Context: The role of expert judgement is essential in our quest to improve software project planning and execution. However, its accuracy is dependent on many factors, not least the avoidance of judgement biases, such as the anchoring bias, arising from being influenced by initial information, even when it's misleading or irrelevant. This strong effect is widely documented. Objective: We aimed to replicate this anchoring bias using professionals and, novel in a software engineering context, explore de-biasing interventions through increasing knowledge and awareness of judgement biases. Method: We ran two series of experiments in company settings with a total of 410 software developers. Some developers took part in a workshop to heighten their awareness of a range of cognitive biases, including anchoring. Later, the anchoring bias was induced by presenting low or high productivity values, followed by the participants' estimates of their own project productivity. Our hypothesis was that the workshop would lead to reduced bias, i.e., work as a de-biasing intervention. Results: The anchors had a large effect (robust Cohen's d = 1.19) in influencing estimates. This was substantially reduced in those participants who attended the workshop (robust Cohen's d = 0.72). The reduced bias related mainly to the high anchor. The de-biasing intervention also led to a threefold reduction in estimate variance. Conclusion: The impact of anchors upon judgement was substantial. Learning about judgement biases does appear capable of mitigating, although not removing, the anchoring bias. The positive effect of de-biasing through learning about biases suggests that it has value.
The cognitive basis of the anchoring bias is disputed and there are at least three different, partly overlapping, explanations: 1) Anchoring as communication (the attitude change theory), i.e., that it is natural for us to give weight to what other people communicate @cite_32 . 2) Anchors as a starting point (the anchoring and adjustment theory), i.e., that the anchor is the starting point and that the adjustment away from the anchor typically is insufficient @cite_31 . 3) Anchors as an activating experience (the selective accessibility theory), i.e., that the anchor activates experiences and that recently activated experience is more likely to be used in the subsequent judgement process @cite_1 . All explanations have supporting evidence and it is possible that they all contribute to the observed anchoring bias.
@cite_31 @cite_1 @cite_32
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Results of four studies support the notion that anchoring effects are mediated by mechanisms of hypothesis-consistent testing and semantic priming. According to the suggested Selective Accessibility Model, judges use a hypothesis-consistent test strategy to solve a comparative anchoring task. Applying this strategy selectively increases the accessibility of anchor-consistent knowledge which is then used to generate the subsequent absolute judgment. Studies 1 and 2 demonstrate that absolute estimates depend on the hypothesis implied in the comparative task, suggesting that a hypothesis-testing strategy is used to solve this task. Study 3 shows that limiting the amount of knowledge generated for the comparative task retards absolute judgments. This suggests that knowledge rendered easily accessible in the comparative judgment is used for the subsequent absolute judgment. Finally, Study 4 suggests that self-generation of knowledge contributes to the robustness of the effect, thus resolving the seeming inconsistency that anchoring effects are at the same time remarkably robust and mediated by typically fragile semantic priming mechanisms. Abstract Effects of extreme versus moderate numerical anchors are investigated. Similar to past results in attitude change, three separate data collections show that extreme anchors can have less influence on judgments than more moderate anchors. Though difficult to account for using traditional “anchor-and-adjust” and recent “selective accessibility” views, the findings are consistent with theories of attitude change. Implications of an attitude-change view of numerical anchoring are discussed. Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88 ), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research. Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.