Combination Of Assumptions Research Articles

Assumptions on decision making and environment can yield multiple steady states in microbial community models

BackgroundMicrobial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically.ResultsHere, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism.ConclusionsSteady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.

An Adaptive Alternating Direction Method of Multipliers

The alternating direction method of multipliers (ADMM) is a powerful splitting algorithm for linearly constrained convex optimization problems. In view of its popularity and applicability, a growing attention is drawn toward the ADMM in nonconvex settings. Recent studies of minimization problems for nonconvex functions include various combinations of assumptions on the objective function including, in particular, a Lipschitz gradient assumption. We consider the case where the objective is the sum of a strongly convex function and a weakly convex function. To this end, we present and study an adaptive version of the ADMM which incorporates generalized notions of convexity and penalty parameters adapted to the convexity constants of the functions. We prove convergence of the scheme under natural assumptions. To this end, we employ the recent adaptive Douglas–Rachford algorithm by revisiting the well-known duality relation between the classical ADMM and the Douglas–Rachford splitting algorithm, generalizing this connection to our setting. We illustrate our approach by relating and comparing to alternatives, and by numerical experiments on a signal denoising problem.

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

The efficacy of international environmental agreements when adaptation matters: Nash-Cournot vs Stackelberg leadership

We analyze the paradox of cooperation, as established by Barrett (1994), and later reiterated by many others, in a more general framework. That is, we show that stable coalitions are either small or if they are large, the potential gains from cooperation are small. First, we argue that the extension to a mitigation-adaptation game is a generalization of Barrett's pure mitigation game. Second, we consider for this extension not only the Nash-Cournot scenario, as in Bayramoglu et al. (2018), but also the Stackelberg scenario. Third, we show generally that if mitigation levels in different countries are strategic substitutes, stable coalitions are larger in the Stackelberg than in the Nash-Cournot scenario. Fourth, this is reversed if mitigation levels are strategic complements, which is possible if the strategic interaction between mitigation and adaptation is sufficiently strong. Fifth, for all possible combination of assumptions, we demonstrate that the paradox of cooperation is robust, except if mitigation and adaptation were strategic complements, which we argue is an assumption not supported by empirical evidence.

Inter-Individual Variability and Non-linear Dose-Response Relationship in Assessing Human Health Impact From Chemicals in LCA: Addressing Uncertainties in Exposure and Toxicological Susceptibility

Current life cycle impact assessment (LCIA) practices use a characterization factor to linearly scale chemical emission to human health impact assuming a homogeneous exposure and toxicological susceptibility for the entire population. However, both exposure and toxicological susceptibility may vary within the population, making the same emission elicit disproportionate impacts. Here we explore how inter-individual variabilities in human exposure and toxicological susceptibility interact to affect the estimated overall health impacts on the population level. For exemplification, we use the PROTEX model to simulate the exposure of the general American population to dieldrin and heptachlor, two organochlorine pesticides that tend to accumulate in food items. Using a Monte-Carlo analysis, we characterize inter-individual variabilities in exposure by considering variations in anthropometrics and dietary patterns between ages, sexes, and racial groups. We assess the overall health impact on the population level in five scenarios with different combinations of assumptions in exposure (homogeneous/heterogeneous) and the dose-response relationship (linear/non-linear, homogeneous/heterogeneous susceptibility). Our results indicate human exposure can vary by a factor of six among the different demographic groups. Combined with a non-linear dose-response relationship with heterogeneous susceptibility, the estimated overall health impact is substantially higher than the results using homogeneous susceptibility. However, the current LCIA practice of using a linear dose-response relationship produces even higher results that may overestimate the health impacts.

On the edge capacitated Steiner tree problem

Given a graph G=(V,E) with a root r∈V, positive capacities {c(e)|e∈E}, and non-negative lengths {ℓ(e)|e∈E}, the minimum-length (rooted) edge capacitated Steiner tree problem is to find a tree in G of minimum total length, rooted at r, spanning a given subset T⊂V of vertices, and such that, for each e∈E, there are at most c(e) paths, linking r to vertices in T, that contain e. We study the complexity and approximability of the problem, considering several relevant parameters such as the number of terminals, the edge lengths and the minimum and maximum edge capacities. For all but one combinations of assumptions regarding these parameters, we settle the question, giving a complete characterization that separates tractable cases from hard ones. The only remaining open case is proved to be equivalent to a long-standing open problem. We also prove close relations between our problem and classic Steiner tree as well as vertex-disjoint paths problems.

A State-of-Affairs-Semantic Solution to the Problem of Extensionality in Free Logic

If one takes seriously the idea that a scientific language must be extensional, and accepts Quine’s notion of truth-value-related extensionality, and also recognizes that a scientific language must allow for singular terms that do not refer to existing objects, then there is a problem, since this combination of assumptions must be inconsistent. I will argue for a particular solution to the problem, namely, changing what is meant by the word ‘extensionality’, so that it would not be the truth-value that had to be preserved under the substitution of co-extensional expressions, but the state of affairs that the sentence described. The question is whether or not elementary sentences containing empty singular terms, such as ‘Vulcan rotates’, are extensional in the substitutivity sense. Five conditions are specified under which extensionality in the substitutivity sense of such sentences can be secured. It is demonstrated that such sentences are state-of-affairs-as-extension-related extensional. This implies (in accordance with the basic idea of state-of-affairs semantics) that such sentences are also truth-value-related extensional in Quine’s sense, but not truth-value-as-extension-related extensional.

One-day-ahead forecast of state of turbulence based on today's economic situation

Research background: In the literature little discussion was made about predicting state of time series in daily manner. The ability to recognize the state of a time series gives, for example, an opportunity to measure the level of risk in a state of tranquility and a state of turbulence independently, which can provide more accurate measurements of the market risk in a financial institution.
 Purpose of the article: The aim of article is to find an appropriate tools to predict, based on today's economic situation, the state, in which time series of financial data will be tomorrow.
 Methods: This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (Logit, Probit, Cloglog), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (un-transformed data, PCA analysis and factor analysis). Additionally, an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, Gini coefficient analysis and CROC criterion based on the ROC curve. The analyses were performed for 43 individual shares and 5 portfolios of shares quoted on the Warsaw Stock Exchange. The study has been conducted for the period from 1 January 2006 to 31 January 2012.
 Findings & Value added: Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability to one-day-ahead forecast of state of turbulence based on today's economic situation.

Learners\u2019 use of domain-specific computer-based feedback to overcome logical circularity in deductive proving in geometry

Much remains under-researched in how learners make use of domain-specific feedback. In this paper, we report on how learners’ can be supported to overcome logical circularity during their proof construction processes, and how feedback supports the processes. We present an analysis of three selected episodes from five learners who were using a web-based proof learning support system. Through this analysis we illustrate the various errors they made, including using circular reasoning, which were related to their understanding of hypothetical syllogism as an element of the structure of mathematical proof. We found that, by using the computer-based feedback and, for some, teacher intervention, the learners started considering possible combinations of assumptions and conclusion, and began realising when their proof fell into logical circularity. Our findings raise important issues about the nature and role of computer-based feedback such as how feedback is used by learners, and the importance of teacher intervention in computer-based learning environments.

Some Ghosts of Statistics Past

Some Ghosts of Statistics Past

Write on the Money?

An unusual combination of assumptions - markets have frictions, but nonetheless are price efficient - is required to justify the generic buy-write strategy. In theory, a buy-write strategy allows investors to earn the variance risk premium (VRP) when they cannot access the variance swap market because of market frictions. But whether the strategy earns the expected VRP is contingent on markets being efficient. We show that the performance of the generic buy-write strategy that writes at-the-money options deteriorates rapidly in the presence of short-lived anomalies. Empirically, an alternative strategy that is designed to protect against market reversals outperforms the generic strategy. Our analysis provides a potential explanation of why, in practice, these popular option-write strategies earn but a fraction of the expected variance risk premium.

Literal grid map models for animal navigation: Assumptions and predictions

Many animals can navigate from unfamiliar locations to a familiar target location with no outward route information or direct sensory contact with the target or any familiar landmarks. Several models have been proposed to explain this phenomenon, one possibility being a literal interpretation of a grid map. In this paper we systematically compare four such models, which we label: Correct Bicoordinate navigation, both Target and Release site based, Approximate Bicoordinate navigation, and Directional navigation. Predictions of spatial patterns of initial orientation errors and efficiencies depend on a combination of assumptions about the navigation mechanism and the geometry of the environmental coordinate fields used as model inputs. When coordinates axes are orthogonal at the target the predictions from the Correct Bicoordinate (Target based) model and Approximate Bicoordinate model are identical. However, if the coordinate axes are non-orthogonal different regional patterns of initial orientation errors and efficiencies can be expected from these two models. Field anomalies produce high magnitudes of orientation errors close to the target, while region-wide nonlinearity leads to orientation errors increasing with distance from the target. In general, initial orientation error patterns are more useful for distinguishing between different assumption combinations than efficiencies. We discuss how consideration of model predictions may be helpful in the design of experiments.

EXISTENCE AND CHARACTERIZATION OF CONDITIONAL DENSITY PROJECTIONS

In this paper we propose primitive conditions under which a projection of a conditional density onto a set defined by conditional moment restrictions exists and is unique. Moreover, we provide an analytic expression of the obtained projection. The range of applications where conditional density projections are used is wide. The derived results are potentially useful in a variety of areas including: semiparametric efficient estimation and optimal testing in (conditional) moment models, Bayesian prior determination and inference in semiparametric models, density forecasting, and simulation-based econometric analysis.Regarding existence, we propose three different combinations of assumptions that are all sufficient to show that the projection exists and is unique. The proposed conditions exhibit a clear trade off between restrictions put on the divergence between the conditional densities and on the moment function which defines the projection set. Depending on the nature of the application, the researcher can pick and choose which set of conditions to use. Our second set of results characterizes the projection. The expression for the projected density is new though not surprising given the previously obtained results for the unconditional case. The projection is characterized by the dual of the original projection problem. In establishing the strong duality, however, we work with a constraint qualification condition that is weaker than that used by Borwein and Lewis (1991a, 1992a, 1993 in their seminal work concerning the unconditional case.

Evil Acts and Malicious Gossip

Although person perception is central to virtually all human social behavior, it is ordinarily studied in isolated individual perceivers. Conceptualizing it as a socially distributed process opens up a variety of novel issues, which have been addressed in scattered literatures mostly outside of social psychology. This article examines some of these issues using a series of multiagent models. Perceivers can use gossip (information from others about social targets) to improve their ability to detect targets who perform rare negative behaviors. The model suggests that they can simultaneously protect themselves against being influenced by malicious gossip intended to defame specific targets. They can balance these potentially conflicting goals by using specific strategies including disregarding gossip that differs from a personally obtained impression. Multiagent modeling demonstrates the outcomes produced by different combinations of assumptions about gossip, and suggests directions for further research and theoretical development.

Beware the black box: investigating the sensitivity of FEA simulations to modelling factors in comparative biomechanics

Finite element analysis (FEA) is a computational technique of growing popularity in the field of comparative biomechanics, and is an easily accessible platform for form-function analyses of biological structures. However, its rapid evolution in recent years from a novel approach to common practice demands some scrutiny in regards to the validity of results and the appropriateness of assumptions inherent in setting up simulations. Both validation and sensitivity analyses remain unexplored in many comparative analyses, and assumptions considered to be ‘reasonable’ are often assumed to have little influence on the results and their interpretation.Here we report an extensive sensitivity analysis where high resolution finite element (FE) models of mandibles from seven species of crocodile were analysed under loads typical for comparative analysis: biting, shaking, and twisting. Simulations explored the effect on both the absolute response and the interspecies pattern of results to variations in commonly used input parameters. Our sensitivity analysis focuses on assumptions relating to the selection of material properties (heterogeneous or homogeneous), scaling (standardising volume, surface area, or length), tooth position (front, mid, or back tooth engagement), and linear load case (type of loading for each feeding type).Our findings show that in a comparative context, FE models are far less sensitive to the selection of material property values and scaling to either volume or surface area than they are to those assumptions relating to the functional aspects of the simulation, such as tooth position and linear load case. Results show a complex interaction between simulation assumptions, depending on the combination of assumptions and the overall shape of each specimen. Keeping assumptions consistent between models in an analysis does not ensure that results can be generalised beyond the specific set of assumptions used. Logically, different comparative datasets would also be sensitive to identical simulation assumptions; hence, modelling assumptions should undergo rigorous selection. The accuracy of input data is paramount, and simulations should focus on taking biological context into account. Ideally, validation of simulations should be addressed; however, where validation is impossible or unfeasible, sensitivity analyses should be performed to identify which assumptions have the greatest influence upon the results.

The Michaelis-Menten-Stueckelberg Theorem

We study chemical reactions with complex mechanisms under two assumptions: (i) intermediates are present in small amounts (this is the quasi-steady-state hypothesis or QSS) and (ii) they are in equilibrium relations with substrates (this is the quasiequilibrium hypothesis or QE). Under these assumptions, we prove the generalized mass action law together with the basic relations between kinetic factors, which are sufficient for the positivity of the entropy production but hold even without microreversibility, when the detailed balance is not applicable. Even though QE and QSS produce useful approximations by themselves, only the combination of these assumptions can render the possibility beyond the “rarefied gas” limit or the “molecular chaos” hypotheses. We do not use any a priori form of the kinetic law for the chemical reactions and describe their equilibria by thermodynamic relations. The transformations of the intermediate compounds can be described by the Markov kinetics because of their low density (low density of elementary events). This combination of assumptions was introduced by Michaelis and Menten in 1913. In 1952, Stueckelberg used the same assumptions for the gas kinetics and produced the remarkable semi-detailed balance relations between collision rates in the Boltzmann equation that are weaker than the detailed balance conditions but are still sufficient for the Boltzmann H-theorem to be valid. Our results are obtained within the Michaelis-Menten-Stueckelbeg conceptual framework.

Technology interactions among low-carbon energy technologies: What can we learn from a large number of scenarios?

Advanced low-carbon energy technologies can substantially reduce the cost of stabilizing atmospheric carbon dioxide concentrations. Understanding the interactions between these technologies and their impact on the costs of stabilization can help inform energy policy decisions. Many previous studies have addressed this challenge by exploring a small number of representative scenarios that represent particular combinations of future technology developments. This paper uses a combinatorial approach in which scenarios are created for all combinations of the technology development assumptions that underlie a smaller, representative set of scenarios. We estimate stabilization costs for 768 runs of the Global Change Assessment Model (GCAM), based on 384 different combinations of assumptions about the future performance of technologies and two stabilization goals. Graphical depiction of the distribution of stabilization costs provides first-order insights about the full data set and individual technologies. We apply a formal scenario discovery method to obtain more nuanced insights about the combinations of technology assumptions most strongly associated with high-cost outcomes. Many of the fundamental insights from traditional representative scenario analysis still hold under this comprehensive combinatorial analysis. For example, the importance of carbon capture and storage (CCS) and the substitution effect among supply technologies are consistently demonstrated. The results also provide more clarity regarding insights not easily demonstrated through representative scenario analysis. For example, they show more clearly how certain supply technologies can provide a hedge against high stabilization costs, and that aggregate end-use efficiency improvements deliver relatively consistent stabilization cost reductions. Furthermore, the results indicate that a lack of CCS options combined with lower technological advances in the buildings sector or the transportation sector is the most powerful predictor of high-cost scenarios.

Generalized framework for robust design of tuned mass damper systems

The primary purpose of this contribution is to develop a novel framework for generalized robust design of tuned mass damper (TMD) systems as passive vibration controllers for uncertain structures. This versatile strategy is intended to be free of any restriction on the structure–TMD system configuration, the performance criterion, and the number of uncertain parameters. The main idea pursued is to adopt methods and concepts from the robust control literature, including: (1) the linear fractional transformation (LFT) formulation pertaining to the structured singular value ( μ) framework; (2) the concept of weighted multi-input multi-output (MIMO) norms for characterizing performance; and (3) a worst-case performance assessment method to avoid the unacceptable computation burden involved with exhaustive search or Monte Carlo methods in the presence of multiple uncertainties. Based on these, the robust design framework is organized into four steps: (1) modeling and casting the overall dynamics into the proposed LFT framework that isolates the TMD system as the controller, and the uncertainties as a structured perturbation to the nominal dynamics; (2) setting up the optimization problem based on generalized indices of nominal performance, robustness, and worst-case performance; (3) implementing a genetic algorithm (GA) for solution of the optimization problem; and (4) post-processing the results for systematic visualization, validation, and selection of preferred designs. This strategy has been implemented on several illustrative design examples involving a seismically excited multi-story building with different combinations of assumptions on the uncertainty, TMD configuration, excitation scenarios, and performance criteria. The resulting solution sets have been studied through various post-processing methods, including visualization of Pareto fronts, uncertain frequency response plots, time-domain simulations, and random vibration analysis.

Transmission dynamic modelling of the impact of human papillomavirus vaccination in the United Kingdom

Many countries are considering vaccination against human papillomavirus (HPV). However, the long-term impact of vaccination is difficult to predict due to uncertainty about the prevalence of HPV infection, pattern of sexual partnerships, progression of cervical neoplasias, accuracy of screening as well as the duration of infectiousness and immunity. Dynamic models of human papillomavirus (HPV) transmission were developed to describe the infection spread and development of cervical neoplasia, cervical cancer (squamous cell and adenocarcinoma) and anogenital warts. Using different combinations of assumptions, 9900 scenarios were created. Each scenario was then fitted to epidemiological data and the best-fitting scenarios used to predict the impact of vaccination. Results suggest that vaccinating 12-year-old girls at 80% coverage will result in a 38–82% reduction in cervical cancer incidence and 44–100% reduction in anogenital warts incidence after 60 years of an ongoing vaccination programme if vaccine protection lasts 20 years on average. The marginal benefit of vaccinating boys depends on the degree of protection achieved by vaccinating girls.

Influenza A Gradual and Epochal Evolution: Insights from Simple Models

The recurrence of influenza A epidemics has originally been explained by a “continuous antigenic drift” scenario. Recently, it has been shown that if genetic drift is gradual, the evolution of influenza A main antigen, the haemagglutinin, is punctuated. As a consequence, it has been suggested that influenza A dynamics at the population level should be approximated by a serial model. Here, simple models are used to test whether a serial model requires gradual antigenic drift within groups of strains with the same antigenic properties (antigenic clusters). We compare the effect of status based and history based frameworks and the influence of reduced susceptibility and infectivity assumptions on the transient dynamics of antigenic clusters. Our results reveal that the replacement of a resident antigenic cluster by a mutant cluster, as observed in data, is reproduced only by the status based model integrating the reduced infectivity assumption. This combination of assumptions is useful to overcome the otherwise extremely high model dimensionality of models incorporating many strains, but relies on a biological hypothesis not obviously satisfied. Our findings finally suggest the dynamical importance of gradual antigenic drift even in the presence of punctuated immune escape. A more regular renewal of susceptible pool than the one implemented in a serial model should be part of a minimal theory for influenza at the population level.