confidence: 99%

Application of the Naive Bayes Classifier for Representation and Use of Heterogeneous and Incomplete Knowledge in Social Robotics

2016

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Abstract:As societies move towards integration of robots, it is important to study how robots can use their cognition in order to choose effectively their actions in a human environment, and possibly adapt to new contexts. When modelling these contextual data, it is common in social robotics to work with data extracted from human sciences such as sociology, anatomy, or anthropology. These heterogeneous data need to be efficiently used in order to make the robot adapt quickly its actions. In this paper we describe a methodology for the use of heterogeneous and incomplete knowledge, through an algorithm based on naive Bayes classifier. The model was successfully applied to two different experiments of human-robot interaction.

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Bayesian Networks for Social Modeling

Cited by 5 publications

References 8 publications

The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking

The use of Bayesian networks for realist evaluation of complex interventions: evidence for prevention of human trafficking

bAIcis: A Novel Bayesian Network Structural Learning Algorithm and Its Comprehensive Performance Evaluation Against Open-Source Software

Application of the Naive Bayes Classifier for Representation and Use of Heterogeneous and Incomplete Knowledge in Social Robotics

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