Table 1.

Theories used in the studies on disruption propagation.

Theory	Number of studies	Study
Agent-Based Simulation	1	Mizgier et al. (2012)
Bayesian Networks	6	Cao et al. (2019), Garvey et al. (2015), Garvey and Carnovale (2020), Hosseini and Ivanov (2019), Hosseini et al. (2019), Ojha et al. (2018)
Complexity theory	5	Basole and Bellamy (2014), Deng et al. (2019), Lei et al. (2020), Levner and Ptuskin (2018), Zeng and Xiao (2014)
Discrete-Event Simulation	4	Dolgui et al. (2020), Ivanov (2017, 2019, 2020)
Entropy	2	Levner and Ptuskin (2018), Zeng and Xiao (2014)
Graph theory	5	Basole and Bellamy (2014), Li et al. (2019), Li and Zobel (2020), Sinha et al. (2019), Sokolov et al. (2016)
Linear/Mixed-Integer Programming	6	Liberatore et al. (2012), Ivanov et al. (2013, 2014, 2015, 2016), Pavlov et al. (2019)
Markov Chains	1	Hosseini et al. (2019)a,b
Monte-Carlo Simulation	1	Pariazar et al. (2017)
Optimal Control	5	Ivanov et al. (2010, 2013, 2014a,b, 2015, 2016)
Petri Nets	1	Blackhurst et al. (2018)
Reliability Theory/Statistical Analysis	4	Han and Shin (2016), Osadchiy et al. (2016), Pavlov et al. (2020), Tang et al. (2016)
Robust Optimization	3	Lu et al. (2015), Özçelik et al. (2020), Zhao and Freeman (2019)
Stochastic Optimization	2	Goldbeck et al. (2020), Pariazar et al. (2017)
Systems Dynamics	2	Bueno-Solano and Cedillo-Campos (2014), Ghadge et al. (2013),