Free Will Pessimism

The immediate aim of this paper is to articulate the essential features of an alternative compatibilist position, one that is responsive to sources of resistance to the compatibilist program based on considerations of fate and luck. The approach taken relies on distinguishing carefully between issues of skepticism and pessimism as they arise in this context. A compatibilism that is properly responsive to concerns about fate and luck is committed to what I describe as free will pessimism, which is to be distinguished from free will skepticism. Free will skepticism is the view that our vulnerability to conditions of fate and luck serve to discredit our view of ourselves as free and responsible agents. Free will pessimism rejects free will scepticism, since the basis of its pessimism rests with the assumption that we are free and responsible agents who are, nevertheless, subject to fate and luck in this aspect of our lives. According to free will pessimism, all the major parties and positions in the free will debate, including that of skepticism, are modes of evasion and distortion regarding our human predicament in respect of agency and moral life

Sorabji and the dilemma of determinism

IN Necessity, Cause and Blame (London: Duckworth, 1980) Richard Sorabji attempts to develop a notion of moral responsibility which does not get caught on either horn of a well known dilemma. One horn is the argument that if an action was caused then it must have been necessary and therefore could not be one for which the agent is responsible. The other horn is the argument that if the action was not caused then it is inexplicable and random and therefore not something which the agent can be responsible for. Sorabji denies that what is caused is always necessitated. Causes are primarily explanatory rather than necessitating. This established, Sorabji hopes to show that action open to moral scrutiny may be caused without being necessitated and the dilemma collapses. I will argue that this strategy runs into serious difficulties

Economies of scale in the library world: the Dr Martin Luther King Jr Library in San Jose, California

Discusses the new Dr Martin Luther King Jr Library in San Jose´, California, which will house the collections of the San Jose´ Public Library’s main branch and the San Jose´ State University’s Library system in one new building. Outlines the conception of the project, the site selection and the planning process. Considers the communities served, usage patterns and services. Focuses on the management structure and operations in light of a, perhaps controversial, aspect of mixing city and university library staff under the same roof, some performing similar functions, but with different supervisors and employing agencies. Discusses the new library in the context of other joint-use libraries and in the context of economies of scale and future trends. Evaluates the arising challenges and opportunities

Ensemble Transport Adaptive Importance Sampling

Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sampling from complex probability distributions. As applications of these methods increase in size and complexity, the need for efficient methods increases. In this paper, we present a particle ensemble algorithm. At each iteration, an importance sampling proposal distribution is formed using an ensemble of particles. A stratified sample is taken from this distribution and weighted under the posterior, a state-of-the-art ensemble transport resampling method is then used to create an evenly weighted sample ready for the next iteration. We demonstrate that this ensemble transport adaptive importance sampling (ETAIS) method outperforms MCMC methods with equivalent proposal distributions for low dimensional problems, and in fact shows better than linear improvements in convergence rates with respect to the number of ensemble members. We also introduce a new resampling strategy, multinomial transformation (MT), which while not as accurate as the ensemble transport resampler, is substantially less costly for large ensemble sizes, and can then be used in conjunction with ETAIS for complex problems. We also focus on how algorithmic parameters regarding the mixture proposal can be quickly tuned to optimise performance. In particular, we demonstrate this methodology's superior sampling for multimodal problems, such as those arising from inference for mixture models, and for problems with expensive likelihoods requiring the solution of a differential equation, for which speed-ups of orders of magnitude are demonstrated. Likelihood evaluations of the ensemble could be computed in a distributed manner, suggesting that this methodology is a good candidate for parallel Bayesian computations