arXiv subjects

Angela Fontan

Publications and source records attributed to Angela Fontan.

At least 19 recordsLinked to original sources

arXiv2026

Sustainable Heating with Karma: A Simulation Study of the KTH Live-In Lab

Space heating in buildings accounts for 10% of the global CO2 footprint. The widespread adoption of energy-efficient heating technology, e.g., heat pumps, could help reduce this figure, but technology alone may not suffice to reach carbon neutrality. Additionally, human occupants have an important role to play by adopting sustainable heating behaviors, e.g., avoid excessive window opening in the winter or (pre-)heat their units while clean energy is abundant. Thus far demand response policies aimed at promoting these behaviors have been monetary, which discriminates against low-income households and exposes human occupants who do not actively engage with real-time control signals to financial risks. This paper instead investigates the suitability of a non-monetary karma economy for promoting sustainable heating behaviors. Karma leverages the repeated and dynamic nature of heating energy allocations to attain climate targets both fairly and efficiently over time without resorting to financial means. As a first step towards experimentally validating the karma concept with real human occupants in the KTH Live-In Lab, we perform a simulation study on a digital model of the Live-In Lab. The study provides initial estimates of expected effects to guide the design of human-in-the-loop experiments, as well as assists with designing and tuning the karma economy in this context. As a specific example, we investigate how incorporating consumption memory in the form of karma affects window opening behaviors in comparison to conventional memory-less heating operation.

eess.SY↗

arXiv2026

On Leadership Emergence in Opinion Dynamics on Social Networks

Leadership in social groups emerges dynamically through interaction and opinion exchange. Empirical evidence indicates that individuals expressing strong opinions tend to gain influence, while sustained leadership critically depends on maintaining alignment with the surrounding social context. Motivated by these observations, we introduce a coupled dynamical model describing the simultaneous evolution of opinions and leadership in a networked population. Extending the Friedkin-Johnsen framework, we represent leadership as a time-varying susceptibility to social influence, which evolves according to a game-theoretic mechanism, consistent with social psychology evidence. Within this setting, agents strengthen their leadership by expressing decisive yet socially coherent opinions, whereas misalignment with the collective state results in a loss of influence. We analyze the coupled dynamics and establish sufficient conditions to identify which agents necessarily emerge as leaders and which act as followers in the social network.

physics.soc-ph↗

arXiv2026

Hyperedge approximation for stochastic processes on higher-order networks

Graphs are a standard framework for describing dynamical processes shaped by pairwise interactions among agents. But many systems involve interactions in groups of three or more agents. Here, we develop a method of "$\ell$-hyperedge approximation", a framework to analyze stochastic population processes on regular hypergraphs, in which each individual belongs to $k$ groups of size $\ell$. The framework accommodates both higher-order interactions that determine payoffs and higher-order processes for updating states in response to payoffs. Applied to evolutionary game dynamics, the framework generalizes the classical pairwise result on benefits and costs, $b/c>k$, that favors the spread of cooperation; and it provides critical benefit-to-cost ratios for nonlinear $\ell$-player public goods games that cannot be reduced to pairwise interactions. Applied to complex contagions, where inheritance of states occurs within hyperedges rather than along parent-offspring edges, the framework gives a closed-form result for the fixation probability, which shows how a complexity parameter governs the spread of rare types. Coupling the two processes produces a single stochastic model of payoff-biased complex contagion in structured populations. These results extend pair approximation from graphs to hypergraphs, accommodating multi-way interactions and inheritance structures with no pairwise analog.

physics.soc-ph↗

arXiv2026

Impact of Attitude and Bounded Rationality on Collective Behavioral Transitions

The theory of planned behavior (TPB) is one of the most influential frameworks in social psychology, stating that a person's behavior is driven by intention, which is primarily shaped by attitude, subjective norms, and perceived behavioral control. Despite its strong empirical support, TPB remains a static conceptual framework without explicit mathematical formulations that capture the temporal evolution of its components. To address this gap, we develop a dynamic agent-based modeling framework that integrates the core principles of TPB with a behavior-to-attitude feedback mechanism. Specifically, we define behaviors based on their feedback effects on attitude and examine when the population undergoes collective transitions by either adopting a beneficial behavior or rejecting a harmful one. Results from our model demonstrate that collective transitions can be effectively controlled by adjusting two key behavioral parameters that reflect agents' attitude influence and decision rationality. These findings provide quantitative insights on TPB, highlighting the key factors that drive collective behavioral transitions and the need for further socio-psychological case studies.

cs.SI↗

arXiv2026

Generalizability of Learning-based Occupancy Detection in Residential Buildings (extended version)

This paper investigates non-intrusive occupancy detection methods for residential buildings using environmental sensor data from the KTH Live-In Lab in Stockholm, Sweden. Three machine learning approaches, namely, logistic regression (LR), support vector machines (SVM), and long short-term memory (LSTM) network enhanced with an attention mechanism, are evaluated in terms of predictive performance and computational complexity. The analysis considers the trade-off between sensor availability (investment cost) and prediction accuracy in real applications, as well as the models' cross-apartment generalizability. Hyperparameters for both the SVM and LSTM models are optimized using Bayesian optimization. All three models are evaluated on data collected from apartments not used during training, and on data generated from a calibrated digital model of the testbed. Results show that all models achieve comparable performance on the same-apartment test data (accuracy of approximately 0.83, F1 score of approximately 0.86). When assessed on cross-apartment data, the LSTM model demonstrates the strongest generalization capability (accuracy of 0.84, F1 score of 0.85), while LR provides a competitive, low-complexity alternative for applications that do not require cross-apartment generalization.

eess.SY↗

arXiv2026

Topology Identification of Dynamical Signed Graphs

We propose an adaptive control protocol for identifying the topology of dynamical networks interconnected over undirected graphs with cooperative and antagonistic interactions. The signed network is modeled using a repelling Laplacian. Topology identification relies on an edge-based formulation of the network and adaptive control protocols through the design of a persistently excited auxiliary network. Our approach guarantees the simultaneous identification and synchronization of the unknown signed network and establishes uniform semiglobal practical asymptotic stability of the estimation errors. Numerical simulations validate our theoretical results.

eess.SY↗

arXiv2026

Flexible Electric Vehicle Charging with Karma

Motivated by the need to develop fair and efficient schemes to facilitate the electrification of transport, this paper proposes a non-monetary karma economy for flexible Electric Vehicle (EV) charging, managing the intertemporal allocation of limited power capacity. We consider a charging facility with limited capacity that must schedule arriving EVs to charge in real-time. For this purpose, the facility adopts online karma auctions, in which each EV user is endowed with non-tradable karma tokens, places a karma bid in each time interval it is present in the facility, and capacity is allocated to the highest bidders, who must pay their bids. These payments are subsequently redistributed to the users to form a closed, indefinitely sustainable economy. The main contribution is to extend previous karma Dynamic Population Game (DPG) formulations to this setting which features novel State of Charge (SOC) dynamics and private trip deadlines in addition to urgency. A Stationary Nash Equilibrium (SNE) of the EV charging karma economy is guaranteed to exist, and it is demonstrated to provide pronounced benefits with respect to benchmark scheduling schemes as it balances between meeting deadlines and prioritizing high urgency.

eess.SY↗

arXiv2026

On the Convergence of an Opinion-Action Coevolution Model with Bounded Confidence

This paper presents a theoretical convergence analysis for an opinion-action coevolution model that integrates the opinion updating rule of the Hegselmann-Krause model with a utility-based decision-making mechanism. The model is reformulated into an augmented state-space representation, where the state matrix induces a time-varying social interaction digraph. The convergence analysis is grounded on two existing theoretical findings that establish convergence for the Hegselmann-Krause type of models and containment control systems with multiple stationary leaders, respectively. Results indicate that, if the structure of the interaction digraph stabilizes within finite time, the model either converges to consensus, where all agents' opinions and actions reach an identical state, or exhibits clustering, where some opinion nodes act as stationary leaders while the remaining nodes approach the convex hull formed by the leaders. Numerical simulations are then provided to validate the theoretical results.

eess.SY↗

arXiv2026

On a Co-evolving Opinion-Leadership Model in Social Networks

Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an agent holds strong yet socially aligned opinions, and declines when such alignment is lost, capturing the trade-off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.

physics.soc-ph↗

arXiv2026

Stability of Open Multi-agent Systems over Dynamic Signed Digraphs

We address the synchronization problem in open multi-agent systems (OMAS) containing both cooperative and antagonistic interactions. In these systems, agents can join or leave the network over time, and the interaction structure may evolve accordingly. To capture these dynamical structural changes, we represent the network as a switched system interconnected over a dynamic and directed signed graph. Additionally, the network may contain one or multiple leader groups that influence the behavior of the remaining agents. In general, we show that the OMAS exhibit a more general form of synchronization, including trivial consensus, bipartite consensus and containment. Our approach uses the signed edge-based agreement protocol, and constructs strict Lyapunov functions for signed networks described by signed edge-Laplacian matrices containing multiple zero eigenvalues. Numerical simulations validate our theoretical results.

eess.SY↗

arXiv2026

Edge-based Synchronization over Signed Digraphs with Multiple Leaders

This work addresses the edge-based synchronization problem in first-order multi-agent systems containing both cooperative and antagonistic interactions with one or multiple leader groups. The presence of multiple leaders and antagonistic interactions means that the multi-agent system typically does not achieve consensus, unless specific conditions (on the number of leaders and on the signed graph) are met, in which case the agents reach a trivial form of consensus. In general, we show that the multi-agent system exhibits a more general form of synchronization, including bipartite consensus and containment. Our approach proposes a signed edge-based agreement protocol for signed networks described by signed edge-Laplacian matrices. In particular, in this work, we present new spectral properties of signed edge-Laplacian matrices containing multiple zero eigenvalues and establish global exponential stability of the synchronization errors. Moreover, we explicitly compute the equilibrium to which all edge states converge, thereby characterizing the resulting synchronization behavior. Numerical simulations validate our theoretical results.

eess.SY↗

arXiv2026

On a Coupled Adoption-Opinion Framework for Competing Innovations

In this paper, we propose a two-layer adoption-opinion model to study the diffusion of two competing technologies within a population whose opinions evolve under social influence and adoption-driven feedback. After adopting one technology, individuals may become dissatisfied and switch to the alternative. We prove the existence and uniqueness of the adoption-diffused equilibrium, showing that both technologies coexist and that neither partial-adoption nor monopoly can arise. Numerical simulations show that while opinions shape the equilibrium adoption levels, the relative market share between the two technologies depends solely on their user-experience. As a consequence, interventions that symmetrically boost opinions or adoption can disproportionately favor the higher-quality technology, illustrating how symmetric control actions may generate asymmetric outcomes.

eess.SY↗

arXiv2025

Adaptive Online Optimization for Microgrids with Renewable Energy Sources

In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid.

math.OC↗

arXiv2025

Collective decision-making dynamics in hypernetworks

This work describes a collective decision-making dynamical process in a multiagent system under the assumption of cooperative higher-order interactions within the community, modeled as a hypernetwork. The nonlinear interconnected system is characterized by saturated nonlinearities that describe how agents transmit their opinion state to their neighbors in the hypernetwork, and by a bifurcation parameter representing the community's social effort. We show that the presence of higher-order interactions leads to the unfolding of a pitchfork bifurcation, introducing an interval for the social effort parameter in which the system exhibits bistability. With equilibrium points representing collective decisions, this implies that, depending on the initial conditions, the community will either remain in a deadlock state (with the origin as the equilibrium point) or reach a nontrivial decision. A numerical example is given to illustrate the results.

math.OC↗

arXiv2025

Dynamical models for distributed social power perception in Friedkin-Johnsen influence networks

Social power quantifies the ability of individuals to influence others and plays a central role in social influence networks. Yet, computing social power typically requires global knowledge and significant computational or storage capability, especially in large-scale networks with stubborn individuals. In this paper, we propose a distributed perception mechanism based on the Friedkin-Johnsen opinion dynamics that enables individuals to estimate their true social power through local interactions. The mechanism starts from independent initial perceptions and relies only on local information: each individual only needs to know its neighbors' stubbornness and the influence weights they accord. We provide rigorous dynamical system analysis that characterizes equilibria, invariant sets, and convergence. Conditions are established for convergence to the true social power in both the static setting with fixed influence weights and the reflected-appraisal setting where influence weights coevolve with perceptions. The proposed mechanism remains reliable under extreme initial perceptions, disconnected influence networks, reflected-appraisal coupling, and variations in timescales. Numerical examples illustrate our results.

eess.SY↗

arXiv2025

Stability of Open Multi-agent Systems over Dynamic Signed Graphs

This paper addresses the bipartite consensus-control problem in open multi-agent systems containing both cooperative and antagonistic interactions. In these systems, new agents can join and new interactions can be formed over time. Moreover, the types of interactions, cooperative or antagonistic, may change. To model these structural changes, we represent the system as a switched system interconnected over a dynamic signed graph. Using the signed edge-based agreement protocol and constructing strict Lyapunov functions for signed edge-Laplacian matrices with multiple zero eigenvalues, we establish global asymptotic stability of the bipartite consensus control. Numerical simulations validate our theoretical results.

eess.SY↗

arXiv2024

Robust Data-Driven Tube-Based Zonotopic Predictive Control with Closed-Loop Guarantees

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations.

eess.SY↗

arXiv2023

Social interactions for a sustainable lifestyle: The design of an experimental case study

Every day we face numerous lifestyle decisions, some dictated by habits and some more conscious, which may or may not promote sustainable living. Aided by digital technology, sustainable behaviors can diffuse within social groups and inclusive communities. This paper outlines a longitudinal experimental study of social influence in behavioral changes toward sustainability, in the context of smart residential homes. Participants are residing in the housing on campus referred to as KTH Live-In Lab, whose behaviors are observed w.r.t. key lifestyle choices, such as food, resources, mobility, consumption, and environmental citizenship. The focus is on the preparatory phase of the case study and the challenges and limitations encountered during its setup. In particular, this work proposes a definition of sustainability indicators for environmentally significant behaviors, and hypothesizes that, through digitalization of a household into a social network of interacting tenants, sustainable living can be promoted. Preliminary results confirm the feasibility of the proposed experimental methodology.

eess.SY↗