arXiv subjects

Niraj Kushwaha

Publications and source records attributed to Niraj Kushwaha.

4 recordsLinked to original sources

arXiv2026

Pulses, waves, and cascades in collective migration dynamics

Decisions to migrate depend on others' decisions. Dependence can produce nontrivial dynamics. We propose a minimal migration model that accounts for social influence alongside individual heterogeneity in mobility as migrants move from region to region. In special locations of parameter space, migrant flows dramatically and spontaneously fluctuate. Such aspects mimic observed fluctuations in migration statistics and thus show how large fluctuations in data can reflect more than response to events like armed conflict and natural disasters. Correspondingly, the impact of exogenous factors can be confounded with the results of collective decisions.

physics.soc-ph↗

arXiv2025

Common indicators hurt armed conflict prediction

Are big conflicts different from small or medium size conflicts? To answer this question, we leverage fine-grained conflict data, which we map to climate, geography, infrastructure, economics, raw demographics, and demographic composition in Africa. With an unsupervised learning model, we find three overarching conflict types representing ``major unrest,'' ``local conflict,'' and ``sporadic and spillover events.'' Major unrest predominantly propagates around densely populated areas with well-developed infrastructure and flat, riparian geography. Local conflicts are in regions of median population density, are diverse socio-economically and geographically, and are often confined within country borders. Finally, sporadic and spillover conflicts remain small, often in low population density areas, with little infrastructure and poor economic conditions. The three types stratify into a hierarchy of factors that highlights population, infrastructure, economics, and geography, respectively, as the most discriminative indicators. Specifying conflict type negatively impacts the predictability of conflict intensity such as fatalities, conflict duration, and other measures of conflict size. The competitive effect is a general consequence of weak statistical dependence. Hence, we develop an empirical and bottom-up methodology to identify conflict types, knowledge of which can hurt predictability and cautions us about the limited utility of commonly available indicators.

physics.soc-ph↗

arXiv2022

Discovering the mesoscale for chains of conflict

Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year developments, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and from tens to a few hundred kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted only from conflict statistics, are identifiable with causal mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict amongst other processes.

physics.soc-ph↗

arXiv2021

Machine Learning assisted Chimera and Solitary states in Networks

Chimera and Solitary states have captivated scientists and engineers due to their peculiar dynamical states corresponding to the co-existence of coherent and incoherent dynamical evolution in coupled units in various natural and artificial systems. It has been further demonstrated that such states can be engineered in systems of coupled oscillators by the suitable implementation of communication delays. Here, using supervised machine learning, we predict (a) the precise value of delay which is sufficient for engineering chimera and solitary states for a given set of system parameters, as well as (b) the intensity of incoherence for such engineered states. The results are demonstrated for two different examples consisting of single layer and multi layer networks. First, the chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators. Then, different machine learning classifiers, KNN, SVM and MLP-Neural Network are employed by feeding the data obtained from the network models. Once a machine learning model is trained using a limited amount of data, it makes predictions for a given unknown systems parameter values. Testing accuracy, sensitivity, and specificity analysis reveal that MLP-NN classifier is better suited than Knn or SVM classifier for the predictions of parameters values for engineered chimera and solitary states. The technique provides an easy methodology to predict critical delay values as well as the intensity of incoherence for designing an experimental setup to create solitary and chimera states.

nlin.AO↗