The Hard Problem Isn’t Getting any Easier: Thoughts on Chalmers’ “Meta-Problem”

Philosophia - Chalmers’ meta-problem of consciousness is the problem of explaining “problem reports”; i.e. reports to the effect that phenomenal consciousness has the various...     

Causal explanation improves judgment under uncertainty,but rarely in a Bayesian way

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes’ rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.     

Projection and Behavior: A Rorschach Study of Assaultive Mental Hospital Patients

Collapsing DSM-III schizotypal and borderline groups to form a borderline population is unwarranted when the results are then related to borderline personality organization. In a comparison of overlapping scoring systems for inferring defensive operations, Lerner, Albert, and Walsh intertwine fact and theory extravagantly and are not justified by their chosen populations nor their assumptions about the theories involved.     

Ethics, Finance, and Automation: A Preliminary Survey of Problems in High Frequency Trading

All of finance is now automated, most notably high frequency trading. This paper examines the ethical implications of this fact. As automation is an interdisciplinary endeavor, we argue that the interfaces between the respective disciplines can lead to conflicting ethical perspectives; we also argue that existing disciplinary standards do not pay enough attention to the ethical problems automation generates. Conflicting perspectives undermine the protection those who rely on trading should have. Ethics in finance can be expanded to include organizational and industry-wide responsibilities to external market participants and society. As a starting point, quality management techniques can provide a foundation for a new cross-disciplinary ethical standard in the age of automation.     

Asymmetries in Benefiting, Harming and Creating

It is often said that while we have a strong reason not to create someone who will be badly off, we have no strong reason for creating someone who will be well off. In this paper I argue that this asymmetry is incompatible with a plausible principle of independence of irrelevant alternatives, and that a more general asymmetry between harming and benefiting is difficult to defend. I then argue that, contrary to what many have claimed, it is possible to harm or benefit someone by bringing her into existence.     

The politicization of deviance: Resisting and reversing degradation and stigmatization

Using the Israeli case of Abu‐hatzira, this paper argues that processes of degradation, stigmatization and deviantization can be reversed. The case itself is documented to be followed by an interpretation clustered along two axes. It is argued, first, that Abu‐hatzira used in his favor an existing ethnic dispute in Israel, coupled with a religious factor. Second, reversal of deviantization can be achieved by politicizing “regular” deviance, subsequently making a political statement within the context of an effective ideology geared to mobilize a receptive audience.¹     

Evaluating the Equal-Interval Hypothesis with Test Score Scales

The axioms of additive conjoint measurement provide a means of testing the hypothesis that testing data can be placed onto a scale with equal-interval properties. However, the axioms are difficult to verify given that item responses may be subject to measurement error. A Bayesian method exists for imposing order restrictions from additive conjoint measurement while estimating the probability of a correct response. In this study an improved version of that methodology is evaluated via simulation. The approach is then applied to data from a reading assessment intentionally designed to support an equal-interval scaling.