Ambiguity Reduction Research Articles

Quantum cognition in management research: A novel methodological framework with an application to causal ambiguity

AbstractWe propose a novel methodological framework based on the emerging field of quantum cognition and illustrate its application to a central problem in management and strategy research: causal ambiguity. The current literature often assumes that causal ambiguity—the difficulty of managers to understand the causal link between resources and outcomes at the basis of a firm's performance—can be reduced through learning. This literature overlooks the fact that causal ambiguity reduction is impossible when causal systems exhibit so‐called complementary properties. Building upon quantum cognition—specifically the idea of complementarity as an alternative to causality—we illustrate that causal ambiguity is only a special case of ambiguity and we offer a novel methodological framework to model what we label as ‘acausal ambiguity’, which refers to the insurmountable limit of managers to achieve causal ambiguity reduction. Managers can, of course, cognize some causal links. However, this comes at the price of being agnostic about complementary ones. The implications of this novel methodological framework applied to causal ambiguity are twofold: while complementarity opacifies the attentional faculties of managers, it also accounts for the cognitive origins of novelty.

Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization

Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization

Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other.

New algorithm for the analysis of nucleotide and amino acid evolutionary relationships based on Klein four-group

Phylogenetics is the study of ancestral relationships among biological species. Such sequence analyses are often represented as phylogenetic trees. The branching pattern of each tree and its topology reflect the evolutionary relatedness between analyzed sequences. We present a Klein four-group algorithm (K4A) for the evolutionary analysis of nucleotide and amino acid sequences. Klein four-group set of operators consists of: identity e (U), and three elements—a = transition (C), b = transversion (G) and c = transition-transversion or complementarity (A). We generated Klein four-group based distance matrices of: 1. Cayley table (CK4), 2. Table rows (K4R), 3. Table columns (K4C), and 4. Euclidean 2D distance (K4E). The performance of the matrices was tested on a dataset of RecA proteins in bacteria, eukaryotes (Rad51 homolog) and archaea (RadA homolog). RecA and its functional homologs are found in all species, and are essential for the repair and maintenance of DNA. Consequently, they represent a good model for the study of evolutionary relationship of protein and nucleotide sequences. The ancestral relationship between the sequences was correctly classified by all K4A matrices concerning general topology. All distance matrices exhibited small variations among species, and overall results of tree classification were in agreement with the general patterns obtained by standard BLOSUM and PAM substitution matrices. During the evolution of a code there is a phase of optimization of system rules, the ambiguity of a code is eliminated, and the system starts producing specific components. Klein four-group algorithm is consistent with the concept of ambiguity reduction. It also enables the use of different genetic code table variants optimized for particular transitions in evolution based on biological specificity.

An Overview of Context Capturing Techniques in NLP

In the NLP context identification has become a prominent way to overcome syntactic and semantic ambiguities. Ambiguities are unsolved problems but can be reduced to a certain level. This ambiguity reduction helps to improve the quality of several NLP processes, such as text translation, text simplification, text retrieval, word sense disambiguation, etc. Context identification, also known as contextualization, takes place in the preprocessing phase of NLP processes. The essence of this identification is to uniquely represent a word or a phrase to improve the decision-making during the transfer phase of the NLP processes. The improved decision-making helps to improve the quality of the output. This paper tries to provide an overview of different context-capturing mechanisms used in NLP.

Creating safe environments: optimal acoustic alarming of laypeople in fire prevention

Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognise dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (N = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were the most effective. In Study 2 (N = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction. Practitioner summary: Warning laypeople is of great importance in time-critical hazards. In two remote testing studies (N Total = 585), auditory alarm sounds with siren-like patterns resulted in the most distinct and emotional perception. Combining the alarm sound with a voice alert adds meaning to the alarm and fosters action intention. Abbreviations: DIN: Deutsches Institut für Normung [German Institute for Standardization]; ISO: International Organization for Standardization; Mixed MANOVA: mixed measures multivariate analysis of variance; rmMANOVA: repeated measures multivariate analysis of variance.

Improved GNSS integer ambiguity resolution method based on the column oriented Cholesky decomposition

Because the traditional Cholesky decomposition algorithm still has some problems such as computational complexity and scattered structure among matrices when solving the GNSS ambiguity, it is the key problem to further improve the computational efficiency of the least squares ambiguity reduction correlation process in the carrier phase integer ambiguity solution. But the traditional matrix decomposition calculation is more complex and time-consuming, to improve the efficiency of the matrix decomposition, in this paper, the decomposition process of traditional matrix elements is divided into two steps: multiplication update and column reduction of square root calculation. The column reduction step is used to perform square root calculation and column division calculation, while the update step is used for the update task of multiplication. Based on the above ideas, the existing Cholesky decomposition algorithm is improved, and a column oriented Cholesky (C-Cholesky) algorithm is proposed to further improve the efficiency of matrix decomposition, so as to shorten the calculation time of integer ambiguity reduction correlation. The results show that this method is effective and superior, and can improve the data processing efficiency by about 12.34% on average without changing the integer ambiguity accuracy of the traditional Cholesky algorithm.

Corpus evidence for the role of world knowledge in ambiguity reduction: Using high positive expectations to inform quantifier scope

Every-negation utterances (e.g., Every vote doesn’t count) are ambiguous between a surface scope interpretation (e.g., No vote counts) and an inverse scope interpretation (e.g., Not all votes count). Investigations into the interpretation of these utterances have found variation: child and adult interpretations diverge (e.g., Musolino 1999) and adult interpretations of specific constructions show considerable disagreement (Carden 1973, Heringer 1970, Attali et al. 2021). Can we concretely identify factors to explain some of this variation and predict tendencies in individual interpretations? Here we show that a type of expectation about the world (which we call a high positive expectation), which can surface in the linguistic contexts of every-negation utterances, predicts experimental preferences for the inverse scope interpretation of different every-negation utterances. These findings suggest that (1) world knowledge, as set up in a linguistic context, helps to effectively reduce the ambiguity of potentiallyambiguous utterances for listeners, and (2) given that high positive expectations are a kind of affirmative context, negation use is felicitous in affirmative contexts (e.g., Wason 1961).

Clutter and Ambiguity Reduction Using 3-dimensional Side-looking Synthetic Aperture Radar

Clutter and Ambiguity Reduction Using 3-dimensional Side-looking Synthetic Aperture Radar

Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction

In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

Efficient SAR Azimuth Ambiguity Reduction in Coastal Waters Using a Simple Rotation Matrix: The Case Study of the Northern Coast of Jeju Island

Azimuth ambiguities, or ghosts on SAR images, represent one of the main obstacles for SAR applications involving coastal monitoring activities such as ship detection. While most previous methods based on azimuth antenna pattern and direct filtering are effective for azimuth ambiguity suppression, they may not be effective for fast cruising small ships. This paper proposes a unique approach for the reduction of azimuth ambiguities or ghosts in SAR single-look complex (SLC) images using a simple rotation matrix. It exploits the fact that the signal powers of azimuth ambiguities are concentrated on narrow bands, while those of vessels or other true ground targets are dispersed over broad bands. Through sub-aperture processing and simple axis rotation, it is possible to concentrate the dispersed energy of vessels onto a single axis while the ghost signal powers are dispersed onto three different axes. Then, the azimuth ambiguities can be easily suppressed by a simple calculation of weighted sum and difference, while preserving vessels. Applied results achieved by processing TerrSAR-X SLC images are provided and discussed. An optimum weight of 0.5 was determined by Receiver Operating Characteristic (ROC) analysis. Capabilities of ship detection from the test image were significantly improved by removing 93% of false alarms. Application results demonstrate its high performance of ghost suppression. This method can be employed as a pre-processing tool of SAR images for ship detection in coastal waters.

Relational model of the standard genetic code

The genetic code is a set of rules that establishes mapping between triplets in messenger RNA and amino acids in proteins. The most common way to display these rules is the Standard Genetic Code (SGC) table. This paper takes an alternative approach, based on the relational data model by Edgar F. Codd (Commun. ACM, 13:377–387, 1970). The relational model (RM) proposes a distributed storage of data into a collection of tables (called relations), that can be connected by shared communality. Basic elements of the table are rows (called records or tuples), and columns (called fields or attributes). The SGC table, according to the relational data model, represents the so called unnormalized form of a table. Using normalization rules it is possible to subdivide the SGC table into four tables. The rows and columns of single tables are defined by the first and second base and individual tables by the third codon base. The result of this model is an approach to managing genetic code data, represented in terms of tuples and grouped into relations, with table structure and language consistent with first-order (predicate) logic. The RM explains that the final step in the development of the SGC was the adoption of coding function by the third base, which makes an informational/functional unit with the first base, despite the different physical location in a triplet. This enabled the synthesis of specific proteins without ambiguity, in accordance with the concept of ambiguity reduction and five phases of the general model on the origin of biological codes by Marcello Barbieri (BioSystems 181:11–19, 2019).

Code biology and the problem of emergence

It should now be recognized that codes are central to life and to understanding its more complex forms, including human culture. Recognizing the ‘conventional’ nature of codes provides solid grounds for rejecting efforts to reduce life to biochemistry and justifies according a place to semantics in life. The question I want to consider is whether this is enough. Focussing on Eigen's paradox of how a complex code could originate, I will argue that along with Barbieri's efforts to account for the origins of life based on the ribosome and then to account for the refined codes through a process of ambiguity reduction, something more is required. Barbieri has not provided an adequate account of emergence, or the basis for providing such an account. I will argue that Stanley Salthe has clarified to some extent the nature of emergence by conceptualizing it as the interpolation of new enabling constraints. Clearly, codes can be seen as enabling constraints. How this actually happens, though, is still not explained. Stuart Kauffman has grappled with this issue and shown that it radically challenges the assumptions of mainstream science going back to Newton. He has attempted to reintroduce real possibilities or potentialities into his ontology, and argued that radically new developments in nature are associated with realizing adjacent possibles. This is still not adequate. What is also involved, I will suggest, utilizing concepts developed by the French natural philosopher Gilbert Simondon, is ‘transduction’ as part of ‘ontogenesis’ of individuals in a process of ‘individuation’, that is, the emergence of ‘individuals’ from preindividual fields or milieux.

Signalling in Initial Coin Offerings: The Key Role of Entrepreneurs’ Self‐efficacy and Media Presence

By analyzing data of more than 1,000 initial coin offerings (ICOs) obtained from seven different ICO information platforms, we investigate the effectiveness of signals used by entrepreneurs to foster ICO funding success. In particular, we examine the effectiveness of venture quality (human capital), level of uncertainty (entrepreneurs’ self‐efficacy and ambiguity reduction), and level of familiarity among potential investors (media presence). Results imply that media presence and entrepreneurs’ self‐efficacy are effective signals in the ICO market and thus can increase funding success. Project initiators who communicate (more actively) via social media collect more funds than those who do not. Analogously, entrepreneurs appearing self‐efficacious with regard to the quality of their venture receive more funds.

On Ambiguity Reduction and the Role of Decision Analysis during the Pandemic.

The COVID‐19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response—especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID‐19 pandemic.

Multidimensional Scientometric indicators for the detection of emerging research topics

The detection of emerging research topics (ERTs) is an important step in the promotion of potentially promising research. In this paper, we first distinguish the concept of ERT from that of common-related topics. We discuss multi-dimensional and practical bibliometric indicators to discover high-impact ERTs at a fine level of granularity. To this end, we focus on three objectives. First, we determine a method for identifying research topics from scholarly publications. Second, we developed a method for uncovering ERTs. Third, we conduct an experimental analysis to demonstrate the operationalization of our method. High-impact ERTs are selected based on the advice of field experts. Then, we divide the identified ERTs into seven patterns and propose different research and development (R&D) strategies for each topic. In general, previous studies have mainly focused on the attributes of “novelty” and “growth.” Compared with extant ERT identification analysis, this study pays more attention to the future economic and social impact of ERTs and the reduction of uncertainty. This article is the first attempt to devise an ERT detection framework by comprehensively measuring its five characteristics (radical novelty, relatively fast growth, persistence and coherence, potential high impact, and uncertainty and ambiguity reduction) and proposing feasible operational indicators and processes. Thus, different R&D layout strategies for different economic and social-impact topics are outlined herein, which contribute significantly to the development of science and technology policies. The evidence presented suggests that our methodology can be used by decision makers to evaluate emerging scientific research.

Government Communication Strategies during Coronavirus Pandemic: United Arab Emirates Lessons

Government communication introduced important lessons during the worldwide experience with the COVID-19 pandemic. It is important to apply known efficacious principles of risk and health communication strategies. The purpose of the study is to depict and explore the United Arab Emirates government communication scenario in tackling the COVID-19 pandemic as well as look at the types of strategies, information and messages delivered via digital mediums to handle challenges that are raised based on the Crisis and Emergency Risk Communication model. The study includes a qualitative analysis of two government bodies’ digital platforms: ‘The Ministry of Health and Prevention’ (mohap) and ‘Crisis and Disasters Management Authority’. Results indicated that the UAE government used different communication aims and strategies to face the pandemic according to the risk management scenario. In the quarantine phase, communication focused on giving people information about the disease, raising awareness about the disease, motivating health and behaviour change, informing people about government decisions and procedures. In the coexistence phase communication focused on emphasising the necessity of adherence the health measures, providing information on re-work in institutions and commercial centres, involving people in the health and social initiatives, confronting non-compliance with health precautions. Government communication also focused on facing rumours and false information. UAE government communication used digital platforms and social media to address more than 200 nationalities living in the state for ensuring that they adhere to the precautionary measures and coordinate with the authorities. Government communication was committed to a set of values including equality between citizens of the state and residents, societal and individual responsibility, recognising the frontline medical staff and acknowledging their sacrifices. UAE implemented an integrated, coherent and effective scenario to deal with the crisis. It developed risk communication strategies in health communication to manage the COVID-19 crisis by following international standards and also took into account its own political, economic, social and cultural features. The UAE government used many strategies to inform and convince people including clarification of measures strategy, reassurance strategy, ambiguity reduction strategy, behaviour efficacy strategy, correcting misinformation and rumours, advising strategy.

CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems

The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.

Ambiguities in Accounting and their Impact on Regulatory Arbitrage

Abstract The revision of the asset and liability definitions is at the core of the International Accounting Standards Board’s (IASB) efforts to reflect more truthfully the economic substance of the underlying business transactions. In the IASB’s revised Conceptual Framework (CF) from 2018, the board redefined assets and liabilities in terms of rights and obligations, thereby explicitly abstaining from a notion of indivisible balance sheet items. This alteration lays the conceptual foundation for carving out pieces of an item in accounting standards, enabling the removal of arbitrary bright line tests, and, eventually seeks to tackle regulatory arbitrage. Drawing upon 18 expert interviews as well as a document analysis, this study sheds light on the process that led to the anchoring of the rights and obligations model in the IASB’s CF. Using literature on ambiguities in accounting as a theoretical frame, this study goes on to show that removing ambiguities in the asset and liability definitions creates new ambiguities and additional discretionary leeway in turn. The paper argues that the perpetual cycle of ambiguity reduction and creation in accounting (Davie, 2000) also includes ambiguity shifting between the conceptual basis of financial reporting and accounting standards. By comparing the previous International Accounting Standard (IAS) 17: Leases, which followed a physicalist, ownership-based notion of assets, with the revised International Financial Reporting Standard (IFRS) 16, the paper demonstrates that the explicit anchoring of the rights and obligations approach does not fully solve the issue of regulatory arbitrage. Instead, it shifts the playing field for structuring activities from the evasion of precise rules to the bending of interpretations.

CODAS HFLTS Method to Appraise Organizational Culture of Innovation and Complex Technological Changes Environments

Sustainable development implies establishing principles, objectives and strategies within organizations that impact the organizational culture in innovation. However, a method needs to be defined in order to know the critical factors that allow the strengthening of the organizational culture in innovation with emphasis on Industry 4.0 and sustainable development in a highly changing environment for a specific organization. In this sense, the paper identifies the set of factors that are documented through reviews and analysis of the literature, subsequently proposes a Multi-Criteria Decision Making (MCDM) methodology using hesitant fuzzy linguistic term sets (HFLTS) and combinative distance-based assessment (CODAS), where factors are evaluated to obtain a score and hierarchy value. Weight values were calculated using the ambiguity reduction method, which incorporates the knowledge acquired by researchers in organizational culture of innovation and expert judgment under the Saaty scale used in analytic hierarchy process (AHP). Finally, a model of organizational culture in innovation is proposed that can be used by organizations to focus strategies on the factors of greater hierarchy and thereby optimize their resources considering the sustainable development and the Industry 4.0 approach.