The strategic interplay between bundling and merging in complementary markets

In this paper, two pairs of complementors have to decide whether to merge and eventually bundle their products. Depending on the degree of competitive pressure in the market, either both pairs decide to merge (with or without bundling), or only one pair merges and bundles, while rivals remain independent. The latter case can very harmful for consumers as it brings surge in prices. We also consider the case in which one pair moves first. Interestingly, we find a parametric region where first movers merge but refrain from bundling, to not induce rivals to merge as well.

Targeted proteomics: a powerful approach providing new insights in biology

Comunicaciones a congreso

A quantum probability account of individual differences in causal reasoning

We use quantum probability (QP) theory to investigate individual differences in causal reasoning. By analyzing data sets from Rehder (2014) on comparative judgments, and from Rehder & Waldmann (2016) on absolute judgments, we show that a QP model can both account for individual differences in causal judgments, and why these judgments sometimes violate the properties of causal Bayes nets. We implement this and previously proposed models of causal reasoning (including classical probability models) within the same hierarchical Bayesian inferential framework to provide a detailed comparison between these models, including computing Bayes factors. Analysis of the inferred parameters of the QP model illustrates how these can be interpreted in terms of putative cognitive mechanisms of causal reasoning. Additionally, we implement a latent classification mechanism that identifies subcategories of reasoners based on properties of the inferred cognitive process, rather than post hoc clustering. The QP model also provides a parsimonious explanation for aggregate behavior, which alternatively can only be explained by a mixture of multiple existing models. Investigating individual differences through the lens of a QP model reveals simple but strong alternatives to existing explanations for the dichotomies often observed in how people make causal inferences. These alternative explanations arise from the cognitive interpretation of the parameters and structure of the quantum probability model

Persistent aggregates in apheresis platelet concentrates are commonly collected from donors with a history of aggregate donation

Platelet apheresis sometimes causes persistent aggregates (PA). This study (n = 211) shows that changing the apheresis settings to reach fixed product volumes instead of yields does not influence PA incidence, even though PA products on average contain more platelets than controls. Furthermore, logistic regression was used to model if PA can be predicted on the basis of certain predonation parameters. PA donation history was the only parameter retained, proving a strong determinant of predictability [AUC = 0.735 (SE = 0.022)]. Consequently, donations from a donor with previous PA history are 7.8 times more likely to contain PA than from a donor without preceding history