The production of public goods, which are fundamental to well-functioning societies, requires the payment of taxes, but taxpayers have clear incentives to free-ride because of the low probability of sanctions. Despite these incentives, many do pay, and research suggests that non-monetary mechanisms are important drivers. Here, we focus on social norms and propose that they drive tax paying and mediate the relationship between institutional quality and tax payments, potentially leading to either virtuous or vicious feedback cycles. Using two studies conducted in Italy, a nationally representative survey and vignette experiment (NStudy I = 1,218) and an online behavioural experiment (NStudy II = 448), we show that: (i) social norms and perceptions of institutional quality predict tax payments; (ii) social norms affect tax payment intentions and behaviour; and (iii) institutional context influences these dynamics either positively or negatively through social norms. Our results highlight the essential role of social norms in shaping tax payments and their link to context-specific institutional factors.

Although publishing peer review reports increases editorial transparency, little is known about the differences in terms of information content, readability and similarity between open and unpublished peer review reports across journals. We compared 140,844 published and 117,250 unpublished peer review reports from 233 medical journals published by Elsevier and Springer Nature between 2016 and 2021 using natural language processing. Our results showed that published peer review reports were longer and had more informative content, with the greatest difference found in the number of “suggestion and solution” sentences. Published peer review reports were also more readable and more similar to each other in terms of content structure. Reports by women had higher information scores and were more readable than reports by men, while reports by reviewers from non-Western institutions had lower information scores and were less readable than reports by reviewers from Western institutions. Our results suggest that increasing the transparency of review reports could lead to more detailed reports focusing on suggestions for improving manuscripts.

The growing number of older adults in our societies has not been matched by balanced representation in the press, which tends to portray older people stereotypically. This study used computational methods to analyze the representation of older people in a large corpus of articles from a sample of major Italian newspapers between 2017 and 2024. The results suggest that, although the range of topics associated with older people has increased compared to previous research, both positive and negative stereotypical views still prevail. However, content related to older adults is more positive than content on the same topics that does not specifically reference older adults. While the pandemic has brought renewed attention to older people, it has also increased the prevalence of negative narratives that focus on their frailty. Nevertheless, Italian newspapers have increased their focus on content related to the cultural and social life of older people, thus paving the way for a more positive perspective on ageing.

St. Francis of Assisi (1181/82-1226) famously called money the devil's dung, and indeed money is often associated with greed, inequality, and corruption. Here, we argue that money can facilitate the formation of circuits of generalized reciprocity across human groups, a crucial mechanism for the evolution of cooperation when monitoring the actions and reputation of potential partners becomes difficult. Using an agent-based evolutionary tournament, we show that money exchange constitutes an evolutionarily stable strategy, promoting cooperation without the cognitive demands of traditional reciprocity mechanisms. In particular, we demonstrate how the monetary exchange strategy can take advantage of, and ultimately displace, reputation-based systems in mixed populations. However, we also find that excessive liquidity can be detrimental because it can distort the informational value of money as a signal of past cooperation, making defection more profitable. Our results suggest that, in addition to institutions that promoted trust and punishment, generalized reciprocity within and across human groups may also have depended on institutions regulating the money supply.

During the Social Simulation Conference 2023 in Glasgow a discussion in one of the general sessions brought the idea to have an informal description of the past, present, and future of agent-based social simulation. Who better to offer such a paper than the collection of ESSA presidents so far? It forms the core of the ESSA organizational memory is you wish, and given the variety in backgrounds, experiences, and interests among them, there is something for everyone, reflecting the multitude of positions in the ESSA and SSC community.

Although it is beneficial to scientific development, data sharing is still uncommon in many research areas. Various organisations, including funding agencies that endorse open science, are working to increase uptake. However, it is difficult to estimate the large-scale implications of different policy interventions on data sharing by funding agencies, especially in the context of intense competition among academics. In this study, we developed an agent-based simulation model to examine the impact of different funding schemes (e.g., highly competitive large grants versus distributive small grants), and the intensity of incentives on the uptake of data sharing by academic teams that adapt their strategy according to the context. Our results show that, in the short term, more competitive funding schemes may lead to higher rates of data sharing, but lower rates in the long term because the uncertainty associated with competitive funding negatively affects the cost/benefit ratio of data sharing. Conversely, more distributive grants imply a drastic reduction in initial uptake compared to more competitive funding schemes because they do not allow academic teams to cover the costs and time required for data sharing. However, they ensure higher long term uptake. Our findings suggest that any attempt to reform reward and recognition systems in line with open science principles must carefully consider the potential impact and longterm side effects of their proposed policies.

At the beginning of July 2025, the global cryptocurrency market capitalisation reached more than $2.8 trillion, with 1 Bitcoin exchanging for more than $105,000. As cryptocurrencies are becoming part of the global financial infrastructure, monitoring their evolution is crucial for determining whether they can be considered a sustainable long-term financial exchange system. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation, and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.

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Serious concerns have been raised on the potentially negative impact of public measures to contain the COVID-19 pandemic on academic research, including the closure of research facilities, and the challenges of lockdown. However, it is unclear whether COVID-related mobility restrictions have penalized academic productivity, and if this is the case, whether it has had an equal impact on all research areas and countries. Here, we examined about 9.2 million submissions to 2689 Elsevier journals in all research areas in 2018–2021 and estimated the impact of anti-COVID mobility restriction policies on submissions to journals. Results showed that anti-contagion public measures had a positive impact on academic productivity. However, submission patterns changed more in non-Western academic countries, with the exception of Italy, which had stringent lock-down measures. During the early stages of the pandemic, the abnormal peak of submission was dominated by health & medical researchers, whereas later, there was an increase in submissions to social science & economics journals. Although anti-contagion public measures have contributed to change academic work, it is difficult to estimate whether they will have any potentially long-term effect on the academic community- either positive or negative.

Advice-seeking typically occurs across organizational boundaries through informal connections. By using Stochastic Actor-Oriented Models (SAOM), previous research has tried to identify the micro-level mechanisms behind these informal connections. Unfortunately, these models assume perfect network information, require agents to perform too cognitively demanding decisions, and do not account for threshold-based critical events, such as simultaneous tie changes. In the context of knowledge-intensive organizations, the shortage of high-skilled professionals could determine complex network effects given that many less-skilled professionals would seek advice from a few easily overloaded, selective high-skilled, who are also sensitive to status demotion. To capture these context-specific organizational features, we have elaborated on SAOM with an agent-based model that assumes local information, status-based tie selection, and simultaneous re-direction of multiple ties. By fitting our simulated networks to Lazega’s advice network used in previous research, we reproduced the same set of macro-level network metrics with a parsimonious model based on more empirically plausible assumptions than previous research. Our findings show the advantage of exploring multiple generative paths of network formation with different models.

Research in social gerontology has suggested that structural complexity of personal networks could moderate cognitive decline of older adults. In line with the environmental complexity hypothesis, their cognitive functioning would benefit from a high number of cohesive subgroups in their own personal networks, i.e., various social foci, thanks to higher cognitive stimuli from various social interactions. Yet, past studies considered only compositional diversity of networks due to lack of data on alter–alter ties. To fill this gap, we collected survey ego-network data on frequent social contacts (including alter–alter ties) and cognitive functioning on a sample of individuals aged ≥ 75 in Brescia, Italy (N=230). As a proxy for social foci, we detected cohesive subgroups within each respondent’s personal networks. Results showed a positive association between the number of cohesive subgroups and cognitive functioning, regardless of the network size, while controlling for relevant socio-demographic attributes and depression symptoms. Our findings testify to the importance of granular network data in studying the link between social relationships and cognitive functioning.

ABSTRACT Exposing women and girls to female role models is considered essential to break down gender-stereotypical beliefs on STEM interest and engagement. However, evidence remains controversial regarding the efficacy of these interventions. Here, we provide a scoping review of fifty-seven empirical studies that considers information about: (1) research type, (2) target, (3) type of intervention, (4) role models’ characteristics, (5) variables of interest and (6) effects of the study. Our findings show that research is considerably heterogeneous in terms of role models, interventions, variables of interest and effects. Role models are frequently female STEM professionals or a mixed-gender group of STEM workers. Interventions mainly consist of asking participants to read a brief article about the role model and the effect of being exposed to a role model is mostly tested on participants’ characteristics, e.g. attitudes toward STEM and performance. This heterogeneity comes at a price, i.e. it is difficult to understand the effectiveness of role models’ exposure. Future research should focus on whether and how the heterogeneous characteristics of role models influence the efficacy of these interventions.

This chapter introduces the proceedings of the Social Simulation Conference 2022 by providing a brief overview of the impact of social simulation in various research areas. By focusing on the key role of agent-based modeling, we argue that social simulation has a unique position in the wider data science area. This is because it can enrich the predominantly inductive, data-driven, pattern oriented approach of computational social science with deductive, hypothesis-driven, explanatory, mechanism-detection models. Furthermore, social simulation can also work in areas and for contexts where data is not available, experiments cannot be performed or in which scenario exploration is paramount. We would also like to focus on areas and aspects where methodological improvement and cross-methodological integration are required to enhance the potential of social simulation in various communities. In the final section, we introduce the structure and sections of the proceedings.

Stereotypes can contribute to the gender gap in STEM by shaping people’s expectations on their own and others’ performance. When gender is salient, expectations on task performance might reflect gender constructs even when information on individual abilities is available. We tested this hypothesis in a network study on students from ten high school classes in Milan, Italy. We asked the students to choose the four best candidates from their classmates for three hypothetical inter-class competitions in reading, math, and science. Results showed that females were more likely to be nominated for the reading competition but less likely for science. We did not find any statistically significant results for the math competition. We also found that female students were less likely to nominate themselves for any competition, regardless of the subject, even controlling for their own performance and self-concept.

Agent-Based Modeling (ABM) is a computational method used to examine social outcomes emerging from interaction between heterogeneous agents by computer simulation. It can be used to understand the effect of initial conditions on complex outcomes by exploring fine-grained (multiple-scale, spatial/temporal) observations on the aggregate consequences of agent interaction. By performing in silico experimental tests on policy interventions where ex ante predictions of outcomes are difficult, it can also reduce costs, explore assumptions and boundary conditions, as well as overcome ethical constraints associated with the use of randomized controlled trials in behavioral policy. Here, we introduce the essential elements of ABM and present two simple examples where we assess the hypothetical impact of certain policy interventions while considering different possible reactions of individuals involved in the context. Although highly abstract, these examples suggest that ABM can be either a complement or an alternative to behavioral policy methods, especially when understanding social processes and exploring direct and indirect effects of interventions are important. Prospects and critical problems of these in silico policy experiments are then discussed. Learning Objectives By studying this chapter, you will: . Learn the basic concepts and methodological principles of agent-based modeling. . Understand the advantages of agent-based modeling compared to other research methods when examining social dynamics. . Understand how to design agent-based modeling for in silico experiments. . Understand the importance of agent-based modeling for policy appraisal. . Practice with two examples of agent-based modeling for policy experiments.