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

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Kyle Reese: And it was over. Skynet was gone. And now one road has become many. Though questions remain, we’ll search for the answers together. But one thing we know for sure. The future is not set

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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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Authors and Affiliations

  1. Educational Psychology Program, University of Alabama, Tuscaloosa, AL, 35487, USA

    Hyemin Han & Firat Soylu

  2. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea

    Kangwook Lee

Authors
  1. Hyemin Han
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  2. Kangwook Lee
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  3. Firat Soylu
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Correspondence to Hyemin Han.

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Han, H., Lee, K. & Soylu, F. Simulating outcomes of interventions using a multipurpose simulation program based on the evolutionary causal matrices and Markov chain. Knowl Inf Syst 57, 685–707 (2018). https://doi.org/10.1007/s10115-017-1151-0

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