Dataset Information

How inter-state amity and animosity complement migration networks to drive refugee flows: A multi-layer network analysis, 1991-2016.

ABSTRACT: What drives the formation and evolution of the global refugee flow network over time? Refugee flows in particular are widely explained as the result of pursuits for physical security, with recent research adding geopolitical considerations for why states accept refugees. We refine these arguments and classify them into explanations of people following existing migration networks and networks of inter-state amity and animosity. We also observe that structural network interdependencies may bias models of migration flows generally and refugee flows specifically. To account for these dependencies, we use a dyadic hypothesis testing method-Multiple Regression- Quadratic Assignment Procedure (MR-QAP). We estimate MR-QAP models for each year during the 1991-2016 time period. K-means clustering analysis with visualization supported by multi-dimensional scaling allows us to identify categories of variables and years. We find support for the categorization of drivers of refugee flows into migration networks and inter-state amity and animosity. This includes key nuance that, while contiguity has maintained a positive influence on refugee flows, the magnitude of that influence has declined over time. Strategic rivalry also has a positive influence on refugee flows via dyad-level correlations and its effect on the structure of the global refugee flow network. In addition, we find clear support for the global refugee flow network shifting after the Arab Spring in 2011, and drivers of refugee flows shifting after 2012. Our findings contribute to the study of refugee flows, international migration, alliance and rivalry relationships, and the application of social network analysis to international relations.

SUBMITTER: Schon J

PROVIDER: S-EPMC7840044 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and [Formula: see text]-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective [Formula: see text]-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

Dataset Information

How inter-state amity and animosity complement migration networks to drive refugee flows: A multi-layer network analysis, 1991-2016.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets