Learning, fatigue and preference formation in discrete choice experiments

While the repeated nature of Discrete Choice Experiments is advantageous from a sampling efficiency perspective, patterns of choice may differ across the tasks, due, in part, to learning and fatigue. Using probabilistic decision process models, we find in a field study that learning and fatigue behavior may only be exhibited by a small subset of respondents. Most respondents in our sample show preference and variance stability consistent with rational pre-existent and
well formed preferences. Nearly all of the remainder exhibit both learning and fatigue effects. An important aspect of our approach is that it enables learning and fatigue effects to be explored, even though they were not envisaged during survey design or data collection.

Strategic behaviour in choice experiments.

Practitioners of environmental economics sometimes use repeated trinary choice experiment surveys to estimate the value of environmental policies and programs for use in policy evaluation. These surveys have several advantages over simpler forms of non-market valuation: (1) researchers can estimate the marginal value of attributes of the good or service in question, making the results useful for benefits transfer; and (2) because respondents make several choices and choose from choice sets containing three options, efficiency of the willingness to pay estimate is improved over one-shot, binary choice formats. Despite these benefits, such surveys may have incentive properties which cause the resulting value estimates to be biased. This paper presents a theoretical demonstration that subjects often have an incentive to choose the second-best option in a repeated trinary choice survey. The model shows that due to the nature of factorial choice set design, the second-best option in the choice set will often be the status quo option. The paper reports a set of experiments designed to test these theoretical predictions in an induced-value setting. The experimental results are consistent with the theoretical predictions, demonstrating that repeated trinary choice experiment surveys can generate biased value estimates under a wide range of conditions.

Optimising element choice for nanoparticle radiosensitisers

There is considerable interest in the use of heavy atom nanoparticles as theranostic contrast agents due to their high radiation cross-section compared to soft tissue. However, published studies have primarily focused on applications of gold nanoparticles. This study applies Monte Carlo radiation transport modelling using Geant4 to evaluate the macro- and micro-scale radiation dose enhancement following X-ray irradiation with both imaging and therapeutic energies on nanoparticles consisting of stable elements heavier than silicon. An approach based on the Local Effect Model was also used to assess potential biological impacts. While macroscopic dose enhancement is well predicted by simple absorption cross-sections, nanoscale dose deposition has a much more complex dependency on atomic number, with local maxima around germanium (Z = 32) and gadolinium (Z = 64), driven by variations in secondary Auger electron spectra, which translate into significant variations in biological effectiveness. These differences may provide a valuable tool for predicting and elucidating fundamental mechanisms of these agents as they move towards clinical application.