Buchak’s risk-weighted expected utility considers not just the probability of an outcome, but also the probability of getting a strictly better outcome, when weighting the contribution that outcome gives to the evaluation of a gamble. It uses a risk-weighting function $R$ sending probabilities in $\left[ {0,1} \right]$ to decision weights $\left[ {0,1} \right]$. I adapt this to allow weights in any real interval. Finite intervals yield nothing new, but if the interval is infinite, then the resulting rule can incorporate maximin or maximax preferences (or both!) while still satisfying stochastic dominance. There are advantages to working with marginal risk-weighting, $R$’s derivative, $r$.

Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between trials within one block. Optimal design theory was applied to find the optimal combination of these three design factors. Furthermore, different error structures were used within a general linear model for the analysis of fMRI data, and the maximin criterion was applied to find designs which are robust against misspecification of model parameters.

This paper proposes new grounds for the legal ambivalence about ‘bad character evidence’. It is suggested that errors based on such evidence are profoundly tragic in the Aristotelian sense: the defendant who previously committed crime is likely to reoffend; nevertheless, she beats the odds and refrains from further crime commission – only to then be falsely convicted based on the very odds she has almost heroically managed to beat. It is further proposed that the tragic nature of such false convictions might make them particularly unfair to the defendant. It is, however, submitted that the likelihood of errors based on such evidence is unknown and probably also unknowable. Accordingly, the maximin rule for decision in conditions of deep ignorance is applied, leading to the conclusion that exclusion is to be preferred.

How should a team allocate its players to achieve the optimal balance between offence and defence? By posing a simple territorial game between two teams, insight into this question is gained. The saddlepoint solution to the game is derived.