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Gourab Ghoshal

Publications and source records attributed to Gourab Ghoshal.

At least 19 recordsLinked to original sources

The hierarchical organization of urban social overlap: How shared social spaces diverge from physical mobility

Cities are commonly characterized through population density, infrastructure, and mobility flows. These quantities describe where people are and how they move, but movement does not uniquely determine where populations repeatedly encounter one another or whether different locations participate in the same social environments. Here, we construct co-presence overlap networks across 13 Brazilian cities to distinguish these dimensions of urban organization. We introduce two complementary measures: colocation, which quantifies recurrent co-presence activity within individual locations, and co-connectedness, which measures the extent to which locations share the same co-present pairs. We find that the spatial concentration of colocation differs substantially from that of mobility, producing social centers that can differ in both number and location from mobility centers. At the network level, co-connectedness exhibits a non-random hierarchical organization in which locations preferentially share recurrent pairs with locations of similar activity. This hierarchy is systematically weaker and substantially more variable across cities than the corresponding mobility hierarchy, demonstrating that cities with similar mobility organization can differ in how recurrent social overlap is organized across locations. Finally, we show that the spatial proximity of colocation activity to social centers relative to residential centers provides a complementary spatial dimension of urban organization. Together, these results separate three features that are conflated when cities are characterized through mobility alone: movement between locations, local opportunities for social interaction, and the organization of recurrent social overlap across locations.

physics.soc-ph

Replicating Human Motivated Reasoning Studies with LLMs

Motivated reasoning - the idea that individuals processing information may be motivated to either arrive at accurate beliefs or arrive at desired conclusions - has been well-explored as a human phenomenon. However, it remains unclear whether base LLMs are affected by motivational manipulations. Replicating 4 prior political motivated reasoning studies, we find that base LLM behavior does not align with expected human behavior. Furthermore, base LLM behavior across models shares some similarities, such as when selecting to abstain from question answering and incorporating provided arguments into opinions. The results suggest that base LLMs may not emulate human motivated reasoning processes. We emphasize the importance of these findings for researchers using LLMs to for certain tasks such as opinion replication and argument assessment.

cs.HC

Can LLMs Emulate Human Belief Dynamics?

Can LLMs simulate how humans form and change beliefs in social networks? We put this to the test by replicating an established study on belief dynamics, evaluating 12 LLMs across multiple model families and parameter sizes. The answer is a clear no, and in systematic ways. LLMs fail to capture initial human belief distributions and tend to be overall more conformist than humans, shifting their responses to align with those around them. They also take a nuanced approach to emulating human homophilic tendencies within networks. Our findings carry a double payoff: they highlight fundamental properties of LLM behavior, and they raise a sharp warning against deploying LLMs as human proxies in social simulations.

cs.SI

Information bounds production in replicator systems

Environmental fluctuations can shape replicator dynamics, with important consequences for both prebiotic and modern ecosystems. However, it remains unclear how simple replicators can acquire and use information about fluctuating environments, given that such information processing is often assumed to require sophisticated mechanisms for sensing and control. Here, we show that even simple replicator networks can increase productivity by exploiting environmental information in a functional way. Using a model of autocatalytic replicators in a flow reactor, we derive an information-theoretic decomposition of productivity, with separate contributions from environmental uncertainty, side information, and distribution mismatch. We derive optimal strategies and universal bounds on the benefit of information and compare our findings with existing work, including ``Kelly gambling'' in information theory. By applying our framework to a model of real-world molecular replicators, we demonstrate the benefits of internal memory and propose an experimental setup for detecting functional information in a minimal chemical system.

physics.bio-ph

How LLMs Fail to Support Fact-Checking

While Large Language Models (LLMs) can amplify online misinformation, they also show promise in tackling misinformation. In this paper, we empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering political misinformation. We implement a two-step, chain-of-thought prompting approach, where models first identify credible sources for a given claim and then generate persuasive responses. Our findings suggest that models struggle to ground their responses in real news sources, and tend to prefer citing left-leaning sources. We also observe varying degrees of response diversity among models. Our findings highlight concerns about using LLMs for fact-checking through only prompt-engineering, emphasizing the need for more robust guardrails. Our results have implications for both researchers and non-technical users.

cs.CL

Spatiotemporal Determinants of Vector-Borne Disease Outbreaks

Vector-borne diseases arise from the coupled dynamics of human mobility and mosquito ecology, producing outbreaks shaped by both spatial distributions and temporal patterns of movement. Here we develop a coarse-grained hub--leaf reduction that isolates the universal principles governing epidemic vulnerability in interconnected populations. By deriving and analyzing the epidemic vulnerability equation, we show how human and vector population ratios, together with mobility parameters that regulate time spent in hub and leaf locations, jointly determine the conditions for outbreak persistence. The analysis reveals that balanced flows of individuals between patches consistently minimize vulnerability, whereas disproportionate concentrations of vectors can shift the dominant risk to specific locations. Across parameter regimes, compensatory mobility emerges as a stabilizing mechanism, while skewed host--vector ratios elevate epidemic risk. These results establish general principles for the spatiotemporal determinants of vector-borne disease spread and provide a theoretical foundation for extending minimal models to more complex epidemiological settings.

q-bio.PE

Personalized Large Language Models Can Increase the Belief Accuracy of Social Networks

Large language models (LLMs) are increasingly involved in shaping public understanding on contested issues. This has led to substantial discussion about the potential of LLMs to reinforce or correct misperceptions. While existing literature documents the impact of LLMs on individuals' beliefs, limited work explores how LLMs affect social networks. We address this gap with a pre-registered experiment (N = 1265) around the 2024 US presidential election, where we empirically explore the impact of personalized LLMs on belief accuracy in the context of social networks. The LLMs are constructed to be personalized, offering messages tailored to individuals' profiles, and to have guardrails for accurate information retrieval. We find that the presence of a personalized LLM leads individuals to update their beliefs towards the truth. More importantly, individuals with a personalized LLM in their social network not only choose to follow it, indicating they would like to obtain information from it in subsequent interactions, but also construct subsequent social networks to include other individuals with beliefs similar to the LLM -- in this case, more accurate beliefs. Therefore, our results show that LLMs have the capacity to influence individual beliefs and the social networks in which people exist, and highlight the potential of LLMs to act as corrective agents in online environments. Our findings can inform future strategies for responsible AI-mediated communication.

cs.SI

Configurational Information Measures, Phase Transitions, and an Upper Bound on Complexity

Configurational entropy (CE) and configurational complexity (CC) are recently popularized information theoretic measures used to study the stability of solitons. This paper examines their behavior for 2D and 3D lattice Ising Models, where the quasi-stability of fluctuating domains is controlled by proximity to the critical temperature. Scaling analysis lends support to an unproven conjecture that these configurational information measures (CIMs) can detect (in)stability in field theories. The primary results herein are the derivation of a model dependent CC-CE relationship, as well as a model independent upper bound on CC. CIM phenomenology in the Ising universality class reveals multiple avenues for future research.

cond-mat.stat-mech

The Physics of Life: Exploring Information as a Distinctive Feature of Living Systems

This paper explores the idea that information is an essential and distinctive feature of living systems. Unlike non-living systems, living systems actively acquire, process, and use information about their environments to respond to changing conditions, sustain themselves, and achieve other intrinsic goals. We discuss relevant theoretical frameworks such as ``semantic information'' and ``fitness value of information''. We also highlight the broader implications of our perspective for fields such as origins-of-life research and astrobiology. In particular, we touch on the transition to information-driven systems as a key step in abiogenesis, informational constraints as determinants of planetary habitability, and informational biosignatures for detecting life beyond Earth. We briefly discuss experimental platforms which offer opportunities to investigate these theoretical concepts in controlled environments. By integrating theoretical and experimental approaches, this perspective advances our understanding of life's informational dynamics and its universal principles across diverse scientific domains.

cond-mat.soft

AI Can Enhance Creativity in Social Networks

Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.

cs.AI

Contrasting and comparing the efficacy of non-pharmaceutical interventions on air-borne and vector-borne diseases

Non-pharmaceutical interventions (NPIs) aimed at limiting human mobility have demonstrated success in curbing the transmission of airborne diseases. However, their effectiveness in managing vector-borne diseases remains less clear. In this study, we introduce a framework that integrates mobility data with vulnerability matrices to evaluate the differential impacts of mobility-based NPIs on both airborne and vector-borne pathogens. Focusing on the city of Santiago de Cali in Colombia, our analysis illustrates how mobility-based policies previously proposed to contain airborne disease can make cities more prone to the spread of vector-borne diseases. By proposing a simplified synthetic model, we explain the limitations of the latter policies and exploit the synergies between both types of diseases to find new interventions reshaping the mobility network for their simultaneous control. Our results thus offer valuable insights into the epidemiological trade-offs of concurrent disease management, providing a foundation for the design and assessment of targeted interventions that reshape human mobility.

physics.soc-ph

Exo-Daisy World: Revisiting Gaia Theory through an Informational Architecture Perspective

The Daisy World model has long served as a foundational framework for understanding the self-regulation of planetary biospheres, providing insights into the feedback mechanisms that may govern inhabited exoplanets. In this study, we extend the classic Daisy World model through the lens of Semantic Information Theory (SIT), aiming to characterize the information flow between the biosphere and planetary environment -- what we term the \emph{information architecture} of Daisy World systems. Our objective is to develop novel methodologies for analyzing the evolution of coupled planetary systems, including biospheres and geospheres, with implications for astrobiological observations and the identification of agnostic biosignatures. To operationalize SIT in this context, we introduce a version of the Daisy World model tailored to reflect potential conditions on M-dwarf exoplanets, formulating a system of stochastic differential equations that describe the co-evolution of the daisies and their planetary environment. Analysis of this Exo-Daisy World model reveals how correlations between the biosphere and environment intensify with rising stellar luminosity, and how these correlations correspond to distinct phases of information exchange between the coupled systems. This \emph{rein control} provides a quantitative description of the informational feedback between the biosphere and its host planet. Finally, we discuss the broader implications of our approach for developing detailed ExoGaia models of inhabited exoplanetary systems, proposing new avenues for interpreting astrobiological data and exploring biosignature candidates.

astro-ph.EP

Information-theoretic description of a feedback-control Kuramoto model

Semantic Information Theory (SIT) offers a new approach to evaluating the information architecture of complex systems. In this study we describe the steps required to {\it operationalize} SIT via its application to dynamical problems. Our road map has four steps: (1) separating the dynamical system into agent-environment sub-systems; (2) choosing an appropriate coarse graining and quantifying correlations; (3) identifying a measure of viability; (4) implementing a scrambling protocol and measuring the semantic content. We apply the road map to a model inspired by the neural dynamics of epileptic seizures whereby an agent (a control process) attempts to maintain an environment (a base process) in a desynchronized state. The synchronization dynamics is studied through the well-known Kuramoto model of phase synchronization. Our application of SIT to this problem reveals new features of both semantic information and the Kuramoto model. For the latter we find articulating the correlational structure for agent and environment(the oscillators), allows us to cast the model in in a novel computational (information theoretic) perspective, where the agent-environment dynamics can be thought of as analyzing a communication channel. For the former we find that all the information in our system is semantic. This is in contrast to previous SIT studies of foragers in which semantic thresholds where seen above which no further semantic content was obtained.

nlin.PS

Exploring the Role of Randomization on Belief Rigidity in Online Social Networks

People often stick to their existing beliefs, ignoring contradicting evidence or only interacting with those who reinforce their views. Social media platforms often facilitate such tendencies of homophily and echo-chambers as they promote highly personalized content to maximize user engagement. However, increased belief rigidity can negatively affect real-world policy decisions such as leading to climate change inaction and increased vaccine hesitancy. To understand and effectively tackle belief rigidity on online social networks, designing and evaluating various intervention strategies is crucial, and increasing randomization in the network can be considered one such intervention. In this paper, we empirically quantify the effects of a randomized social network structure on belief rigidity, specifically examining the potential benefits of introducing randomness into the network. We show that individuals' beliefs are positively influenced by peer opinions, regardless of whether those opinions are similar to or differ from their own by passively sensing belief rigidity through our experimental framework. Moreover, people incorporate a slightly higher variety of different peers (based on their opinions) into their networks when the recommendation algorithm provides them with diverse content, compared to when it provides them with similar content. Our results indicate that in some cases, there might be benefits to randomization, providing empirical evidence that a more randomized network could be a feasible way of helping people get out of their echo-chambers. Our findings have broader implications in computing and platform design of social media, and can help combat overly rigid beliefs in online social networks.

cs.SI

Characterizing network circuity among heterogeneous urban amenities

The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality, and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely determined by urban density alone. These measures are unable to gauge how efficiently street networks can navigate between amenities, since they neglect the circuity component of accessibility. Existing measures also often require ad hoc modeling choices, making them less flexible for different applications and difficult to apply in cross-sectional analyses. Here we develop a simple, principled, and flexible measure to characterize the circuity of accessibility among heterogeneous amenities in a city, which we call the pairwise circuity (PC). The PC quantifies the excess travel distance incurred when using the street network to route between a pair of amenity types, summarizing both spatial and topological correlations among amenities. Measures developed using our framework exhibit significant statistical associations with a variety of urban prosperity and accessibility indicators when compared to an appropriate null model, and we find a clear separation in the PC values of cities according to development level and geographic region.

physics.soc-ph

Semantic Information in a model of Resource Gathering Agents

We explore the application of a new theory of Semantic Information to the well-motivated problem of a resource foraging agent. Semantic information is defined as the subset of correlations, measured via the transfer entropy, between agent $A$ and environment $E$ that is necessary for the agent to maintain its viability $V$. Viability, in turn, is endogenously defined as opposed to the use of exogenous quantities like utility functions. In our model, the forager's movements are determined by its ability to measure, via a sensor, the presence of an individual unit of resource, while the viability function is its expected lifetime. Through counterfactual interventions -- scrambling the correlations between agent and environment via noising the sensor -- we demonstrate the presence of a critical value of the noise parameter, $η_c$, above which the forager's expected lifetime is dramatically reduced. On the other hand, for $η< η_c$ there is little-to-no effect on its ability to survive. We refer to this boundary as the semantic threshold, quantifying the subset of agent-environment correlations that the agent actually needs to maintain its desired state of staying alive. Each bit of information affects the agent's ability to persist both above and below the semantic threshold. Modeling the viability curve and its semantic threshold via forager/environment parameters, we show how the correlations are instantiated. Our work provides a useful model for studies of established agents in terms of semantic information. It also shows that such semantic thresholds may prove useful for understanding the role information plays in allowing systems to become autonomous agents.

cond-mat.stat-mech

Dynamic predictability and spatio-temporal contexts in human mobility

Human travelling behaviours are markedly regular, to a large extent, predictable, and mostly driven by biological necessities (\eg sleeping, eating) and social constructs (\eg school schedules, synchronisation of labour). Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual (\eg preference, choices) and social (\eg household, groups) all the way to global scale (\eg mobility restrictions in a pandemic). In this work, we explore how spatio-temporal patterns in individual-level mobility, which we refer to as \emph{predictability states}, carry a large degree of information regarding the nature of the regularities in mobility. Our findings indicate the existence of contextual and activity signatures in predictability states, pointing towards the potential for more sophisticated, data-driven approaches to short-term, higher-order mobility predictions beyond frequentist/probabilistic methods.

physics.soc-ph

Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia

In the absence of vaccines, the most widespread reaction to curb COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their outcome in terms of mobility reduction and the subsequent limitation of social activity. Here we investigate the impact of lockdown policies on the mobility patterns of different socioeconomic classes in the three major cities of Colombia during the first wave of COVID-19 pandemic. In global terms, we find a consistent positive correlation between the reduction in mobility levels and the socioeconomic stratum of the population in the three cities, implying that those with lower incomes were less capable of adopting the aforementioned policies. Our analysis also suggests a strong restructuring of the mobility network of lowest socioeconomic strata during COVID-19 lockdown, which increased their mixing while hampering their connections with wealthiest areas due to a sharp reduction in long-distance trips.

physics.soc-ph