• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.935-946
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• 2016

This study aims to explore cognitive biases relating the core competences of science and instructional strategy in reducing the level of cognitive biases. The literature review method was used to explore cognitive biases and science education experts discussed the relevance of cognitive biases to science education. Twenty nine cognitive biases were categorized into five groups (limiting rational causal inference, limiting diverse information search, limiting self-regulated learning, limiting self-directed decision making, and category-limited thinking). The cognitive biases in limiting rational causal inference group are teleological thinking, availability heuristic, illusory correlation, and clustering illusion. The cognitive biases in limiting diverse information search group are selective perception, experimenter bias, confirmation bias, mere thought effect, attentional bias, belief bias, pragmatic fallacy, functional fixedness, and framing effect. The cognitive biases in limiting self-regulated learning group are overconfidence bias, better-than-average bias, planning fallacy, fundamental attribution error, Dunning-Kruger effect, hindsight bias, and blind-spot bias. The cognitive biases in limiting self-directed decision-making group are acquiescence effect, bandwagon effect, group-think, appeal to authority bias, and information bias. Lastly, the cognitive biases in category-limited thinking group are psychological essentialism, stereotyping, anthropomorphism, and outgroup homogeneity bias. The instructional strategy to reduce the level of cognitive biases is disused based on the psychological characters of cognitive biases reviewed in this study and related science education methods.

• Journal of Information Science Theory and Practice
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• v.8 no.2
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• pp.18-31
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• 2020

Cognitive biases can influence human information behaviour and decisions made in information behaviour and use. This study aims to identify the biases involved in some aspects of information behaviour and the role they play in information behaviour and use. Twenty-five semi-structured face-to-face interviews were conducted in an exploratory qualitative study with graduate (MA and PhD) students who were at the stage of their dissertation/thesis research. Eisenberg & Berkowitz Big6^TM Skills for Information Literacy was adopted as a framework for interviews and the analysis was done using grounded theory coding method. The findings revealed the presence of twenty-eight biases in different stages of information behaviour, including availability bias (affects the preference for information seeking strategies), attentional bias (leads to biased attention to some information), anchoring effect (persuades users to anchor in special parts of information), confirmation bias (increases the tendency to use information that supports one's beliefs), and choice-supportive bias (results in confidence in information seeking processes). All stages of information seeking were influenced by some biases. Biases might result in a lack of clarity in defining the information needs, failure in looking for the right information, misinterpretation of information, and might also influence the way information is presented.

• Journal of Mechanical Science and Technology
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• v.16 no.11
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• pp.1367-1378
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• 2002

This paper investigates observability properties of strapdown INS aided by a GPS antenna array. The motivation to consider a GPS antenna array is that the lever-arms between the GPS antennas and IMU play an important role in the estimation of vehicle attitude and biases of IMU. It is shown that time-invariant INS error models are observable with measurements from at least three GPS antennas. It is also shown that time-varying error models are instantaneously observable with measurements from three antennas. Numerical simulation results are given to show the effectiveness of multiple GPS antennas on estimating vehicle attitude and biases of IMU when IMU has considerable magnitude of biases.

• Journal of Distribution Science
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• v.13 no.11
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• pp.31-37
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• 2015

Purpose - This study is an effort to assess the role of behavioral biases in investment decision making, specifically for mutual funds, and the moderating role of the investor. Individual investment behavior is concerned with choices about purchasing various securities. However, behavioral finance disputes the concept of perfect rationality and identifies psychological factors and their impact on decision-making. Research design, data, and methodology - A survey questionnaire was designed and used to collect responses using a judgmental sampling technique from 290 investors in the Gwalior Region. Cronbach's Alpha, factor analysis, and linear regression were all used to test the influence of behavioral biases on investment decision. Results - We found that the behavioral biases have a positive impact on investment decisions. Conclusions - This study's results identified three factors influencing investor behavior(rationale, investment skills, and profit making) and four factors influencing investor decisions (profit maker, market analysis, investment plan, seller). The overall results of the study also show that there is no significant relationship between investor behavior and investment decisions by gender in the market.

• Ocean and Polar Research
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• v.38 no.4
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• pp.259-270
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• 2016

Sea Surface Salinity (SSS) observations from the Aquarius satellite in the East Sea show large systematic biases mainly caused by the surrounding lands and Radio Frequency Interferences (RFI) along the descending orbits on which the satellite travels from the Asian continent to the East Sea. To develop a technique for correcting the systematic biases unique to the East Sea, the least square regression between in situ observations of salinity and the reanalyzed salinities by HYCOM is first performed. Then monthly mean reanalyzed salinities fitted to the in situ salinities are compared with monthly mean Aquarius salinities to calculate mean biases in $1^{\circ}{\times}1^{\circ}$ boxes. Mean biases in winter (December-March) are found to be considerably larger than those in other seasons possibly caused by the inadequate correction of surface roughness in the sea surrounded by the land, and thus the mean bias corrections are performed using two bias tables. Large negative biases are found in the area near the coast of Japan and in the areas with islands. In the northern East Sea, data sets using the ascending orbit only (SCIA) are chosen for correction because of large RFI errors on the descending orbit (SCID). Resulting mean biases between the reanalysis salinities fitted to in situ observations and the bias corrected Aquarius salinities are less than 0.2 psu in all areas. The corrected mean salinity distributions in March and September demonstrate marked improvements when compared with mean salinities from the World Ocean Atlas (WOA [2005-2012]). In September, salinity distributions based on the corrected Aquarius and on the WOA (2005-2012) show similar distributions of Changjiang Diluted Water (CDW) in the East Sea.

• Smart Media Journal
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• v.13 no.2
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• pp.44-51
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• 2024

The construction of a stress measurement detaset plays a curcial role in various modern applications. In particular, for the efficient training of artificial intelligence models for stress measurement, it is essential to compare various biases and construct a quality-controlled dataset. In this paper, we propose the construction of a stress measurement dataset with quality management through the comparison of various biases. To achieve this, we introduce strss definitions and measurement tools, the process of building an artificial intelligence stress dataset, strategies to overcome biases for quality improvement, and considerations for stress data collection. Specifically, to manage dataset quality, we discuss various biases such as selection bias, measurement bias, causal bias, confirmation bias, and artificial intelligence bias that may arise during stress data collection. Through this paper, we aim to systematically understand considerations for stress data collection and various biases that may occur during the construction of a stress dataset, contributing to the construction of a dataset with guaranteed quality by overcoming these biases.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.185-196
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• 2023

This paper investigates the effects of entrepreneurs' cognitive biases on business opportunity evaluation, given their strong entrepreneurial spirit, which is characterized by innovation, proactivity, and risk-taking. When making decisions related to business activities, entrepreneurs typically make rational judgments based on their knowledge, experience, and the advice of external experts. However, in situations of extreme stress or when quick decisions are required, they often rely on heuristics based on their cognitive biases. In particular, we often see cases where entrepreneurs fail because they make decisions based on heuristics in the process of evaluating and selecting new business opportunities that are planned to guarantee the growth and sustainability of their companies. This study was conducted in response to the need for research to clarify the effects of entrepreneurs' cognitive biases on new business opportunity evaluation, given that the cognitive biases of entrepreneurs, which are formed by repeated successful experiences, can sometimes lead to business failure. Although there have been many studies on the effects of cognitive biases on entrepreneurship and opportunity evaluation among university students and general people who aspire to start a business, there have been few studies that have clarified the relationship between cognitive biases and social networks among entrepreneurs. In contrast to previous studies, this study conducted empirical surveys of entrepreneurs only, and also conducted research on the relationship with social networks. For the study, a survey was conducted using a parallel survey method using online mobile surveys and self-report questionnaires from 150 entrepreneurs of small and medium-sized enterprises. The results of the study showed that 'overconfidence' and 'illusion of control', among the independent variables of entrepreneurs' cognitive biases, had a statistically significant positive(+) effect on business opportunity evaluation. In addition, it was confirmed that the moderating variable, social network, moderates the effect of overconfidence on business opportunity evaluation. This study showed that entrepreneurs' cognitive biases play a role in the process of evaluating and selecting new business opportunities, and that social networks play a role in moderating the structural relationship between entrepreneurs' cognitive biases and business opportunity evaluation. This study is expected to be of great help not only to entrepreneurs, but also to entrepreneur education and policy making, by showing how entrepreneurs can use cognitive biases in a positive way and the influence of social networks.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.35-50
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• 2020

This study aimed to investigate the changes of emotional perception biases according to the level of social phobia and the relationship with visuospatial attention allocation ability. This study used the Korean self-report assessment test (Korean version of social avoidance and distress, K-SAD) to measure the level of social phobia. Moreover, the emotional perception and useful field of view (UFOV) tasks were employed for measuring emotional perception biases and visuospatial attention allocation ability, respectively. A total of 118 participants participated in this experiment, and only 107 (males: 94, females: 13) data were analyzed due to the exclusion of response errors and other statistical problems. The average age of the participants used in the analysis was 21 years (SD: 3.64), and those participants were divided into three groups according to the K-SAD scores. Consequently, all experimental groups showed negative emotional perception biases in the emotional perception task, but the magnitudes of the biases of each group were not significantly different. Furthermore, the positive emotional perception biases were higher at higher levels of social phobia, which could be related to the tendency of interpreting positive stimuli negatively. In the UFOV task, the higher the level of social phobia, the lower the visuospatial attention allocation ability. These results suggest that the deterioration of visuospatial attention allocation ability potentially contributes to the increase of positive emotional perception biases by being difficult to perceive external stimuli. Thus, this paper discusses the potential contribution of visuospatial attention allocation ability to the increased perceptual biases of positive emotions as the level of social phobia increases.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Proceedings of the Korean Association for Survey Research Conference
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• 2002.11a
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• pp.255-274
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• 2002

We classify rotation sampling designs into two classes. The first class replaces sample units within the same rotation group while the second class replaces sample units between different rotation groups. The first class is specified by the three-way balanced design which is a multi-level version of previous balanced designs. We introduce an extended generalized composite estimator (EGCE) and derive its variance and mean squared error for each of the two classes of design, cooperating two types of correlations and three types of biases. Unbiased estimators are derived for difference between interview time biases, between recall time biases, and between rotation group biases. Using the variance and mean squared error, since any rotation design belongs to one of the two classes and the EGCE is a most general estimator for rotation design, we evaluate the efficiency of EGCE to simple weighted estimator and the effects of levels, design gaps, and rotation patterns on variance and mean squared error.