Regulation and the Management of Expectations - Rating Agencies Revisited

In the aftermath of the 2008 crisis, scholars have begun to revise their conceptions of how market participants interact. While the traditional “rationalist optic” posits market participants who are able to process decisionrelevant information and thereby transform uncertainty into quantifiable risks, the increasingly popular “sociological optic” stresses the role of uncertainty in expectation formation and social conventions for creating confidence in markets. Applications of the sociological optic to concrete regulatory problems are still limited. By subjecting both optics to the same regulatory problem—the role of credit rating agencies (CRAs) and their ratings in capital markets—this paper provides insights into whether the sociological optic offers advice to tackle concrete regulatory problems and discusses the potential of the sociological optic in complementing the rationalist optic. The empirical application suggests that the sociological optic is not only able to improve our understanding of the role of CRAs and their ratings, but also to provide solutions complementary to those posited by the rationalist optic.

A new yardstick for rating dental arch relationship in patients with complete bilateral cleft lip and palate

Objective: To develop yardsticks for assessment of dental arch relationship in young individuals with repaired complete bilateral cleft lip and palate appropriate to different stages of dental development. Participants: Eleven cleft team orthodontists from five countries worked on the projects for 4 days. A total of 776 sets of standardized plaster models from 411 patients with operated complete bilateral cleft lip and palate were available for the exercise. Statistics: The interexaminer reliability was calculated using weighted kappa statistics. Results: The interrater weighted kappa scores were between .74 and .92, which is in the "good" to "very good" categories. Conclusions: Three bilateral cleft lip and palate yardsticks for different developmental stages of the dentition were made: one for the deciduous dentition (6-year-olds' yardstick), one for early mixed dentition (9-year-olds' yardstick), and one for early permanent dentition (12-year-olds' yardstick).

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Measuring motor activity in major depression: the association between the Hamilton Depression Rating Scale and actigraphy

Despite the use of actigraphy in depression research, the association of depression ratings and quantitative motor activity remains controversial. In addition, the impact of recurring episodes on motor activity is uncertain. In 76 medicated inpatients with major depression (27 with a first episode, 49 with recurrent episodes), continuous wrist actigraphy for 24h and scores on the Hamilton Depression Rating Scale (HAMD) were obtained. In addition, 10 subjects of the sample wore the actigraph over a period of 5 days, in order to assess the reliability of a 1-day measurement. Activity levels were stable over 5 consecutive days. Actigraphic parameters did not differ between patients with a first or a recurrent episode, and quantitative motor activity failed to correlate with the HAMD total score. However, of the motor-related single items of the HAMD, the item activities was associated with motor activity parameters, while the items agitation and retardation were not. Actigraphy is consistent with clinical observation for the item activities. Expert raters may not correctly rate the motor aspects of retardation and agitation in major depression.

German validation of the Conners Adult ADHD Rating Scales–self-report (CAARS-S) I: Factor structure and normative data

Attention-deficit/hyperactivity disorder (ADHD) often persists into adulthood. Instruments for diagnosing ADHD in childhood are well validated and reliable, but diagnosis of ADHD in adults remains problematic. Attempts have been made to develop criteria specific for adult ADHD, resulting in the development of self-report and observer-rated questionnaires. To date, the Conners Adult ADHD Rating Scales (CAARS) are the international standard for questionnaire assessment of ADHD. The current study evaluates a German version of the CAARS self-report (CAARS-S).