Complexity analysis of sustainable peace: Mathematical models and data science measurements

L. S. Liebovitch, P. T. Coleman, A. Bechhofer, C. Colon, J. Donahue, C. Eisenbach, L. Guzmán-Vargas, D. Jacobs, A. Khan, C. Li, D. Maksumov, J. Mucia, M. Persaud, M. Salimi, L. Schweiger, Q. Wang

    Research output: Contribution to journalArticlepeer-review

    5 Scopus citations

    Abstract

    Peace is not merely the absence of war and violence, rather 'positive peace' is the political, economic, and social systems that generate and sustain peaceful societies. Our international and multidisciplinary group is using physics inspired complex systems analysis methods to understand the factors and their interactions that together support and maintain peace. We developed causal loop diagrams and from them ordinary differential equation models of the system needed for sustainable peace. We then used that mathematical model to determine the attractors in the system, the dynamics of the approach to those attractors, and the factors and connections that play the most important role in determining the final state of the system. We used data science ('big data') methods to measure quantitative values of the peace factors from structured and unstructured (social media) data. We also developed a graphical user interface for the mathematical model so that social scientists or policy makers, can by themselves, explore the effects of changing the variables and parameters in these systems. These results demonstrate that complex systems analysis methods, previously developed and applied to physical and biological systems, can also be productively applied to analyze social systems such as those needed for sustainable peace.

    Original languageEnglish
    Article number073022
    JournalNew Journal of Physics
    Volume21
    Issue number7
    DOIs
    StatePublished - 8 Jul 2019

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