This excerpt from the first chapter of The Sciences of the Artificial (1969; 1996) by Herbert A. Simon establishes the epistemological foundations for distinguishing natural sciences from the “sciences of the artificial.” While natural sciences seek hidden patterns to explain how things are, the sciences of the artificial deal with objects synthesised by human beings, characterised by functions, goals, and normative imperatives—addressing how things “ought” to be. Simon introduces the crucial concept of the artefact as an “interface” between an “inner” environment (the substance and organisation of the object itself) and an “outer” environment (the context in which it operates); the artefact’s effectiveness depends on the successful adaptation of these two environments to one another. The text further explores the role of simulation as a source of new knowledge that can reveal the hidden implications of known premises. It defines both computers and the human mind as “physical symbol systems.” According to Simon, intelligence is fundamentally the work of these systems, which can encode information, manipulate structures, and adapt to their environment.The re-proposal of this classic text within the contemporary context of urban studies and Artificial Intelligence (PlanAIr) is driven by three fundamental reasons. First, Simon provides a critical ontological definition, reminding us that the world we inhabit is predominantly man-made. In this view, the city is the artefact par excellence: not a natural phenomenon to be passively observed, but a complex, designed system that must answer to human purposes, thus legitimising urban planning as a rigorous science of the artificial. Second, the vision of the artefact as a “meeting point” between inner and outer environments offers a powerful metaphor for Urban AI. Intelligent technologies in the city act as an interface between physical infrastructure and citizens’ social or environmental dynamics, requiring mutual adaptation to function effectively. Finally, as Simon’s work is foundational to symbolic Artificial Intelligence, revisiting it today allows us to grasp the theoretical roots of rule-based and logical AI. This historical perspective is crucial for distinguishing and potentially integrating symbolic approaches with the currently dominant data-driven paradigms, thereby recovering the capacity to reason about goals, meanings, and design imperatives rather than relying solely on raw data processing.In this article, we examine these ontological issues, discuss existing frameworks that aim to unify fragmented information, and explore the practical implications for urban AI applications. The thesis is that ontologies—structured and formal representations of knowledge—offer a powerful tool to address the challenges outlined above, while serving as a blueprint for defining, categorizing, and interrelating the entities present in urban environments and putting them to work in urban planning.
Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the expertise required. The ability of large language models (LLMs) to perform complex reasoning is well-enhanced by prompting learning such as Chain-of-thought (CoT) but still confined to left-to-right decision-making processes or limited paths during inference. This means they can fall short in problems that require exploration and uncertainty searching. In response, a novel large language model prompting method, called Random Forest of Thoughts (RFoT), is proposed for generating uncertainty reasoning to fit the area of computational social science. The RFoT allows LLMs to perform deliberate decision-making by generating diverse thought space and randomly selecting the sub-thoughts to build the forest of thoughts. It can extend the exploration and prediction of overall performance, benefiting from the extensive research space of response. The method is applied to optimize computational social science analysis on two datasets covering a spectrum of social survey analysis problems. Our experiments show that RFoT significantly enhances language models' abilities on two novel social survey analysis problems requiring non-trivial reasoning.
We propose a model of cross-pollination among online social media (OSM) websites, where the dynamics of user interactions mimic insect-mediated pollen transfer by pollinators. A pollinator acts as a vehicle enabling users to visit multiple social media sites- akin to visiting different plants in the same field- within a single browsing session. This approach frames geitonogamy in self-incompatible plant species as analogous to the distribution of web traffic across the social media landscape. A theoretical pollinator, allowing users to choose among social media sites multiple times per trip, drives uneven increases in web traffic across platforms, disproportionately benefiting the largest social networks while providing tangible competitive advantages to smaller OSMs. This heterogeneous landscape fosters monopolistic competition among niche platforms, incentivizing smaller sites to promote cross-pollination despite the larger relative gains to their bigger competitors. Our findings underscore the broader value of cross-platform user engagement, highlighting how cross-pollination dynamics can intensify network effects and bolster interconnectivity. Cross pollination via new pass-through apps facilitates the movement of attention, deepening and distributing engagement across multiple destinations. As pass-through apps gain traction, their disproportionate impact on traffic to social media platforms will incentivize social media platforms, large and small, to embrace cross-pollination dynamics.
Healthy snacks are often one of the forgotten options for some people, with the mindset “Healthy snacks are not tasty, healthy snacks are tasteless.” However, not all healthy snacks are bland, it's more about the food we eat with the original or natural flavour of the food itself and no MSG added to the food. As awareness about the importance of healthy eating increases, there is a need to change consumer perceptions that healthy snacks are also delicious. Besides, there are some consumers who are confused to find healthy yet tasty snacks. In this case, an analysis of consumer perception for healthy snacks in Batam city will be conducted. This research will use qualitative methods in the form of interview, observation and the design thinking approach. The design thinking approach includes the stages of empathize, define, ideate, prototype and test to understand consumer needs and formulate problems. With this method, it is expected to create solutions to consumer perception for healthy snacks and provide one example of a healthy snack product idea.
As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.
Long Kim, Thanapa Chouykaew, Siwarit Pongsakornrungsilp
et al.
Promoting repurchase intention to existing consumers is a crucial advantage that helps businesses manage sufficient supply for their customers and ensure their business sustainability. Business managers must understand factors that can significantly promote repurchase intention. Therefore, this study aims to examine the influence of brand love, brand preference, and brand loyalty on the intention to repurchase Covid-19 antigen test kits among a sample of Thai consumers. To achieve this aim, 670 Thai people, who had used the antigen test kit for their Covid-19 testing, were invited to answer questionnaires using an online Google Forms survey. After clearing outliers, only 523 responses were deemed valid and reliable and kept for further path analysis. The research findings showed that brand love and brand preference demonstrated positive relationships with brand loyalty. In addition, brand love and brand preference displayed positive relationships with repurchase intention. In conclusion, the results emphasized brand loyalty as the primary driver of repurchase intention because of its significant impact on Thai customers.
José Elguero Videgaray fue uno de los hispanistas mexicanos más destacados de la primera mitad del siglo XX. Abogado por formación, aunque periodista por oficio y vocación, cultivó el ejercicio de la palabra impresa en infinidad de artículos publicados en importantes periódicos de la época, como El País o Excélsior. Su pluma no aceptó ataduras y, por ello, acabó padeciendo la experiencia del exilio en varias ocasiones. De este periodista, escritor y académico michoacano, que vivió el régimen porfiriano y las primeras décadas de la revolución mexicana, versará el presente artículo con vistas a alcanzar un acercamiento a su desconocida trayectoria de vida.
Abstract This study aimed to examine the influence of emotional media information on information-processing mechanisms in the current post-truth era. A cognitive conflict monitoring and evaluation (CCME) model was proposed to explore news audiences’ attention and implicit attitudes. The study had a 2 (information type, emotional vs. neutral) × 2 (condition, compatible vs. incompatible) × 3 (electrode position: Fz vs. Cz vs. Pz) design, and an implicit association test (IAT) was administered, with event-related potential (ERP) data collected. The results revealed that emotional information evoked different information-processing mechanisms than neutral information. First, in the early conflict-monitoring stage, emotional information altered arousal, and more attentional resources were allocated to semantic processing. Second, in the late evaluation stage, the lack of attentional resources (due to prior allocation) reduced the late-stage evaluation of the target stimuli by participants. Thus, in this post-truth era, attentional resources may be exhausted by processing emotional information in unnecessary media cues irrelevant to facts, inducing early cognitive conflict and prolonged late-stage evaluation of news articles.
History of scholarship and learning. The humanities, Social Sciences
The National Stock Exchange of India (NSE) has presented Nifty 50 Shariah and Nifty 500 Shariah indices to provide unconventional indices for Sharia-compliant companies. These indices follow Sharia laws and can be used in portfolios that are culturally dependable commodities for investors who do not wish to put their money into the undesired business. NSE witnessed big movements in the indices during the Covid-19 period. This study seeks to understand the association between Nifty 500 Sharia and Nifty 50 Sharia and their respective selected indexes, Nifty 500 and Nifty 50, during the Covid-19 pandemic. The period from 27/01/2020 to 31/05/2022 has been taken for this study. The techniques applied, like correlation, co-integration, GMM, etc. based on the objectives of this paper. We conclude that the return of Sharia indices is better compared to the other indices. Also, stocks compliant with Sharia Indices are less risky and a better alternative for the portfolio during pandemic times.
The urban planning process is complex, involving social, economic, environmental, and political systems. Knowledge of how these systems interact is the domain of professional planners. Advances in artificial intelligence (AI) present planners with a ripe opportunity to critically assess their approaches and explore how new data collection, analysis, and methods can augment the understanding of places as they seek to anticipate futures with improved quality of life. AI can offer access to more and better information about travel patterns, energy consumption, land utilization, and environmental impacts, while also helping to better integrate these systems, which is what planners do. The adoption process will likely be gradual and involve significant time and resources. This article highlights several topics and issues that should be considered during this process. It is argued that planners will be well-served by approaching AI tools in a strategic manner that involves the topics discussed here.
Geography. Anthropology. Recreation, Social Sciences
Due to their essential role as places for socialization, "third places" - social places where people casually visit and communicate with friends and neighbors - have been studied by a wide range of fields including network science, sociology, geography, urban planning, and regional studies. However, the lack of a large-scale census on third places kept researchers from systematic investigations. Here we provide a systematic nationwide investigation of third places and their social networks, by using Facebook pages. Our analysis reveals a large degree of geographic heterogeneity in the distribution of the types of third places, which is highly correlated with baseline demographics and county characteristics. Certain types of pages like "Places of Worship" demonstrate a large degree of clustering suggesting community preference or potential complementarities to concentration. We also found that the social networks of different types of social place differ in important ways: The social networks of 'Restaurants' and 'Indoor Recreation' pages are more likely to be tight-knit communities of pre-existing friendships whereas 'Places of Worship' and 'Community Amenities' page categories are more likely to bridge new friendship ties. We believe that this study can serve as an important milestone for future studies on the systematic comparative study of social spaces and their social relationships.
Significant social change brings with it significant linguistic change. The recent global emergency caused by Covid-19, which has remorselessly spread all over the world in a few months, has changed significantly our lives and, consequently, our language. What is extraordinary is the rapidity with which this alteration in language has happened, so much so that the Oxford English Dictionary broke its quarterly publication cycle to update its coverage in April 2020. The aim of this paper is to give an overview on how language use has changed over a few weeks in response to an extraordinary event such as the Coronavirus pandemic. On the one hand, taking as a starting point the OED update I will highlight the way technical terms have entered everyday language. On the other hand, I will observe to what extent common words and expressions have come to assume new connotative meanings.
How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive. We use the connectivity kernel of conditionally-independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals, and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies. Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from eleven ego-network datasets collected in four surveys in the United Kingdom and United States. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life.
A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.
Knowledge about the software used in scientific investigations is necessary for different reasons, including provenance of the results, measuring software impact to attribute developers, and bibliometric software citation analysis in general. Additionally, providing information about whether and how the software and the source code are available allows an assessment about the state and role of open source software in science in general. While such analyses can be done manually, large scale analyses require the application of automated methods of information extraction and linking. In this paper, we present SoftwareKG - a knowledge graph that contains information about software mentions from more than 51,000 scientific articles from the social sciences. A silver standard corpus, created by a distant and weak supervision approach, and a gold standard corpus, created by manual annotation, were used to train an LSTM based neural network to identify software mentions in scientific articles. The model achieves a recognition rate of .82 F-score in exact matches. As a result, we identified more than 133,000 software mentions. For entity disambiguation, we used the public domain knowledge base DBpedia. Furthermore, we linked the entities of the knowledge graph to other knowledge bases such as the Microsoft Academic Knowledge Graph, the Software Ontology, and Wikidata. Finally, we illustrate, how SoftwareKG can be used to assess the role of software in the social sciences.
Christophe Heinkelé, Pierre Charbonnier, Philippe Foucher
et al.
Le présent article décrit l'implémentation d'une méthode de pré-location décimétrique d'images au sein de grands volumes de données dans des tunnels navigables et routiers. Elle repose sur une technique d'odométrie visuelle simplifiée, ce qui la rend rapide et facile à mettre en oeuvre. Cette méthode permet de structurer les données afin d'améliorer les traitements postérieurs, comme par exemple la reconstruction 3D par photogrammétrie. La méthode est évaluée sur la précision de la localisation par comparaison avec des techniques de localisation plus conventionnelles. La structuration des données qui découle de cette localisation des images au sein de l'ouvrage constitue l'aspect le plus important du travail présenté ici.
Instruments and machines, Applied optics. Photonics