How strength asymmetries shape multi-sided conflicts

Governments and multilateral organisations often attempt to influence multi-sided violent conflicts by supporting or undermining one of the conflicting parties. We investigate the (intended and unintended) consequences of strengthening or weakening an agent in a multi-sided conflict. Using a conflict network based on Franke and Öztürk (J Public Econ 126:104–113, 2015), we study how changing the strength of otherwise symmetric agents creates knock-on effects throughout the network. Increasing or decreasing an agent’s strength has the same unintended consequences. Changes in the strength of an agent induce a relocation of conflict investments: Distant conflicts are carried out more fiercely. In line with previous results, asymmetry reduces aggregate conflict investments. In the case of bipartite networks, with two conflicting tacit groups with aligned interests, agents in the group of the (now) strong or weak agent face more intense conflicts. Furthermore, in conflicts where the (now strong or weak) agent is not involved, the probabilities of winning remain unchanged compared to the symmetric case.</p

Prosocial behavior among human workers in robot-augmented production teams : a field-in-the-lab experiment

Human-machine interaction has raised a lot of interest in various academic disciplines, but it is still unclear how human-human interaction is affected when robots join the team. Robotics has already been integral to manufacturing since the 1970s. With the integration of AI, however, they are increasingly working alongside humans in shared spaces. We conducted an experiment in a learning factory to investigate how a change from a human-human work context to a hybrid human-robot work context affects participants\u27 valuation of their production output as well as their pro-sociality among each other. Learning factories are learning, teaching, and research environments in engineering university departments. These factory environments allow control over the production environment and incentives for participants. Our experiment suggests that the robot\u27s presence increases sharing behavior among human workers, but there is no evidence that rewards earned from production are valued differently. We discuss the implications of this approach for future studies on human-machine interaction

Don't Fear the Robots: Automatability and Job Satisfaction

We analyse the correlation between job satisfaction and automatability - the degree to which an occupation can be or is at risk of being replaced by computerised equipment. Using multiple survey datasets matched with various measures of automatability from the literature, we find that there is a negative and statistically significant correlation that is robust to controlling for worker and job characteristics. Depending on the dataset, a one standard deviation increase in automatability leads to a drop in job satisfaction of about 0.73% to 1.85% for the average worker. Unlike other studies, we provide evidence that it is not the fear of losing the job that mainly drives this result, but the fact that monotonicity and low perceived meaning of the job drive both automatability as well as low job satisfaction

Generalising Conflict Networks

We investigate the behaviour of agents in bilateral contests within arbitrary network structures when valuations and efficiencies are heterogenous. These parameters are interpreted as measures of strength. We provide conditions for when unique, pure strategy equilibria exist. When a player starts attacking one player more strongly, others join in on fighting the victim. Different efficiencies in fighting make players fight those of similar strength. Centrality of a player (having more enemies) makes a player weaker and her opponents are more likely to attack with more effort

Prosocial behavior among human workers in robot-augmented production teams—A field-in-the-lab experiment

IntroductionHuman-machine interaction has raised a lot of interest in various academic disciplines, but it is still unclear how human-human interaction is affected when robots join the team. Robotics has already been integral to manufacturing since the 1970s. With the integration of AI, however, they are increasingly working alongside humans in shared spaces.MethodsWe conducted an experiment in a learning factory to investigate how a change from a human-human work context to a hybrid human-robot work context affects participants' valuation of their production output as well as their pro-sociality among each other. Learning factories are learning, teaching, and research environments in engineering university departments. These factory environments allow control over the production environment and incentives for participants.ResultsOur experiment suggests that the robot's presence increases sharing behavior among human workers, but there is no evidence that rewards earned from production are valued differently.DiscussionWe discuss the implications of this approach for future studies on human-machine interaction

Do Personalized AI Predictions Change Subsequent Decision-Outcomes? The Impact of Human Oversight

Regulators of artificial intelligence (AI) emphasize the importance of human autonomy and oversight in AI-assisted decision-making (European Commission, Directorate-General for Communications Networks, Content and Technology, 2021; 117th Congress, 2022). Predictions are the foundation of all AI tools; thus, if AI can predict our decisions, how might these predictions influence our ultimate choices? We examine how salient, personalized AI predictions affect decision outcomes and investigate the role of reactance, i.e., an adverse reaction to a perceived reduction in individual freedom. We trained an AI tool on previous dictator game decisions to generate personalized predictions of dictators’ choices. In our AI treatment, dictators received this prediction before deciding. In a treatment involving human oversight, the decision of whether participants in our experiment were provided with the AI prediction was made by a previous participant (a ‘human overseer’). In the baseline, participants did not receive the prediction. We find that participants sent less to the recipient when they received a personalized prediction but the strongest reduction occurred when the AI’s prediction was intentionally not shared by the human overseer. Our findings underscore the importance of considering human reactions to AI predictions in assessing the accuracy and impact of these tools as well as the potential adverse effects of human oversight

Different Pattern of Immunoglobulin Gene Usage by HIV-1 Compared to Non-HIV-1 Antibodies Derived from the Same Infected Subject

A biased usage of immunoglobulin (Ig) genes is observed in human anti-HIV-1 monoclonal antibodies (mAbs) resulting probably from compensation to reduced usage of the VH3 family genes, while the other alternative suggests that this bias usage is due to antigen requirements. If the antigen structure is responsible for the preferential usage of particular Ig genes, it may have certain implications for HIV vaccine development by the targeting of particular Ig gene-encoded B cell receptors to induce neutralizing anti-HIV-1 antibodies. To address this issue, we have produced HIV-1 specific and non-HIV-1 mAbs from an infected individual and analyzed the Ig gene usage. Green-fluorescence labeled virus-like particles (VLP) expressing HIV-1 envelope (Env) proteins of JRFL and BaL and control VLPs (without Env) were used to select single B cells for the production of 68 recombinant mAbs. Ten of these mAbs were HIV-1 Env specific with neutralizing activity against V3 and the CD4 binding site, as well as non-neutralizing mAbs to gp41. The remaining 58 mAbs were non-HIV-1 Env mAbs with undefined specificities. Analysis revealed that biased usage of Ig genes was restricted only to anti-HIV-1 but not to non-HIV-1 mAbs. The VH1 family genes were dominantly used, followed by VH3, VH4, and VH5 among anti-HIV-1 mAbs, while non-HIV-1 specific mAbs preferentially used VH3 family genes, followed by VH4, VH1 and VH5 families in a pattern identical to Abs derived from healthy individuals. This observation suggests that the biased usage of Ig genes by anti-HIV-1 mAbs is driven by structural requirements of the virus antigens rather than by compensation to any depletion of VH3 B cells due to autoreactive mechanisms, according to the gp120 superantigen hypothesis

Induction of Antibodies in Rhesus Macaques That Recognize a Fusion-Intermediate Conformation of HIV-1 gp41

A component to the problem of inducing broad neutralizing HIV-1 gp41 membrane proximal external region (MPER) antibodies is the need to focus the antibody response to the transiently exposed MPER pre-hairpin intermediate neutralization epitope. Here we describe a HIV-1 envelope (Env) gp140 oligomer prime followed by MPER peptide-liposomes boost strategy for eliciting serum antibody responses in rhesus macaques that bind to a gp41 fusion intermediate protein. This Env-liposome immunization strategy induced antibodies to the 2F5 neutralizing epitope 664DKW residues, and these antibodies preferentially bound to a gp41 fusion intermediate construct as well as to MPER scaffolds stabilized in the 2F5-bound conformation. However, no serum lipid binding activity was observed nor was serum neutralizing activity for HIV-1 pseudoviruses present. Nonetheless, the Env-liposome prime-boost immunization strategy induced antibodies that recognized a gp41 fusion intermediate protein and was successful in focusing the antibody response to the desired epitope