Wrong-doers may try to collaborate to achieve greater gains than would be possible alone. Yet potential collaborators face two issues: they need to accurately identify other cheaters and trust that their collaborators do not betray them when the opportunity arises. These concerns may be in tension, since the people who are genuine cheaters could also be the likeliest to be untrustworthy. We formalise this interaction in the ‘villain’s dilemma’ and use it in a laboratory experiment to study three questions: what kind of information helps people to overcome the villain’s dilemma? Does the villain’s dilemma promote or hamper cheating relative to individual settings? Who participates in the villain’s dilemma and who is a trustworthy collaborative cheater? We find that information has important consequences for behaviour in the villain’s dilemma. Public information about actions is important for supporting collaborative dishonesty, while more limited sources of information lead to back-stabbing and poor collaboration. We also find that the level of information, role of the decision maker, and round of the experiment affect whether dishonesty is higher or lower in the villain’s dilemma than in our individual honesty settings. Finally, individual factors are generally unrelated to collaborating but individual dishonesty predicts untrustworthiness as a collaborator.

In International Relations (IR) scholarship, there is a growing body of research on the connections between emotions, stigma, and norm violations. It is often presumed that for stigma imposition to be successful, norm violators should feel shame. We argue instead that the emotional dynamics that inform the management of stigma are more complex and involve overlooked emotions such as anxiety, sadness, and hopelessness. We substantiate this by analysing the successful stigmatisation of anti-war voices in Azerbaijan during the 2020 Karabakh war. While the vast majority of the Azerbaijani population supported the war, a small minority contested its legitimacy and the related emotional obligation to express hatred against Armenians. However, these anti-war voices became stigmatised as ‘traitors to the homeland’, and were ultimately pushed to self-silence. We contribute to the growing IR scholarship on emotions and stigma in two ways. First, we show how successful stigmatisation of norm violators may involve emotional dynamics that go beyond shame. Second, we discuss the power of emotion norms of hatred, which, especially in times of war, can push ‘ordinary people’ to pro-actively and vehemently stigmatise norm-violators. In conclusion, we elaborate on the potential future implications of stigma on peacebuilding activities between Armenia and Azerbaijan.

This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as a creative idea generation tool to assist novice product designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an active ideation scenario, which helps foster continuous dialog-based interaction, context-sensitive conversation, and prolific idea generation. An empirical study was conducted with 30 novice product designers to generate multiple ideas for given problems using traditional methods and the new CAI-based interface. The ideas generated by both methods were qualitatively evaluated by a panel of experts. The findings demonstrated the relative superiority of the proposed tool for generating prolific, meaningful, novel, and diverse ideas. The interface was enhanced by incorporating a prompt-engineered structured dialog style for each ideation stage to make it uniform and more convenient for the product designers. A pilot study was conducted and the resulting responses of such a structured CAI interface were found to be more succinct and aligned toward the subsequent design stage. The article thus established the rich potential of using generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

Older adults have largely been excluded from health research despite bearing a disproportionate disease burden. The Community Engagement Studio (CES) model, initially developed at Vanderbilt University in 2009, allows potential research participants to help shape research to promote greater inclusion. The University of Pittsburgh adapted the CES model for older adults (OA-CES). Tailored specifically to older adults, OA-CES addresses underrepresentation in research by gathering valuable feedback that allows investigators to make research more accessible and relevant to older people. An OA-CES toolkit will help in adapting the model in other research areas to close the gap in research inclusion.

Research into the causes of mood disorders has been going on for decades. However, most of the hypotheses put forward have been insufficiently substantiated by living biomarkers. Much research is therefore being conducted into biomarkers that help us better assess the diagnosis and course of mood disorders. Neural autoantibodies are one such biomarker potentially appearing in a subgroup of mood disorders. The aim of this narrative review is to describe the spectrum of autoantibodies in mood disorders while substantiating their prevalence, about which there is very inconsistent evidence. In addition, we discuss autoantibodies in the context of systemic autoimmune diseases associated with depressive symptoms. The pathogenicity of individual autoantibodies has occasionally been demonstrated in animal models, and there is evidence that the severity of depressive symptoms correlates with certain autoantibodies. Possible models of autoimmunity are also explained, such as involvement of the B-cell system, the complement system, and systemic inflammation in autoimmune processes. Nevertheless, note that the mere presence of autoantibodies does not justify the assumption of an autoimmune genesis, as more evidence is needed. The aim of this review is to describe the concepts behind targeting autoantibodies in mood disorders.

This study aims to illustrate a process approach for the calculation of minimum dietary diversity (MDD) indicators for interpretation of dietary diversity (DD) scores and to validate the MDD indicator as a proxy for adequate micronutrient intake using an existing dataset for 2 to younger than 10-year-old South African children. The DD scores were derived from nine food groups, adjusted from the ten food groups for women of reproductive age by combining pulses, nuts and seeds. Three reference methods were used to inspect micronutrient adequacy, namely the mean adequacy ratio and the mean probability of adequacy (MPA) using a single 24-h recall, and the MPA derived from usual intake using more than one 24-hour recall in a sub-sample. Adequacy threshold levels and candidate MDD indicators were inspected and validated using several performance criteria. Results show that the mean and median DD scores were 3·6 and 3·1, respectively. The resulting MDD indicators varied between 3 and 4 out of nine food groups favouring the identification of children with adequate and inadequate intake, respectively, depending on the method used and the age group. Our results and those from others furthermore support a simplified method or ‘rule of thumb’ for the determination of an MDD indicator to establish the integer values below and above the median of the DD scores. We conclude that finding a valid MDD indicator can be done using different methodologies and that results underscore the potential of a simplified method for determining an MDD indicator.

Southern European welfare states have historically been characterised by the absence of national safety nets. However, these countries have witnessed a succession of policy reforms over the past three decades. In 2020, the Spanish Parliament unanimously approved the ‘Ingreso Mínimo Vital’ to finally cease being the only European country without a minimum income scheme at the national level. This article investigates the political and institutional mechanisms that enabled this policy reform, which was particularly unexpected due to the fragmentation of the party system along both ideological and territorial dimensions. To solve this puzzle, I employ the ‘explaining-outcome’ process tracing method and rely on qualitative data from official documents, party manifestos and parliamentary interventions spanning from 2015 to 2020. In addition, I build on ten expert interviews with relevant actors directly involved in the reform, conducted between 2022 and 2024. Findings illustrate that inclusive path departure was possible through two political stages. First, left-wing party competition and strong socio-political demand (2014–2020) allowed for the introduction of the scheme into the political agenda. Second, territorial politics and the external shock of the coronavirus disease 2019 (COVID-19) pandemic ultimately contributed to mitigating expected political cleavages during the parliamentary process.

Parent factors impact adolescent’s emotion regulation, which has key implications for the development of internalizing psychopathology. A key transdiagnostic factor which may contribute to the development of youth internalizing pathology is parent anxiety sensitivity (fear of anxiety-related physiological sensations). In a sample of 146 adolescents (M/SDage = 12.08/.90 years old) and their parents (98% mothers) we tested whether parent anxiety sensitivity was related to their adolescent’s brain activation, over and above the child’s anxiety sensitivity. Adolescents completed an emotion regulation task in the scanner that required them to either regulate vs. react to negative vs. neutral stimuli. Parent anxiety sensitivity was associated with adolescent neural responses in bilateral orbitofrontal cortex (OFC), anterior cingulate, and paracingulate, and left dorsolateral prefrontal cortex, such that higher parent anxiety sensitivity was associated with greater activation when adolescents were allowed to embrace their emotional reaction(s) to stimuli. In the right OFC region only, higher parent anxiety sensitivity was also associated with decreased activation when adolescents were asked to regulate their emotional responses. The findings are consistent with the idea that at-risk adolescents may be modeling the heightened attention and responsivity to environmental stimuli that they observe in their parents.

Research shows that meritocratic recruitment (MR) in public administration is positively related to improved government performance and developmental outcomes. However, the mechanisms behind these improvements remain understudied theoretically and empirically. This paper addresses this gap by theorising and testing two simultaneous pathways through which MR influences development outcomes. First, by prioritising competence over nepotism or political expedience, MR enhances the epistemic quality of bureaucratic personnel (the competence mechanism). Second, by creating incentive misalignment between bureaucrats and politicians, it enables bureaucrats to resist undue political influence, prioritise public interests in governance, and ultimately contribute to development (the impartiality mechanism). Applying mediation analysis to fourteen years of cross-national data, we examine whether changes in recruitment systems are associated with competence- and impartiality-laden indicators of government performance and developmental outcomes. The findings provide robust empirical support for these mechanisms, advancing theoretical understanding and empirical insights into the effects of MR.

One of the most interesting facts about economic history is that income levels remained flat for most of recorded human history and then began to increase in a sustained fashion during the 17th century, providing citizens of industrialized countries with standards of living that would have been unimaginable for most of human history (see Figure 1).

Graphs can help people arrive at data-supported conclusions. However, graphs might also induce bias by shifting the amount of evidence needed to make a decision, such as deciding whether a treatment had some kind of effect. In 2 experiments, we manipulated the early base rates of treatment effects in graphs. Early base rates had a large effect on a signal detection measure of bias in future graphs even though all future graphs had a 50% chance of showing a treatment effect, regardless of earlier base rates. In contrast, the autocorrelation of data points within each graph had a larger effect on discriminability. Exploratory analyses showed that a simple cue could be used to correctly categorize most graphs, and we examine participants’ use of this cue among others in lens models. When exposed to multiple graphs on the same topic, human judges can draw conclusions about the data, but once those conclusions are made, they can affect subsequent graph judgment.

When aiming to change behavior, policymakers confront the challenge of implementing behavioral interventions across contexts. However, the effectiveness of behavioral solutions often hinges on context, posing a significant hurdle to scaling interventions. This study explores the application of a behavioral pattern language approach as a means to enhance intervention efficacy and support policymakers and practitioners who seek to solve problems at scales that cross diverse contexts. The study demonstrates how a pattern language can inform contextually aware solutions, fostering collaboration and knowledge sharing among stakeholders. Additionally, the research finds practitioners deploy multiple solutions within complex systems to achieve more difficult behavioral change goals. Despite challenges related to replicability and evolving methodologies, the findings suggest that pattern languages offer a promising avenue for systematically generating and disseminating behavioral insights. This research contributes to advancing applied behavioral science by providing a structured approach for collaborative policymaking and research endeavors that are contextually relevant and effective.