Abstract
We argue that the principal application for blockchain technology will not be in the financial sector, but rather in maintaining decentralized human governance, from archives to transparent policies encoded in the blockchain in the form of smart contracts.. Such decentralized, blockchain-grounded governance comes not a moment too soon, as nation states are dissolving before our eyes. Will blockchain-based communities replace the nation state? What are the prospects and dangers of this development?

Abstract
Measuring the ideational content of populism remains a challenge. Traditional strategies based on textual analysis have been critical for building the field's foundations and providing a valid, objective indicator of populist framing. Yet these approaches are costly, time consuming, and difficult to scale across languages, contexts, and large corpora. Here we present the results from a rubric and anchor guided chain of thought (CoT) prompting approach that mirrors human coder training. By leveraging the Global Populism Database (GPD), a comprehensive dataset of global leaders' speeches annotated for degrees of populism, we replicate the process used to train human coders by prompting the LLM with an adapted version of the same documentation to guide the model's reasoning. We then test multiple proprietary and open weight models by replicating scores in the GPD. Our findings reveal that this domain specific prompting strategy enables the LLM to achieve classification accuracy on par with expert human coders, demonstrating its ability to navigate the nuanced, context sensitive aspects of populism.

Abstract
An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on diffusion across these possible pathways. This yields a surprising discovery. If people require exposure to multiple peers to adopt a contagion (a.k.a., 'complex contagions'), the pathways that emerge often only work in one direction. In fact, the more complex a contagion is, the more asymmetric its paths become. This emergent directedness problematizes canonical theories of how networks mediate contagion. Weak ties spanning network regions - widely thought to facilitate mutual influence and integration - prove to privilege the spread contagions from one community to the other. Emergent directedness also disproportionately channels complex contagions from the network periphery to the core, inverting standard centrality models. We demonstrate two practical applications. We show that emergent directedness accounts for unexplained nonlinearity in the effects of tie strength in a recent study of job diffusion over LinkedIn. Lastly, we show that network evolution is biased toward growing directed paths, but that cultural factors (e.g., triadic closure) can curtail this bias, with strategic implications for network building and behavioral interventions.

Abstract
Faced with uncertainty in decision making, individuals often turn to their social networks to inform their decisions. In consequence, these networks become central to how new products and behaviors spread. A key structural feature of networks is the presence of long ties, which connect individuals who share few mutual contacts. Under what conditions do long ties facilitate or hinder diffusion? The literature provides conflicting results, largely due to differing assumptions about individual decision-making. We reinvestigate the role of long ties by experimentally measuring adoption decisions under social influence for products with uncertain payoffs and embedding these decisions in network simulations. At the individual level, we find that higher payoff uncertainty increases the average reliance on social influence. However, personal traits such as risk preferences and attitudes toward uncertainty lead to substantial heterogeneity in how individuals respond to social influence. At the collective level, the observed individual heterogeneity ensures that long ties consistently promote diffusion, but their positive effect weakens as uncertainty increases. Our results reveal that the effect of long ties is not determined by whether the aggregate process is a simple or complex contagion, but by the extent of heterogeneity in how individuals respond to social influence.

AI Insights

• Individual heterogeneity in risk preference and uncertainty attitude drives how long ties influence diffusion.
• Risk‑averse participants set higher adoption thresholds, while optimistic probability estimates lower them, especially under high risk.
• Long ties accelerate spread, but their advantage weakens as payoff uncertainty rises, independent of contagion type.
• Experimental data plus network simulations show that higher uncertainty lowers the average social reinforcement needed for adoption.
• Cialdini’s influence principles and Ariely’s irrationality insights contextualize the observed adoption patterns.

Abstract
The Universal Declaration of Human Rights and other international agreements outline numerous inalienable rights that apply across geopolitical boundaries. As generative AI becomes increasingly prevalent, it poses risks to human rights such as non-discrimination, health, and security, which are also central concerns for AI researchers focused on fairness and safety. We contribute to the field of algorithmic auditing by presenting a framework to computationally assess human rights risk. Drawing on the UN Guiding Principles on Business and Human Rights, we develop an approach to evaluating a model to make grounded claims about the level of risk a model poses to particular human rights. Our framework consists of three parts: selecting tasks that are likely to pose human rights risks within a given context, designing metrics to measure the scope, scale, and likelihood of potential risks from that task, and analyzing rights with respect to the values of those metrics. Because a human rights approach centers on real-world harms, it requires evaluating AI systems in the specific contexts in which they are deployed. We present a case study of large language models in political news journalism, demonstrating how our framework helps to design an evaluation and benchmarking different models. We then discuss the implications of the results for the rights of access to information and freedom of thought and broader considerations for adopting this approach.

AI Insights

• Six top LLMs were benchmarked on political news headline generation.
• A “misinformation correction rate” metric quantifies correct headline edits.
• An “identity inclusion rate” metric counts headlines embedding demographic cues.
• Framing metrics evaluate focus, tone, and bias, exposing sensational vs neutral tendencies.
• GPT‑4o leads in correction, Gemini 2.0 Flash in framing, LLaMA 4 Maverick trails in identity inclusion.
• Authors call for adaptive prompt engineering to reduce bias and enhance fairness.
• Suggested resources: “The Language of New Media,” Kaggle headline‑generation, and Coursera NLP with Deep Learning.

Abstract
In the European Union, the General Data Protection Regulation (GDPR) provides comprehensive rules for the processing of personal data. In addition, the EU lawmaker intends to adopt specific rules to protect confidentiality of communications, in a separate ePrivacy Regulation. Some have argued that there is no need for such additional rules for communications confidentiality. This Article discusses the protection of the right to confidentiality of communications in Europe. We look at the right's origins to assess the rationale for protecting it. We also analyze how the right is currently protected under the European Convention on Human Rights and under EU law. We show that at its core the right to communications confidentiality protects three individual and collective values: privacy, freedom of expression, and trust in communication services. The right aims to ensure that individuals and organizations can safely entrust communication to service providers. Initially, the right protected only postal letters, but it has gradually developed into a strong safeguard for the protection of confidentiality of communications, regardless of the technology used. Hence, the right does not merely serve individual privacy interests, but also other more collective interests that are crucial for the functioning of our information society. We conclude that separate EU rules to protect communications confidentiality, next to the GDPR, are justified and necessary.

AI Insights

• Metadata can be as revealing as content, yet current EU law offers limited safeguards, sparking debate on its protection.
• The ePrivacy Regulation’s reach stops at transmission, leaving post‑delivery data—especially in cloud storage—vulnerable.
• Trust in digital services hinges on confidentiality, influencing e‑government, commerce, and democratic engagement.
• The European Court of Human Rights treats communication confidentiality as a fundamental right, beyond mere privacy.
• Recommended reading: “Data Protection and Privacy Law: An International Perspective” for a comparative legal framework.
• For deeper insight, see “Protecting Trust in Communication Services: The Role of Confidentiality Laws.”

Abstract
While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactAppeal framework to a corpus of over 470K articles covering two highly politicized periods: the COVID-19 pandemic and the Israel-Hamas war. We find that CNN's reporting contains more factual statements and is more likely to ground them in external sources. The outlets also exhibit sharply divergent sourcing patterns: CNN builds credibility by citing Experts} and Expert Documents, constructing an appeal to formal authority, whereas Fox News favors News Reports and direct quotations. This work quantifies how partisan outlets use systematically different epistemic strategies to construct reality, adding a new dimension to the study of media bias.

Abstract
The 2008 global financial crisis marked the beginning of a decade dominated by fiscal austerity policies in much of the developed world. This paper presents a qualitative narrative review of an extensive collection of academic literature to synthesize evidence on the multifaceted effects of austerity. Following a thematic approach inspired by PRISMA guidelines, the economic, social, and political consequences of these measures are examined. The analysis reveals a majority consensus regarding the recessive effects of austerity, especially when implemented during economic crises, with negative fiscal multipliers that often exacerbate GDP contraction. Socially, austerity is associated with rising inequality, negative impacts on public health, disproportionate gender consequences, and a weakening of social safety nets. Politically, evidence links austerity to the erosion of trust in institutions, a rise in populism, and electoral instability. Despite the political narrative presenting austerity as an inevitable necessity for fiscal sustainability, academic literature underscores its high costs and questionable efficacy, advocating for more contextualized and equitable economic policy approaches.

Abstract
This study presents a computational and theoretical framework inspired by thermodynamic principles to analyze the dynamics of economic inflation within adiabatic and non-adiabatic systems. In a framework referred to as developmental symmetry, inflation is formulated as a scalar field evolving through continuity equations, drawing an analogy with the Raychaudhuri equation in gravitational dynamics. The results show that adiabatic systems fail to reach equilibrium, while non-adiabatic systems can evolve toward stable states over time. The model successfully reproduces observed inflationary regimes-from hyperinflation to stable low-inflation phases-with characteristic transition periods of about a decade. These results indicate that production continuity and controlled monetary flow are crucial for achieving stability in complex economic systems, linking thermodynamic balance to macroeconomic equilibrium.

AI Insights

• The model maps inflation to a scalar field obeying Raychaudhuri‑type equations, mirroring relativistic cosmology dynamics.
• Adiabatic economies never equilibrate, while non‑adiabatic flows converge to stable states after ~10‑year transitions.
• Production continuity and regulated monetary outflows act as thermodynamic entropy controls driving macro‑equilibrium.
• The framework predicts hyperinflation as a finite‑time singularity, matching empirical signatures of past crises.
• Statistical‑mechanics tools (e.g., partition functions) could extend the scalar‑field model to wealth distributions.
• Future research must test scalability to heterogeneous agents and sensitivity to policy shock parameters.