arXiv subjects

Vítor V. Vasconcelos

Publications and source records attributed to Vítor V. Vasconcelos.

At least 19 recordsLinked to original sources

arXiv2026

Slow Context, Fast Symptoms: Multiscale Temporal Dynamics and Context-Induced Coupling in Psychological Systems

Psychological dynamics unfold across multiple timescales: symptoms and other psychological states can change rapidly, whereas social, environmental, biological, and developmental conditions often evolve more slowly. We formulate this structure as a stochastic slow-fast system in which binary symptom states form a fast interacting network embedded within a slow contextual field. Pairwise symptom coupling governs interactions within the fast layer, while the contextual field shifts symptom-specific activation tendencies and can itself receive feedback from sustained symptom activation. Simulations show that changes in the slow field can shift the macroscopic activation state of the symptom system even when the underlying interaction matrix remains fixed. Perturbations to the field generate transient increases in symptom activation followed by recovery, while feedback between the fast and slow layers delays recovery and, when sufficiently strong, produces dependence on initial conditions. We further show that when between-person variation in the contextual field is omitted from network estimation, the inferred system exhibits stronger total coupling and nonzero couplings between symptom pairs that are uncoupled in the data-generating model. Thus, slowly varying context can alter both the dynamics and the apparent interaction structure of a fast psychological system. The framework connects psychological network models with slow-fast dynamical systems and provides a formal basis for distinguishing changes in activation from changes in coupling.

physics.soc-ph↗

arXiv2026

Rethinking Group Differences in Psychopathology Networks: A Slow-Fast Perspective on Context and Symptom Activation

Groups differing in social and economic circumstances often differ markedly in depressive symptom levels, yet their estimated symptom networks show few clear differences. Such null results are often read as evidence that the groups' symptom systems are the same, but they only show that no difference was detected in how symptoms interact. How readily symptoms become present, and how this depends on the contextual conditions under which they are observed, is rarely compared. Building on a slow-fast perspective, we treat depressive symptoms as a relatively fast-changing system embedded in more persistent social, economic, psychosocial, health, and lifestyle conditions that shape symptom activation. Using a cohort of 23,689 adults from Amsterdam, we illustrate this with PHQ-9 depressive symptoms and a composite index of persistent socioeconomic, psychosocial, and health-related conditions, which we call Slow Risk Load (SRL). Participants with high SRL had substantially higher symptom levels than those with low SRL. A standard Network Comparison Test detected no overall difference in network structure, although global strength was modestly higher in the high-SRL group. Ising models, which separate how symptoms co-occur from how readily each becomes present, indicate higher activation under high SRL for eight of nine symptoms, and model comparison consistently favored group-specific activation over group-specific interactions. The slow-fast perspective reframes group network comparisons as a multiple-timescale problem in which network structure, symptom activation, and context should be considered jointly. It places symptom-focused and context-focused interventions at different layers of the same coupled system rather than in competition.

physics.soc-ph↗

arXiv2026

Structuring International Governance through the Space of Concerns

When institutions decide by consensus, the official record shows agreement but hides who shaped what was decided. We introduce a way to recover that hidden structure from the one trace consensus cannot suppress: the documentary record of what actors choose to work on. Adapting tools from economic complexity, we map a ``space of concerns'' in which issues lie close when the same actors repeatedly specialize in both -- turning a flat agenda into a measurable topology of attention. Across six decades of the Antarctic Treaty (6,591 documents, 66 actors), engagement is structured, local, and persistent, and the most specialized actors produce binding law at roughly five times the baseline rate. The approach generalizes to any document-rich consensus forum, showing that unanimity does not erase political structure -- it relocates it upstream, into the organization of attention.

cs.SI↗

arXiv2026

Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty

Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems. However, as qualitative and static representations, CLDs are limited in their ability to support dynamic analysis and inform intervention strategies. We propose Diagrams-to-Dynamics (D2D), a method for converting CLDs into exploratory system dynamics models in the absence of empirical data. With minimal user input - following a protocol to label variables as stocks, flows or auxiliaries, and constants - D2D utilizes the structural information already encoded in CLDs, namely the existence and polarity of causal connections, to simulate hypothetical interventions and explore potentially influential places to intervene, known as 'leverage points,' under uncertainty. Results: D2D helps distinguish between high- and low-ranked leverage points. We compare D2D to a calibrated system dynamics model constructed from the same CLD and variable labels. D2D showed greater consistency with the calibrated model than did static network centrality analysis, while also providing uncertainty estimates and guidance for future data collection. Conclusions: The D2D method is implemented in an open-source Python package and a web-based application to support further testing and to lower the barrier to dynamic modeling for researchers working with CLDs. Future studies could help establish the approach's utility across a broad range of cases and domains.

cs.LG↗

arXiv2026

Combining opinion and structural similarity in link recommendations to counter extreme polarization

Recommendation algorithms, used in online social networks, shape interactions between users. In particular, link-recommendation algorithms suggest new connections and affect how individuals interact and exchange information. These algorithms' efficacy relies on key mechanisms governing the creation of social ties, such as triadic closure and homophily. The first is achieved through structural similarity and represents a heightened chance of recommending users to one another given mutual friends; the second is related to opinion similarity and conveys an increased chance of recommending a connection given similar individual characteristics. These two mechanisms jointly shape the evolution of social networks and behaviors unfolding over them. Their combined effect on the co-evolution of opinion and structure dynamics remains, however, poorly understood. Here, we study how social networks and opinions co-evolve given the joint effect of rewiring based on opinion and structural similarity. We show that both similarity metrics lead to polarized states, but differ in how they impact network fragmentation and opinion diversity. While strongly relying on opinion similarity leads to a higher variation of opinion, rewiring via network similarity leads to a larger number of (dis)connected components, resulting in fragmented networks that lean towards one of the signed opinions. Under strong homophilic settings, introducing a weak dependence on structural similarity prevents network fragmentation and favors moderate opinions. This work can inform the design of new recommender algorithms that explicitly account for interacting social and recommendation mechanisms, with the potential to foster moderate opinion coexistence even in inherently polarizing settings.

cs.SI↗

arXiv2025

Social influence on complex networks as a perturbation to individual behavior

Cooperation is fundamental to the functioning of biological and social systems in both human and animal populations, with the structure of interactions playing a crucial role. Previous studies have used networks to describe interactions and explore the evolution of cooperation, but with limited transposability to social settings due to biologically relevant assumptions. Exogenous processes -- that affect the individual and are not derived from social interactions -- even if unbiased, have a role in supporting cooperation over defection, and this role has been largely overlooked in the context of network-based interactions. Here, we show that selection can favor either cooperation or defection depending on the frequency of exogenous, even if neutral, processes in any population structure. Our framework allows for deriving analytically the conditions for favoring a specific behavior in any network structure strongly affected by non-social environments (frequent exogenous forcing, FEF), which contrasts with previous computationally prohibitive methods. Our results demonstrate that the requirements for favoring cooperation under FEF do not match those in the rare-mutation limit, establishing that underlying neutral processes can be considered a mechanism for cooperation. We reveal that, under FEF, populations are less cooperative, and network heterogeneity can provide an advantage only if targeting specific network properties, clarifying seemingly contradictory experimental results and evolutionary predictions. While focused on cooperation, our assumptions generalize to any decision-making process involving a choice between alternative options. Our framework is particularly applicable to non-homogeneous human populations, offering a new perspective on cooperation science in the context of cultural evolution, where neutral and biased processes within structured interactions are abundant.

physics.soc-ph↗

arXiv2025

Policy, Risk, and Norms Shape Collective Behaviors Worldwide

Societal responses to environmental change vary widely, even under comparable shocks, reflecting differences in both policy measures and public reactions shaped by cultural and socioeconomic contexts. We examine mask-wearing dynamics across 47 countries during the COVID-19 pandemic using a process-based, utility-driven model of individual behavior with three evolving drivers: policy stringency, disease risk, and social norms to understand emergent collective behavior. Calibrated with daily data on mask usage, COVID-19 deaths, and policy mandates, the model reproduces diverse national trajectories with minimal complexity. Policy and norms are crucial for explaining variation, and we find significant associations between weights for all three drivers and cultural and socioeconomic indicators. Our findings demonstrate how mechanistic models can uncover the processes shaping collective behavior, enabling policymakers to anticipate the magnitude and timing of behavioral change and design more effective, context-sensitive interventions.

physics.soc-ph↗

arXiv2025

Understanding Heterogeneity in Adaptation to Intermittent Water Supply: Clustering Household Types in Amman, Jordan

More than a billion people around the world experience intermittence in their water supply, posing challenges for urban households in Global South cities. An intermittent water supply (IWS) system prompts water users to adapt to service deficits which entails coping costs. Adaptation and its impacts can vary between households within the same city, leading to intra-urban inequality. Studies on household adaptation to IWS through survey data are limited to exploring income-based heterogeneity and do not account for the multidimensional and non-linear nature of the data. There is a need for a standardized methodology for understanding household responses to IWS that acknowledges the heterogeneity of households characterized by sets of multiple underlying factors and that is applicable across different settings. Here, we develop an analysis pipeline that applies hierarchical clustering analysis (HCA) in combination with the Welch-two-sample t-test on household survey data from Amman, Jordan. We identify three clusters of households distinguished by a set of characteristics including income, water social network, supply duration, relocation and water quality problems and identify their group-specific adaptive strategies such as contacting the utility or accessing an alternate water source. This study uncovers the unequal nature of IWS adaptation in Amman, giving insights into the link between household characteristics and adaptive behaviors, while proposing a standardized method to reveal relevant heterogeneity in households adapting to IWS.

stat.AP↗

arXiv2025

Targeted incentives for social tipping in heterogeneous networked populations

Many societal challenges, such as climate change or disease outbreaks, require coordinated behavioral changes. For many behaviors, the tendency of individuals to adhere to social norms can reinforce the status quo. However, these same social processes can also result in rapid, self-reinforcing change. Interventions may be strategically targeted to initiate endogenous social change processes, often referred to as social tipping. While recent research has considered how the size and targeting of such interventions impact their effectiveness at bringing about change, they tend to overlook constraints faced by policymakers, including the cost, speed, and distributional consequences of interventions. To address this complexity, we introduce a game-theoretic framework that includes heterogeneous agents and networks of local influence. We implement various targeting heuristics based on information about individual preferences and commonly used local network properties to identify individuals to incentivize. Analytical and simulation results suggest that there is a trade-off between preventing backsliding among targeted individuals and promoting change among non-targeted individuals. Thus, where the change is initiated in the population and the direction in which it propagates is essential to the effectiveness of interventions. We identify cost-optimal strategies under different scenarios, such as varying levels of resistance to change, preference heterogeneity, and homophily. These results provide insights that can be experimentally tested and help policymakers to better direct incentives.

physics.soc-ph↗

arXiv2025

The Paradox of Intervention: Resilience in Adaptive Multi-Role Coordination Networks

Complex adaptive networks exhibit remarkable resilience, driven by the dynamic interplay of structure (interactions) and function (state). While static-network analyses offer valuable insights, understanding how structure and function co-evolve under external interventions is critical for explaining system-level adaptation. Using a unique dataset of clandestine criminal networks, we combine empirical observations with computational modeling to test the impact of various interventions on network adaptation. Our analysis examines how networks with specialized roles adapt and form emergent structures to optimize cost-benefit trade-offs. We find that emergent sparsely connected networks exhibit greater resilience, revealing a security-efficiency trade-off. Notably, interventions can trigger a "criminal opacity amplification" effect, where criminal activity increases despite reduced network visibility. While node isolation fragments networks, it strengthens remaining active ties. In contrast, deactivating nodes (analogous to social reintegration) can unintentionally boost criminal coordination, increasing activity or connectivity. Failed interventions often lead to temporary functional surges before reverting to baseline. Surprisingly, stimulating connectivity destabilizes networks. Effective interventions require precise calibration to node roles, connection types, and external conditions. These findings challenge conventional assumptions about connectivity and intervention efficacy in complex adaptive systems across diverse domains.

physics.soc-ph↗

arXiv2025

Heterogeneous Update Processes Shape Information Cascades in Social Networks

A common assumption in the literature on information diffusion is that populations are homogeneous regarding individuals' information acquisition and propagation process: Individuals update their informed and actively communicating state either through imitation (simple contagion) or peer influence (complex contagion). Here, we study the impact of the mixing and placement of individuals with different update processes on how information cascades in social networks. We consider Simple Spreaders, which take information from a random neighbor and communicate it, and Threshold-based Spreaders, which require a threshold number of active neighbors to change their state to active communication. Even though, in a population made exclusively of Simple Spreaders, information reaches all elements of any (connected) network, we show that, when Simple and Threshold-based Spreaders coexist and occupy random positions in a social network, the number of Simple Spreaders systematically amplifies the cascades only in degree heterogeneous networks (exponential and scale-free). In random and modular structures, this cascading effect originated by Simple Spreaders only exists above a critical mass of these individuals. In contrast, when Threshold-based Spreaders are assorted preferentially in the nodes with a higher degree, the cascading effect of Simple Spreaders vanishes, and the spread of information is drastically impaired. Overall, the study highlights the significance of the strategic placement of different roles in networked structures, with Simple Spreaders driving widespread cascades in heterogeneous networks and Threshold-based Spreaders playing a critical regulatory role in information spread with a tunable effect based on the threshold value.

cs.MA↗

arXiv2024

Criminal organizations exhibit hysteresis, resilience, and robustness by balancing security and efficiency

The interplay between criminal organizations and law enforcement disruption strategies is crucial in criminology. Criminal enterprises, like legitimate businesses, balance visibility and security to thrive. This study uses evolutionary game theory to analyze criminal networks' dynamics, resilience to interventions, and responses to external conditions. We find strong hysteresis effects, challenging traditional deterrence-focused strategies. Optimal thresholds for organization formation or dissolution are defined by these effects. Stricter punishment doesn't always deter organized crime linearly. Network structure, particularly link density and skill assortativity, significantly influences organization formation and stability. These insights advocate for adaptive policy-making and strategic law enforcement to effectively disrupt criminal networks.

physics.soc-ph↗

arXiv2024

Towards participatory multi-modeling for policy support across domains and scales: a systematic procedure for integral multi-model design

Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scales or relying on different modeling paradigms offer a potential solution. Such multi-models can be assembled from existing computational models (i.e., integrated modeling) or be designed conceptually as a whole before their computational implementation (i.e., integral modeling). Integral modeling is particularly valuable for novel policy problems, such as those faced in the early stages of a pandemic, where relevant models may be unavailable or lack standard documentation. Designing such multi-models through an integral approach is, however, a complex task requiring the collaboration of modelers and experts from various domains. In this collaborative effort, modelers must precisely define the domain knowledge needed from experts and establish a systematic procedure for translating such knowledge into a multi-model. Yet, these requirements and systematic procedures are currently lacking for multi-models that are both multiscale and multi-paradigm. We address this challenge by introducing a procedure for developing multi-models with an integral approach based on clearly defined domain knowledge requirements derived from literature. We illustrate this procedure using the case of school closure policies in the Netherlands during the COVID-19 pandemic, revealing their potential implications in the short and long term and across the healthcare and educational domains. The requirements and procedure provided in this article advance the application of integral multi-modeling for policy support in multiscale and multidomain contexts.

stat.ME↗

arXiv2023

How Social Rewiring Preferences Bridge Polarized Communities

Recently, social debates have been marked by increased polarization of social groups. Such polarization not only implies that groups cannot reach a consensus on fundamental questions but also materializes in more modular social spaces/networks that further amplify the risks of polarization in less polarizing topics. How can network adaptation bridge different communities when individuals reveal homophilic or heterophilic social rewiring preferences? Here, we consider information diffusion processes that capture a continuum from simple to complex contagion processes. We use a computational model to understand how fast and to what extent individual rewiring preferences bridge initially weakly connected communities and how likely it is for them to reach a consensus. We show that homophilic and heterophilic rewiring have different impacts depending on the type of opinion spread. First, in the case of complex opinion diffusion, we show that even polarized social networks can reach a population-wide consensus without reshaping their underlying network. When polarized social structures amplify opinion polarization, heterophilic rewiring preferences play a key role in creating bridges between communities and facilitating a population-wide consensus. Secondly, in the case of simple opinion diffusion, homophilic rewiring preferences are more capable of fostering consensus and avoiding a co-existence (dynamical polarization) of opinions. Hence, across a broad profile of simple and complex opinion diffusion processes, only a mix of heterophilic and homophilic rewiring preferences avoids polarization and promotes consensus.

physics.soc-ph↗

arXiv2023

Rate-Induced Transitions in Networked Complex Adaptive Systems: Exploring Dynamics and Management Implications Across Ecological, Social, and Socioecological Systems

Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.

physics.soc-ph↗

arXiv2023

Optimization of institutional incentives for cooperation in structured populations

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives respectively for a structured population of individuals whose conflicting interactions are characterized by a prisoner's dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world, and scale-free networks.

cs.GT↗

arXiv2022

The evolution of forecasting for decision making in dynamic environments

Global change is reshaping ecosystems and societies. Strategic choices that were best yesterday may be sub-optimal tomorrow; and environmental conditions that were once taken for granted may soon cease to exist. In this setting, how people choose behavioral strategies has important consequences for environmental dynamics. Economic and evolutionary theories make similar predictions for strategic behavior in a static environment, even though one approach assumes perfect rationality and the other assumes no cognition whatsoever; but predictions differ in a dynamic environment. Here we explore a middle ground between economic rationality and evolutionary myopia. Starting from a population of myopic agents, we study the emergence of a new type that forms environmental forecasts when making strategic decisions. We show that forecasting types can have an advantage in changing environments, even when the act of forecasting is costly. Forecasting types can invade but not overtake the population, producing a stable coexistence with myopic types. Moreover, forecasters provide a public good by reducing the amplitude of environmental oscillations and increasing mean payoff to forecasting and myopic types alike. We interpret our results for understanding the evolution of different modes of decision-making. And we discuss implications for the management of environmental systems of great societal importance.

q-bio.PE↗

arXiv2021

Combination of institutional incentives for cooperative governance of risky commons

Finding appropriate incentives to enforce collaborative efforts for governing the commons in risky situations is a long-lasting challenge. Previous works have demonstrated that both punishing free-riders and rewarding cooperators could be potential tools to reach this goal. Despite weak theoretical foundations, policy-makers frequently impose a punishment-reward combination. Here, we consider the emergence of positive and negative incentives and analyze their simultaneous impact on sustaining risky commons. Importantly, we consider institutions with fixed and flexible incentives. We find that a local sanctioning scheme with pure reward is the optimal incentive strategy. It can drive the entire population towards a highly cooperative state in a broad range of parameters, independently of the type of institutions. We show that our finding is also valid for flexible incentives in the global sanctioning scheme, although the local arrangement works more effectively.

physics.soc-ph↗