Reflexive Monism

Reflexive monism is, in essence, an ancient view of how consciousness relates to the material world that has, in recent decades, been resurrected in modern form. In this paper I discuss how some of its basic features differ from both dualism and variants of physicalist and functionalist reductionism, focusing on those aspects of the theory that challenge deeply rooted presuppositions in current Western thought. I pay particular attention to the ontological status and seeming “out-thereness” of the phenomenal world and to how the “phenomenal world” relates to the “physical world”, the “world itself”, and processing in the brain. In order to place the theory within the context of current thought and debate, I address questions that have been raised about reflexive monism in recent commentaries and also evaluate competing accounts of the same issues offered by “transparency theory” and by “biological naturalism”. I argue that, of the competing views on offer, reflexive monism most closely follows the contours of ordinary experience, the findings of science, and common sense

The hidden subgroup problem and quantum computation using group representations

The hidden subgroup problem is the foundation of many quantum algorithms. An efficient solution is known for the problem over abelian groups, employed by both Simon's algorithm and Shor's factoring and discrete log algorithms. The nonabelian case, however, remains open; an efficient solution would give rise to an efficient quantum algorithm for graph isomorphism. We fully analyze a natural generalization of the algorithm for the abelian case to the nonabelian case and show that the algorithm determines the normal core of a hidden subgroup: in particular, normal subgroups can be determined. We show, however, that this immediate generalization of the abelian algorithm does not efficiently solve graph isomorphism

Dominance Measuring Method Performance under Incomplete Information about Weights.

In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one