Stephen Leeds

This paper investigates the possibility of extending the likelihood treatment of support to situations in which the evidence and the hypotheses supported by the evidence are all outcomes of a chance process. An example is when we ask how much support the observed sequence of heads and tails gives to the hypothesis that the next toss will be a head. I begin by discussing Sober’s approach to a problem of this type: that of estimating how much support the observation that I have a mind gives to the hypothesis that you do. I criticize his approach, and offer a general solution to the problem.

This paper is a discussion of David Albert's approach to the foundations of classical statistical menchanics. I point out a respect in which his account makes a stronger claim about the statistical mechanical probabilities than is usually made, and I suggest what might be motivation for this. I outline a less radical approach, which I attribute to Boltzmann, and I give some reasons for thinking that this approach is all we need, and also the most we are likely to get. The issue between the two accounts turns out to be one about the explanatory role probabilities play in statistical mechanics.

I argue that one good reason for Scientific Realists to be interested in correspondence theories is the hope they offer us of being able to state and defend realistic theses in the face of well-known difficulties about modern physics: such theses as, that our theories are approximately true, or that they will tend to approach the truth. I go on to claim that this hope is unlikely to be fulfilled. I suggest that Realism can still survive in the face of these difficulties, as a claim about the kind of theories we want to aim for. I relate this conception of Realism to various contemporary discussions, both by realists and antirealists.