Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where there is both belief updating based on direct evidence and also belief combination between agents. In particular, we consider the scenario in which a population of agents collaborate on the best-of-n problem where the aim is to reach a consensus about which is the best (alternatively, true) state from amongst a set of states, each with a different quality value (or level of evidence). Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators for this multi-agent consensus formation problem: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. The convergence properties of the operators are considered and simulation experiments are conducted for different evidence rates and noise levels. Results show that a combination of updating on direct evidence and belief combination between agents results in better consensus to the best state than does evidence updating alone. We also find that in this framework the operators are robust to noise. Broadly, Yager's rule is shown to be the better operator under various parameter values, i.e. convergence to the best state, robustness to noise, and scalability.Comment: 13th international conference on Scalable Uncertainty Managemen

Causes of regional variation in Dutch healthcare expenditures: Evidence from movers

We assess the relative importance of demand and supply factors as determinants of regional variation in healthcare expenditures in the Netherlands. Our empirical approach follows individuals who migrate between regions. We use individual data on annual healthcare expenditures for the entire Dutch population between the years 2006 and 2013. Regional variation in healthcare expenditures is mostly driven by demand factors, with an estimated share of around 70%. The relative importance of different causes varies with the groups of regions being compared

A probabilistic analysis of argument cogency

This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and may indeed serve to correct, the informal understanding and applications of the RSA criteria concerning their conceptual dependence, their function as update-thresholds, and their status as obligatory rather than permissive norms, but also show how these formal and informal normative approachs can in fact align

Relevance and Reason Relations

The present paper examines precursors and consequents of perceived relevance of a proposition A for a proposition C. In Experiment 1, we test Spohn’s (2012, ch. 6) assumption that ∆P = P(C|A) – P(C|¬A) is a good predictor of ratings of perceived relevance and reason relations, and we examine whether it is a better predictor than the difference measure (P(C|A) – P(C)). In Experiment 2, we examine the effects of relevance on probabilistic coherence in Cruz, Baratgin, Oaksford, and Over’s (2015) uncertain “and-to-if” inferences. The results suggest that ∆P predicts perceived relevance and reason relations better than the difference measure and that participants are either less probabilistically coherent in “and-to-if” inferences than initially assumed or that they do not follow P(if A, then C) = P(C|A) (“the Equation”). Results are discussed in light of recent results suggesting that the Equation may not hold under conditions of irrelevance or negative relevance

Imaging markers associated with the development of post-stroke depression and apathy: results of the cognition and affect after stroke - a prospective evaluation of risks study

Introduction: It has been suggested that the development of post-stroke apathy (PSA) and depression (PSD) may be more strongly associated with generalised brain pathology, rather than the stroke lesion itself. The present study aimed to investigate associations between imaging markers of lesion-related and generalised brain pathology and the development of PSA and PSD during a one-year follow-up.Patients and methods: In a prospective cohort study, 188 stroke patients received 3-Tesla MRI at baseline (three months post-stroke) for evaluation of lesion-related, vascular, and degenerative brain pathology. Presence of lacunes, microbleeds, white matter hyperintensities, and enlarged perivascular spaces was summed to provide a measure of total cerebral small vessel disease (cSVD) burden (range 0-4). The Mini International Neuropsychiatric Interview and Apathy Evaluation Scale were administered at baseline and repeated at 6- and 12-month follow-up to define presence of PSD and PSA, respectively.Results: Population-averaged logistic regression models showed that global brain atrophy and severe cSVD burden (score 3-4) were significantly associated with the odds of having PSA (ORGEE 5.33, 95% CI 1.99-14.25 and 3.04, 95% CI 1.20-7.69, respectively), independent of stroke lesion volume and co-morbid PSD. Medium cSVD burden (score 2) was significantly associated with the odds of having PSD (ORGEE 2.92, 95% CI 1.09-7.78), independent of stroke lesion volume, co-morbid PSA, and pre-stroke depression. No associations were found with lesion-related markers.Conclusions: The results suggest that generalised degenerative and vascular brain pathology, rather than lesion-related pathology, is an important predictor for the development of PSA, and less strongly for PSD