Vaccine mandates featuring a deadline, i.e. time-limited, can raise uptake either by pulling forward vaccinations that would have occurred later or by inducing additional vaccinations that would not have occurred absent the mandate. This paper asks how such mandates change vaccination behaviour, how the overall effect decomposes into the pull-forward and induction components, and which features of the mandate and public-health context drive that composition. Empirically, we study a low-coercion time-limited mandate targeting graduating high-school students in Western Australia and identify its causal effects using regression discontinuity designs based on strict school-age eligibility rules, applied to population-wide administrative records on first-dose COVID-19 vaccinations. We estimate both a static RDD at the deadline and a dynamic RDD that estimates the treatment effect over time. The mandate increased short-run first-dose uptake by 9.3 percentage points (12.7%) among the targeted cohort, but the dynamic evidence shows that this effect is entirely driven by pull-forward behavior: uptake converges in the long run, implying no vaccinations were induced. Students advanced vaccina
When governments mandate collaboration, shared data systems can serve both as tools for coordination and instruments of control. This study examines U.S. homelessness service networks, where Continuums of Care (CoCs) coordinate service providers through the federally mandated Homeless Management Information System (HMIS). With client consent, providers enter data into HMIS and access cross-provider service histories to support coordinated care. At the same time, HMIS embeds standards and governance rules that shape who can collect, access, interpret, and act on data, and thus who holds decision authority. Using qualitative interviews with six experts, we show that standardization can facilitate collaboration and shared learning. However, unequal resources, analytic capacity, and authority limit equitable participation and often shift some participants toward compliance-focused roles. We contribute to public-interest design research on civic data infrastructures by illustrating how mandated data sharing can simultaneously enable coordination and accountability while reproducing power asymmetries in data interpretation and decision-making.
Over the last two decades, research funders have adopted Open Access (OA) mandates, with various forms and success. While some funders emphasize gold OA through article processing charges, others favour green OA and repositories, leading to a fragmented policy landscape. Compliance with these mandates depends on several factors, including disciplinary field, monitoring, and availability of repository infrastructure. Based on 5 million papers supported by 36 funders from 20 countries, 11 million papers funded by other organisations, and 10 million papers without any funding reported, this study explores how different policies influence the adoption of OA. Findings indicate a sustained growth in OA overall, especially hybrid and gold OA, and that funded papers are more likely to be OA than unfunded papers. Those results suggest that policies such as Plan S, as well as read-and-publish agreements, have had a strong influence on OA adoption, especially among European funders. However, the global low uptake of Diamond OA and limited indexing of OA outputs in Latin American countries highlight ongoing disparities, influenced by funding constraints, journal visibility, and regional infras
We have now tested the Finch Committee's Hypothesis that Green Open Access Mandates are ineffective in generating deposits in institutional repositories. With data from ROARMAP on institutional Green OA mandates and data from ROAR on institutional repositories, we show that deposit number and rate is significantly correlated with mandate strength (classified as 1-12): The stronger the mandate, the more the deposits. The strongest mandates generate deposit rates of 70%+ within 2 years of adoption, compared to the un-mandated deposit rate of 20%. The effect is already detectable at the national level, where the UK, which has the largest proportion of Green OA mandates, has a national OA rate of 35%, compared to the global baseline of 25%. The conclusion is that, contrary to the Finch Hypothesis, Green Open Access Mandates do have a major effect, and the stronger the mandate, the stronger the effect (the Liege ID/OA mandate, linked to research performance evaluation, being the strongest mandate model). RCUK (as well as all universities, research institutions and research funders worldwide) would be well advised to adopt the strongest Green OA mandates and to integrate institutional an
At the core of most socio-technical systems lies a scarce resource that is allocated among agents: highway lanes, public transit, road space, water rights, energy access, grid capacity, user attention, pollution rights, etc. With further automation of the underlying allocation processes, control engineers are increasingly tasked to make decisive assumptions regarding what society wants. In practice to date, design choices are largely driven by industry norms and conventions rather than a result of conscientiously responsible and ethical design. In this paper, we look at tools available to control engineers to design systems in a more principled manner in order to match the societal mandate. We consider three control design paradigms: online feedback optimization, control of Markov decision processes, and model predictive control. Beginning with aggregating individual agents' preferences into control design objectives, subsequently ensuring and certifying the fulfillment of those specifications, we argue that the feedback nature of control systems enables appropriate allocation of the shared resources in ways hitherto unparalleled.
Security mandates today are often in the form of checklists and are generally inflexible and slow to adapt to changing threats. This paper introduces an alternate approach called open mandates, which mandate that vendors must dedicate some amount of resources (e.g. system speed, energy, design cost, etc.) towards security but unlike checklist security does not prescribe specific controls that must be implemented. The goal of open mandates is to provide flexibility to vendors in implementing security controls that they see fit while requiring all vendors to commit to a certain level of security. In this paper, we first demonstrate the usefulness of open security mandates: for instance, we show that mandating 10% of resources towards security reduces defenders losses by 8% and forestalls attackers by 10%. We then show how open mandates can be implemented in practice. Specifically, we solve the problem of identifying a system's overhead due to security, a key problem towards making such an open mandate enforceable in practice. As examples we demonstrate our open mandate system -- COMMAND -- for two contemporary software hardening techniques and show that our methodology can predict se
We model the impact of local vaccine mandates on the spread of vaccine-preventable infectious diseases, which in the absence of vaccines will mainly affect children. Examples of such diseases are measles, rubella, mumps and pertussis. To model the spread of the pathogen, we use a stochastic SIR (Susceptible, Infectious, Recovered) model with two levels of mixing in a closed population, often referred to as the household model. In this model individuals make local contacts within a specific small subgroup of the population (e.g.\ within a household or a school class), while they also make global contacts with random people in the population at a much lower rate than the rate of local contacts. We consider what happens if schools are given freedom to impose vaccine mandates on all of their pupils, except for the pupils that are exempt from vaccination because of medical reasons. We investigate how such a mandate affects the probability of an outbreak of a disease and the probability that a pupil that is medically exempt from vaccination, gets infected during an outbreak. We show that if the population vaccine coverage is close to the herd-immunity level then both probabilities may in
Recent application programming interface (API) restrictions on major social media platforms challenge compliance with the EU Digital Services Act [20], which mandates data access for algorithmic transparency. We develop a structured audit framework to assess the growing misalignment between regulatory requirements and platform implementations. Our comparative analysis of X/Twitter, Reddit, TikTok, and Meta identifies critical ``audit blind-spots'' where platform content moderation and algorithmic amplification remain inaccessible to independent verification. Our findings reveal an ``accountability paradox'': as platforms increasingly rely on AI systems, they simultaneously restrict the capacity for independent oversight. We propose targeted policy interventions aligned with the AI Risk Management Framework of the National Institute of Standards and Technology [80], emphasizing federated access models and enhanced regulatory enforcement.
We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negative ESG premium arises. A result that is supported by empirical data. The negative ESG premium is due to the ESG constraint imposed on institutional investors and is not associated with a risk factor.
Background: A recent epidemiological analysis of staggered policy implementation reported a 29.4% reduction in COVID-19 cases by maintaining school mask mandates in the greater Boston area during the first half of 2022. The robustness of their results and the appropriateness of methodology are explored. Methods: Using data from the Massachusetts Department of Elementary and Secondary Education and the Centers for Disease Control and Prevention, we re-analyze differences in COVID-19 incidence in school districts that did and did not lift mask mandates using the same districts as the original study and expanded the analysis to the entire state of Massachusetts. We present changes in case rates and differences in prior immunity in areas with different mask lifting policies. Results: The Boston and Chelsea districts, which maintained mask mandates, were outliers in terms of size, demographics, and testing. We failed to find a notable change in student cases in mask mandate districts compared with the 70 districts in the original study (-0.08/1000; p=0.98) and found a slight increase compared with a statewide control group +3.63/1000 (p=0.291). Results were similar for students and staf
This paper examines how risk and budget limits on investment mandates affect the bidding strategy in a uniform-price auction for issuing corporate bonds. I prove the existence of symmetric Bayesian Nash equilibrium and explore how the risk limits imposed on the mandate may mitigate severe underpricing, as the symmetric equilibrium's yield positively relates to the risk limit. Investment mandates with low-risk acceptance inversely affect the equilibrium bid. The equilibrium bid provides insights into the optimal mechanism for pricing corporate bonds conveying information about the bond's valuation, market power, and the number of bidders. These findings contribute to auction theory and have implications for empirical research in the corporate bond market.
Home fiber connections are largely realized by using passive optical networks, in their most common form today relying on the GPON standard. Among other things, this standard specifies how the first node inside of customers' homes, the so called ONU or ONT, has to behave, and which security features have to be supported. Currently, customers in some European countries, including Germany, have freedom of choice between using terminal equipment provided by the ISP or a self-selected open market device.We analyze the security implications resulting from this freedom of choice and whether or not ISP-mandated hardware would increase the security of the GPON. Our review reveals that there are no differences between an ISP-mandated ONU/ONT and a standard conforming subscriber-selected ONU/ONT that would justify the security based recommendation of an ISP-mandated ONU/ONT.
Face masks are one of the cheapest and most effective non-pharmaceutical interventions available against airborne diseases such as COVID-19. Unfortunately, they have been met with resistance by a substantial fraction of the populace, especially in the U.S. In this study, we uncover the latent moral values that underpin the response to the mask mandate, and paint them against the country's political backdrop. We monitor the discussion about masks on Twitter, which involves almost 600k users in a time span of 7 months. By using a combination of graph mining, natural language processing, topic modeling, content analysis, and time series analysis, we characterize the responses to the mask mandate of both those in favor and against them. We base our analysis on the theoretical frameworks of Moral Foundation Theory and Hofstede's cultural dimensions. Our results show that, while the anti-mask stance is associated with a conservative political leaning, the moral values expressed by its adherents diverge from the ones typically used by conservatives. In particular, the expected emphasis on the values of authority and purity is accompanied by an atypical dearth of in-group loyalty. We find
The paper examines the trajectory of crime, tracing its evolution from traditional forms to digital manifestations in cybercrime, and proposes "Hypercrime" as the latest frontier. Leveraging insights from Michael McGuire's "Hypercrime: The New Geometry of Harm," the study calls for a paradigm shift in law enforcement strategies to meet the challenges posed by AI-driven hypercrime. Emphasis is placed on understanding hypercrime's complexity, developing proactive policies, and embracing technological tools to mitigate risks associated with AI misuse.
Although the topic of opinion polarization receives much attention from the media, public opinion researchers and political scientists, the phenomenon itself has not been adequately characterized in either the lay or academic literature. To study opinion polarization among the public, researchers compare the distributions of respondents to survey questions or track the distribution of responses to a question over time using ad-hoc methods and measures such as visual comparisons, variances, and bimodality coefficients. To remedy this situation, we build on the axiomatic approach in the economics literature on income bipolarization, specifying key properties a measure of bipolarization should satisfy: in particular, it should increase as the distribution spreads away from a center toward the poles and/or as clustering below or above this center increases. We then show that measures of bipolarization used in public opinion research fail to satisfy one or more of these axioms. Next, we propose a $p$-Wasserstein polarization index that satisfies the axioms we set forth. Our index measures the dissimilarity between an observed distribution and a distribution with all the mass clustered o
MELIBEA is a Spanish database that uses a composite formula with eight weighted conditions to estimate the effectiveness of Open Access mandates (registered in ROARMAP). We analyzed 68 mandated institutions for publication years 2011-2013 to determine how well the MELIBEA score and its individual conditions predict what percentage of published articles indexed by Web of Knowledge is deposited in each institution's OA repository, and when. We found a small but significant positive correlation (0.18) between MELIBEA score and deposit percentage. We also found that for three of the eight MELIBEA conditions (deposit timing, internal use, and opt-outs), one value of each was strongly associated with deposit percentage or deposit latency (immediate deposit required, deposit required for performance evaluation, unconditional opt-out allowed for the OA requirement but no opt-out for deposit requirement). When we updated the initial values and weights of the MELIBEA formula for mandate effectiveness to reflect the empirical association we had found, the score's predictive power doubled (.36). There are not yet enough OA mandates to test further mandate conditions that might contribute to ma
Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a -80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport and biomass use for CHP and industrial heat are im
The deployment of autonomous AI agents capable of executing commercial transactions has motivated the adoption of mandate-based payment authorization protocols, including the Universal Commerce Protocol (UCP) and the Agent Payments Protocol (AP2). These protocols replace interactive, session-based authorization with cryptographically issued mandates, enabling asynchronous and autonomous execution. While AP2 provides specification-level guarantees through signature verification, explicit binding, and expiration semantics, real-world agentic execution introduces runtime behaviors such as retries, concurrency, and orchestration that challenge implicit assumptions about mandate usage. In this work, we present a security analysis of the AP2 mandate lifecycle and identify enforcement gaps that arise during runtime in agent-based payment systems. We propose a zero-trust runtime verification framework that enforces explicit context binding and consume-once mandate semantics using dynamically generated, time-bound nonces, ensuring that authorization decisions are evaluated at execution time rather than assumed from static issuance properties. Through simulation-based evaluation under high c
As generative AI advances, global governance frameworks increasingly mandate verifiable content provenance. However, existing watermarking techniques face a critical policy-to-technology disconnect: sampling-based methods require computationally prohibitive inversion, while fine-tuning approaches are tethered to specific model checkpoints, hindering standardized, cross-model oversight. To bridge this gap, we introduce DiffMark, a plug-and-play multi-bit watermarking framework. DiffMark embeds a persistent, learned perturbation into every denoising step of a frozen diffusion model, accumulating a recoverable signal in the final latent space. To enable efficient training through the frozen network, we utilize Latent Consistency Models (LCMs) as a differentiable training bridge. DiffMark achieves 64-bit extraction in a single 16.4 ms forward pass, which is a $45\times$ speed-up over inversion baselines. By enabling per-image key flexibility and cross-architecture transferability without retraining, DiffMark provides the practical, scalable technical tooling necessary to operationalize user accountability and enforce emerging AI governance mandates.
We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 user-funded agents traded real ETH in a bounded onchain market. Users configured vaults through structured controls and natural-language strategies, but only agents could choose normal buy/sell trades. The system produced 7.5M agent invocations, roughly 300K onchain actions, about $20M in volume, more than 5,000 ETH deployed, roughly 70B inference tokens, and 99.9% settlement success for policy-valid submitted transactions. Long-running agents accumulated thousands of sequential decisions, including 6,000+ prompt-state-action cycles for continuously active agents, yielding a large-scale trace from user mandate to rendered prompt, reasoning, validation, portfolio state, and settlement. Reliability did not come from the base model alone; it emerged from the operating layer around the model: prompt compilation, typed controls, policy validation, execution guards, memory design, and trace-level observability. Pre-launch testing exposed failures that text-only benchmarks rarely measure, in