From disparities in the number of exhibiting artists to auction opportunities, there is evidence of women's under-representation in visual art. Here we explore the exhibition history and auction sales of 65,768 contemporary artists in 20,389 institutions, revealing gender differences in the artist population, exhibitions and auctions. We distinguish between two criteria for gender equity: gender-neutrality, when artists have gender-independent access to exhibition opportunities, and gender-balanced, that strives for gender parity in representation, finding that 58\% of institutions are gender-neutral but only 24\% are gender-balanced, and that the fraction of man-overrepresented institutions increases with institutional prestige. We define artist's co-exhibition gender to capture the gender inequality of the institutions that an artist exhibits. Finally, we use logistic regression to predict an artist's access to the auction market, finding that co-exhibition gender has a stronger correlation with success than the artist's gender. These results help unveil and quantify the institutional forces that relate to the persistent gender imbalance in the art world.
Institutions play a critical role in enabling communities to manage common-pool resources and avert tragedies of the commons. However, a fundamental issue arises: Individuals typically perceive participation as advantageous only after an institution is established, creating a paradox: How can institutions form if no one will join before a critical mass exists? We term this conundrum the institution bootstrapping problem and propose that misperception, specifically, agents' erroneous belief that an institution already exists, could resolve this paradox. By integrating well-documented psychological phenomena, including cognitive biases, probability distortion, and perceptual noise, into a game-theoretic framework, we demonstrate how these factors collectively mitigate the bootstrapping problem. Notably, unbiased perceptual noise (e.g., noise arising from agents' heterogeneous physical or social contexts) drastically reduces the critical mass of cooperators required for institutional emergence. This effect intensifies with greater diversity of perceptions. We explain this counter-intuitive result through asymmetric boundary conditions: proportional underestimation of low-probability s
This paper examines how institutional belonging shapes long-term development by comparing Spain and Uruguay, two small democracies with similar historical endowments whose trajectories diverged sharply after the 1960s. While Spain integrated into dense European institutional architectures, Uruguay remained embedded within the Latin American governance regime, characterized by weaker coordination and lower institutional coherence. To assess how alternative institutional embeddings could have altered these paths, the study develops a generative counterfactual framework grounded in economic complexity, institutional path dependence, and a Wasserstein GAN trained on data from 1960-2020. The resulting Expected Developmental Shift (EDS) quantifies structural gains or losses from hypothetical re-embedding in different institutional ecosystems. Counterfactual simulations indicate that Spain would have experienced significant developmental decline under a Latin American configuration, while Uruguay would have achieved higher complexity and resilience within a European regime. These findings suggest that development is not solely determined by domestic reforms but emerges from a country's st
Each major technological revolution inverts a particular scarcity and rebuilds institutions around the shift. The near-consensus diagnosis of the AI revolution holds that AI collapses the cost of prediction while judgment remains scarce. This Opinion argues the inversion has now flipped: competent-looking judgment (selecting, ranking, attributing, certifying) is produced at scale and at marginal cost approaching zero, and four complements become scarce: verified signal, legitimacy, authentic provenance, and integration capacity (the community's tolerance for delegated cognition). Because judgment is the substance of institutions, the institutions built to manufacture legitimate judgment (courts, journals, licensing bodies, legislatures) now compete with the technology for the same functional role. The piece traces the pattern across scientific institutions, professional licensing, intellectual property, democratic legitimacy, and foundation-model concentration, and closes with a three-move agenda: reframe AI policy as institutional redesign, build provenance and verification as commons, and develop the formal apparatus for institutional composition under strategic agents.
Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.
Institutional research computing infrastructure plays a vital role in Australia's research ecosystem, complementing and extending national facilities. This paper analyses research computing capabilities across Australian universities and organisations, showing how institutional systems support research excellence through local compute resources, specialised hardware, and cluster solutions. Our study finds that nearly 112,258 CPU cores and 2,241 GPUs serve over 6,000 researchers as essential bridges between desktops and national facilities, enabling workflows from development to large-scale computations. The estimated replacement value of this infrastructure is $144M AUD. Drawing on detailed data from multiple institutions, we identify key patterns in deployment, utilisation, and strategic alignment with research priorities. Institutional resources provide critical support for data-intensive projects, facilitate training and higher-degree student research, enable prototyping and development, and ensure data sovereignty compliance when required. The analysis shows how these facilities leverage national investments while addressing institution-specific needs that national systems cann
This article provides an overview of IG Parser, a software that facilitates qualitative content analysis of formal (e.g., legal) rules or informal (e.g., social) norms, and strategies (such as conventions) -- referred to as institutions -- that govern social systems and operate configurally to describe institutional systems. To this end, the IG Parser employs a distinctive syntax that ensures rigorous encoding of natural language, while automating the transformation into various formats that support the downstream analysis using diverse analytical techniques. The conceptual core of the IG Parser is an associated syntax, IG Script, that operationalizes the conceptual foundations of the Institutional Grammar, and more specifically the Institutional Grammar 2.0, an analytical paradigm for institutional analysis. This article presents the IG Parser, including its conceptual foundations, the syntax specification of IG Script, and its architectural principles. This overview is augmented with selective illustrative examples that highlight its use and the associated benefits.
Indirect reciprocity is a plausible mechanism for sustaining cooperation: people cooperate with those who have a good reputation, which can be acquired by helping others. However, this mechanism requires the population to agree on who has good or bad moral standing. Consensus can be provided by a central institution that monitors and broadcasts reputations. But how might such an institution be maintained, and how can a population ensure that it is effective and incorruptible? Here we explore a simple mechanism to sustain an institution of reputational judgment: a compulsory contribution from each member of the population, i.e., a tax. We analyze the maximum possible tax rate that individuals will rationally pay to sustain an institution of judgment, which provides a public good in the form of information, and we derive necessary conditions for individuals to resist the temptation to evade their tax payment. We also consider the possibility that institution members may be corrupt and subject to bribery, and we analyze how often an institution must be audited to prevent bribery. Our analysis has implications for the establishment of robust public institutions that provide social info
In this article we propose a novel method to perform unsupervised clustering of different forms of Institute names. We use only author and affiliation metadata to perform the clustering without any string or pattern matching. After analysing only 50000 articles from Crossref database, we see encouraging results which can be scaled up to provide even better results. We compare our clustering with what a well-known method using string matching does and found that the results were complementary. This can help perform institute disambiguation better when integrated with existing systems, especially to provide aliases for cases where traditional string matching fails. The code of this open-source methodology can be found at: https://github.com/Jeet009/Institute-Disambiguation-using-Author-Institution-Co-Occurrence
The conflict between individual and collective interests makes fostering cooperation in human societies a challenging task, requiring drastic measures such as the establishment of sanctioning institutions. These institutions are costly because they have to be maintained regardless of the presence or absence of offenders. Here we revisit some improvements to the standard $N$-person prisoner's dilemma formulation with institutional punishment in a well-mixed population, namely the elimination of overpunishment, the requirement of a minimum number of contributors to establish the sanctioning institution, and the sharing of its maintenance costs once this minimum number is reached. In addition, we focus on large groups or communities for which sanctioning institutions are ubiquitous. Using the replicator equation framework for an infinite population, we find that by sufficiently fining players who fail to contribute either to the public good or to the sanctioning institution, a population of contributors immune to invasion by these free riders can be established, provided that the contributors are sufficiently numerous. In a finite population, we use finite-size scaling to show that, f
We show that in a situation where individuals have a choice between a costly institute and a free institute to perform a collective action task, the existence of a participation cost promotes cooperation in the costly institute. Despite paying for a participation cost, costly cooperators, who join the costly institute and cooperate, can out-perform defectors, who predominantly join a free institute. This, not only promotes cooperation in the costly institute but also facilitates the evolution of cooperation in the free institute. A costly institute out-performs a free institute when the profitability of the collective action is low. On the other hand, a free institute performs better when the collective action's profitability is high. Furthermore, we show that in a structured population, when individuals have a choice between different institutes, a mutualistic relation between cooperators with different institute preferences emerges and helps the evolution of cooperation.
Scientific institutions play a crucial role in driving intellectual, social, and technological progress. Their capacity to innovate depends mainly on their ability to attract, retain, and nurture scientific talent and ultimately make it available to other organizations, industries, or the economy. As researchers change institutions during their careers, their skills are also transferred. The extent and mechanisms by which academic institutions manage their internal portfolio of scientific skills by attracting and sending researchers are far from being understood. We examine 25 million publication histories of 9.2 million scientists extracted from a large-scale bibliographic database covering thousands of research institutions worldwide to understand how the skills of mobile scientists align with those present in-house. We find a clear association between top-ranked institutions and greater skill alignment, i.e., the degree to which skills of incoming academics match those of their colleagues at the institution. We uncover similar high-alignment for scientists leaving top-ranked institutions. This type of academic alignment is more pronounced in engineering and life, health, earth,
The US higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship with prestige and scientific impact. We observe a Howard-Harvard effect, in which the topical profile of minoritized scholars are amplified in mission-driven institutions and decreased in prestigious institutions. Results demonstrate a consistent pattern of inequality in topics and research impact. Specifically, we observe statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and highly negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. Academic institutions and funders are called to creat
This paper studies the response of stock markets relative to the banking sector to innovation by using a panel of 75 countries from 1982 to 2021. We find that innovation increases the activity, efficiency and size of stock markets relative to the banking sector, moderated by proximity to technological frontier and institutional quality. The moderating effect of institutional quality is positive for activity and efficiency but negative for size. Moreover, the moderating effect can be nonlinear depending on specific indicators. The marginal effect of innovation on the activity is persistent over many years, but the moderating effect of institutional quality gradually fades away.
Research institutions provide the infrastructure for scientific discovery, yet their role in the production of knowledge is not well characterized. To address this gap, we analyze interactions of researchers within and between institutions from millions of scientific papers. Our analysis reveals that the number of collaborations scales superlinearly with institution size, though at different rates (heterogeneous densification). We also find that the number of institutions scales with the number of researchers as a power law (Heaps' law) and institution sizes approximate Zipf's law. These patterns can be reproduced by a simple model with three mechanisms: (i) researchers collaborate with friends-of-friends, (ii) new institutions trigger more potential institutions, and (iii) researchers are preferentially hired by large institutions. This model reveals an economy of scale in research: larger institutions grow faster and amplify collaborations. Our work provides a new understanding of emergent behavior in research institutions and how they facilitate innovation.
The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact asymmetry on the market condition. For most of the stocks institutional sales have a larger price impact than institutional purchases, and larger impact of institutional purchases only exists in few stocks with primarily increasing tendencies. We further study the mean return of trades surrounding institutional transactions, and find the asymmetric behavior also exists before and after institutional transactions. A new variable is proposed to investigate the order book structure, and it can partially explain the price impact of institutional transactions. A linear regression for the price impact of institutional transactions further confirms our finding that institutional sales primarily have a larger price impact than institutional purchases in the bearish year 2003.
Artificial intelligence (AI) has transformed various sectors and institutions, including education and healthcare. Although AI offers immense potential for innovation and problem solving, its integration also raises significant ethical concerns, such as privacy and bias. This paper delves into key considerations for developing AI policies within institutions. We explore the importance of interpretability and explainability in AI elements, as well as the need to mitigate biases and ensure privacy. Additionally, we discuss the environmental impact of AI and the importance of energy-efficient practices. The culmination of these important components is centralized in a generalized framework to be utilized for institutions developing their AI policy. By addressing these critical factors, institutions can harness the power of AI while safeguarding ethical principles.
This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.
India is now among the major knowledge producers of the world, ranking among the top 5 countries in total research output, as per some recent reports. The institutional setup for Research & Development (R&D) in India comprises a diverse set of Institutions, including Universities, government departments, research laboratories, and private sector institutions etc. It may be noted that more than 45% share of India's Gross Expenditure on Research and Development (GERD) comes from the central government. In this context, this article attempts to explore the quantum of research contribution of centrally funded institutions and institution systems of India. The volume, proportionate share and growth patterns of research publications from the major centrally funded institutions, organised in 16 groups, is analysed. These institutions taken together account for 67.54% of Indian research output during 2001 to 2020. The research output of the centrally funded institutions in India has increased steadily since 2001 with a good value for CAGR. The paper presents noteworthy insights about scientific research production of India that may be useful to policymakers, researchers and science
Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to understand the research landscape of an institution. The process of exploring the research landscape of institutions has a vague information need, no precise goal, and is open-ended. Current applications are not designed to fulfill the requirements for exploratory search in research institutions. In this paper, we analyze exploratory search in research institutions and propose a knowledge graph-based approach to enhance this process.