Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...

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Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Theoretical models of financial markets built on the assumption that some investors are overconfident yield one central prediction: overconfident investors will trade too much. We test this prediction by partitioning investors on the basis of a variable that provides a natural proxy for overconfidence--gender. Psychological research has established that men are more prone to overconfidence than women. Thus, models of investor overconfidence predict that men will trade more and perform worse than women. Using account data for over 35,000 households from a large discount brokerage firm, we analyze the common stock investments of men and women from February 1991 through January 1997. Consistent with the predictions of the overconfidence models, we document that men trade 45 percent more than women and earn annual risk-adjusted net returns that are 1.4 percent less than those earned by women. These differences are more pronounced between single men and single women; single men trade 67 percent more than single women and earn annual risk-adjusted net returns that are 2.3 percent less than those earned by single women.

https://papers.ssrn.com/sol3/Delivery.cfm/98110615.pdf?abstractid=139415&rulid=175370&mirid=3

Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment

Theoretical models predict that overconedent investors trade excessively We test this prediction by partitioning investors on gender Psychological research demonstrates that, in areas such as enance, men are more overconedent than women Thus, theory predicts that men will trade more excessively than women Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997 We document that men trade 45 percent more than women Trading reduces men’s net returns by 265 percentage points a year as opposed to 172 percentage points for women It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so Josh Billings, nineteenth century American humorist It is difecult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors Rational investors make periodic contributions and withdrawals from their investment portfolios, rebalance their portfolios, and trade to minimize their taxes Those possessed of superior information may trade speculatively, although rational speculative traders will generally not choose to trade with each other It is unlikely that rational trading needs account for a turnover rate of

/pdf/boys-will-be-boys-gender-overconfidence-and-common-stock-2ctwfizezu.pdf

Boys will be Boys: Gender, Overconfidence, and Common Stock Investment

/pdf/research-design-qualitative-quantitative-and-mixed-methods-1m92m1x5gg.pdf

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

The rational choice of "error"

This paper describes the principles of human-centred automation from the point of view of cognitive systems engineering. Five main aspects of automation are identified: level of automation, interface, computer support, training, and task allocation. A matrix is proposed which describes the dependencies between these five aspects, thus maintaining the joint systems perspective.

Cognitive functions and automation: principles of human-centred automation

Efforts to account for incidents and accidents usually include one or more instances of human actions gone wrong, which in turn are explained as 'human error'. An abundance of theories, models and methods have been developed to help clarify what exactly 'human errors' are and how they can be reduced, although with limited success. Most of this research has focused on what happens when things go wrong, and has neglected to look at what happens when things go right. This paper argues that we cannot understand the former without understanding the latter, and that we therefore need theories and models of normal actions rather than of 'human error'. It is proposed that both normal performance and failures should be explained in terms of how people adjust their actions to achieve an acceptable compromise between resources and demands, hence that neither can be attributed to nor explained by failures of specific components or functions. The paper discusses how human performance can be seen as a dynamic trade-off between efficiency and thoroughness, taking place both at the sharp end and the blunt end.

Time to think and time to do? I can fail, and so can you!

There is one main problem in defining what an intelligent system is: there are no good definitions of intelligence. There is generally more agreement on the behaviours referred to by the term (the phenomenology of intelligence) than there is on how they can be interpreted or categorised. In psychology, intelligence can be defined either as the general cognitive ability underlying all processes of complex reasoning, or as the performance on tests of intelligence - to mention two extremes. It is therefore not sufficient to define an intelligent system with reference to human intelligence. An alternative is to focus on the salient aspects of intelligent performance and refer to a cognitive system as follows [1]:

The Intelligent Use of Intelligent Systems

The study reported here reviewed and analyzed multi-team organizations in offshore operations to identify and understand the factors that are essential for good operation. It was found that the most prominent contribution to good operations was the balancing of structure and flexibility during work (anchor handling operations). The enabling factor towards this balance seems to be a process of team adaptation, including adjustments and corrections, when performing operations. The multi-teams operate in an open climate with a commonly accepted allowance to stop any safety-threatened operational activity. In practice, this gives the operational teams a “license” to intervene and adjust or adapt to suddenly occurring anomalies. The operations are in turn based on a period of planning and preparing. During the actual balancing, the teams relied on communication and information sharing, coordination and cooperation, and anticipation (proactive behavior) and empowerment/autonomy as effective enablers of team adaptation.

Erik Hollnagel

Papers

The rational choice of "error"

Cognitive functions and automation: principles of human-centred automation

Time to think and time to do? I can fail, and so can you!

The Intelligent Use of Intelligent Systems

License to intervene: the role of team adaptation in balancing structure and flexibility in offshore operations