Toward a quantitative approach to migrants integration

TL;DR

This paper introduces a novel quantitative framework using statistical mechanics to model and understand the complex process of migrant integration within host communities, emphasizing the role of social interactions and collective memory.

Contribution

It develops a minimal theoretical model based on bi-partite spin-glass systems to define and analyze migrant integration quantitatively, incorporating key social parameters.

Findings

The model captures the emergence of social consciousness among host citizens.

It provides a quantitative measure of migrant integration.

The approach demonstrates robustness under various model refinements.

Abstract

Migration phenomena and all the related issues, like integration of different social groups, are intrinsically complex problems since they strongly depend on several competitive mechanisms as economic factors, cultural differences and many others. By identifying a few essential assumptions, and using the statistical mechanics of complex systems, we propose a novel quantitative approach that provides a minimal theory for those phenomena. We show that the competitive interactions in decision making among a population of $N$ host citizens and $P$ immigrants, a bi-partite spin-glass, give rise to a "social consciousness" inside the host community in the sense of the associative memory of neural networks. The theory leads to a natural quantitative definition of migrant's "integration" inside the community. From the technical point of view this minimal picture assumes, as control parameters, only general notions like strength of the random interactions, the ratio among the two party sizes and the cultural influence. Few steps forward, toward more refined models, which include some structure on the random interaction topology (as dilution to avoid the plain mean field approach) and correlations of experiences felt among the two parties (biasing the distribution of the coupling) are discussed at the end, where we show the robustness of our approach.