Peter Vranas

We can distinguish two concepts of respect for persons: appraisal respect , an attitude based on a positive appraisal of a person's moral character, and recognition respect , the practice of treating persons with consideration based on the belief that they deserve such treatment. After engaging in an extended analysis of these concepts, I examine two "truisms" about them. We justifiably believe of some persons that they have good character and thus deserve our esteem . Frequently it pays to be disrespectful; e.g., insulting those who insult us may put them in their place. By using empirical results from social and personality psychology and techniques from decision theory in addition to conceptual considerations, I argue that, surprisingly, the above two "truisms" are false. Extensive psychological evidence indicates that most persons are indeterminate---overall neither good nor bad nor intermediate---and that our information about specific persons almost never distinguishes those who are indeterminate from those who are not. The strategy of habitually avoiding disrespectful behavior maximizes long-term expected utility. In sum, we have good pragmatic reason to treat persons respectfully, but we have good epistemic reason to avoid esteeming or despising them

Why does classical equilibrium statistical mechanics work? Malament and Zabell (1980) noticed that, for ergodic dynamical systems, the unique absolutely continuous invariant probability measure is the microcanonical. Earman and Rédei (1996) replied that systems of interest are very probably not ergodic, so that absolutely continuous invariant probability measures very distant from the microcanonical exist. In response I define the generalized properties of epsilon-ergodicity and epsilon-continuity, I review computational evidence indicating that systems of interest are epsilon-ergodic, I adapt Malament and Zabell’s defense of absolute continuity to support epsilon-continuity, and I prove that, for epsilon-ergodic systems, every epsilon-continuous invariant probability measure is very close to the microcanonical.

[1] You have a crystal ball. Unfortunately, it’s defective. Rather than predicting the future, it gives you the chances of future events. Is it then of any use? It certainly seems so. You may not know for sure whether the stock market will crash next week; but if you know for sure that it has an 80% chance of crashing, then you should be 80% confident that it will—and you should plan accordingly. More generally, given that the chance of a proposition A is x%, your conditional credence in A should be x%. This is a chance-credence principle: a principle relating chance (objective probability) with credence (subjective probability, degree of belief). Let’s call it the Minimal Principle (MP).