Search Results (32)

Historic urban landscapes, despite their cultural significance, often face neglect, limiting their potential to increase the value of historical centers. Defined as a complex sociotechnical network that involves a variety of agencies incorporating material, immaterial, natural, and artificial elements, these landscapes present significant challenges for architects because of their layered and diverse components. Actor–network theory (ANT) is used as a methodological and ontological framework to address this complexity. However, a notable research gap exists on the basis of the lack of clear representation and practical application of ANT to address the complexity of these historic urban landscapes. To bridge this gap, this study uses Bey’s palace as a case study to develop a comprehensive framework based on a digital mapping approach rooted in ANT. This framework traces, visualizes, and analyzes historic urban landscapes as intricate systems of agencies, leveraging graph theoretical algorithms and computational analysis tasks from network analysis tools to increase their effectiveness. This investigation is based on two key concepts: the actor/actant and the actor network. The research employed Bruno Latour’s concepts of translation, agency, and the mapping controversies technique grounded in graph-theoretic algorithm tasks to decipher the complexities of Bey’s palace system. The results identify seven clusters as actor networks and highlight the roles of key actors/actants, such as Ahmed Bey, decorative elements, courtyard gardens, and Moorish architecture. This methodological approach provides architects and urban planners with practical tools to better understand, analyze and preserve historic urban landscapes, enriching their cultural and historical value. By transforming contested discourses into measurable networks indicators, this interdisciplinary framework directly supports SDG11 (Sustainable Cities and Communities), especially Target 11.4, in safeguarding cultural heritage by enabling the prioritization, monitoring and governance of cultural, social and infrastructural assets in historic urban landscapes. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Online aggression and abusive language on social media pose a growing threat to democratic discourse, as they contribute to polarization, delegitimization of political actors, and the erosion of civil debate. While much of the current research relies on computational methods to detect hate speech, fewer studies investigate how online aggression functions discursively in specific socio-political contexts. This study addresses this gap by analyzing patterns of verbal aggression on Facebook and Twitter during two key events in Poland in 2020: the presidential election and the Women’s Strike. Adopting a mixed-method approach (combining sentiment analysis, content analysis, and discourse analysis) and comparing two socio-political events that generated extensive online debate, this study investigates the patterns and communicative functions of hostile and aggressive language on Facebook and Twitter. The study reveals that neutral posts dominated both datasets, but negative and aggressive posts were significantly more frequent during the Women’s Strike, where verbal aggression was used not only to reinforce group identity but also to express moral outrage, trauma, and demands for change. In contrast, aggression during the election campaign was less frequent but more calculated. It functioned as a strategic tool to delegitimize political opponents and reinforce partisan divides. Users employed vitriolic language and profanity as rhetorical tools to undermine authority, reinforce group identity, and mobilize supporters. The study also reveals asymmetric patterns of aggression, with public figures and institutions, particularly the ruling party, Church, and police, being primary targets. The findings have significant implications for understanding the dynamics of online debates and aggression patterns in social media. Full article

The European Green Deal (EGD) aims at driving the green transition in the EU and positions cities as pivotal actors in achieving climate neutrality and environment protection. Despite ambitious policy commitments, significant implementation gaps persist at the local level impeding urban green transitions. This study assesses barriers to the EGD urban implementation by integrating several methods (scoping literature review, expert consultations, and computational network analysis) to identify structural conditions hampering change. Barriers are clustered into five domains and reviewed by experts to distill eight structural conditions perpetuating the status quo of urban development, hindering transformative change. The findings illustrate how the emerged structural conditions, ranked by their in-degree centrality, regard insufficient policy implementation; upgrade of consolidated built environments’ layout; short-term mindset; lack of knowledge and data sharing among stakeholders; silos in policymaking and development processes; competition among stakeholders over space use; limited social acceptance; and limited financial resources. Conversely, high-out-degree barriers—such as limited technical expertise in urban departments and GDP-oriented paradigms—emerge as system triggers where targeted interventions could catalyze change. This research provides actionable insights for policymakers by identifying leverage points which could promote urban green transitions and enhance the EGD local implementation for accelerating urban green transitions. Full article

Service robots (SRs) are increasingly deployed in commercial settings, yet the factors influencing their acceptance, particularly emotional design elements, remain understudied. This research investigates SR acceptance factors by integrating the technology acceptance model, the Computers Are Social Actors (CASA) framework, Kansei engineering (KE), and social presence theory (SPT) to examine how design elements influence user responses to SRs. Using structural equation modeling of survey data from 318 shoppers and hotel guests in China, we tested relationships between CASA attributes, emotional perceptions, social presence, and usage intention. The results revealed that communication style produced the strongest effects across all emotional dimensions, with cuteness and coolness directly influencing usage intention, while warmth and novelty operate through social presence mediation. Multi-group analysis identified significant gender differences in response patterns: male users prioritized communication-driven perceptions while female users responded more strongly to coolness attributes. These findings extend our understanding of acceptance factors in service robot adoption, highlighting the critical roles of emotional design, communication style, and gender differences, while suggesting differentiated design approaches for diverse user segments. Full article

Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain. Full article

Interaction is typically at the core of the value co-creation process through operant resource exchange in online collaborative innovation communities (OCICs). While some studies emphasize the facilitating effect of interaction on value co-creation, others have drawn opposite conclusions, such as more peer interaction leads to less idea generation. Thus, the purpose of this paper is to utilize the service ecosystem framework to clarify the overall relationship between interaction and value co-creation and to explore the moderating factors that may have contributed to the divergence and inconsistency of previous studies. We conducted a meta-analysis of 65 effect sizes obtained from 63 articles with a cumulative sample size of 25,185 between 2004 and 2023, using a random effects model. The results indicate that interaction has a significantly positive impact on user value co-creation within OCICs (r = 0.453, 95%CI [0.405, 0.499]), and the heterogeneity among studies was significant (Q = 1409.29, p < 0.001). The strength of this correlation was moderated by the types of interaction (human–computer or human–human interactions), the types of OCICs (business-sponsored or socially constructed online communities), and the number of involved OCICs (one or multiple online communities), but not by the cultural background. These findings support the service ecosystem perspective rather than resource scarcity theory by resolving the mixed findings regarding the relationship between interaction and user value co-creation. Furthermore, this study systematically examined the contingent factors separately across three levels, micro (types of actor interactions), meso (types and number of OCICs), and macro (cultural background), combining the whole and the part insights, and empirically integrating service ecosystems as the foundational paradigm and unit of analysis for value co-creation research for the first time. This research contributes to theoretical frameworks in service ecosystems and offers actionable insights for management practices in business and marketing. Full article

Homophily, which means similarity breeds association, is one of the most fundamental principles in social organization. However, in some cases, homophily is not significant, because actors’ perceptions of others differ from the real situation. In this article, we use the term “subjective homophily” to describe the homophily where the perceived similarity of objects is considered. In addition, we also consider social influence, which is closely related to homophily and represents the diffusion of some attributes through associations. In short, the dynamic temporal logic ${\mathrm{LoSH}}_{\mathrm{G},\mathrm{M}}^{\mathrm{SC}}$ we propose in this article is based on computation tree logic (CTL), which is used to describe the evolution of networks by subjective homophily, and dynamic logic (DL), which provides the dynamic update operator for representing active social influence. Furthermore, we prove that the model checking problem and the validity checking problem for ${\mathrm{LoSH}}_{\mathrm{G},\mathrm{M}}^{\mathrm{SC}}$ are both PSPACE-complete. Finally, we use an example, named false consensus, to illustrate how logic captures the subjective homophily evolution of networks and the impact of active social influence on evolution and structure. Full article

This exploratory study examines the socio-technical dynamics of Artificial Intelligence Companions (AICs), focusing on user interactions with AI platforms like Replika 9.35.1. Through qualitative analysis, including user interviews and digital ethnography, we explored the nuanced roles played by these AIs in social interactions. Findings revealed that users often form emotional attachments to their AICs, viewing them as empathetic and supportive, thus enhancing emotional well-being. This study highlights how AI companions provide a safe space for self-expression and identity exploration, often without fear of judgment, offering a backstage setting in Goffmanian terms. This research contributes to the discourse on AI’s societal integration, emphasizing how, in interactions with AICs, users often craft and experiment with their identities by acting in ways they would avoid in face-to-face or human-human online interactions due to fear of judgment. This reflects front-stage behavior, in which users manage audience perceptions. Conversely, the backstage, typically hidden, is somewhat disclosed to AICs, revealing deeper aspects of the self. Full article

Trajectory prediction is essential for the safe driving of autonomous vehicles. With the advancement of advanced sensors and deep learning technologies, attempts have been made to reflect complex interactions. In this study, we propose a deep learning-based Multimodal Trajectory Prediction method that reflects traffic light conditions in complex urban intersection situations. Based on existing state-of-the-art research, the multi-path of multi-agents was predicted using a generative model, and the actor’s trajectory information, state, social interaction, and traffic light state, and scene context were reflected. Performance evaluation was conducted using metrics commonly used to evaluate the performance of stochastic trajectory prediction models. This study is meaningful in that trajectory prediction was performed by reflecting realistic elements of traffic lights in a complex urban environment. Future research will need to be conducted on efficient ways to reduce time and computational performance while reflecting different real-world environments. Full article

The study of opinion formation and dynamics is one of the core topics in sociophysics. In this paper, the results of computer simulation of opinion dynamics based on social impact theory are presented. The simulations are based on Latané theory in its computerised version proposed by Nowak, Szamrej and Latané. The active parameters of the model describe the volatility of the actors (social temperature T) and the effective range of interaction (governed by an exponent $\alpha$ in a scaling function of distance between actors). Initially, every actor i has his/her own opinion. Our results indicate that ultimately at least 90% of the initial opinions available are removed from the society. For a low social temperature and a long range of interaction, only one opinion survives. Also, a rough sketch of the system phase diagram is presented. It indicates a set of ($\alpha ,T$) leading either to (1) the dominance of the unanimity of the opinions or (2) mixtures of unanimity and polarisation, or (3) taking random opinions by actors, or (4) a mixture of the final fates of the systems. The drastic reduction of finally observed opinions vs. their initial variety may be generic for many sociophysical models of opinions formation but masked by assuming an initially small pool of available opinions (in the worst case, in models with only binary opinions). Full article

While conventional innovation has boosted economic growth in certain regions, it has not contributed to closing the social and economic gap in most developing countries. Humanity is going through a historic moment of great challenges. One of them is social exclusion, a matrix of factors that prevent human beings from achieving well-being: poverty, hunger, inequality, lack of access to basic resources and services, and lack of social ties that help improve these circumstances, among others. This study holds two hypotheses: (1) in this context, inclusive innovation emerges as a response to the inability of conventional innovation to contribute to solve the persistent challenge of social exclusion and (2) universities—key actors in innovation dynamics—should play a fundamental role in the generation of inclusive innovation, especially considering their natural commitment to society. Although the role of the university in innovation has been widely acknowledged and studied, no formal theoretical model has represented inclusive innovation in developing countries adopting a systemic, complex, adaptive, and functional approach and incorporating a diversity of agents, interactions, capabilities, learning processes, knowledge, and directionalities—this would enable us to understand the role of the university in inclusive innovation. This paper argues that innovation dynamics should be understood from a systemic perspective and using computational modeling and simulation methods, so that the inherent complexity of these systems can be taken into account. The analysis of innovation scenarios based on a formal theoretical model and its operationalization through computer simulation should contribute to the understanding of the role of the university in these system dynamics, which can be used to propose effective strategies to strengthen its participation. Therefore, this paper proposes a formal systemic agent-based conceptual model that can be used to study the role of the university in inclusive innovation and establish guidelines to improve its performance. This study implemented standard computer modeling and simulation, specifically adapted for agent-based modeling. The results obtained from the simulation scenarios were comparatively analyzed using statistical tests (ANOVA and Tukey) to determine the presence of statistically significant differences. As the main finding of the research, the proposed conceptual model was validated and proved to be useful for studying the role of the university in reducing social exclusion in the Global South, through the design and execution of computer simulation scenarios. Full article

Many financial technology (fintech) applications have incorporated interactive computer agents to act as mediators between the user and the fintech system. This paper provides a comprehensive review of interactive fintech agents from technological and social science perspectives. First, we explain the general fintech landscape and define interactive fintech agents. Next, we review the major technologies involved in creating fintech: (1) artificial intelligence and machine learning, (2) big data, (3) cloud computing, and (4) blockchain; as well as the specific key technologies enabling the following aspects of interactive fintech agents: (1) intelligence, (2) understanding of users, and (3) manifestation as social actors. Following the technology review, we examine issues and theories related to human-fintech agent interaction in the following areas: (1) agents’ understanding of users, (2) agents’ manifestation as social actors (via embodiment, emotion, and personality), and (3) users’ social interaction with agents. Finally, we suggest directions for future research on fintech agents. Full article

In this paper, the results of computer simulations based on the Nowak–Szamrej–Latané model with multiple (from two to five) opinions available in the system are presented. We introduce the noise discrimination level (which says how small the clusters of agents could be considered negligible) as a quite useful quantity that allows qualitative characterization of the system. We show that depending on the introduced noise discrimination level, the range of actors’ interactions (controlled indirectly by an exponent in the distance scaling function, the larger the exponent, the more influential the nearest neighbors are) and the information noise level (modeled as social temperature, which increases results in the increase in randomness in taking the opinion by the agents), the ultimate number of the opinions (measured as the number of clusters of actors sharing the same opinion in clusters greater than the noise discrimination level) may be smaller than the number of opinions available in the system. These are observed in small and large information noise limits but result in either unanimity, or polarization, or randomization of opinions. Full article

Under the influence of COVID-19, various emergency supplies have repeatedly broken links, seriously affecting normal life and hindering the sustainable development of enterprises and society. Therefore, suitable emergency suppliers are crucial. To prioritize and select suitable emergency suppliers, key indicators were determined, and evaluation models were established based on the characteristics of epidemic situations and epidemic prevention materials. The application of the MCDM (multi-criteria decision-making) issue combining fuzzy SWARA (the stepwise weight assessment ratio analysis) and the actor analysis method to emergency supplier selection studies, called the fuzzy SWARA-based actor analysis method, is used to identify appropriate suppliers for optimizing pre-preparation. Results of evaluation system weight computations by the Fuzzy SWARA-based actor analysis method show that the overall prioritization of four non-economic factors in ranking orders are “Delivery Capacity”, “Flexible Supply Capacity”, “Quality”, and “Social Evaluation and Reputation”. For the inclusion of conflicting standards and the unquantifiable feature, economic and non-economic factors were discussed separately and evaluated by language variables. Additionally, the fuzzy actor analysis indicated that economic factors and non-economic factors need to be considered comprehensively for emergency supplier selection. This method has good operability and reference value, convenient for the final choice making according to actual situation. Full article