arXiv subjects

Adarsh Prabhakaran

Publications and source records attributed to Adarsh Prabhakaran.

4 recordsLinked to original sources

arXiv2026

SimPol: Simulating polarisation in political belief networks in European countries

Here we combine empirical network analysis with agent-based modelling to understand how different ways of structuring belief systems may affect the polarisation drive, and how the diversity of belief systems in Europe may result in different polarisation trajectories. Using the 2016 European Social Survey, we infer belief networks across 23 European countries via a Bayesian algorithm, revealing that belief systems are predominantly organised around immigration, LGBT rights, and economic interventionism, reflecting the influence of populist discourse across the continent. We further verify a Western-Eastern divide across the national belief networks: in Western European countries, left-right self-identification is a more reliable predictor of broader belief alignment, whereas in Eastern Europe this relationship breaks down. By applying these empirical belief networks into a sociologically grounded agent-based model, we further show that polarisation is amplified by high individual belief rigidity and low susceptibility to social influence, and that cross-country differences in polarisation levels mirror the same geographic divide observed in belief network topology. These findings establish belief networks topologies as a structural driver of political polarisation, with implications for understanding and anticipating polarisation dynamics across diverse European contexts. We find that populations are not polarised when little attention is placed on maintaining internal coherence and polarisation levels are moderate when high attention is placed in both keeping internal coherence and agreement in beliefs with others.

physics.soc-ph↗

arXiv2026

PrologMCP: A Standardized Prolog Tool Interface for LLM Agents

Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly. Symbolic delegation offers a complementary route: a language model translates the problem, while a solver performs the inference. However, current autoformalization pipelines for logic programming are typically bespoke integrations tied to particular tasks or agents. We introduce PrologMCP, a task-agnostic, open-source server that exposes Prolog as a stateful tool through the Model Context Protocol (MCP). Its compact tool interface, structured error reporting, and per-session isolation make the translate-run-inspect-repair loop a reusable primitive for MCP-capable agents. We evaluate a formalizer agent enhanced with PrologMCP against standard and reasoning LLMs (Claude Sonnet 4.6, GPT-4.1, and o4-mini) on two subsets of PARARULE-Plus: a general-purpose sample and a more challenging one targeting a specific failure mode of natural-language reasoning. On the general sample, the formalizer matches or exceeds reasoning LLMs (accuracy 1.00 vs.\ 1.00 / 0.998), with the largest gains over standard models (0.762 for GPT-4.1). On the challenging subset, the formalizer remains near-perfect (1.00 / 0.99) while reasoning LLMs drop to 0.95 / 0.94. These results suggest that delegating inference to Prolog via MCP is a robust and inspectable alternative to extended natural-language reasoning.

cs.AI↗

arXiv2024

Disentangling individual-level from location-based income uncovers socioeconomic preferential mobility and impacts segregation estimates

Segregation encodes information about society, such as social cohesion, mixing, and inequality. However, most past and current studies tackled socioeconomic (SE) segregation by analyzing static aggregated mobility networks, often without considering further individual features beyond income and, most importantly, without distinguishing individual-level from location-based income. Accessing individual-level income may help mapping macroscopic behavior into more granular mobility patterns, hence impacting segregation estimates. Here we combine a mobile phone dataset of daily mobility flows across Spanish districts stratified and adjusted by age, gender and income with census data of districts median income. We build mobility-based SE assortativity matrices for multiple demographics and observe mobility patterns of three income groups with respect to location-based SE classes. We find that SE assortativity differs when isolating the mobility of specific income groups: we observe that groups prefer to visit areas with higher average income than their own, which we call preferential mobility. Our analysis suggests substantial differences between weekdays and weekends SE assortativity by age class, with weekends characterized by higher SE assortativity. Our modeling approach shows that the radiation model, which typically performs best at reproducing inter-municipal population mobility, best fits middle income and middle-aged flows, while performing worse on young and low income groups. Our double-sided approach, focusing on assortativity patterns and mobility modeling, suggests that state of the art mobility models fail at capturing preferential mobility behavior. Overall, our work indicates that mobility models considering the interplay of SE preferential behavior, age and gender gaps may sensibly improve the state of the art models performance.

physics.soc-ph↗

arXiv2022

Improving tobacco social contagion models using agent-based simulations on networks

Over the years, population-level tobacco control policies have considerably reduced smoking prevalence worldwide. However, the rate of decline of smoking prevalence is slowing down. Therefore, there is a need for models that capture the full complexity of the smoking epidemic. These models can then be used as test-beds to develop new policies to limit the spread of smoking. Current models of smoking dynamics mainly use ordinary differential equation (ODE) models, where studying the effect of an individual's contact network is challenging. They also do not consider all the interactions between individuals that can lead to changes in smoking behaviour, implying that they do not consider valuable information on the spread of smoking behaviour. In this context, we develop an agent-based model (ABM), calibrate and then validate it on historical trends observed in the US and UK. Our ABM considers spontaneous terms, interactions between agents, and the agent's contact network. To explore the effect of the underlying network on smoking dynamics, we test the ABM on six different networks, both synthetic and real-world. In addition, we also compare the ABM with an ODE model. Our results suggest that the dynamics from the ODE model are similar to the ABM only when the network structure is fully connected (FC). The FC network performs poorly in replicating the empirical trends in the data, while the real-world network best replicates it amongst the six networks. Further, when information on the real-world network is unavailable, our ABM on Lancichinetti-Fortunato-Radicchi benchmark networks (or networks with a similar average degree as the real-world network) can be used to model smoking behaviour. These results suggest that networks are essential for modelling smoking behaviour and that our ABM can be used to develop network-based intervention strategies and policies for tobacco control.

physics.soc-ph↗