Information Bias Research Articles

An experimental test of whether financial incentives constitute undue inducement in decision-making

Around the world, laws limit the incentives that can be paid for transactions such as human research participation, egg donation or gestational surrogacy. A key reason is concerns about ‘undue inducement’—the influential but empirically untested hypothesis that incentives can cause harm by distorting individual decision-making. Here I present two experiments (n = 671 and n = 406), including one based on a highly visceral transaction (eating insects). Incentives caused biased information search—participants offered a higher incentive to comply more often sought encouragement to do so. However, I demonstrate theoretically that such behaviour does not prove that incentives have harmful effects; it is consistent with Bayesian rationality. Empirically, although a substantial minority of participants made bad decisions, incentives did not magnify them in a way that would suggest allowing a transaction but capping incentives. Under the conditions of this experiment, there was no evidence that higher incentives could undermine welfare for transactions that are permissible at low incentives.

Collective group drift in a partial-differential-equation-based opinion dynamics model with biased perception kernels.

In the age of technology, individuals accelerate their biased gathering of information, which in turn leads to a population becoming extreme and more polarized. Here we study a partial-differential-equationmodel for opinion dynamics that exhibits collective behavior subject to nonlocal interactions. We developed an interaction kernel function to represent biased information gathering. Through a linear stability analysis, we show that biased populations can still form opinionated groups. However, a population that is too heavily biased can no longer come to a consensus, that is, the initial homogeneous mixed state becomes stable. Numerical simulations with biased information gathering show the ability for groups to collectively drift towards one end of the opinion space. This means that a small bias in each individual will collectively lead to groups of individuals becoming extreme together. The characteristic time scale for a group's existence is captured from numerical experiments using the temporal correlation function. Supplementing this, we included a measure of how different each population is after regular time intervals using a form of the Manhattan and Euclidean distance metrics. We conclude by exploring how wall boundary conditions induce pattern formation initially on the most extreme sides of the domain.

Deployment of attention to facial expressions varies as a function of emotional quality-but not in alexithymic individuals.

Alexithymia is a risk factor for emotional disorders and is characterized by differences in automatic and controlled emotion processing. The multi-stimulus free-viewing task has been used to detect increased negative and reduced positive attentional biases in depression and anxiety. In the present eye-tracking study, we examined whether lexical emotional priming directs attention toward emotion-congruent facial expressions and whether alexithymia is related to impairments in lexical priming and spontaneous attention deployment during multiple face perception. A free-viewing task with happy, fearful, angry, and neutral faces shown simultaneously was administered to 32 alexithymic and 46 non-alexithymic individuals along with measures of negative affect and intelligence. Face presentation was preceded by masked emotion words. Indices of initial orienting and maintenance of attention were analyzed as a function of prime or target category and study group. Time to first fixation was not affected by prime category or study group. Analysis of fixation duration yielded a three-way interaction. Alexithymic individuals exhibited no prime or target category effect, whereas non-alexithymic individuals showed a main effect of target condition, fixating happy faces longer than neutral and angry faces and fearful faces longer than angry faces. Our results show evidence of attentional biases for positive and fearful social information in non-alexithymic individuals, but not in alexithymic individuals. The lack of spontaneous attentional preference for these social stimuli in alexithymia might contribute to a vulnerability for developing emotional disorders. Our data also suggest that briefly presented emotion words may not facilitate gaze orientation toward emotion-congruent stimuli.

Risk factors and predictive model for prenatal depression: A large retrospective study in China

BackgroundPrenatal depression, associated with adverse effects on mothers and fetuses, has received little attention. We conducted a large-sample study to investigate the risk factors of, and develop a predictive model for, prenatal depression in the Chinese population. MethodsThis study enrolled 14,329 pregnant women who delivered at the West China Second University Hospital, Sichuan University from January 2017 to December 2020. Participants were divided into a training or validation cohort. Multiple variables were collected and selected using univariate logistic regression and least absolute shrinkage and selection operator penalty regression. After multivariate logistic analysis, a predictive model was developed and validated internally and externally. ResultsNine variables (employment, planned pregnancy, pregnancy number, conception methods, gestational diabetes mellitus, twin pregnancy, placenta previa, umbilical cord encirclement, and educational attainment) were identified as independent risk factors for prenatal depression. Receiver operating characteristic curves in both the training and validation cohorts showed excellent discrimination of the predictive model (the area under the curve: 0.746 and 0.732, respectively). LimitationsThe results of this retrospective study may be affected by confounding and information bias. Some important variables were excluded, such as family history of mental disorders. The study was conducted in China; its results may not be generalizable to other regions. ConclusionOur study identified nine significant risk factors for prenatal depression and constructed an accurate predictive model. This model could be applied as a clinical decision aid for individualized risk estimates and prevention of prenatal depression.

Communicating socially acceptable risk judgments: The role of impression information insufficiency in the risk information seeking and processing model

AbstractThe COVID‐19 pandemic has created uncertainty and controversy around risk‐related issues such as vaccine mandates. People expressing their opinions on these issues to important others, such as employers, may face significant consequences, such as rewards or rejection. Therefore, people may try to find, avoid, or use information in a way that helps them express risk judgments that are socially acceptable in different social situations. This study investigated how people seek, avoid, and process risk information when they are concerned about their impression management. It also introduced the concept of impression information insufficiency (the perceived gap between the information one has and the information one needs to convey socially acceptable judgments and meet interpersonal needs in social situations) and examined its antecedents and outcomes within the risk information seeking and processing model. We conducted an online survey with 1673 Hong Kong adults during the COVID‐19 pandemic. The results showed that fear and social norms related to greater impression information insufficiency, which thereby was associated with biased risk information seeking, avoidance, and processing.

Behavioral Aspects of Investments in Renewable Energy Sources on the Example of Podkarpackie Province

Abstract Subject and purpose of work: The subject of the study consists of the biasess made when investing in photovoltaic panels. The purpose of the study is to identify biasess associated with investing in renewable energy sources. Materials and methods: Data from a survey conducted among owners of photovoltaic installations in Subcarpathia were used. The literature has identified papers on factors other than behavioral errors affecting investments in photovoltaic panels. Results: Respondents succumbed to the anchoring effect, the error of positive retrospection, information bias, choice-supportive bias and framing bias when deciding to invest in photovoltaic panels. Conclusions: The research conducted makes it possible to confirm that people’s behavior while investing is influenced by non-economic factors. Knowledge of behavioral biases can allow one to calibrate financial incentives in order to influence customers more effectively.

Abstract 1830 The neural pathway for internal bias and sensory information interaction in decision making

Abstract 1830 The neural pathway for internal bias and sensory information interaction in decision making

Occupational safety and health among fishing boat workers in Thailand

The Thai fishing industry has been under close scrutiny for conditions faced by fishing boat workers, yet data are scarce with regards to occupational safety and health (OSH) among its workers. The objectives of this study are to describe: i) water, sanitation, and hygiene (WASH) conditions, and; ii) OSH conditions faced by migrant fishing boat workers in Thailand. We conducted a mixed-methods study with a quantitative survey in 200 migrant fishing boat workers and focus group discussions with 11 workers and 6 stakeholders in southern Thailand. We analyzed quantitative and qualitative data using descriptive statistics and content analysis, respectively. Nearly all participants (95.0%) reported that drinking water on board was stored in a sealed container, but only 5.5% of participants reported that there was latrine on board for defecation. Nearly all participants reported proper storage of objects on board, although findings on maintenance varied. Use of personal protective equipment was scarce as equipments obstruct the workers’ movement. The findings highlighted areas where improvements in WASH and OSH are needed. However, caveats regarding potential information bias and lack of generalizability should be considered in the interpretation of the study findings.

새로운 노멀, 한국의 부동산과 신뢰에 관한 소고

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 This study conducted research on Korean real estate and trust facing the new normal. The purpose of the thesis is to explore the problems and future that arise from the combination of Korean real estate and artificial intelligence amid changes in the rules of the game called the new normal. This paper pointed out the importance of social capital called trust and explored the sustainability of the Korean real estate industry.
 (2) RESEARCH METHOD
 This study applied descriptive research methods to new normal, artificial intelligence, real estate in Korea, and trusts. Through this, the paper attempted to discuss research topics in an explanatory and normative manner and draw implications.
 (3) RESEARCH FINDINGS
 This study was conducted on the topic of real estate and trust in the new normal. The main keywords of this research were new normal, Korean real estate, trust, Artificial Intelligence(AI), and the 4th industrial revolution(4IR). This paper differs from previous studies. Existing prior research approaches the 4IR(AI) and Korean real estate with a focus on economic utility and technological benefit. In contrast, this study pointed out the importance of social capital called trust and attempted to explore the sustainability of the Korean real estate industry.
 2. RESULTS
 In the new normal, real estate in Korea is presented as three major issues: 1. Problems of Korean real estate in transition, 2. Industrial revolution and collapse of trust, and 3. Transformation of the times and changes in social codes. In relation to this, this study derived three implications: first, compliance with the rule of law, second, discussion and direction on ethical guidelines, and third, response and preparation for information asymmetry and bias. This study predicts that Korea's real estate industry, facing a new normal, will minimize risks based on trust and develop the benefits and utilities provided by artificial intelligence and intelligent information technology in a desirable direction.

A hybrid model for stock price prediction based on multi-view heterogeneous data

Literature shows that both market data and financial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of information is difficult because of their completely different characteristics. This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine (SVM). It works by simply inputting heterogeneous multi-view data simultaneously, which may reduce information loss. Compared with the ARIMA and classic SVM models based on single- and multi-view data, our hybrid model shows statistically significant advantages. In the robustness test, our model outperforms the others by at least 10% accuracy when the sliding windows of news and market data are set to 1–5 days, which confirms our model’s effectiveness. Finally, trading strategies based on single stock and investment portfolios are constructed separately, and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

The Future of Research in an Artificial Intelligence-Driven World

Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.

Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

PurposePlatform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.Design/methodology/approachThis paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.FindingsIn this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.Originality/valueFirstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

Supplemental Material for The Roles of Social Information, Asocial Information, and Initial Bias in Nest-Building Decisions

Supplemental Material for The Roles of Social Information, Asocial Information, and Initial Bias in Nest-Building Decisions

The roles of social information, asocial information, and initial bias in nest-building decisions.

Animals can use asocial (e.g., environmental cues) or social (e.g., conspecific behavior) information when making decisions. We investigated decisions made by zebra finches when asocial and social sources conveyed agreeing or conflicting information, and assessed the influence of initial bias on decision making. Finches completed an initial preference test ranking preference for three colors of nest-building material. Birds in the agree group (n = 14) then observed demonstrators build nests using nonpreferred color material (social information) that matched the environment color (asocial information). Birds in the conflict group (n = 15) observed demonstrators build nests with nonpreferred color material that did not match the cage environment (another nonpreferred color). A final preference test assessed any changes in color preference. The agree group reduced average preference for their initially preferred color, but did not significantly increase average preference for the asocial/social colors. The conflict group also reduced average preference for the initially preferred color and also increased preference for the socially demonstrated color. Observers with stronger initial bias were less likely to choose the socially demonstrated color than observers with weaker initial bias. This shows that social information informs nest-building decisions, even when in conflict with asocial information. However, bias influences social information use and adds nuance to how different individuals use information when making decisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Information bias for case definitions and mortality between and within studies over time undermines our understanding of COVID-19 transmission and disease severity

Information bias for case definitions and mortality between and within studies over time undermines our understanding of COVID-19 transmission and disease severity

DEVELOPMENT OF INTELLIGENT SYSTEMS FOR AUTOMATIC ASSESSMENT OF STUDENTS' ACADEMIC ACHIEVEMENTS

to analyse the development of artificial intelligence systems for automatic assessment of students' learning achievements. Methodology: to achieve this goal, the scientific methods of analysis and synthesis, content analysis, SWOT analysis, comparison, and typology were used. Results: it has been established that among the key advantages is a significant increase in the objectivity of the assessment of students' knowledge and skills. It is important to consider the acceleration of the process of checking the results, which saves time and effort for teachers. Another important advantage is the provision of real-time feedback during assessment. Scientific novelty: It has been established that one of the major problems is the possibility of bias and inequality in the educational system. Given that intelligent systems are based on certain algorithms, any bias or false information in the initial data can lead to biased results. Additional challenges include the excessive mechanisation of the assessment process, which does not always allow for the individual characteristics of each student, as well as ensuring appropriate protection of personal data. Conclusions: Intelligent student assessment systems are a powerful tool in countering corruption schemes in the educational system, especially in developing countries.

Altruistic Collaborative Learning.

This article proposes a new learning paradigm based on the concept of concordant gradients for ensemble learning strategies. In this paradigm, learners update their weights if and only if the gradients of their cost functions are mutually concordant in a sense given by paper. The objective of the proposed concordant optimization framework is robustness against uncertainties by postponing to a later epoch, the consideration of examples associated with discordant directions during a training phase. Concordance constrained collaboration is shown to be relevant, especially in intricate classification issues where exclusive class labeling involves information bias due to correlated disturbances affecting almost all training examples. The first learning paradigm applies on a gradient descent strategy based on allied agents, subjected to concordance checking before moving forward in training epochs. The second learning paradigm is related to multivariate dense neural matrix fusion, where the fusion operator is itself a learnable neural operator. In addition to these paradigms, this article proposes a new categorical probability transform to enrich the existing collection and propose an alternative scenario for integrating penalized SoftMax information. Finally, this article assesses the relevance of the above contributions with respect to several deep learning frameworks and a collaborative classification involving dependent classes.

Irrelevant information enhances a sense of knowledge and curses our understanding of other minds.

This study shows that exposure to topic-related but irrelevant information enhances both estimates of peer knowledge and our own sense of knowledge. In Experiment 1, participants were more confident in their answers to general knowledge questions and gave higher estimates of peer knowledge when such questions were accompanied by short paragraphs containing topic-related yet nondiagnostic information than when they were not. The inflated peer knowledge estimates were independent of the classic curse of knowledge. Experiments 2, 3, 5, and 6 demonstrated that irrelevant information biases knowledge estimation via its semantic relatedness to the test questions; response latencies were measured in Experiments 5 and 6 to examine the possible role of retrieval fluency in the semantic relatedness effect. Experiment 4 attributed the bias to information content (e.g., "it is generally known that keratin is responsible"), not comments on knowledge popularity (e.g., "what is responsible is generally known"). Importantly, the effect of irrelevant information on estimates of peer knowledge was fully mediated by confidence in own knowledge in Experiments 1, 2, 4, and 5. Experiment 6 manipulated retrieval fluency and failed to find conclusive evidence for its involvement in the semantic relatedness effect. We conclude that irrelevant information boosts peer knowledge estimation through its semantic relatedness to the problem at hand, and the effect is mostly explained by a corresponding increase in the individual's own sense of knowledge. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Randomized controlled trials of mind-body interventions for posttraumatic stress disorder: a systematic review.

Mind-body interventions (MBIs) include mindfulness-based interventions (MiBIs), meditation- and mantra-based interventions (MMIs), and movement-based interventions (MoBIs). These approaches have demonstrated preliminary efficacy in improving posttraumatic stress disorder (PTSD) symptoms. However, previous systematic reviews and meta-analyses have noted that this area of research is limited by inadequate comparator conditions, heterogeneity of measurement, and absence of objective outcome measures. For these reasons, an updated review of the highest-quality evidence available is warranted. We used the Agency for Healthcare Research and Quality (AHRQ)-funded evidence tables for the PTSD-Repository to identify relevant studies and assess the risk of bias as follows: The search was conducted between June 2018 and June 2022, and databases included PTSDpubs (formerly PILOTS), Ovid® MEDLINE®, Cochrane CENTRAL, Embase®, the Cumulative Index to Nursing and Allied Health Literature (CINAHL®), SCOPUS, and PsycINFO®. Twenty-six randomized controlled trials met our inclusion criteria. After identifying studies and retrieving risk of bias information from the PTSD-Repository evidence tables, we extracted additional data and synthesized the evidence. The strength of evidence was rated as low for MiBIs and MMIs, largely due to contradicting results, inconsistent use of active versus passive comparators, and high risk of bias. The strength of evidence for MoBIs was rated as moderate due to individual studies consistently favoring the intervention and a relatively large number of studies and participants. Of the 26 included studies, only two included objective outcome measures. Implications for future MBI research and clinical applications for treating PTSD are discussed.

Risk of Bias Assessment and Risk Minimisation Strategies in COVID-19 Diagnostic Test Accuracy Study

It is paramount to assess the risk of biases in may arise from diagnostic test accuracy (DTA) study as it will affect the accuracy and validity of the tests. These biases can be found in published researches and here we look at COVID-19 DTA studies. The evaluation of bias risk in diagnostic research is mainly performed using QUADAS-2. The aim of this review was to determine potential selection and information biases in diagnostic test accuracy studies and strategies to minimize risk of biases. Literature review related to diagnostic test accuracy study is identified through an online search of databases namely PubMed, ScienceDirect, Research Gate, Google Scholar, and official government websites range. Six potential biases in four QUADAS-2 domains are identified in COVID-19 diagnostic test accuracy study which are 1) spectrum bias in patient selection; 2) interpretation bias in index test; 3) differential misclassification bias and nondifferential misclassification bias in reference standard; and 4) partial verification bias and differential verification bias in patient flow. The identified biases exert effects on accuracy of COVID-19 diagnostic tests. Six strategies are recommended to reduce these biases, hence, improving the accuracy of COVID-19 diagnostic tests. The best diagnostic test can give benefits to the population in the mass screening program during COVID-19.