Central Assumption Research Articles

A new approach for assessing the value of informal care in Alzheimer's disease.

As most informal caregivers providing help for patients with Alzheimer's disease (AD) are retired spouses or unemployed people, there is no market value for their time. Most articles that tried to estimate the cost of informal care in AD rely on the so-called "replacement" methodology, which assumes that one hour of informal care has the same value as one hour of professional care. Little attention has been dedicated to exploring the validity of this assumption. In this paper, we determine the relationship between the price of informal caregiving and professional care from the first-order condition of a theoretical model that maximizes informal caregivers' satisfaction with providing care. This article formalizes the marginal substitution rate between informal and formal care. We assume that the caregiver's utility depends on the caregiver's burden and the patient's quality of life. After explaining the parameters of the marginal utility of caregivers, we estimate each of these parameters using PLASA data. Our results show how the value of informal care increases as the care contributes to improving patients' quality of life but decreases as the burden on the caregiver increases and professionals contribute to patients' quality of life. The central assumption of the replacement cost method of perfect substitution between informal and formal care leads to a misestimation of the value of informal care. The effects of informal care must be considered (direct effect on the burden and indirect effects on the patient's quality of life).

Effective Rates for Iterations Involving Bregman Strongly Nonexpansive Operators

We develop the theory of Bregman strongly nonexpansive maps for uniformly Fréchet differentiable Bregman functions from a quantitative perspective. In that vein, we provide moduli witnessing quantitative versions of the central assumptions commonly used in this field on the underlying Bregman function and the Bregman strongly nonexpansive maps. In terms of these moduli, we then compute explicit and effective rates for the asymptotic regularity of Picard iterations of Bregman strongly nonexpansive maps and of the method of cyclic Bregman projections. Further, we also provide similar rates for the asymptotic regularity and metastability of a strongly convergent Halpern-type iteration of a family of such mappings and we use these new results to derive rates for various special instantiations like a Halpern-type proximal point algorithm for monotone operators in Banach spaces as well as Halpern-Mann- and Tikhonov-Mann-type methods.

Testing the Goodwin Model: Can Distribution Battles Trigger Business Cycles? Evidence from a Behavioral Experiment

AbstractThe Goodwin model provides an important explanation for business cycles with its investment decisions based on pro-cyclical wage fluctuations. The purpose of this paper is to test the central hypothesis of the Goodwin model, namely that wage developments influence investments, which creates business cycles. The model used to test this hypothesis was that of a simplified company, where labor demand is related to investments. Thus, more investments lead to higher wages and vice versa. In the behavioral experiment the test subjects played the manager of a company who maximizes their profits through investments. The central assumptions and conclusions of the Goodwin model are supported by behavioral science.

Language learners, chess champions, and piano prodigies – insights from research on language contact and expert behavior

Abstract One of the central assumptions in Cognitive Linguistics is that the cognitive mechanisms underlying our language use are domain-general and thus apply to human behavior beyond language. Examples of such cognitive mechanisms are, among others, our ability to focus attention, to memorize and categorize, as well as processes related to chunking, generalization, and inhibitory control. Testing this core assumption, however, is often difficult, as it requires us to move beyond linguistic research and to actively look for links between our language use and other areas of human cognition. This paper is an illustration of what such links could look like, particularly focusing on links between research on (Dutch-German) language contact and expert behavior. In doing so, it shows (a) that there are many shared links, for example, regarding the cognitive mechanisms of entrenchment and chunking, and (b) that these shared links (as well as potential differences across the fields) can be used to improve our linguistic theorizing. In particular, I argue that linguistic research can benefit from the insights from research on expert behavior, especially from its more advanced insights modelling individual variation, and that the shared links can help us to test the core assumption that the cognitive mechanisms underlying our language use are indeed domain-general.

Dual-objective optimization of solar driven alkaline electrolyzer system for on-site hydrogen production and storage: Current and future scenarios

Achieving cost competitiveness of renewable hydrogen could accelerate the transition to the deeply decarbonized energy system. In this article, we develop and apply a dual-objective optimization model to explore the photovoltaic (PV) hydrogen production pathway and costs development from the present through 2050. The model is applied for optimal capacity allocation of the megawatt-scale off-grid PV-Hydrogen system to achieve maximum production at the minimal levelized cost of hydrogen (LCOH). Methodology includes a smoothing control strategy. Simulation is performed utilizing measured meteorological data for one year with hourly resolution and considering electrolyzer's load flexibility constraint. It has been found that the smoothing control strategy is indispensable for maximizing PV energy utilization, enhancing the electrolyzer's capacity factor and reducing the power curtailments. The analysis shows that the off-grid solar hydrogen in Algeria lacks economic competitiveness currently. Components CAPEX reduction turns out to be the fundamental condition towards the future LCOH decrease. LCOH could decline from 4.2 $/kg in 2025 to 2.24 $/kg in 2050 under central assumptions and to roughly 1.4 $/kg under optimistic assumptions. Alkaline electrolysis step cost could reduce by 0.28 $/kg every decade. Hydrogen storage autonomy could rise the LCOH by 7.1 c$ per day of autonomy in 2050.

Costly Signaling as an Integral Part of the Systemic Approach to Religion

In this commentary on Purzycki and Sosis’ book Religion Evolving: Cultural, Cognitive, and Ecological Dynamics (2022), I subject their central assumption, namely that religious costs function as honest signals of commitment to systematic scrutiny. Many studies show that people who partake in (costlier) rituals cooperate (to a larger extent), but these studies are often non-experimental and say only a little about the causal relationship between signals and signaled quality, i.e., willingness to cooperate. Reviewing the existing literature on religious costly signaling, I argue that the core costly signaling model originally developed in evolutionary biology (and economics) is only partially supported when applied to religious signaling. At the same time, I show that little is known about the interactions between costly signals and supernatural beliefs. I discuss the results of the recent experimental work that tested basic assumptions of human costly signaling within the religious context.

An analysis of the use of historical control data in the assessment of regulatory pesticide toxicity studies

The concurrent control group is the most important reference for the interpretation of toxicity studies. However, pooled information on control animals from independent studies, i.e., historical control data (HCD), is also used for the interpretation of results. Currently, an overview on actual HCD use in regulatory toxicology is lacking. Therefore, we evaluated the HCD use of the Joint FAO/WHO Meeting on Pesticide Residues from 2004 to 2021 and compared it with recommendations in regulatory guidelines and in the literature. We found that HCD was used routinely and exclusively to avoid potential false positive decisions regarding the treatment-relatedness of effects, mostly using the HCD range, i.e., the most extreme values, as a benchmark. HCD were not used to avoid potential false negative decisions or for quality control of the index study. The central assumption of the HCD use, namely that the HCD and control group of the index study follow the same underlying distribution because they are samples of the same data generation process, was not investigated, although numerous factors potentially contribute to effect variation between the different control groups pooled in the HCD. We recommend that the existing guidelines be revised to improve the robustness and transparency of toxicological assessments.

Making Formulog Fast: An Argument for Unconventional Datalog Evaluation

With its combination of Datalog, SMT solving, and functional programming, the language Formulog provides an appealing mix of features for implementing SMT-based static analyses (e.g., refinement type checking, symbolic execution) in a natural, declarative way. At the same time, the performance of its custom Datalog solver can be an impediment to using Formulog beyond prototyping—a common problem for Datalog variants that aspire to solve large problem instances. In this work we speed up Formulog evaluation, with some surprising results: while 2.2× speedups can be obtained by using the conventional techniques for high-performance Datalog (e.g., compilation, specialized data structures), the big wins come by abandoning the central assumption in modern performant Datalog engines, semi-naive Datalog evaluation. In the place of semi-naive evaluation, we develop eager evaluation, a concurrent Datalog evaluation algorithm that explores the logical inference space via a depth-first traversal order. In practice, eager evaluation leads to an advantageous distribution of Formulog’s SMT workload to external SMT solvers and improved SMT solving times: our eager evaluation extensions to the Formulog interpreter and Soufflé’s code generator achieve mean 5.2× and 7.6× speedups, respectively, over the optimized code generated by off-the-shelf Soufflé on SMT-heavy Formulog benchmarks. All in all, using compilation and eager evaluation (as appropriate), Formulog implementations of refinement type checking, bottom-up pointer analysis, and symbolic execution achieve speedups on 20 out of 23 benchmarks over previously published, hand-tuned analyses written in F ♯ , Java, and C++, providing strong evidence that Formulog can be the basis of a realistic platform for SMT-based static analysis. Moreover, our experience adds nuance to the conventional wisdom that traditional semi-naive evaluation is the one-size-fits-all best Datalog evaluation algorithm for static analysis workloads.

Strategic autonomy of the EU in the light of CSDP and the changing world order

The development of the European Union’s activity as a security actor is closely linked to the need for global capacity for action in this domain, with a clearly “softer” role definition than NATO, which is derived from a collective security concept. The aim of this study is to identify how and under what circumstances the European Union’s self-definition in the field of security and defense policy has evolved, how it has attempted to make the EU be present in the changing world order as an independent actor asserting its strategic autonomy with a specific voice. The paper looks back not only on the development of the EU security and defense policy, but also highlights the two decades of Hungarian operational involvement in it. Our central assumption is that the strengthening of the EU’s strategic autonomy, in which CSDP is one of the core instruments, cannot be avoided amid the challenges of the modern era if the EU wants to preserve its competitiveness and adaptive responsiveness.

Why and How Consumers Perform Online Reviewing Differently

Abstract Reviewing products and services is a widespread consumer activity in which millions engage. Why and how do consumers review differently from one another? Prior work assumes that consumers commonly understand what reviewing is. Consequently, it attributes differences in reviewing to individual variations in psychological, motivational, and sociodemographic characteristics, consumption experiences, and expertise. This central assumption is problematic because it fails to consider that differences in how consumers understand reviewing may explain why they approach and perform reviewing differently. To address this gap, we analyze a large qualitative dataset composed of reviews and interviews with their authors. Our insights complement prior work by theorizing the sociocultural shaping of reviewing. We answer why consumers review differently by inductively theorizing the concept of reviewing orientation—a cultural model comprising a set of interconnected characteristics that shapes how consumers review and translates into a distinct reviewer voice—a reviewer’s standpoint expressed within a review. We answer how consumers review differently by developing three reviewing orientations: communal sharing, systemic evaluation, and competitive punditry. Finally, we discuss the transferability of the findings, the role of institutional dynamics in reviewing, and recommendations for online review platforms and marketers.

Entrepreneurs' use of imagination in pivot decision: Toward a conceptual model

AbstractThe purpose of the paper is to open the black box of pivot decisions and to deepen our understanding of how entrepreneurs use their imagination in such a crucial moment of the entrepreneurial process. A pivot is a strategic decision made after a failure and challenges central assumptions about the entrepreneurial project. In turn, entrepreneurs can remain quite conservative in the generation of new options for their business and expose themselves to the risks of path dependency and escalation of commitment. Reconfiguring an initial idea requires specific cognitive skills and mechanisms. In this paper, we sketch a conceptual model explaining the relationships between creative, social, and practical imaginativeness and the generation of new business options, and how founders' cognitive mechanisms may moderate this relationship.

Analysis of Decentralization in the Central Government's Acquisition of Provincial Road Management

Infrastructure is a crucial aspect necessary for a state to perform its various functions. One type of infrastructure that plays a vital role in ensuring the smooth mobility of the general public is roads. Unfortunately, in Indonesia, the quality of road networks cannot be described as ideal. The substandard condition of these roads has drawn public concern, particularly following a social media post by a resident of Lampung Province criticizing the quality of roads there. In response to this public attention, the Central Government decided to assume the duties, authority, and responsibility for repairing 15 (fifteen) roads in Lampung Province. This decision has sparked new concerns among the public regarding the centralization of duties, authority, and responsibilities, which could undermine the spirit of decentralization. Based on the findings presented in this article, it can be asserted that the central government's assumption of the duties, authority, and responsibilities of local governments does not violate prevailing laws and regulations and is in accordance with them. Nevertheless, the phenomenon of the central government taking over these responsibilities warrants attention. This case highlights that the Lampung Provincial Government is not entirely capable of managing the tasks mandated by the Central Government. Therefore, from the findings of this case, it is evident that there is still a significant need for the maturation of Regional Governments in fulfilling the duties, authority, and responsibilities entrusted to them.

Temporal multiplexing of perception and memory codes in IT cortex

A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli1. Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code2–4. Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here we examined how familiar faces are encoded in the IT anterior medial face patch (AM), perirhinal face patch (PR) and temporal pole face patch (TP). In AM and PR we observed that the encoding axis for familiar faces is rotated relative to that for unfamiliar faces at long latency; in TP this memory-related rotation was much weaker. Contrary to previous claims, the relative response magnitude to familiar versus unfamiliar faces was not a stable indicator of familiarity in any patch5–11. The mechanism underlying the memory-related axis change is likely intrinsic to IT cortex, because inactivation of PR did not affect axis change dynamics in AM. Overall, our results suggest that memories of familiar faces are represented in AM and perirhinal cortex by a distinct long-latency code, explaining how the same cell population can encode both the percept and memory of faces.

Integration of Chemical Looping Combustion in the Graz Power Cycle

: Effective decarbonization of the power generation sector requires a multi-pronged approach, including the implementation of CO2 capture and storage (CCS) technologies. The Graz cycle features oxy-combustion CO2 capture in a power production scheme which can result in higher thermal efficiencies than that of a combined cycle. However, the auxiliary consumption required by the air separation unit to provide pure O2 results in a significant energy penalty relative to an unabated plant. In order to mitigate this penalty, the present study explores the possibility of chemical looping combustion (CLC) as an alternative means to supply oxygen for conversion of the fuel. For a midscale power plant, despite reducing the levelized cost of electricity (LCOE) by approximately 12.6% at a CO2 tax of EUR 100/ton and a natural gas price of EUR 6.5/GJ and eliminating the energy penalty of CCS relative to an unabated combined cycle, the cost reductions of CLC in the Graz cycle were not compelling relative to commercially available post-combustion CO2 capture with amines. Although the central assumptions yielded a 3% lower cost for the Graz-CLC cycle, an uncertainty quantification study revealed an 85.3% overlap in the interquartile LCOE range with that of the amine benchmark, indicating that the potential economic benefit is small compared to the uncertainty of the assessment. Thus, this study indicates that the potential of CLC in gas-fired power production is limited, even when considering highly efficient advanced configurations like the Graz cycle.

Competitive Words: Identity Counts in Large Amounts

This essay argues that central assumptions underlying identity politics in literary writing—inclusion, visibility, diversity—are progressive in character. Yet in conditions of social precarity, literary writing and especially literary discussions that derive progressive potential from identity claims involuntarily and inevitably act under the aegis of neoliberal progressivism (as critiqued by Nancy Fraser and many others). Caution about identity politics (rather than its outright rejection) is a necessary minimum in response to the corruption inherent in the systemic placement of literature. When literary discussions, and this does not just mean scholarly research but also literary critique, book presentations and discussions in different reading groups and book clubs, fall short of this critical minimum, they risk missing that common and universal feature of the general intellect upon which revolutionary ideas of emancipation and solidarity once rested. Following Ulrich Brand and Markus Wissen's study of the imperial mode of living, this essay examines the phenomenon of what I call 'literary identity politics'—namely the implementation of identity politics in and through literary writing—in terms of the interval between literature's efforts to right the wrongs of exclusion and its simultaneous entanglement in the system of global injustice.

A critical inquiry into the discourses of war and occupation in the wake of the wars in Ukraine and Gaza

The aim of this article is to use one central assumption of Wittgenstein’s philosophy – language games – to review some important aspects of communication and language issues that typically have arisen in the aftermath of the wars in Ukraine and Gaza. I draw specifically on a critical inquiry of purposefully selected samples of discursive and linguistic practices accompanying the war in Ukraine and the occupation of its territory by Russia, and the war in Gaza and the occupation of Palestinian territories by Israel. References will also be made to the types of language games used by mainstream western media to categorize other wars and occupations discursively and socially, such as those of Iraq, Yemen and Afghanistan. I argue that the use of language is crucial for the understanding and representation of these wars and occupations, and a cause of failure in intercultural interaction. The central argument is as follows: While language games have different senses and not all people attach the same meaning to them, in case of conflict and controversy, those with power attach additional or different interpretations to them in a way they think is reasonable to change or at least rearrange their meanings. According to Wittgenstein’s deliberations of language games, the meaning of each of the various linguistic utterances, like words, sentences or symbols, is defined in terms of its setting and use. To unravel how and why certain linguistic practices are reinforced, and others are thwarted, the article supplements Wittgenstein’s deliberations of language with Hollihan and Baaskes’ definition of rhetorical source credibility, Thomas Hobbes’ ‘Leviathan discourse’ and Foucault’s notion of discourse and power.

Principles for Adapting Assessments of Executive Function across Cultural Contexts.

Direct assessments of executive functions (EFs) are increasingly used in research and clinical settings, with a central assumption that they assess "universal" underlying skills. Their use is spreading globally, raising questions about the cultural appropriateness of assessments devised in Western industrialized countries. We selectively reviewed multidisciplinary evidence and theory to identify sets of cultural preferences that may be at odds with the implicit assumptions of EF assessments. These preferences relate to motivation and compliance; cultural expectations for interpersonal engagement; contextualized vs. academic thinking; cultural notions of speed and time; the willingness to be silly, be incorrect, or do the opposite; and subject-matter familiarity. In each case, we discuss how the cultural preference may be incompatible with the assumptions of assessments, and how future research and practice can address the issue. Many of the cultural preferences discussed differ between interdependent and independent cultures and between schooled and unschooled populations. Adapting testing protocols to these cultural preferences in different contexts will be important for expanding our scientific understanding of EF from the narrow slice of the human population that has participated in the research to date.

Artificial Intelligence, Rationalization, and the Limits of Control in the Public Sector: The Case of Tax Policy Optimization

In this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.

Invariant measures for -free systems revisited

Abstract For $\mathscr {B} \subseteq \mathbb {N} $ , the $ \mathscr {B} $ -free subshift $ X_{\eta } $ is the orbit closure of the characteristic function of the set of $ \mathscr {B} $ -free integers. We show that many results about invariant measures and entropy, previously only known for the hereditary closure of $ X_{\eta } $ , have their analogues for $ X_{\eta } $ as well. In particular, we settle in the affirmative a conjecture of Keller about a description of such measures [G. Keller. Generalized heredity in $\mathcal B$ -free systems. Stoch. Dyn.21(3) (2021), Paper No. 2140008]. A central assumption in our work is that $\eta ^{*} $ (the Toeplitz sequence that generates the unique minimal component of $ X_{\eta } $ ) is regular. From this, we obtain natural periodic approximations that we frequently use in our proofs to bound the elements in $ X_{\eta } $ from above and below.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.