Pathways for socio-economic system transitions expressed as a Markov chain

Cross-impact balance (CIB) analysis provides a system-theoretical view of scenarios useful for investigating complex socio-economic systems. CIB can synthesize a variety of qualitative or quantitative inputs and return information suggestive of system evolution. Current software tools for CIB are limited to identifying system attractors as well as describing system evolution from only one scenario of initial conditions at a time. Through this study, we enhance CIB by developing and applying a method that considers all possible system evolutions as transitions in a Markov chain. We investigated a simple three-variable system (27 possible scenarios) of the demographic transition and were able to generally replicate the findings of traditional CIB. Through our experiments with four possible approaches to produce CIB Markov chains, we found that information about transition pathways is gained; however, information about system attractors may be lost. Through a comparison of model results to a recent literature review on human demography, we found that low-income countries are more likely to remain stuck in a demographic trap if economic development is not prioritized alongside educational gains. Future work could test our comparative methodological findings for systems comprised of more than three variables.

Automated summary

Cross-impact balance (CIB) analysis is a system-theoretical approach to investigate complex socio-economic systems. By considering all possible system evolutions as transitions in a Markov chain, CIB can provide information about system evolution. In this study, we applied this approach to a three-variable system and found that it generally replicated the findings of traditional CIB. However, the information about system attractors may be lost in the process. Furthermore, we compared our model results to a literature review on human demography and found that economic development is crucial to prevent low-income countries from getting stuck in a demographic trap.