Measure Of Typicality Research Articles

The Effect of Categorical Typicality on Target Selection in Mergers and Acquisitions

The use of shared categories reduces uncertainty in markets, thus facilitating exchanges. Acquisitions are typical contexts in which uncertainty plays a dominant role since prospective acquirers usually face a high risk of adverse selection. Yet, prior research did not study the effects of categories on M&A activity. This paper argues that the extent to which a firm fits a clear category (how typical it is) will affect how easy it is to use category-specific schemas to interpret information about the firm and, in turn, how easily it can be evaluated as a target for an acquisition, thus influencing the likelihood of it being acquired. Conversely, we submit that information about difficult-to-classify firms and/or those that do not fit with a single category (atypical firms) is more complex to interpret and that they are thus comparatively less likely to be acquired. We test our hypotheses on a sample of 9,061 US-listed firms from 1995 to 2018, which represent the whole population of listed firms net of missing data. Our measure of typicality is constructed starting from annual reports (forms 10-K) and using a new methodology to locate the firm in the semantic space based on the meaning of the words contained in its annual report. Results indicate that typical firms are more likely to be acquisition targets. The acquirer's experience in acquisitions and with the target's category negatively moderate this effect. Using a sample of 4,528,585 dyads, we also analyze the categorical typicality of the acquiring firm as a boundary condition of our main effect and find that a typical acquirer is more likely to acquire typical firms.

Gaussianity and typicality in matrix distributional semantics

Constructions in type-driven compositional distributional semantics associate large collections of matrices of size D to linguistic corpora. We develop the proposal of analysing the statistical characteristics of this data in the framework of permutation invariant matrix models. The observables in this framework are permutation invariant polynomial functions of the matrix entries, which correspond to directed graphs. Using the general 13-parameter permutation invariant Gaussian matrix models recently solved, we find, using a dataset of matrices constructed via standard techniques in distributional semantics, that the expectation values of a large class of cubic and quartic observables show high Gaussianity at levels between 90 to 99 percent. Beyond expectation values, which are averages over words, the dataset allows the computation of standard deviations for each observable, which can be viewed as a measure of typicality for each observable. There is a wide range of magnitudes in the measures of typicality. The permutation invariant matrix models, considered as functions of random couplings, give a very good prediction of the magnitude of the typicality for different observables. We find evidence that observables with similar matrix model characteristics of Gaussianity and typicality also have high degrees of correlation between the ranked lists of words associated to these observables.

Category norms with a cross-sectional sample of adults in the United States: Consideration of cohort, age, and historical effects on semantic categories.

This paper describes normative data for newly collected exemplar responses to 70 semantic categories described in previous norming studies (Battig & Montague, Journal of Experimental Psychology, 80(3, pt.2): 1-46, 1969; Van Overschelde, Rawson, & Dunlosky, Journal of Memory and Language, 50(3): 289-335, 2004; Yoon et al., Psychology and Aging, 19(3), 379-393, 2004). These categories were presented to 246 young (18-39 years), middle (40-59 years), and older (60 years and older) English-speaking adults living in the United States who were asked to generate as many category exemplars as possible for each of the 70 categories. In order to understand differences in normative responses, we analyzed these responses a) between age groups within the current sample and b) in comparison to three previously published sets of norms. Experimental studies using such norms typically assume invariance of normative likelihoods across age and historical time. We replicate previous findings such that exemplar frequency correlations suggest moderate stability in generated category members between age groups and cohorts for many, but not all, categories. Further, analyses of rank-order correlations highlight that the traditional measure of typicality may not capture all aspects of typicality, namely that for some categories there is high consistency in the frequency of exemplars across age groups and/or norms, but the ordering of those exemplars differs significantly. We include a cluster analysis to aid in grouping categories based on relative stability across time, cohort, and age groups. These results emphasize the importance of maintaining and updating age-differentiated category norms.

Over and above Gender Differences in Cyberbullying: Relationship of Gender Typicality to Cyber Victimization and Perpetration in Adolescents

ABSTRACT The present study used a unique measure of self-perceived gender typicality to better examine the association of gender with cyber victimization and perpetration. Participants were 297 adolescent males and females recruited from independent schools in grade 8 (Mage = 13.8) and grade 10 (Mage = 15.8) who completed a self-report survey. Multiple regression analyses revealed that only for males, high other-gender typicality and low same-gender typicality were associated with high cyber victimization, but when same-gender typicality was high there was no association. Independent associations of same- and other-gender typicality with cyber perpetration were present only for males. Findings highlight that the importance of considering same- and other-gender typicality for adolescent boys’ engagement in cyberbullying.

One-Class Classification with Application to Forensic Analysis

Summary The analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected at the crime scene (control cases) may help the police to identify the offender(s) correctly. The forensic issue can be framed as a one-class classification problem. One-class classification is a recently emerging and special classification task, where only one class is fully known (the so-called target class), whereas information on the others is completely missing. We propose to consider Gini's classical transvariation probability as a measure of typicality, i.e. a measure of resemblance between an observation and a set of well-known objects (the control cases). The aim of the proposed transvariation-based one-class classifier is to identify the best boundary around the target class, i.e. to recognize as many target objects as possible while rejecting all those deviating from this class.

Effects of temperature and mass conservation on the typical chemical sequences of hydrogen oxidation.

Macroscopic properties of reacting mixtures are necessary to design synthetic strategies, determine yield, and improve the energy and atom efficiency of many chemical processes. The set of time-ordered sequences of chemical species are one representation of the evolution from reactants to products. However, only a fraction of the possible sequences is typical, having the majority of the joint probability and characterizing the succession of chemical nonequilibrium states. Here, we extend a variational measure of typicality and apply it to atomistic simulations of a model for hydrogen oxidation over a range of temperatures. We demonstrate an information-theoretic methodology to identify typical sequences under the constraints of mass conservation. Including these constraints leads to an improved ability to learn the chemical sequence mechanism from experimentally accessible data. From these typical sequences, we show that two quantities defining the variational typical set of sequences-the joint entropy rate and the topological entropy rate-increase linearly with temperature. These results suggest that, away from explosion limits, data over a narrow range of thermodynamic parameters could be sufficient to extrapolate these typical features of combustion chemistry to other conditions.

Learning the mechanisms of chemical disequilibria.

When at equilibrium, large-scale systems obey thermodynamics because they have microscopic configurations that are typical. "Typical" states are a fraction of those possible with the majority of the probability. A more precise definition of typical states underlies the transmission, coding, and compression of information. However, this definition does not apply to natural systems that are transiently away from equilibrium. Here, we introduce a variational measure of typicality and apply it to atomistic simulations of a model for hydrogen oxidation. While a gaseous mixture of hydrogen and oxygen combusts, reactant molecules transform through a variety of ephemeral species en route to the product, water. Out of the exponentially growing number of possible sequences of chemical species, we find that greater than 95% of the probability concentrates in less than 1% of the possible sequences. Overall, these results extend the notion of typicality across the nonequilibrium regime and suggest that typical sequences are a route to learning mechanisms from experimental measurements. They also open up the possibility of constructing ensembles for computing the macroscopic observables of systems out of equilibrium.

On the Faithful Interpretation of Pure Wave Mechanics

Given Hugh Everett III’s understanding of the proper cognitive status of physical theories, his relative-state formulation of pure wave mechanics arguably qualifies as an empirically acceptable physical theory. The argument turns on the precise nature of the relationship that Everett requires between the empirical substructure of an empirically faithful physical theory and experience. On this view, Everett provides a weak resolution to both the determinate record and the probability problems encountered by pure wave mechanics, and does so in a way that avoids unnecessary metaphysical complications. Taking Everett’s goal to be showing the empirical faithfulness of the relative-state formulation agrees well with his characterization of his project as one of seeking a model for observation in the correlation structure described by pure wave mechanics and seeking a measure of typicality over this empirical substructure that covaries with our empirically warranted expectations.

A crossed random effects diffusion model for speeded semantic categorization decisions

Choice reaction times (RTs) are often used as a proxy measure of typicality in semantic categorization studies. However, other item properties have been linked to choice RTs as well. We apply a tailored process model of choice RT to a speeded semantic categorization task in order to deconfound different sources of variability in RT. Our model is based on a diffusion model of choice RT, extended to include crossed random effects (of items and participants). This model retains the interesting process interpretation of the diffusion model’s parameters, but it can be applied to choice RTs even in the case where there are few or no repeated measurements of each participant–item combination. Different aspects of the response process are then linked to different types of item properties. A typicality measure turns out to predict the rate of information uptake, while a lexicographic measure predicts the stimulus encoding time. Accessibility measures cannot reliably predict any component of the decision process.

Top-k typicality queries and efficient query answering methods on large databases

Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognitive science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. Three types of top-k typicality queries are formulated. To answer questions like “Who are the top-k most typical NBA players?”, the measure of simple typicality is developed. To answer questions like “Who are the top-k most typical guards distinguishing guards from other players?”, the notion of discriminative typicality is proposed. Moreover, to answer questions like “Who are the best k typical guards in whole representing different types of guards?”, the notion of representative typicality is used. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations: (1) the randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers; (2) the direct local typicality approximation using VP-trees provides an approximation quality guarantee; (3) a local typicality tree data structure can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree. An extensive performance study using two real data sets and a series of synthetic data sets clearly shows that top-k typicality queries are meaningful and our methods are practical.

‘Most advanced, yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design

Typicality and novelty have often been shown to be related to aesthetic preference of human artefacts. Since a typical product is rarely new and, conversely, a novel product will not often be designated as typical, the positive effects of both features seem incompatible. In three studies it was shown that typicality (operationalized as 'goodness of example') and novelty are jointly and equally effective in explaining the aesthetic preference of consumer products, but that they suppress each other's effect. Direct correlations between both variables and aesthetic preference were not significant, but each relationship became highly significant when the influence of the other variable was partialed out. In Study 2, it was furthermore demonstrated that the expertise level of observers did not affect the relative contribution of novelty and typicality. It was finally shown (Study 3) that a more 'objective' measure of typicality, central tendency - operationalized as an exemplar's average similarity to all other members of the category - yielded the same effect of typicality on aesthetic preference. In sum, all three studies showed that people prefer novel designs as long as the novelty does not affect typicality, or, phrased differently, they prefer typicality given that this is not to the detriment of novelty. Preferred are products with an optimal combination of both aspects.

A note on a fuzzy measure of typicality

The concept of typicality is studied and the relationship between a typical value and the mode is explored. It is suggested that a typical value should be a fuzzy subset. A measure of typicality associated with any fuzzy subset is introduced. We provide an algorithm for finding the best typical value which is of the form of an interval. © 1997 John Wiley & Sons, Inc.

A note on a fuzzy measure of typicality

A note on a fuzzy measure of typicality