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.
Social Norms of Cooperation in Small-Scale Societies Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M
PLOS computational biology/PLoS computational biology,
01/2016, Letnik:
12, Številka:
1
Journal Article
Recenzirano
Odprti dostop
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.
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'.
Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ...ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors - coined as collective-risk dilemmas - by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.
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.
The growing environmental business uncertainties have forced companies to focus on developing more flexible supply chains. Digital transformation has been considered a key means to achieving such ...flexibility, but the literature lacks empirical evidence about how digital technologies effectively contribute to it. Thus, this study aims to analyze how Smart Supply Chain (i.e., a supply chain enabled by digital transformation) contributes to supply chain flexibility and operational performance in environments surrounded by customer and supplier uncertainty. We adopt the organizational information-processing theory to explain the fit between information needs to reduce these uncertainties through more supply chain flexibility (sourcing, delivery, and manufacturing) and information capabilities provided by three main dimensions of the Smart Supply Chain (digital transformation strategy, digital base technologies, and digital front-end technologies). We relate these information-processing fit between Smart Supply Chain and flexibility with the boundary conditions of environmental uncertainty and operational performance improvements. Such relationships are analyzed through moderation and mediation regression tests based on 379 manufacturing companies surveyed. Our findings show that Smart Supply Chain has a statistical association with operational performance through the sequential mediating role of the three supply chain flexibility dimensions. We also found that environments with high customer uncertainty increase the use of base technologies (IoT, cloud, big data, AI, and blockchain) to reach delivery flexibility and support manufacturing flexibility. When companies face high supplier uncertainty, they use front-end technologies (i.e., robotics, 3D printing, simulation, and augmented reality) to increase sourcing flexibility. We show new advances in supply chain flexibility through digital transformation.
•Digital transformation strategies and technologies enable a Smart Supply Chain.•Smart Supply help overcome uncertainties and increase supply chain flexibility.•Supply chain flexibility mediates between Smart Supply Chain and performance.•Customer and Supply uncertainty moderate the Smart Supply Chain effects.•We provide evidence on these relationships through a survey with 379 companies.
Graphics processing units (GPUs) are playing a critical role in convolutional neural networks (CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments, reliability is ...becoming a growing concern. In this paper, we evaluate and propose strategies to improve the reliability of object detection algorithms, as run on three NVIDIA GPU architectures. We consider three algorithms: 1) you only look once; 2) a faster region-based CNN (Faster R-CNN); and 3) a residual network, exposing live hardware to neutron beams. We complement our beam experiments with fault injection to better characterize fault propagation in CNNs. We show that a single fault occurring in a GPU tends to propagate to multiple active threads, significantly reducing the reliability of a CNN. Moreover, relying on error correcting codes dramatically reduces the number of silent data corruptions (SDCs), but does not reduce the number of critical errors (i.e., errors that could potentially impact safety-critical applications). Based on observations on how faults propagate on GPU architectures, we propose effective strategies to improve CNN reliability. We also consider the benefits of using an algorithm-based fault-tolerance technique for matrix multiplication, which can correct more than 87% of the critical SDCs in a CNN, while redesigning maxpool layers of the CNN to detect up to 98% of critical SDCs.
Chiral natural product molecules are generally assumed to be biosynthesized in an enantiomerically pure or enriched fashion. Nevertheless, a significant amount of racemates or enantiomerically ...enriched mixtures has been reported from natural sources. This number is estimated to be even larger since the enantiomeric purity of secondary metabolites is rarely checked in the natural product isolation pipeline. This latter fact may have drastic effects on the evaluation of the biological activity of chiral natural products. A second bottleneck is the determination of their absolute configurations. Despite the widespread use of optical rotation and electronic circular dichroism, most of the stereochemical assignments are based on empirical correlations with similar compounds reported in the literature. As an alternative, the combination of vibrational circular dichroism and quantum chemical calculations has emerged as a powerful and reliable tool for both conformational and configurational analysis of natural products, even for those lacking UV-Vis chromophores. In this review, we aim to provide the reader with a critical overview of the occurrence of enantiomeric mixtures of secondary metabolites in nature as well the best practices for their detection, enantioselective separation using liquid chromatography, and determination of absolute configuration by means of vibrational circular dichroism and density functional theory calculations.
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