Aggregating Computers and Experts

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Human experts have opinions that are the product of enviable experience and pronounced cognitive biases. Calibrated computer simulations can make impressively precise predictions albeit with questionable accuracy. Given that both experts and computers each answer queries with different unknown biases and accuracies, it is unclear how we make best aggregate the information to assess what we know and what we would gain from further inputs from either a computer or an expert. Techniques (e.g., Gaussian Processes) exist to use data from either an expert or a computer and to interpolate between previously measured data-points. These techniques rely on some notion of the extent to which fluctuations in input parameters can result in fluctuations in output data. This notion can be captured mathematically in the "kernel". It is possible to use recently-developed distributed numerical Bayesian techniques (Sequential Monte Carlo samplers) to efficiently search the huge space of kernels: these techniques are better able to exploit distributed hardware than pre-existing alternatives (e.g., Markov chain Monte Carlo). This ability to search efficiently is of paramount importance when the kernel also captures the extent to which biases can exist between the output of an expert and a computer. The problem of aggregating computer outputs and expert judgement is pertinent to Unilever's ability to accelerate the development of new products. Such aggregation would make it possible to: ascertain the utility of requesting additional expert input and/or running additional computer simulation; estimate the biases present; identify the optimal compromise between trusting the experts and relying on the apparent fidelity of the computer simulations and ultimately the level of acceptance and adoption of such simulations to the scientists in their product design activities. Unilever will work with the student to identify and access data pertinent to a specific instance of this challenge as well as to understand how the technology being developed could be deployed in the context of the formulation of new products.