搜索结果：Social science & medicine (1982)

History of Medicine15 August 1990Medical Authorship: Traditions, Trends, and TribulationsW. Bruce Fye, MD, MAW. Bruce Fye, MD, MASearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-113-4-317 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail ExcerptThe biomedical literature serves many purposes. From it we learn what is known and what we seek to know. Communication of new concepts and discoveries is a critical part of the advance of medicine—whether at the bedside or in the laboratory. There are many components of what may be termed the industry of biomedical communication; this paper focuses on the dynamics of medical authorship. Among the themes considered are the incentives that motivate physicians and scientists to write, the rewards of authorship, the relationships of medical authors and editors, and the consequences of the current structure and philosophy of academic...References1. Fye W. The Development of American Physiology: Scientific Medicine in the Nineteenth Century. Baltimore: Johns Hopkins University Press; 1987. Google Scholar2. Ludmerer K. Learning to Heal: The Development of American Medical Education. New York: Basic Books; 1985. Google Scholar3. Taylor J. What constitutes a practical medical paper? Monthly Cyclopaedia of Practical Medicine. 1907;10:360. Google Scholar4. Sprengel K. Preface. In: Neue Litterarische Nachrichten für Aerzte, Wundaerzte und Naturforscher. 1786;1:1. Translated in: Kronick DA. A History of Scientific and Technical Periodicals: The Origins and Development of the Scientific and Technological Press, 1665-1790. 2d ed. Metuchen, New Jersey: Scarecrow Press; 1976:197. 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The Social Transformation of American Medicine: The Rise of a Sovereign Profession and the Making of a Vast Industry. New York: Basic Books; 1982. Google Scholar This content is PDF only. To continue reading please click on the PDF icon. Author, Article, and Disclosure InformationAuthors: W. Bruce Fye, MD, MAThis paper was presented at a symposium, "The Medical Journal: Past, Present, and Future," held in honor of Edward J. Huth, Editor of Annals of Internal Medicine, on 13 September 1989. Dr. Huth retired as Editor on 30 June 1990. The papers from the symposium will be published in a Festschrift in his honor. 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An analysis of publication trendsTeaching dietetic interns to write for lay and professional audiencesPolicy mattersStyle and substance: further improvements.AuthorshipAngelo Taranta, MDDocumenting Contributions to AuthorshipG. B. John Mancini, MD 15 August 1990Volume 113, Issue 4Page: 317-325KeywordsForecastingMotivationResearch laboratoriesScientists ePublished: 1 December 2008 Issue Published: 15 August 1990 Copyright & Permissions© 1990 American College of PhysiciansPDF downloadLoading ...

To the benefit of all of us, Paul Starr has published an updated version of Social Transformation of American Medicine. Nearly everyone in health policy, health politics, or health law who has taken undergraduate or graduate study in the subject—to say nothing of the study of professions, the history of science, the sociology and history of medicine, and the political economy of health—will be familiar with this magisterial 1982 volume. Reading the updated edition gives students and scholars alike a chance to reengage with Starr's centuries-spanning narrative of the rise of the American medical profession to combined social, economic, and political dominance over the sphere of American health care. To the classic material, Starr appends a new epilogue surveying the last 35 years in light of what came before it.Revisiting the text in the twenty-first century provides an opportunity to examine Starr's history in light of subsequent academic developments, as well as the intervening years. Starr's second chapter, “Medicine in a Democratic Culture, 1760–1850,” reads as ever more perceptive in light of four decades of historical, sociological, and political science inquiry that connect the American Revolution more tightly to democratizing patterns in antebellum America. These years saw the undermining of ecclesiastical authority (Hatch 1989), of patriarchal household dominance (Cott 1977; Salmon 1986), of slaveholding magisterium (Sinha 2016), and of landed rentier economies (Huston 2000; McCurdy 2006). It saw the democratization of American political life (Wilentz 2005) and even the democratization of capital through free banking (Lamoreaux 1997; Moss and Brennan 2001). Starr's description of democratic culture during the “long Revolution” provides a necessary basis for the later liberation of professional expertise. The rise in meritocratic professional authority required first, in some sense, the rupture of earlier barriers that hindered scientific advance. The power accumulated by the medical profession in the late nineteenth century replaced another form of dominance, weakened by cultural democratization.Rereading Starr's chapters on “The Consolidation of Authority, 1850–1930” and “The Escape from the Corporation, 1900–1930” reminds us of other work written since that sketches the role of professional and state forces in weakening corporate power. The hitching of doctors' authority to that of the hospital required the capitalization of medicine's physical plant, as well as a freer vision of municipal government's role in health policy, as detailed in such works as Judith Walzer Leavitt's The Healthiest City (1996) on progressive Milwaukee, or Charles Rosenberg's insightful study “From Almshouse to Hospital” (1982) on the transformation of Philadelphia Hospital, published the same year as Starr's first edition. The decline of quackery-based alternatives to organized medicine came in part through the assertion of Progressive and New Deal Era state power, as shown by such narratives as Stephen Skowronek's Building a New American State (1982), Theda Skocpol's Protecting Soldiers and Mothers (1992), and my own Forging of Bureaucratic Autonomy (Carpenter 2001) and Reputation and Power (Carpenter 2010). Starr's prescient assignment of a central role to the Mayo Clinic in the transformation of organizational models in twentieth-century medicine develops a narrative that deserves a book of its own.Starr's book 2 on physicians, the state, and the emergence of the medical corporation has probably received the most attention from scholars. Important reinterpretations to the story have been rendered by the sociologist Andrew Abbott (1988) and the political scientist Jacob Hacker. Hacker in particular develops Starr's earlier point that incremental innovations in health insurance actually undermined the long-term effort for comprehensive health insurance provision in America. In his celebrated article “The Historical Logic of National Health Insurance” (1998) that compares the American experience to that of Britain and Canada, and in his book The Divided Welfare State (2002) contrasting the trajectories of social insurance and health insurance, Hacker showed how each seemingly momentary pause in national health insurance reform efforts allowed private insurers to populate an exclusive marketplace (after the failure of the Truman effort, for example), or politically fortified the exclusive nature of health provision (after Medicare). In many ways, however, these reinterpretations merely reinforce the outlines of the Starr narrative.Starr's epilogue to this new edition of Social Transformation of American Medicine is a deft, wide-ranging summary of complicated and intertwining developments in the political economy of American health care. Readers may expect, and they indeed receive, summaries of the battle over the Clinton health care plan and Obamacare. Yet they also see some of the most thoughtful summaries yet written of changes in health insurance policies and organizations, the emergence of preferred-provider organizations and point-of-service plans, the rise of capitation-based payment schemes, and the introduction of tax-favored high-deductible health plans (456–61). Starr links these patterns to aggregate risk shifting in a decentralized but no less incessant fashion (466–68), what Hacker has demonstrated to be a society-wide phenomenon in his volume The Great Risk Shift (2008).To this reader, the most important contribution of the epilogue consists in an apt summary of the provider concentration that has buffeted American health care in the last three decades. Mergers and acquisitions in the hospital sphere exploded ninefold from 1990 to 1996, and the pace reaccelerated after 2010. Starr relates the deeply precedent-setting merger of Massachusetts General and Brigham to form Partners HealthCare in 1994, followed in 2000 by a collusive agreement between Partners and Blue Cross that hiked health care prices statewide. Partners HealthCare became so powerful that it was able to bully Tufts Health Plan to accede to its terms. Hospitals have integrated not only horizontally by merging with other hospitals but also vertically by buying up practice groups such as radiologists, surgical units, skilled nursing facilities, and home health providers. An abundance of health policy research now demonstrates increases in cost and price in these regional monopolies. These developments were made possible by the weakening of antitrust and merger regulations at the state and federal level, which were themselves facilitated by changes in academic and legal doctrine, not only the Chicago school of law and economics (as Starr points out more generally) but also more specifically the doctrine of contestable markets in which a plain monopoly can be considered less threatening because there always lurks the possibility, if not the actuality, of an entrant.Starr concludes the epilogue with a poetic cautionary note, reminding us that the formal reliance on price systems has led to an informal surrender to market power. It is a deft echo of the idea that the dream of reason forgot to account for power.Starr's volume stands sufficiently on its own, but I would like to see scholars attend to two other recent developments that Starr left aside. While Starr notes the dramatic growth of technology-intensive medicine, there is room in academia for a treatment of the way that medical technology and medical practice have combined to create new centers of wealth and political power. The presence of pharmaceutical treatment was a known variable in the first edition of Social Transformation. To this has been added a vast market for medical devices (at $180 billion in industrial revenues per year and rapidly growing)—artificial knees and hips, coronary stents, pacemakers and the like. In recent years the growth in medical device aggregate spending has generally been well above that for pharmaceuticals.Medical devices have reempowered surgeons in ways that are beginning to remap American medicine. And with the aging of the US population, accompanied by changes in obesity, these patterns are likely to grow only stronger. The marginal benefit for physicians to prescribe most drugs is pretty minimal, unless the treatment is administered in a hospital setting, but the marginal benefit of “prescribing” a device is far larger. The administration of treatment by device almost always involves surgery of some sort, and cardiologists and orthopedic practitioners have been making millions of dollars from these treatments. The medical device industry is highly concentrated industrially in the United States, and it counts surgical specialty associations as among its most important political allies. Compared to the pharmaceutical industry, it has been far less stringently regulated by the Food and Drug Administration.A second development also concerns wealth and power, and its outlines at this moment are forming before our eyes. For generations the American medical profession made its money from a broad swath of the American population. This was due in part to the distribution of wealth and income in the United States and in part to government insurance programs. As income and especially wealth are ever more concentrated in the hands of a smaller portion of society since 1970 (see Piketty 2014), a larger share of the demand for medical services comes from the wealthy. Where employees are covered by generous employer-provided health insurance, or where they can pay for a larger share of costs out of pocket, they and their problems begin to attract more attention from physicians and hospitals. Those physicians and medical practices that can forgo Medicare and Medicaid do so because they have lucrative alternatives. Starr reflects on some of this, but there is another treatment waiting. The rise of boutique medical clinics, concierge medicine, and specialized hospitals that cater to wealthy patients (including those who live overseas) is beginning to restructure opportunities for citizens and medical professionals alike. There may be an association between the rise of these services and the growth of average patient wait times in large cities. Not only do wealthy patients foot the bill for services like these, but some also donate immense sums to hospitals, to university medical centers, and to physician practice groups. The upward redistribution of wealth is remaking medical queues and the very physical plant of American health care. In the rise of medical technology and its reempowerment of select specialties, and in the restructuring of American medicine upon boutique and systematic service to the wealthy, new forms of power need to be taken into account. Starr's magisterial treatment points us in the right direction.

This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.

Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, compari

This article explores citing and referencing systems in Social Sciences and Medicine articles from different theoretical and practical perspectives, considering bibliographic references as a facet of descriptive representation. The analysis of citing and referencing elements (i.e. bibliographic references, mentions, quotations, and respective in-text reference pointers) identified citing and referencing habits within disciplines under consideration and errors occurring over the long term as stated by previous studies now expanded. Future expected trends of information retrieval from bibliographic metadata was gathered by approaching these referencing elements from the FRBR Entities concepts. Reference styles do not fully accomplish with their role of guiding authors and publishers on providing concise and well-structured bibliographic metadata within bibliographic references. Trends on representative description revision suggest a predicted distancing on the ways information is approached by bibliographic references and bibliographic catalogs adopting FRBR concepts, including the description levels adopted by each of them under the perspective of the FRBR Entities concept. This stu

The discovery of phenomena in social networks has prompted renewed interests in the field. Data in social networks however can be massive, requiring scalable Big Data architecture. Conversely, research in Big Data needs the volume and velocity of social media data for testing its scalability. Not only so, appropriate data processing and mining of acquired datasets involve complex issues in the variety, veracity, and variability of the data, after which visualisation must occur before we can see fruition in our efforts. This article presents topical, multimodal, and longitudinal social media datasets from the integration of various scalable open source technologies. The article details the process that led to the discovery of social information landscapes within the Twitter social network, highlighting the experience of dealing with social media datasets, using a funneling approach so that data becomes manageable. The article demonstrated the feasibility and value of using scalable open source technologies for acquiring massive, connected datasets for research in the social sciences.

Although beneficial information abounds on social media, the dissemination of harmful information such as so-called ``fake news'' has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 10\%--50\% of links from a social network, the size of cascades after link deletion is estimated to be only 50\% the original size under the optimistic estimation, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.

In 1977, the American pathologist and psychiatrist George Engel (1913-1999) published in the Journal Science his paper "TheIn 1977, the American pathologist and psychiatrist George Engel (1913-1999) published in the Journal Science his paper "Theneed for a new medical model: A challenge for biomedicine", introducing the term Biopsychosocial Model. This model basedon the results of Engel's studies in ulcerative colitis, depression and psychogenic pain, constituted a challenge for biomedicineand the biomedical model. The basic principles of the model included the biological, psychological and social dimensions of the person's life and theperception that the person suffers as a whole and not as isolated organs. The doctor should use a holistic approach regardingillness and should consider his/her relation with the patient socially equal. The personality and the emotional reserves of thepatient, as well as the particular environmental conditions in which the person lives in should be taken into account. There is no doubt that the biopsychosocial model has established a more empathetic and compassionate approach in medical practice. Already since 1936, the general adaptation syndrome had been proposed by the Austrian-Canadian endocrinologist HansSeley (1907-1982), who emphasized that psychological stressful factors may have injurious consequences on health, while theresponse systems to stress may be dysregulated not only by genetic factors, but also from experiences and stressful life events,as well as by harmful behaviors such as smoking, alcohol consumption and lack of physical exercise. Psychosocial factors may co-determine the patient's vulnerability and the illness's severity and course. The biopsychosocialmodel consider the interactions with genetic susceptibility, personality, stressful events and, generally, with the patient's socialcontext. Environmental factors increase the probability of the clinical expression of a mental disorder, play a role in the time ofonset of an illness's manifestation, and they can also protect a vulnerable person from the disease. Stressful experiences modify immunological response and influence treatment compliance. Non adherence to pharmacotherapy,as well as to the psychosocial interventions, may cause defective recovery of psychosocial functioning, recurrence ofthe disorder, as well as insufficient use of health resources and a higher health care cost. The psychoeducation of patients andtheir relatives by the application of the biopsychosocial model plays an important role in psychiatric therapeutics, and it mayalso be used via Internet in the frame of telepsychiatry. Results from neuroimaging studies have shown that the different kinds of human experiences, traumatic or therapeutic, havemeasurable influences on the brain function. Psychotherapy may modify the neuronal connections of the brain in the frame ofits plasticity, as was found by the discovery of synaptogenesis in response to learning and can, thus, be considered not only as astrictly psychological but also as a biopsychosocial form of treatment. Among the disadvantages of the biopsychosocial model have been reported the lack of a concise theoretical frameworkregarding its function and content, that it is complicated, difficulties in its coordination and assignment of responsibilities, aswell as problems with the education on it being multifaceted. The biopsychosocial model has been criticized that it does notconstitute a scientific or philosophical model, it does not provide an answer to the crucial question of how the biological, psychologicaland social variables interact in the disease's expression, that it does not provide guidance on the exact time of itsapplication and, finally, that it allows for a wide range of interventions without providing specific guidelines of a concrete therapeutic scheme. The person-centered diagnosis is based on the biopsychosocial model, connects science with humanism and uses all thepossible ways so that the clinicians, the patients and their families collaborate for a more effective management of the disease.This approach has been established by the World Psychiatric Association (WPA, 2007) as the program "Psychiatry for the Person". Psychiatry in everyday practice presents particularities versus other medical disciplines due to the complexity and polymorphismPsychiatry in everyday practice presents particularities versus other medical disciplines due to the complexity and polymorphismof the expression of mental disorders, their close relation to psychosocial factors, the lack of explicit pathognomonicelements and the stigmatization of mental illness. For these reasons, the biopsychosocial model is particularly applicable inpsychiatric disorders, but it should not be over looked also in somatic illnesses. The biopsychosocial model, despite the criticism it was subjected to, continues to offer valuable clinical, educational andresearch services, as well as to provide an important contribution to the formation of health policies, not only for psychiatry, butfor the whole of medicine as well.

In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% pred

This article introduces into the whole section on Social Sciences, edited by A. Nowak for this Encyclopedia, concentrating on the applications of mathematics and physics. Here under "mathematics" we include also all computer simulations if they are not taken from physics, while physics applications include simulations of models which basically existed already in physics before they were applied to social simulations. Thus obviously there is no sharp border between applications from physics and from mathematics in the sense of our definition. Also social science is not defined precisely. We will include some economics as well as some linguistics, but not social insects or fish swarms, nor human epidemics or demography. Also, we mention not only this section by also the section on agent-based modelling edited by F. Castiglione as containing articles of social interest.

Over the past decade, an explosion in the availability of education-related datasets has enabled new computational research in education. Much of this work has investigated digital traces of online learners in order to better understand and optimize their cognitive learning processes. Yet cognitive learning on digital platforms does not equal education. Instead, education is an inherently social, cultural, economic, and political process manifesting in physical spaces, and educational outcomes are influenced by many factors that precede and shape the cognitive learning process. Many of these are social factors like children's connections to schools (including teachers, counselors, and role models), parents and families, and the broader neighborhoods in which they live. In this article, we briefly discuss recent studies of learning through large-scale digital platforms, but largely focus on those exploring sociological aspects of education. We believe computational social scientists can creatively advance this emerging research frontier-and in doing so, help facilitate more equitable educational and life outcomes.

Social media plays a central role in shaping public opinion and behavior, yet performing experiments on these platforms and, in particular, on feed algorithms is becoming increasingly challenging. This guide offers practical recommendations for researchers developing and deploying field experiments focused on real-time reranking of social media feeds. The article is organized around two contributions. First, we provide an overview of an experimental method using web browser extensions that intercepts and reranks content in real time, enabling naturalistic reranking field experiments. We then describe feed interventions and measurements that this paradigm enables on participants' actual feeds, without requiring the involvement of social media platforms. Second, we offer concrete technical recommendations for intercepting and reranking social media feeds with minimal user-facing delay, and provide an open-source implementation. This document aims to summarize lessons learned in running field experiments on social media, provide concrete implementation details, and foster the ecosystem of independent social media research. Finally, we release the source code that serves as a blueprint

The two Journal Citation Reports of the Science Citation Index 2004 and the Social Science Citation Index 2004 were combined in order to analyze and map journals and specialties at the edges and in the overlap between the two databases. For journals which belong to the overlap (e.g., Scientometrics), the merger mainly enriches our insight into the structure which can be obtained from the two databases separately; but in the case of scientific journals which are more marginal in either database, the combination can provide a new perspective on the position and function of these journals (e.g., Environment and Planning B-Planning and Design). The combined database additionally enables us to map citation environments in terms of the various specialties comprehensively. Using the vector-space model, visualizations are provided for specialties that are parts of the overlap (information science, science & technology studies). On the basis of the resulting visualizations, "betweenness"--a measure from social network analysis--is suggested as an indicator for measuring the interdisciplinarity of journals.

The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This paper presents a community detection method based on nonnegative matrix tri-factorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices which distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps. It also discusses the importance of breaking down data silos, accelerating convergence among GeoAI research methods, as well as moving GeoAI beyond geospatial benefits.

The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio astronomy in general and pulsar astronomy in particular. The eleven articles in this special issue on pulsar science with the SKA Observatory describe its impact on different areas of pulsar science. In this lead article, a brief description of the two telescopes highlighting the relevant features for pulsar science is presented followed by an overview of each accompanying article, exploring the inter-relationship between different pulsar science use cases.

The rise of social media has fundamentally transformed how people engage in public discourse and form opinions. While these platforms offer unprecedented opportunities for democratic engagement, they have been implicated in increasing social polarization and the formation of ideological echo chambers. Previous research has primarily relied on observational studies of social media data or theoretical modeling approaches, leaving a significant gap in our understanding of how individuals respond to and are influenced by polarized online environments. Here we present a novel experimental framework for investigating polarization dynamics that allows human users to interact with LLM-based artificial agents in a controlled social network simulation. Through a user study with 122 participants, we demonstrate that this approach can successfully reproduce key characteristics of polarized online discourse while enabling precise manipulation of environmental factors. Our results provide empirical validation of theoretical predictions about online polarization, showing that polarized environments significantly increase perceived emotionality and group identity salience while reducing expressed

In this paper, we address the challenge of discovering hidden nodes in unknown social networks, formulating three types of hidden-node discovery problems, namely, Sybil-node discovery, peripheral-node discovery, and influencer discovery. We tackle these problems by employing a graph exploration framework grounded in machine learning. Leveraging the structure of the subgraph gradually obtained from graph exploration, we construct prediction models to identify target hidden nodes in unknown social graphs. Through empirical investigations of real social graphs, we investigate the efficiency of graph exploration strategies in uncovering hidden nodes. Our results show that our graph exploration strategies discover hidden nodes with an efficiency comparable to that when the graph structure is known. Specifically, the query cost of discovering 10% of the hidden nodes is at most only 1.2 times that when the topology is known, and the query-cost multiplier for discovering 90% of the hidden nodes is at most only 1.4. Furthermore, our results suggest that using node embeddings, which are low-dimensional vector representations of nodes, for hidden-node discovery is a double-edged sword: it is

Conventional economic and socio-behavioural models assume perfect symmetric access to information and rational behaviour among interacting agents in a social system. However, real-world events and observations appear to contradict such assumptions, leading to the possibility of other, more complex interaction rules existing between such agents. We investigate this possibility by creating two different models for a doctor-patient system. One retains the established assumptions, while the other incorporates principles of reflexivity theory and cognitive social structures. In addition, we utilize a microbial genetic algorithm to optimize the behaviour of the physician and patient agents in both models. The differences in results for the two models suggest that social systems may not always exhibit the behaviour or even accomplish the purpose for which they were designed and that modelling the social and cognitive influences in a social system may capture various ways a social agent balances complementary and competing information signals in making choices.