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Knowledge and Information Systems

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03-01-2018 | Regular Paper

Simulating outcomes of interventions using a multipurpose simulation program based on the evolutionary causal matrices and Markov chain

Authors: Hyemin Han, Kangwook Lee, Firat Soylu

Published in: Knowledge and Information Systems | Issue 3/2018

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Abstract

Predicting long-term outcomes of interventions is necessary for educational and social policy-making processes that might widely influence our society for the long term. However, performing such predictions based on data from large-scale experiments might be challenging due to the lack of time and resources. In order to address this issue, computer simulations based on evolutionary causal matrices and Markov chain can be used to predict long-term outcomes with relatively small-scale laboratory data. In this paper, we introduce Python classes implementing a computer simulation model and presented some pilot implementations demonstrating how the model can be utilized for predicting outcomes of diverse interventions. We also introduce the class-structured simulation module both with real experimental data and with hypothetical data formulated based on social psychological theories. Classes developed and tested in the present study provide researchers and practitioners with a feasible and practical method to simulate intervention outcomes prospectively.

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Title: Simulating outcomes of interventions using a multipurpose simulation program based on the evolutionary causal matrices and Markov chain
Authors: Hyemin Han
Kangwook Lee
Firat Soylu
Publication date: 03-01-2018
Publisher: Springer London
Published in: Knowledge and Information Systems / Issue 3/2018
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-017-1151-0

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