Algorithms of Adaptation in Inductive Inference

We investigate the idea that human concept inference utilizes local incremental search within a compositional mental theory space. To explore this, we study judgments in a challenging task, where participants actively gather evidence about a symbolic rule governing the behavior of a simulated environment. Participants construct mini-experiments before making generalizations and explicit guesses about the hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants’ initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guesses. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants’ revisions better than a range of alternatives. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.

Evidence-based priority setting

This paper describes evidence-based priority setting and resource allocation undertaken by a Division of the Women's & Children's Hospital, Adelaide during 1998-1999. We describe the methods used to combine program budgeting marginal analysis (PBMA), evidence based and "community values" approaches into one decision-making framework.Previous organisational changes involving the formation of multidisciplinary team and program management were pivotal in setting a framework to successfully complete the priority setting process.