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Objective: Our aim was to review empirical studies of complacency and bias in human interaction with automated and decision support systems and provide an integrated theoretical model for their explanation. Background: Automation-related complacency and automation bias have typically been considered separately and independently. Methods: Studies on complacency and automation bias were analyzed with respect to the cognitive processes involved. Results: Automation complacency occurs under conditions of multiple-task load, when manual tasks compete with the automated task for the operator's attention. Automation complacency is found in both naive and expert participants and cannot be overcome with simple practice. Automation bias results in making both omission and commission errors when decision aids are imperfect.Automation bias occurs in both naive and expert participants, cannot be prevented by training or instructions, and can affect decision making in individuals as well as in teams.While automation bias has been conceived of as a special case of decision bias, our analysis suggests that it also depends on attentional processes similar to those involved in automation-related complacency. Conclusion: Complacency and automation bias represent different manifestations of overlapping automation-induced phenomena, with attention playing a central role. An integrated model of complacency and automation bias shows that they result from the dynamic interaction of personal, situational, and automation-related characteristics. Application: The integrated model and attentional synthesis provides a heuristic framework for further research on complacency and automation bias and design options for mitigating such effects in automated and decision support systems.

This study analyzes the extent of downward bias in the calculation of inequality of opportunity for continuous outcomes such as income. A typically recognized source of bias is the unobserved circumstances as there is a limited set of variables available in household and labor force surveys. Another previously overlooked source is the likely unobservable nature of top incomes. Using Monte Carlo simulations where the underlying inequality of opportunity is predetermined at various levels, the study presents three key findings. First, the omission of a relevant circumstance can bias the inequality of opportunity estimate by as much as 80 percent, depending on how much variation of the outcome such circumstance explains. Second, not observing the top 5 percent of the income distribution can lead to downward biases of anywhere between 12 and 35 percent, and the combination of missing the most favored population and even one relevant circumstance exacerbates the bias of the empirical estimates. The third key result is that the estimated inequality of opportunity is strongly correlated with the amount of variation in the outcome variable explained by the combination of circumstances (measured by the R2). This result suggests that in empirical applications, the inequality of opportunity estimate can be roughly (and quickly) approximated using simple econometric techniques.

Representative bureaucracy theory explores the effects of representation on bureaucracies, but less attention has been paid to date as to how agents represent values or interests. Addressing this omission, this article highlights the unconscious dimension of active representation and, more specifically, the role of unconscious bias in representation. Unconscious bias has received limited attention to date in public administration, but has clear relevance for understanding how representation occurs at the individual level. This article proposes a framework for understanding unconscious bias. Drawing on Bourdieu's habitus, but making explicit its unconscious dimension, I argue that unconscious bias enhances our understanding of how active representation occurs in bureaucracies today. The article applies these insights to the case of unconscious gender bias as found in the Australian Public Service (APS) and concludes by exploring the methodological challenges involved in building a research agenda into tackling unconscious bias.

In an ultimatum game, participants were randomly assigned to the role of allocator or recipient and to interact face-to-face (FtF) or over computer text chat (computer-mediated communication [CMC]). The allocator was given money to divide. The recipient was unaware of the amount given, so the allocator could deceive the recipient. Perception of the allocator having a dishonest demeanor increased recipient suspicion of deception, but reduced detection accuracy for truths. Demeanor cues did not help detect deception. Recipients were better at detecting lies CMC than FtF. Overall, truth bias did not differ between CMC and FtF. Rates of deception did not differ between CMC and FtF, but type of deception marginally differed. There was more deceptive omission used in FtF and more deceptive commission (bald-faced lies) used in CMC. Results are discussed in terms of demeanor and truth bias.

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well-documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under-reported and unexplored native ranges.

This paper seeks to address the role of European public policy in addressing the problem of Covid-19 vaccine-rollout policy. Currently in Europe, instead of market-based allocation a centralized command-based approach has been implemented to address the essential questions of production and distribution of vaccines throughout the EU. This is centralized, command-based decision-making on the allocation of vaccines which is leading to political and sociological tensions among EU Member States. Paper argues that in order to mitigate these shortcomings European public policy could employ a more nuanced approach. While employing law and economics tools this paper addresses the questions on how European societies should allocate vaccine and, more importantly, who should make this allocation decisions. Moreover, identified moral negative externalities, status quo and omission biases, planning fallacy, risk aversion, administrative rigidity, notorious type-I-type-II error fallacy and related unraveled markets phenomena might result in vaccine-rollout failures.

In this study, we examine the dynamic causal relationship between remittances and economic growth in South Africa during the period from 1970 to 2017. Although South Africa is well-known for being a source of cross-border remittances to various countries, especially in the African continent, remittance inflows to South Africa have grown in the recent past. The growth in remittances, on one hand, and the need to fight against poverty and inequality in South Africa and ultimately improve economic growth, on the other hand, prompted the need for this study. The study uses the autoregressive distributed lag (ARDL) approach within a multivariate Granger-causality setting to examine the remittance-growth causal link—in an effort to address the variable-omission bias. The empirical findings of the study show that remittances and economic growth are not causally related in South Africa, irrespective of whether the estimations are done in the long run or in the short run. This finding, though contrary to the expectation, is not surprising, given the level of financial sector development in South Africa.

AbstractIntroductionRoB 2 is a tool used by systematic reviewers to assess risk of bias in randomized trials. Over a period of 19 months working as editors for Cochrane, we saw many instances where users of RoB 2 frequently applied the tool in ways the developers had not intended, despite availability of detailed guidance, webinars and FAQs.MethodsIn this paper we highlight the ten main issues that we observed, with the aims of optimising the application of the RoB 2 tool, avoiding some of the frequent misapplications of the tool.ResultsIssues noted included failure to state an effect of interest, applying the tool to an entire study rather than to a specific numerical result, omitting key signaling questions and relying on outdated views of causes of bias.ConclusionSuch omissions and misapplications can lead to overly harsh or lenient assessments of bias with potential to change the confidence we have in an evidence base of randomized trials. We recommend that teams planning to use RoB 2 include at least one member familiar with the RoB 2 detailed guidance and that they use the free resources, such as webinars and FAQs, from the developers of RoB 2 and Cochrane. Our ten tips should be useful to non‐Cochrane systematic reviewers as well as to peer reviewers and editors in Cochrane and other journals.

This paper explores the asymmetric nexus between globalization, technological innovation and unemployment within non-linear Autoregressive Distributed Lag (NARDL) framework, Granger causality approach and forecast error variance decomposition technique, covering the period from 2000 to 2018, using quarterly data in Nigeria. To avoid variable omission bias, the study incorporated control variables. The findings showed that both the positive and negative shock of our variables of study indicate varying signs and magnitudes and there is a mixed presence of asymmetries among the nexus, as indicated in the variables for both short and long runs. The results implies that trade globalization, financial globalization and technological innovation biased process innovation contributed to the persistent increase in unemployment rate except technological innovation biased product innovation, which supported the unemployment reduction in Nigeria. These findings were not consistent with existing theories. The results based on the model and empirical data suggest that the authority needs to initiate socially suitable and economic schemes and strategies by ensuring effective and efficient connectivity between globalization and technology via integrating science, technology, research and development. Also, creating an enabling environment that supports and promotes sound and credible government institutions and education including adequate mechanism to ensure optimum utilization of scare resources and avoidance of embezzlement of these limited resources by corrupt officials.

On the 29th of May 2001 Kees B. was found guilty for the rape and murder of Nienke Kleiss, a ten year old Dutch girl. There was virtually no forensic evidence that he had committed this crime, but he had confessed, and so the police was convinced of his guilt. The fact that he retracted his confession soon after his crucial interrogation was not heavily weighted by the police. After four years of imprisonment, Kees B. was released, because Wik H. admitted he was the one who killed Nienke (van Koppen, 2003). Indeed, DNA of Wik H. was found at the crime scene. How come Kees B. was found guilty while, besides a striking lack of evidence, DNA found at the crime scene did not match his DNA? And why does one conclude so easily that someone is guilty once his DNA is found at the crime scene? Put shortly, how come one ignores evidence of absence so easily, whereas one gives so much weight to physically present evidence? The phenomenon described above (i.e., ignoring absent information while putting a lot of weight on present information) has been named the 'feature positive-effect' (FPE; Jenkins & Sainsbury, 1969), and it is the topic of this dissertation. The FPE originates from literature on Pavlovian conditioning. It was found that pigeons learn associations more quickly when the presence of a stimulus predicts (e.g., blue light is on) the presence of another stimulus (e.g., food can be obtained) rather than when the absence of a stimulus (e.g., blue light is off) predicts the presence of another stimulus. This difficulty with absent information relative to present information has also been described in other domains (e.g., omission bias). The fact that people seem to treat present and absent information differently is not necessarily a bad thing. However, as the example above illustrates, underweighting absent forensic evidence can have severe consequences. Sometimes, the underweighting of absent evidence is irrational and thus constitutes a bias. Unlike other biases that can influence the process of legal decision making, such as the confirmation bias (Nickerson, 1998), the FPE is relatively unknown and unstudied. However, given its possible severe consequences, studying this effect is important

PurposeThe purpose of this paper is to examine the effect of option choice reversibility on the number of options chosen, total spending, and upset/regret from actions/inaction, using 124 Singaporean adults.Design/methodology/approachThe experiment employs two levels of option choice reversibility: fully reversible without a penalty vs strictly irreversible. Participants add options to a base model or delete options from a full model and are either allowed or not allowed to change options in a condominium purchase scenario.FindingsCompared to participants in the irreversible choice condition, those in the reversible choice select more options and end up with higher total spending. In the irreversible option choice condition, participants anticipate more upset (one aspect of regret) when they take actions than inaction, but in the reversible option choice condition, the reverse is true.Research limitations/implicationsThe study uses only one decision stimulus, which is a condominium purchase, and the purchase scenario might not be as realistic as an actual purchase decision.Practical implicationsRefunds and option change permission policies make consumers feel they can reverse their buying decisions, making them feel the decisions are less risky and thus inducing them to buy more than when no refunds or option change is allowed after purchase. To drive consumers to take actions, marketers should allow consumers to change their mind after making decisions and assure them of such policy.Originality/valueThe paper shows the effect of decision reversibility on the total spending (i.e. the total costs of choices made) and extends the theory about omission biases by demonstrating that regrets from actions/inaction depend on decision reversibility.

"In news reporting, every semantic choice, nuanced omission, prioritization, and bias holds the power to shape how readers interpret information, particularly evident during wars such as the conflict in Gaza."

The debate on the strategy of banking spread reduction in Brazil has been extended for a long time and was fundamentally concentrated in macroeconomics aspects. This paper has the goal of evaluate the new policy of banking spread reduction implemented by the federal government, which has added microeconomic aspects to this tendency. In order to do so, a mathematical model was presented that combines microeconomics aspects, such as was developed by Nakane (2001), with macroeconomics aspects, originally presented by Ho and Saunders (1981). This model was tested for the 25 largest banks in the period of March 2009 to March 2013, using the Panel Data Methodology. The GMM System model was the one that best fitted the data gathered and the results showed that both aspects are relevant in explaining the banking spread in Brazil, and should not be analyzed separately, as is frequently made in the literature, considering the econometric problems related to the omission bias of relevant variables. ; O debate acerca da estratégia de redução do spread bancário no Brasil estendeu-se por um longo período e concentrava-se fundamentalmente em aspectos macroeconômicos. Este artigo tem por objetivo avaliar a nova política de redução do spread bancário implementada pelo governo federal, a qual adicionou os aspectos microeconômicos. Para atingir este objetivo, foi apresentado um modelo matemático que combina aspectos microeconômicos, desenvolvido por Nakane (2001), com aspectos macroeconômicos, originalmente apresentados por Ho & Saunders (1981). Este modelo foi testado para os 25 maiores bancos no período de março de 2009 a março de 2013, utilizando a metodologia de Dados em Painel. O Modelo GMM System foi o que mais se adequou aos dados analisados e seus resultados mostraram que ambos aspectos são relevantes para explicar o spread bancário no Brasil e não devem ser analisados separadamente, como é frequentemente realizado pela literatura, em função dos problemas econométricos relacionados ao viés de omissão de variáveis relevantes.

Data from a fully enumerated general pop sample were used estimate the magnitude of nonresponse bias & the effect of personal follow-up in a mail survey. An area-probability sample of 4,735 addresses in Alameda County, Calif yielded 8,267 adults. Of these, 8,083 were represented in an initial 'enumeration' survey in which demographic & SE data were gathered by interviewers. These data are the 'parameters' with which subsequent 1 returns were compared. Questionaires were left for each adult in the household at the time of enumeration, with the request that they be completed & mailed back within a week. 2 further attempts-by letter & telegram-were made to obtain responses. Finally, interviewers visited those who had not yet responded, to retrieve completed questionnaire's or help with their completion. Of the 8,083 enumerated adults, 70% responded by mail, 16% were retrieved by interviewers, & 14% did not respond. The nonR's were so small a proportion of the enumeration that their omission had very little effect on the sample estimates for the 13 characteristics considered; they were older, & more likely to be white, M, skilled, & to live in small households without children, but the largest cliff between the questionaire sample & the total/sum enumerated sample was only 1.4% points. Comparisons between mail R's & those whose questionaires were retrieved by interviewers showed that the 2 response groups were very similar. Personal contacts did improve estimates of racial & some related SE distributions, but at a per-R cost of over twice that of the mail response. The personally-retrieved questionnaire's raised estimates of some categories of people easy-tofind-at-home above the actual distribution. These findings indicate that surveys conducted entirely by mail can achieve a good representation of the sample universe, given the high overall return rate that characterized this survey. AA.