Contributions in Merlin

The paper re-examines Sweden’s price level targeting during the 1930s which is regarded as a precursor of today’s inflation targeting. According to conventional wisdom, the Riksbank was the first central bank to adopt price level targeting, although in practice giving priority to exchange rate stabilisation. Based on Bayesian econometric techniques and the evaluation of new archival sources, we come to the conclusion that defending a fixed exchange rate is hard to reconcile with the claim of adopting price level targeting. This finding has implications for the prevailing view of the 1930s as a decade of great policy innovations.

The book provides a simple, intuitive introduction to regression models for qualitative and discrete dependent variables, to sample selection models, and to event history models, all in the context of maximum likelihood estimation. It presents a wide range of commonly used models. The book thereby enables the reader to become a critical consumer of current empirical social science research and to conduct own empirical analyses. The book includes numerous examples, illustrations, and exercises. It can be used as a textbook for an advanced undergraduate, a Master`s or a first-year Ph.D. course in microdata analysis, and as a reference for practitioners and researchers.

Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other approaches in the presence of outliers. We expect that this new random effects method will prove useful for a wide range of group studies, not only in the context of DCM, but also for other modelling endeavours, e.g. comparing different source reconstruction methods for EEG/MEG or selecting among competing computational models of learning and decision-making.

A key distinction among theories of civil war is between those that are built upon motivation and those that are built upon feasibility. We analyze a comprehensive global sample of civil wars for the period 1965-2004 and subject the results to a range of robustness tests. The data constitute a substantial advance on previous work. We find that variables that are close proxies for feasibility have powerful consequences for the risk of a civil war. Our results substantiate the 'feasibility hypothesis' that where civil war is feasible it will occur without reference to motivation.

Processing of speech and nonspeech sounds occurs bilaterally within primary auditory cortex and surrounding regions of the superior temporal gyrus; however, the manner in which these regions interact during speech and nonspeech processing is not well understood. Here, we investigate the underlying neuronal architecture of the auditory system with magnetoencephalography and a mismatch paradigm. We used a spoken word as a repeating "standard" and periodically introduced 3 "oddball" stimuli that differed in the frequency spectrum of the word's vowel. The closest deviant was perceived as the same vowel as the standard, whereas the other 2 deviants were perceived as belonging to different vowel categories. The neuronal responses to these vowel stimuli were compared with responses elicited by perceptually matched tone stimuli under the same paradigm. For both speech and tones, deviant stimuli induced coupling changes within the same bilateral temporal lobe system. However, vowel oddball effects increased coupling within the left posterior superior temporal gyrus, whereas perceptually equivalent nonspeech oddball effects increased coupling within the right primary auditory cortex. Thus, we show a dissociation in neuronal interactions, occurring at both different hierarchal levels of the auditory system (superior temporal versus primary auditory cortex) and in different hemispheres (left versus right). This hierarchical specificity depends on whether auditory stimuli are embedded in a perceptual context (i.e., a word). Furthermore, our lateralization results suggest left hemisphere specificity for the processing of phonological stimuli, regardless of their elemental (i.e., spectrotemporal) characteristics.

Environmental markets have several institutional features that provide a new context for the use of auctions and that have not been studied previously. This paper reports on laboratory experiments testing three auction forms — niform and discriminatory price sealed-bid auctions and an ascending clock auction. We test the ability of subjects to tacitly or explicitly collude in order to maximize profits. Our main result is that the discriminatory and uniform price auctions produce greater revenues than the clock auction, both with and without explicit communication. The clock appears to facilitate successful collusion, both because of its sequential structure and because it allows bidders to focus on one dimension of cooperation (quantity) rather than two (price and quantity).

BACKGROUND: Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity. RESULTS: The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling. CONCLUSION: During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.

This paper studies coordination in a multi-stage elimination tournament with large monetary incentives and a diversified subject pool drawn from the adult British population. In the tournament, members of an ad hoc team earn money by answering general knowledge questions and then eliminate one contestant by plurality voting without prior communication. We find that in the early rounds of the tournament, contestants use a focal principle and coordinate on one of the multiple Nash equilibria in pure strategies by eliminating the weakest member of the team. However, in the later rounds, contestants switch to playing a mixed strategy Nash equilibrium.