Implicit Assumption Research Articles

A transaction-cost model of chronic specie scarcity and the evolution of monetary structures in constrained colonial economies

Abstract A transaction-cost model of monetary choice is used to justify colonists’ claims that specie money for executing within-colony trades and paying local taxes was chronically scarce in Britain’s pre-nineteenth century North American colonies. This scarcity is shown to be the result of individual rational maximizing choice behavior given the constraints imposed on the colonies by their mother country. By contrast, the conventional quantity-theory-of-money, specie-flow model indicates that a chronic specie scarcity equilibrium is impossible. Implicit assumptions in the quantity-theory-of-money model are shown to not apply to these colonial economies. The transaction-cost model developed here builds on the Walrasian–Arrow–Debreu general equilibrium model by incorporating transaction costs and media-of-exchange structures into the market clearing mechanism. Specie (outside money) and non-specie-money media-of-exchange structures (inside monies), which have differing transaction costs, are added to the model. Those additions, along with import substitution and trade-control constraints, identify the plausible circumstances that yield a chronic specie scarcity outcome in a colony. Whether the individual rational maximizing monetary choices that produce chronic specie scarcity in a colony lead to sub-optimal or to optimal social welfare outcomes in that colony depends on what non-specie media-of-exchange structures emerge as the inside money in that colony.

Group-to-individual generalizability and individual-level inferences in cognitive neuroscience.

Much of cognitive neuroscience research is focused on group-averages and interindividual brain-behavior associations. However, many theories core to the goal of cognitive neuroscience, such as hypothesized neural mechanisms for a behavior, are inherently based on intraindividual processes. To accommodate this mismatch between study design and theory, research frequently relies on an implicit assumption that group-level, between-person inferences extend to individual-level, within-person processes. The assumption of group-to-individual generalizability, formally referred to as ergodicity, requires that a process be both homogenous within a population and stationary within individuals over time. Our goal in this review is to assess this assumption and provide an accessible introduction to idiographic science (study of the individual) for the cognitive neuroscientist, ultimately laying a foundation for increased focus on the study of intraindividual processes. We first review the history of idiographic science in psychology to connect this longstanding literature with recent individual-level research goals in cognitive neuroscience. We then consider two requirements of group-to-individual generalizability, pattern homogeneity and stationarity, and suggest that most processes in cognitive neuroscience do not meet these assumptions. Consequently, interindividual findings are inappropriate for the intraindividual inferences that many theories are based on. To address this challenge, we suggest precision imaging as an ideal path forward for intraindividual study and present a research framework for complementary interindividual and intraindividual study.

Analysis of Receiver Operating Characteristic Curves for Cure Survival Data and Mismeasured Biomarkers

Cure models and receiver operating characteristic (ROC) curve estimation are two important issues in survival analysis and have received attention for many years. In the development of biostatistics, these two topics have been well discussed separately. However, a rare development in the estimation of the ROC curve has been made available based on survival data with the cure fraction. On the other hand, while a large body of estimation methods have been proposed, they rely on an implicit assumption that the variables are precisely measured. In applications, measurement errors are generally ubiquitous and ignoring measurement errors can cause unexpected bias for the estimator and lead to the wrong conclusion. In this paper, we study the estimation of the ROC curve and the area under curve (AUC) when variables or biomarkers are subject to measurement error. We propose a valid procedure to handle measurement error effects and estimate the parameters in the cure model, as well as the AUC. We also make an effort to establish the theoretical properties with rigorous justification.

Within-session propulsion asymmetry changes have a limited effect on gait asymmetry post-stroke

BackgroundBiomechanical gait impairments, such as reduced paretic propulsion, are common post-stroke. Studies have used biofeedback to increase paretic propulsion and reduce propulsion asymmetry, but it is unclear if these changes impact overall gait asymmetry. There is an implicit assumption that reducing propulsion asymmetry will improve overall gait symmetry, as paretic propulsion has been related to numerous biomechanical impairments. However, no work has investigated the impact of reducing propulsion asymmetry on overall gait asymmetry. We aimed to understand how within-session changes in propulsion asymmetry affect overall gait asymmetry in individuals post-stroke, operationalized as the combined gait asymmetry metric (CGAM). We hypothesized that decreasing propulsion asymmetry would reduce CGAM. Methods. Participants completed twenty minutes of biofeedback training designed to increase paretic propulsion. We calculated the change in propulsion asymmetry magnitude (Δ|PA|) and the change in CGAM (ΔCGAM) during biofeedback relative to baseline. Then, we fit a robust linear mixed-effects model with ΔCGAM as the outcome and a fixed effect for Δ|PA|. Results. We found a positive association between Δ|PA| and ΔCGAM (β = 2.6, p = 0.002). The average Δ|PA| was -0.09, suggesting that, on average, we would expect a CGAM change of 0.2, which is 0.5% of the average baseline CGAM value. Conclusions. Reducing propulsive asymmetry using biofeedback is unlikely to produce substantial reductions in overall gait asymmetry, suggesting that biofeedback-based approaches to reduce propulsion asymmetry may need to be combined with other interventions to improve overall gait asymmetry. Clinical Trial Registration. NCT04411303.

Public Involvement to Enhance Care Home Research; Collaboration on a Minimum Data Set for Care Homes.

Information on care home residents in England is captured in numerous data sets (care home records, General Practitioner records, community nursing, etc.) but little of this information is currently analysed in a way that is useful for care providers, current or future residents and families or that realises the potential of data to enhance care provision. The DACHA study aimed to develop and test a minimum data set (MDS) which would bring together data that is useful to support and improve care and facilitate research. It is that utility that underscores the importance of meaningful public involvement (PI) with the range of groups of people affected. This paper analyses the involvement of family members of care home residents and care home staff through a PI Panel. The objective for the PI activities was to consistently bring the knowledge and perspectives of family members and care home staff to influence the ongoing design and conduct of the DACHA study. The bespoke methods of PI included a dedicated PI team and a PI Panel of public contributors. Meetings were recorded and minutes agreed, resulting actions were tracked and reflections on the PI recorded. A democratic, social relations approach was used to frame the analysis. A PI panel met 17 times. All meetings included both family members and care home staff. Analysis of the records and reflections developed the following themes about the operation of the PI: deepened understanding of the data environment in care homes; Influence on the pilot MDS; aiming for best research practices with care homes; personal/professional development for PI members; expectations of the project. Learning points for future research projects are developed. PI shaped the design and conduct of the DACHA study, grounding it in the needs and perspectives of people using and providing social care. Data research has a huge responsibility to accurately incorporate relevant public perspectives. There is an implicit assumption that records and data are objective and 'speak for themselves' however there can be unintended consequences from introduction of new data requirements in practice. Public contributors to this manuscript include family members of older people living in care homes and staff of care homes. The wider study also involved as the public, older people living in care homes. Public contributors helped develop the project, contributed throughout the conduct of the study and some chose to be involved in preparing this manuscript.

Playing with Privacy: Exploring the Social Construction of Privacy Norms Through a Card Game

Investigating digital privacy behavior requires consideration of its contextual nuances and the underlying social norms. This study delves into users' joint articulation of such norms by probing their implicit assumptions and "common sense" surrounding privacy conventions. To achieve this, we introduce Privacy Taboo, a card game designed to serve as a playful breaching interview method, fostering discourse on unwritten privacy rules. Through nine interviews involving pairs of participants (n=18), we explore the decision-making and collective negotiation of privacy's vagueness. Our findings demonstrate individuals' ability to articulate their information needs when consenting to fictive data requests, even when contextual cues are limited. By shedding light on the social construction of privacy, this research contributes to a more comprehensive understanding of usable privacy, thereby facilitating the development of democratic privacy frameworks. Moreover, we posit Privacy Taboo as a versatile tool adaptable to diverse domains of application and research.

Exploring Presuppositions In Podcast Interviews: A Pragmatic Analysis Of Shawn Mendes' Narrative On Jay Shetty's Youtube Channel

This study investigates the role of presuppositions in spoken media, focusing on podcast interviews, to understand how they shape audience perceptions. Presupposition, a concept in pragmatics, refers to implicit assumptions embedded in a speaker’s words, which are often accepted by listeners without question. This research applies Yule’s (1996) presupposition theory to a podcast episode featuring Shawn Mendes on Jay Shetty’s YouTube channel. Employing a descriptive qualitative methodology, the study categorizes six types of presuppositions: existential, factive, lexical, structural, non-factive, and counterfactual. The analysis highlights how these presuppositions structure Mendes' personal and professional narrative, guiding listeners' interpretations in real time. The study concludes that identifying presupposition types contributes to understanding the subtle ways spoken media influences audience engagement and narrative construction. This research provides insights into pragmatic analysis in digital spoken media, offering a foundation for further exploration in this dynamic field

The curious case of dopaminergic prediction errors and learning associative information beyond value.

Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.

Within-session propulsion asymmetry changes have a limited effect on gait asymmetry post-stroke.

Biomechanical gait impairments, such as reduced paretic propulsion, are common post-stroke. Studies have used biofeedback to increase paretic propulsion and reduce propulsion asymmetry, but it is unclear if these changes impact overall gait asymmetry. There is an implicit assumption that reducing propulsion asymmetry will improve overall gait symmetry, as paretic propulsion has been related to numerous biomechanical impairments. However, no work has investigated the impact of reducing propulsion asymmetry on overall gait asymmetry. We aimed to understand how within-session changes in propulsion asymmetry affect overall gait asymmetry, operationalized as the combined gait asymmetry metric (CGAM). We hypothesized that decreasing propulsion asymmetry would reduce CGAM. Methods. Participants completed twenty minutes of biofeedback training designed to increase paretic propulsion. We calculated the change in propulsion asymmetry magnitude (Δ|PA|) and the change in CGAM (ΔCGAM) during biofeedback relative to baseline. Then, we fit a robust linear mixed-effects model with ΔCGAM as the outcome and a fixed effect for Δ|PA|. Results. We found a positive association between Δ|PA| and ΔCGAM (β = 2.6, p = 0.002). The average Δ|PA| was - 0.09, suggesting that, on average, we would expect a CGAM change of 0.2, which is 0.5% of the average baseline CGAM value. Conclusions. Reducing propulsive asymmetry using biofeedback is unlikely to produce substantial reductions in overall gait asymmetry, suggesting that biofeedback-based approaches to reduce propulsion asymmetry may need to be combined with other interventions to improve overall gait asymmetry. Clinical Trial Registration . NCT04411303.

Addressing bias in the use of buffers for focal and geographically weighted analyses

Focal analyses (also known as ‘buffer’ or ‘neighbourhood’ analyses) seek to characterise a location based on its surroundings and are commonplace in GIS applications across many fields and disciplines. However, the implicit assumptions made by researchers in these analyses result in an unintended bias towards the periphery of the focal window, which we term Focal Area Bias (FAB). FAB can have a substantial impact upon the resulting values, and in the most extreme cases can result in paradoxical outcomes. Where geographical weighting functions are used, the interaction between the weighting function and FAB means that it will not have the expected effect, leading to the misinterpretation of results. This research characterises the issue of FAB, before presenting a corrective function to remove it. The efficacy of the proposed corrective function and the spatial characteristics of FAB are then evaluated to demonstrate the importance of this issue. We recommend that researchers and practitioners should consider the impact of FAB when undertaking focal analysis and make use of the corrective functions presented here to remove this issue, particularly where geographical weighting is desired.

Cell membranes sustain phospholipid imbalance via cholesterol asymmetry.

Membranes are molecular interfaces that compartmentalize cells to control the flow of nutrients and information. These functions are facilitated by diverse collections of lipids, nearly all of which are distributed asymmetrically between the two bilayer leaflets. Most models of biomembrane structure and function often include the implicit assumption that these leaflets have similar abundances of phospholipids. Here, we show that this assumption is generally invalid and investigate the consequences of lipid abundance imbalances in mammalian plasma membranes (PM). Using quantitative lipidomics, we discovered that cytoplasmic leaflets of human erythrocyte membranes have >50% overabundance of phospholipids compared to exoplasmic leaflets. This imbalance is enabled by an asymmetric interleaflet distribution of cholesterol, which regulates cellular cholesterol homeostasis. These features produce unique functional characteristics, including low PM permeability and resting tension in the cytoplasmic leaflet that regulates protein localization. These largely overlooked aspects of membrane asymmetry represent an evolution of classic paradigms of biomembrane structure and physiology.

All You Need to Know About Allometric Scaling: An Integrative Review on the Theoretical Basis, Empirical Evidence, and Application in Human Pharmacology.

Scaling approaches are used to describe or predict clearance for paediatric or obese populations from normal-weight adult values. Theoretical allometry assumes the existence of a universal bodyweight-based scaling relationship. Although theoretical allometry is highly disputed, it is commonly applied in pharmacological data analyses and clinical practice. The aim of the current review is to (1) increase pharmacologists' understanding of theoretical allometry to better understand the (implicit) assumptions and (dis)advantages and (2) highlight important methodological considerations with the application of this methodology. Theoretical allometry originated in an empirical, and later debated, observation by Kleiber of a scaling exponent of 0.75 between basal metabolic rate and body mass of mammals. The mathematical framework of West, Brown, and Enquist provides one possible explanation for this value. To date, multiple key assumptions of this framework have been disputed or disproven, and an increasing body of evidence is emerging against the existence of one universal allometric exponent. The promise of ease and universality of use that comes with theoretical approaches may be the reason they are so strongly sought after and defended. However, ecologists have suggested that the theory should move from a 'Newtonian approach', in which physical explanations are sought for a universal law and variability is of minor importance, to a 'Darwinian approach', in which variability is considered of primary importance for which evolutionary explanations can be found. No scientific support was found for the application of allometry for within-species scaling, so the application of basal metabolic rate-based scaling principles to clearance scaling remains unsubstantiated. Recent insights from physiologically based modelling approaches emphasise the interplay between drugs with different properties and physiological variables that underlie drug clearance, which drives the variability in the allometric scaling exponent in the field of pharmacology. To deal with this variability, drug-specific or patient-specific adaptations to theoretical allometric scaling are proposed, that introduce empiric elements and reduce the universality of the theory. The use of allometric scaling with an exponent of 0.75 may hold empirical merit for paediatric populations, except for the youngest individuals (aged ≤ 5 years). Nevertheless, biological interpretations and extrapolation potential attributed to models based on 0.75 allometric scaling are theoretically unfounded, and merits of the empirical application of this function should, as for all models, always be supported by appropriate model validation procedures. In this respect, it is not the value of the allometric exponent but the description and prediction of individual clearance values and drug concentrations that are of primary interest.

Biodiversity conservation, consistency, and Mus musculus.

The house mouse (Mus musculus) is often thought of as a pest species in biological conservation, in agriculture, and in urban areas. As a result, populations are frequently targeted for control and eradication. However, M. musculus has notable within-species biodiversity: it has genetically, morphologically, and behaviorally distinct subpopulations. Conserving biodiversity is usually considered the paramount goal of conservation biology, not least because biodiversity is claimed to have intrinsic value. But the biodiversity in mouse populations is often overlooked. In particular, conservationists do not call for the unique diversity represented by threatened mouse populations to be protected. This is illustrative of the inconsistent valuing of biodiversity in conservation. If biodiversity is intrinsically valuable, then it should be valued; however, it reveals itself. And yet, in examples presented here, unique populations of house mice with clear biodiversity value are threatened by eradication campaigns on islands and by changing agricultural practices on the Swiss-Italian border. The inconsistent valuing of biodiversity in the case of M. musculus raises important questions about whether the intrinsic value of biodiversity in conservation is, in practice, conditional on other implicit assumptions.

Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement

ABSTRACTBackgroundMonitoring how the incidence of influenza infections changes over time is important for quantifying the transmission dynamics and clinical severity of influenza. Infection incidence is difficult to measure directly, and hence, other quantities which are more amenable to surveillance are used to monitor trends in infection levels, with the implicit assumption that they correlate with infection incidence.MethodsHere, we demonstrate, through mathematical reasoning using fundamental mathematical principles, the relationship between the incidence of influenza infections and three commonly reported surveillance indicators: (1) the rate per unit time of influenza‐like illness reported through sentinel healthcare sites, (2) the rate per unit time of laboratory‐confirmed influenza infections and (3) the proportion of laboratory tests positive for influenza (‘test‐positive proportion’).ResultsOur analysis suggests that none of these ubiquitously reported surveillance indicators are a reliable tool for monitoring influenza incidence. In particular, we highlight how these surveillance indicators can be heavily biassed by the following: the dynamics of circulating pathogens (other than influenza) with similar symptom profiles, changes in testing rates and differences in infection rates, symptom rates and healthcare‐seeking behaviour between age‐groups and through time. We make six practical recommendations to improve the monitoring of influenza infection incidence. The implementation of our recommendations would enable the construction of more interpretable surveillance indicator(s) for influenza from which underlying patterns of infection incidence could be readily monitored.ConclusionsThe implementation of all (or a subset) of our recommendations would greatly improve understanding of the transmission dynamics, infection burden and clinical severity of influenza, improving our ability to respond effectively to seasonal epidemics and future pandemics.

Partial Multiview Representation Learning With Cross-View Generation.

Multiview learning has made significant progress in recent years. However, an implicit assumption is that multiview data are complete, which is often contrary to practical applications. Due to human or data acquisition equipment errors, what we actually get is partial multiview data, which existing multiview algorithms are limited to processing. Modeling complex dependencies between views in terms of consistency and complementarity remains challenging, especially in partial multiview data scenarios. To address the above issues, this article proposes a deep Gaussian cross-view generation model (named PMvCG), which aims to model views according to the principles of consistency and complementarity and eventually learn the comprehensive representation of partial multiview data. PMvCG can discover cross-view associations by learning view-sharing and view-specific features of different views in the representation space. The missing views can be reconstructed and are applied in turn to further optimize the model. The estimated uncertainty in the model is also considered and integrated into the representation to improve the performance. We design a variational inference and iterative optimization algorithm to solve PMvCG effectively. We conduct comprehensive experiments on multiple real-world datasets to validate the performance of PMvCG. We compare the PMvCG with various methods by applying the learned representation to clustering and classification. We also provide more insightful analysis to explore the PMvCG, such as convergence analysis, parameter sensitivity analysis, and the effect of uncertainty in the representation. The experimental results indicate that PMvCG obtains promising results and surpasses other comparative methods under different experimental settings.

Volumetric Artificial Intelligence Analysis of Pre and Post Rupture Cerebral Aneurysms: Assessment of Morphologic Change

BackgroundCerebral aneurysm rupture is a major cause of potential years of life lost. Research on rupture risk has often compared unruptured and ruptured aneurysms with the implicit assumption that the rupture event does not significantly change aneurysm morphology. However, aneurysm morphology is charged by rupture, though precisely how remains a matter of debate. MethodsWe used a semi-automated artificial intelligence volumetric aneurysm analysis tool to study pre and post rupture aneurysms (N=6). Aneurysms were assessed quantitatively with conventional linear measurements as well as by using volume and surface area. Qualitative side-by-side and superimposed volumetric analysis was performed to assess for the likely point of rupture. ResultsThe morphologic changes as measured using linear measurement showed both increases and decreases in size for maximum dimension, dome height, dome width, and neck width. Volume, however, increased in all cases – by a minimum of 11% (range 11-47%). In all cases, the point of rupture was clear from co-registration or side-by-side 3D volumetric comparisons. ConclusionAll aneurysms in our cohort grew substantially in volume between pre and post rupture when measured with a semi-automated AI volumetric measurement tool. Linear measurements, however, showed both increases and decreases in size. Rupture site was easy to detect in all cases by qualitative volumetric assessment.

Is more always better? The returns to alcohol by volume—Evidence from the Austrian “Spirits Trophy 2023”

Abstract Hedonic models that seek to explain the observable variation in wine and beer prices have so far included alcohol by volume (ABV) only as a control variable without paying much attention to the magnitude of the coefficient and without questioning the implicit assumption that the impact of ABV on bottle prices is indeed linear. Using data from the “Austrian Spirits Trophy 2023,” we find the relationship between ABV and bottle prices to be nonlinear, with statistically significant effects observed for linear, squared, and cubic terms of alcohol content. Moreover, we find expert evaluations of spirits to demonstrate a nonlinear relationship with ABV too.

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

Lost in the shuffle: Testing power in the presence of errorful network vertex labels

Two-sample network hypothesis testing is an important inference task with applications across diverse fields such as medicine, neuroscience, and sociology. Many of these testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. This assumption is often untrue, and the power of the subsequent test can degrade when there are misaligned/label-shuffled vertices across networks. This power loss due to shuffling is theoretically explored in the context of random dot product and stochastic block model networks for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where the power loss across multiple recently proposed tests in the literature is considered. Lastly, the impact that shuffling can have in real-data testing is demonstrated in a pair of examples from neuroscience and from social network analysis.

Rejoinder: “Consumer Uncertainty and Purchase Decision Reversals: Theory and Evidence”

An implicit assumption from prior research is made explicit to clarify the conditions for which information that can reduce uncertainty about a purchase can increase the likelihood the purchase is cancelled or returned.