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Classification algorithms are widely used in critical domains such as healthcare, bank loans, credit and fraud detection. These systems should be transparent, yet it remains unclear how individuals will use explanations to adjust their own features. Individuals often access multiple sources of information, from insights provided by institutions to experiences shared among peers. Based on the information received, individuals may decide to strategically adapt to obtain a favourable outcome, honestly improving or attempting to game the system. This paper studies the impact of transparency and social information on strategic classification. We assume that agents adapt based on best response and behavioural imitation along the edges of social networks. We observe that increasingly opaque decision rules can negatively impact the utility of institutions, especially in dense social networks. The number of False Positives is reduced in networks with a lower average degree, when users imitate the average behaviour, as opposed to the most extreme behaviours. When imitating the most extreme behaviour among their connections, users change their features to a large extent in networks with a higher average degree (i.e., higher density). This applies to both honest improving and gaming, with more pronounced impacts in the case of the latter, creating an additional source of risk for institutions. Our model and results reveal that behavioural imitation patterns and social network effects influence the downstream effects of algorithmic transparency.

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

In a binary decision game in which players strategically help certain individuals but not others, simple moral principles maximize cooperation, even when including the historical reputations of players. The complexity of cooperation Of all the schemes invoked in the quest to discover how altruism evolved in a world that's red in tooth and claw, indirect reciprocity is perhaps the most complex. It means that an agent can punish a defector and incur a cost, in the expectation of being rewarded later by a third party. Moral choices are required, and the response of the third party depends on the reputation of the agent and the defector. The various options are so complex that some believe the human mind incapable of computing them in a reasonable time frame, and yet people make such choices easily. How can this be? Here the authors simulate choices faced by third parties in situations of indirect reciprocity and find that the choices that lead to the greatest cooperation needn't be the most complex. 'Stern judging', whereby 'all that matters is what you do and the reputation of your opponent when you act: help good people and refuse help otherwise, and we shall be nice to you; otherwise, you will be punished', is of merely second-order complexity, yet even pre-verbal infants use it. Indirect reciprocity is the most elaborate and cognitively demanding 1 of all known cooperation mechanisms 2 , and is the most specifically human 1 , 3 because it involves reputation and status. By helping someone, individuals may increase their reputation, which may change the predisposition of others to help them in future. The revision of an individual’s reputation depends on the social norms that establish what characterizes a good or bad action and thus provide a basis for morality 3 . Norms based on indirect reciprocity are often sufficiently complex that an individual’s ability to follow subjective rules becomes important 4 , 5 , 6 , even in models that disregard the past reputations of individuals, and reduce reputations to either ‘good’ or ‘bad’ and actions to binary decisions 7 , 8 . Here we include past reputations in such a model and identify the key pattern in the associated norms that promotes cooperation. Of the norms that comply with this pattern, the one that leads to maximal cooperation (greater than 90 per cent) with minimum complexity does not discriminate on the basis of past reputation; the relative performance of this norm is particularly evident when we consider a ‘complexity cost’ in the decision process. This combination of high cooperation and low complexity suggests that simple moral principles can elicit cooperation even in complex environments.

Indirect reciprocity (IR) is a key mechanism to understand cooperation among unrelated individuals. It involves reputations and complex information processing, arising from social interactions. By helping someone, individuals may improve their reputation, which may be shared in a population and change the predisposition of others to reciprocate in the future. The reputation of individuals depends, in turn, on social norms that define a good or bad action, offering a computational and mathematical appealing way of studying the evolution of moral systems. Over the years, theoretical and empirical research has unveiled many features of cooperation under IR, exploring norms with varying degrees of complexity and information requirements. Recent results suggest that costly reputation spread, interaction observability and empathy are determinants of cooperation under IR. Importantly, such characteristics probably impact the level of complexity and information requirements for IR to sustain cooperation. In this review, we present and discuss those recent results. We provide a synthesis of theoretical models and discuss previous conclusions through the lens of evolutionary game theory and cognitive complexity. We highlight open questions and suggest future research in this domain. This article is part of the theme issue 'The language of cooperation: reputation and honest signalling'.

Indirect reciprocity, besides providing a convenient framework to address the evolution of moral systems, offers a simple and plausible explanation for the prevalence of cooperation among unrelated individuals. By helping someone, an individual may increase her/his reputation, which may change the pre-disposition of others to help her/him in the future. This, however, depends on what is reckoned as a good or a bad action, i.e., on the adopted social norm responsible for raising or damaging a reputation. In particular, it remains an open question which social norms are able to foster cooperation in small-scale societies, while enduring the wide plethora of stochastic affects inherent to finite populations. Here we address this problem by studying the stochastic dynamics of cooperation under distinct social norms, showing that the leading norms capable of promoting cooperation depend on the community size. However, only a single norm systematically leads to the highest cooperative standards in small communities. That simple norm dictates that only whoever cooperates with good individuals, and defects against bad ones, deserves a good reputation, a pattern that proves robust to errors, mutations and variations in the intensity of selection.

Meeting today's major scientific and societal challenges requires understanding dynamics of prosociality in complex adaptive systems. Artificial intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic environments. In various settings, prosocial actions are socially desirable yet individually costly, thereby introducing a social dilemma of cooperation. How can AI enable cooperation in such domains? How to understand long‐term dynamics in adaptive populations subject to such cooperation dilemmas? How to design cooperation incentives in multiagent learning systems? These are questions that I have been exploring and that I discussed during the New Faculty Highlights program at AAAI 2023. This paper summarizes and extends that talk.

Microbial products are essential for developing various therapeutic agents, including antibiotics, anticancer drugs, vaccines, and therapeutic enzymes. Genetic engineering techniques, functional genomics, and synthetic biology unlock previously uncharacterized natural products. This review highlights major advances in microbial biotechnology, focusing on gene‐based technologies for medical applications. Microbial biotechnology, the technological application of microorganisms, has been instrumental in producing significant natural bioactive products. These include antibiotics, antifungals, anticancer drugs, antiparasitics, antivirals, immunosuppressants, toxoid vaccines, and therapeutic enzymes. Certain microbial components have proven invaluable in the creation of genetic tools, such as CRISPR‐Cas systems and thermostable DNA polymerase enzymes. These tools are essential for the development of genetic engineering strategies. Genetic engineering, as a discipline, plays a crucial role in the rational and precise advancement of microbial biotechnology. Consequently, these two conceptual themes—microbial biotechnology and genetic engineering—exhibit a positive interplay. This review presents major advancements in microbial biotechnology, with a particular emphasis on gene‐based technologies within the medical field.

Covering 35% of Europe’s land area, forest ecosystems play a crucial role in safeguarding biodiversity and mitigating climate change. Yet, forest degradation continues to undermine key ecosystem services that forests deliver to society. Here we provide a spatially explicit assessment of the condition of forest ecosystems in Europe following a United Nations global statistical standard on ecosystem accounting, adopted in March 2021. We measure forest condition on a scale from 0 to 1, where 0 represents a degraded ecosystem and 1 represents a reference condition based on primary or protected forests. We show that the condition across 44 forest types averaged 0.566 in 2000 and increased to 0.585 in 2018. Forest productivity and connectivity are comparable to levels observed in undisturbed or least disturbed forests. One third of the forest area was subject to declining condition, signalled by a reduction in soil organic carbon, tree cover density and species richness of threatened birds. Our findings suggest that forest ecosystems will need further restoration, improvements in management and an extended period of recovery to approach natural conditions. Monitoring ecosystem conditions in quantitative and standardized ways could facilitate transnational coordination of conservation and land management policies. Here, the authors use a spatially explicit ecosystem accounting approach to assess the state of European forests and recent trends.

This research was supported by Fundacao para a Ciencia e Tecnologia (FCT) through grants PTDC/EEISII/5081/2014 and PTDC/MAT/STA/3358/2014 and by multiannual funding of INESC-ID and CBMA (under the projects UID/CEC/50021/2019 and UID/BIA/04050/2013). F.P.S. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems Postdoctoral Fellowship Award. All authors declare no competing financial or non-financial interests in relation to the work described.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks. •Two prominent theories on mappings between structural and functional networks are:•Functional networks can be explained by all possible walks in the structural network.•Functional networks can be explained by the eigenmodes of the structural network.•We show that these two approaches are equivalent using empirical and simulated data.•We provide explicit expressions for model coefficients for both approaches.