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Tversky and Kahneman (1974) described an effect they called insensitivity to prior probability of outcomes', better known as base rate neglect (Bar-Hillel, 1980). This describes people's tendency to underweight prior information in favor of new data. Probability theory requires these prior probabilities to be taken into account, via Bayes' theorem, when determining an event's posterior probability. The fact that most people fail to do so has been taken as evidence of human irrationality and, by other authors, of a mismatch between our cognitive processes and the questions being asked (Cosmides & Tooby, 1996;Gigerenzer & Hoffrage, 1995). In contrast to both views, we suggest that simplistic Bayesian updating using given base rates is not always a rational strategy. Instead, we reconsider Bar-Hillel's original relevance theory, and argue that, since base rates differ in their perceived degree of trustworthiness they are, accordingly, rationally discounted by people.

Large-bodied, top- and apex predators (e.g., crocodilians, sharks, wolves, killer whales) can exert strong top-down effects within ecological communities through their interactions with prey. Due to inherent difficulties while studying the behavior of these often dangerous predatory species, relatively little is known regarding their feeding behaviors and activity patterns, information that is essential to understanding their role in regulating food web dynamics and ecological processes. Here we use animal-borne imaging systems (Crittercam) to study the foraging behavior and activity patterns of a cryptic, large-bodied predator, the American alligator (Alligator mississippiensis) in two estuaries of coastal Florida, USA. Using retrieved video data we examine the variation in foraging behaviors and activity patterns due to abiotic factors. We found the frequency of prey-attacks (mean = 0.49 prey attacks/hour) as well as the probability of prey-capture success (mean = 0.52 per attack) were significantly affected by time of day. Alligators attempted to capture prey most frequently during the night. Probability of prey-capture success per attack was highest during morning hours and sequentially lower during day, night, and sunset, respectively. Position in the water column also significantly affected prey-capture success, as individuals’ experienced two-fold greater success when attacking prey while submerged. These estimates are the first for wild adult American alligators and one of the few examples for any crocodilian species worldwide. More broadly, these results reveal that our understandings of crocodilian foraging behaviors are biased due to previous studies containing limited observations of cryptic and nocturnal foraging interactions. Our results can be used to inform greater understanding regarding the top-down effects of American alligators in estuarine food webs. Additionally, our results highlight the importance and power of using animal-borne imaging when studying the behavior of elusive large-bodied, apex predators, as it provides critical insights into their trophic and behavioral interactions.

It has been repeatedly demonstrated [1–5] that humans are prone to what are called cognitive biases - discrepancies between calculated, optimal decisions and those made using intuition. Such demonstrations of cognitive bias in expert decision makers, however, are often criticized on the basis of their content being irrelevantto the participants. We present results of a study, based on a range of questions designed to highlight a number of well-known biases and including an extension of Capen's [1] study of overconfidence in SPE members, put to industry personnel from a variety of companies. To limit the objection raised above, all questions related to the O&G industry. The authors discuss the biases exhibited and their potential impact on Oil and Gas industry decisions with a special focus on remediation. Despite domain familiarity, industry personnel exhibited a range of cognitive biases indicating that, in accordance with the literature, familiarity with subject material does not remove their impact; nor does industry experience alleviate it. The authors conclude that the biases demonstrated are likely to already be impacting industry decisions. The effect of having had specific risk training is also discussed in light of the results herein with the conclusion that, if risk training does have a beneficial effect, said effects are largely eroded by the passage of time. Finally, we argue that a better understanding of the biases that can affect decisions made within the industry, not just in terms of their existence but also their mechanism of action, will allow more meaningful attempts at remediation. It would also allow current techniques designed to lessen bias impact to be examined critically and both the strengths and weaknesses of these processes to be exposed. Introduction The study of bias in oil and gas (O&G) industry decision making dates back at least to Capen's [1] work in introducing the concept of overconfidence to O&G professionals in the 1970s. Despite this early start, however, the industry continues to report problems that seem to result from known cognitive biases [6, 7]. Biases, such as anchoring [5] and overconfidence [8], are observed in the decisions of most people working under conditions of uncertainty and risk - defining features of O&G. Therefore, it is in the industry's best intereststo follow research on risk, biases and their remediation (see, for example, Begg, Bratvold & Campbell [9]). Despite this, and the best efforts of a segment of the industry, there remains resistance to the acceptance and application of findings. Many O&G personnel question the applicability of laboratory-based findings to their work in the field. Questions used in Capen's [1] assessment of overconfidence among O&G personnel are described as "arcane" and thus, despite repeated demonstrations of overconfidence, some personnel feel that their actual work is beyond the reach of this type of bias. Similar objections have been raised about the generalisability of other biases. To counter these objections, a discussion of known biases and the evidence that they may be impacting the O&G industry will be presented. The questions commonly used to assess these biases will be discussed, in terms of their function and how this relates to their form. Then, the creation of a new battery of bias questions designed using O&G examples will be discussed, along with expectations as to the effect that this will have on responses. Following the presentation of the biases, the role of experience in improving decision making will be discussed, particularly in light of the sorts of training currently employed within the industry.

The assessment of an athlete’s performance can play a key role in determining their current state, their readiness to compete, the impact of an experimental manipulation, and/or the influence of an intervention. At present, there is limited empirical evidence stating the indicators that encapsulate individual performance within any esport. To identify the variables that are historically associated with indicating Counter-Strike: Global Offensive performance, a literature review was conducted. Identified variables were accumulated and presented to three technical expert panels composed of world-class esport athletes, researchers, and practitioners. We utilized a modified Delphi method to provide direction concerning the examination of performance in esports. The expert panelists presented numerous opinions on what encapsulates performance, considerations for best practices, and concerns associated with the semantics of performance. This study presents the opinions of various domain-specific experts and encourages the use of more explicit terminology when discussing performance measurement. It was the intention of the project to generate an open discussion rather than draw a unified conclusion on best practices.