Cognitive cascades: How to model (and potentially counter) the spread of fake news

Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”

Searching for the Most Suitable Loss Model Set for Subsonic Centrifugal Compressors Using an Improved Method for Off-Design Performance Prediction

This work investigates loss model sets based on empirical loss correlations for subsonic centrifugal compressors. These loss models in combination with off-design performance prediction algorithms make up an essential tool in predicting off-design behaviour of turbomachines. This is important since turbomachines rarely work under design conditions. This study employs an off-design performance prediction algorithm based on an iterative process from Galvas. Modelling of ten different loss mechanisms and physical phenomena is involved in this approach and is thoroughly described in this work. Geometries of two subsonic compressors were reconstructed and used in the evaluation of individual loss correlations in order to obtain a suitable loss model. Results of these variations are compared to experimental data. In addition, 4608 loss model sets were created by taking all possible combinations of individual loss estimations from which three promising candidates were selected for further investigation. Finally, off-design performance of both centrifugal compressors was computed. These results were compared to experimental data and to other loss model sets from literature. The newly composed loss model set No. 2137 approximates experimental data over a 21.2% better in relative error than the recent Zhang set and nearly a 36.7% better than the outdated Oh’s set. Therefore, set No. 2137 may contribute to higher precision of centrifugal turbomachines’ off-design predictions in the upcoming research.

ON HIGHER DIMENSIONAL ARITHMETIC PROGRESSIONS IN MEYER SETS

In this paper we study the existence of higher dimensional arithmetic progressions in Meyer sets. We show that the case when the ratios are linearly dependent over ${\mathbb Z}$ is trivial and focus on arithmetic progressions for which the ratios are linearly independent. Given a Meyer set $\Lambda $ and a fully Euclidean model set with the property that finitely many translates of cover $\Lambda $ , we prove that we can find higher dimensional arithmetic progressions of arbitrary length with k linearly independent ratios in $\Lambda $ if and only if k is at most the rank of the ${\mathbb Z}$ -module generated by . We use this result to characterize the Meyer sets that are subsets of fully Euclidean model sets.

OP267 Evidence for Health Technology Assessment: The Capability Approach

IntroductionHealthcare services, such as cochlear implants and subsequent rehabilitation, aim to increase valuable activities and opportunities of those affected. Their impact may be inferred from the extent that they protect or restore capability, which reflects the real freedoms that people have to be or do things they have reason to value. Capability emerges from the dynamic interaction between available resources, individual, social, and environmental conversion factors, and functionings. This model sets the informational requirements of the capability approach.MethodsOn the basis of interviews with thirty-three hearing impaired children and thirty hearing peers, information on capability elements (values, resources, conversion factors, and functionings) was collected. Qualitative results were triangulated with standardized clinical audiological and psycholinguistic quantitative measures.ResultsHearing impaired children and their hearing peers concurred in terms of the doings and beings they valued, but differed in terms of conversion factors to realize capability. Parents of hearing impaired children played a more upfront role, hearing impairment predominated many areas of life, and communicating through hearing aids required more energy than was usually acknowledged by the people around them.ConclusionsThe capability approach offers opportunities not only to assess impact of technology on dimensions that are important to patients, but also to better understand the mechanisms that are involved in value generation.

On the Emergence of Spacetime and Matter from Model Sets

Starting from first principles and the mathematics of model sets we propose a framework where emergence of spacetime and matter can be addressed.

An Implementation of Deep-Connections for Multi-Level Modeling

<p>Traditional object-oriented programming languages only support two logical domain classification levels, i.e. classes and objects. However, if the problem involves more than two classification levels, then to model a multi-level scenario within two classification levels, a mapping approach is required which introduces accidental complexity and destroys the desirable property of “direct mapping”. Therefore “Multi-level modeling” was proposed. It supports an unbounded number of classification levels, that can support “direct mapping” without introducing accidental complexity. Many supporting features have been proposed for “multi-level” modeling such as “deep instantiation”, potency, clabjects, etc. To date most of the research effort was focusing on the entities (clabjects), while the relationships between entities were receiving much less attention and remained under-explored. The “Melanee” tool was developed to support multi-level modeling both for academics and practitioners. “Melanee” supports an unbounded number of classification levels for domain modeling and it treats relationships like clabjects. It mainly supports “constructive modeling” by creating models using a “top-down” approach, whereas “explanatory modeling”, which is creating models using “bottom-up” approach, is not well supported and lacks support to ensure the integrity of the created models. Hence, to further explore relationships in multi-level modeling and to provide a better modeling environment, there are two main focuses in this thesis: First, based on existing, I further explore relationships between entities and extend the LML (Level Agnostic Modeling Language) supported by Melanee accordingly. Second, I extend Melanee’s functionality to support “explanatory modeling”. Considering that Melanee is an open source tool I first discuss Melanee’s structure and its principles in order contribute to future extensions to Melanee. The knowledge of Melanee is currently known by its principle developer, Ralph Gerbig, with whom I had contacts in the beginning phase of the “deep-connection” development for advices. Next I use the work proposed in the paper “A Unifying Approach to Connections for Multi-Level Modeling” by Atkinson et al. as a foundation and stepping stone, to further explore relationships between entities. I extended Melanee to support the “Deep-connections” feature by adding potency to connections and their monikers, and further allow connections to have “deep-multiplicities”. I developed these features, as well as respective validation functions to ensure the well-formedness of models. Then I extended LML so that user-specified type names can be used to indicate the names of types for clabjects. Instead of relying on modelers to fully manually define type- of classification relations between different levels, I introduce “connection conformance” and “entity conformance” to introduce classification support to Melanee. Potentially matching types are calculated and ordered per their matching scores. Respective suggestions to modelers including messages for each possible matching type about how to fix the current connection instance so that it matches the potential type whenever applicable. The suggestions are made available as so-called “quick-fixes” and I extended this approach with a second-stage dialog that allows modelers to select amongst many fix alternatives. Finally, I evaluate my design using model sets taken from existing papers and a systematic exploration involving 57 different scenarios.</p>

An Implementation of Deep-Connections for Multi-Level Modeling

<p>Traditional object-oriented programming languages only support two logical domain classification levels, i.e. classes and objects. However, if the problem involves more than two classification levels, then to model a multi-level scenario within two classification levels, a mapping approach is required which introduces accidental complexity and destroys the desirable property of “direct mapping”. Therefore “Multi-level modeling” was proposed. It supports an unbounded number of classification levels, that can support “direct mapping” without introducing accidental complexity. Many supporting features have been proposed for “multi-level” modeling such as “deep instantiation”, potency, clabjects, etc. To date most of the research effort was focusing on the entities (clabjects), while the relationships between entities were receiving much less attention and remained under-explored. The “Melanee” tool was developed to support multi-level modeling both for academics and practitioners. “Melanee” supports an unbounded number of classification levels for domain modeling and it treats relationships like clabjects. It mainly supports “constructive modeling” by creating models using a “top-down” approach, whereas “explanatory modeling”, which is creating models using “bottom-up” approach, is not well supported and lacks support to ensure the integrity of the created models. Hence, to further explore relationships in multi-level modeling and to provide a better modeling environment, there are two main focuses in this thesis: First, based on existing, I further explore relationships between entities and extend the LML (Level Agnostic Modeling Language) supported by Melanee accordingly. Second, I extend Melanee’s functionality to support “explanatory modeling”. Considering that Melanee is an open source tool I first discuss Melanee’s structure and its principles in order contribute to future extensions to Melanee. The knowledge of Melanee is currently known by its principle developer, Ralph Gerbig, with whom I had contacts in the beginning phase of the “deep-connection” development for advices. Next I use the work proposed in the paper “A Unifying Approach to Connections for Multi-Level Modeling” by Atkinson et al. as a foundation and stepping stone, to further explore relationships between entities. I extended Melanee to support the “Deep-connections” feature by adding potency to connections and their monikers, and further allow connections to have “deep-multiplicities”. I developed these features, as well as respective validation functions to ensure the well-formedness of models. Then I extended LML so that user-specified type names can be used to indicate the names of types for clabjects. Instead of relying on modelers to fully manually define type- of classification relations between different levels, I introduce “connection conformance” and “entity conformance” to introduce classification support to Melanee. Potentially matching types are calculated and ordered per their matching scores. Respective suggestions to modelers including messages for each possible matching type about how to fix the current connection instance so that it matches the potential type whenever applicable. The suggestions are made available as so-called “quick-fixes” and I extended this approach with a second-stage dialog that allows modelers to select amongst many fix alternatives. Finally, I evaluate my design using model sets taken from existing papers and a systematic exploration involving 57 different scenarios.</p>

Analysis of the speed and curvature of the trajectory in the problem of pursuing a set of targets

Introduction. A kinematic model of group pursuit of a set of targets on a plane is considered. Pursuers use a technique similar to parallel approach method to achieve goals. Unlike the parallel approach method, the speed vectors of pursuers and targets are directed arbitrarily. In the parallel approach method, the instantaneous directions of movement of the pursuer and the target intersect at a point belonging to the circle of Apollonius. In the group model of pursuing multiple goals, the pursuers try to adhere to a network of predictable trajectories.Materials and Methods. The model sets the task of achieving goals by pursuers at designated points in time. This problem is solved by the methods of multidimensional descriptive geometry using the Radishchev diagram. The predicted trajectory is a composite line that moves parallel to itself when the target moves. On the projection plane “Radius of curvature — speed value”, the permissible speed range of the pursuer is displayed in the form of level lines (these are straight lines parallel to one of the projection planes). Images of speed level lines are displayed on the projection plane “Radius of curvature — time to reach the goal”. The search for points of intersection of the speed line images and the appointed time level line is being conducted. Along the communication lines, the values of the intersection points are lowered to the plane “Radius of curvature — speed value”. Using the obtained points, we construct an approximating curve and look for the intersection point with the line of the assigned speed. As a result, we get values of the radius of the circle at the predicted line of the trajectory of the pursuer.Results. Based on the results of the conducted research, test programs have been created, and animated images have been made in the computer mathematics system.Discussion and Conclusions. This method of constructing trajectories of pursuers to achieve a variety of goals at a given time values can be in demand by developers of autonomous unmanned aerial vehicles.

A Model for Predicting the Class of Illicit Drug Suspects and Offenders

In this study, the artificial neural network was deployed to develop a classification model for predicting the class of a drug-related suspect into either the drug peddler or non-drug peddler class. A dataset consisting of 262 observations on drug suspects and offenders in central Nigeria was used to train the model which uses parameters such as exhibit type, suspect’s age, exhibit weight, and suspect’s gender to predict the class of a suspect, with a predictive accuracy of 83%. The model sets the pace for the implementation of a full system for use at airports, seaports, police stations, and by security agents concerned with drug-related matters. The accurate classification of suspects and offenders will ensure a faster and correct reference to the sections of the drug law that correspond to a particular offence for appropriate actions such as prosecution or rehabilitation.