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.
Contemporary commentators describe the current period as “an era of fake news” in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.
A series of weaknesses in creativity, research design, and quality of writing continue to handicap energy social science. Many studies ask uninteresting research questions, make only marginal contributions, and lack innovative methods or application to theory. Many studies also have no explicit research design, lack rigor, or suffer from mangled structure and poor quality of writing. To help remedy these shortcomings, this Review offers suggestions for how to construct research questions; thoughtfully engage with concepts; state objectives; and appropriately select research methods. Then, the Review offers suggestions for enhancing theoretical, methodological, and empirical novelty. In terms of rigor, codes of practice are presented across seven method categories: experiments, literature reviews, data collection, data analysis, quantitative energy modeling, qualitative analysis, and case studies. We also recommend that researchers beware of hierarchies of evidence utilized in some disciplines, and that researchers place more emphasis on balance and appropriateness in research design. In terms of style, we offer tips regarding macro and microstructure and analysis, as well as coherent writing. Our hope is that this Review will inspire more interesting, robust, multi-method, comparative, interdisciplinary and impactful research that will accelerate the contribution that energy social science can make to both theory and practice.
This book is concerned with the study of collective preference, in particular with the relationship between the objectives of social action and the preferences and aspirations of society's members. Professor Sen's approach is based on the assumption that the problem of collective choice cannot be satisfactorily discussed within the confines of economics. While collective choice forms a crucial aspect of economics, the subject pertains also to political science, the theory of the state, and to the theory of decision procedures. The author has therefore used material from these disciplines, plus philosophical aspects from ethics and the theory of justice.
The rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field's major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.
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Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers’ gold references. We conclude that the performance of today’s LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in social science analysis in partnership with humans.
The varied effects of recent extreme weather events around the world exemplify the uneven impacts of climate change on populations, even within relatively small geographic regions. Differential human vulnerability to environmental hazards results from a range of social, economic, historical, and political factors, all of which operate at multiple scales. While adaptation to climate change has been the dominant focus of policy and research agendas, it is essential to ask as well why some communities and peoples are disproportionately exposed to and affected by climate threats. The cases and synthesis presented here are organized around four key themes (resource access, governance, culture, and knowledge), which we approach from four social science fields (cultural anthropology, archaeology, human geography, and sociology). Social scientific approaches to human vulnerability draw vital attention to the root causes of climate change threats and the reasons that people are forced to adapt to them. Because vulnerability is a multidimensional process rather than an unchanging state, a dynamic social approach to vulnerability is most likely to improve mitigation and adaptation planning efforts.
Accumulated evidence identifies discernible gender gaps across many dimensions of professional academic careers including salaries, publication rates, journal placement, career progress, and academic service. Recent work in political science also reveals gender gaps in citations, with articles written by men citing work by other male scholars more often than work by female scholars. This study estimates the gender gap in citations across political science subfields and across methodological subfields within political science, sociology, and economics. The research design captures variance across research areas in terms of the underlying distribution of female scholars. We expect that subfields within political science and social science disciplines with more women will have smaller gender citation gaps, a reduction of the “Matthew effect” where men’s research is viewed as the most central and important in a field. However, gender citation gaps may persist if a “Matilda effect” occurs whereby women’s research is viewed as less important or their ideas are attributed to male scholars, even as a field becomes more diverse. Analysing all articles published from 2007–2016 in several journals, we find that female scholars are significantly more likely than mixed gender or male author teams to cite research by their female peers, but that these citation rates vary depending on the overall distribution of women in their field. More gender diverse subfields and disciplines produce smaller gender citation gaps, consistent with a reduction in the “Matthew effect”. However, we also observe undercitation of work by women, even in journals that publish mostly female authors. While improvements in gender diversity in academia increase the visibility and impact of scholarly work by women, implicit biases in citation practices in the social sciences persist.
Careful bias management and data fidelity are key Advances in artificial intelligence (AI), particularly large language models (LLMs), are substantially affecting social science research. These transformer-based machine-learning models pretrained on vast amounts of text data are increasingly capable of simulating human-like responses and behaviors (1, 2), offering opportunities to test theories and hypotheses about human behavior at great scale and speed. This presents urgent challenges: How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research?
Data sharing, research ethics, and incentives must improve The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.
As a key node in the transportation network, ports connect the inland hinterland with the outside world, providing strong guarantees for the sustainable development of domestic trade and economy. However, the increasing port activities, while promoting regional economic growth, have also brought increasingly serious environmental problems to the local area. Promoting port emission reduction is a key way and important lever for China’s transportation industry to promote ecological civilization construction. By using CiteSpace to systematically review the literature on port emission reductions in recent years in the China National Knowledge Infrastructure (CNKI) and Web of Science databases, it was found that significant achievements have been made in port emission reduction technology, policy system construction, and emission reduction effect evaluation. However, there are still problems such as insufficient research on regional differences and insufficient in-depth analysis of emission reduction costs and benefits. Analyzing the hotspots and trends of port emission reduction research at home and abroad can provide reference for the theoretical research and practical path of coordinated emission reduction governance in Chinese ports. Future research on port emission reduction should focus on regional differences, cost–benefit analysis, and in-depth exploration of the synergistic effects of port emission reduction and regional economic development, in order to provide more comprehensive theoretical support and practical guidance for the green sustainable development of ports.