Information, learning, and risk in financial markets

Cette thèse comprend trois essais qui abordent l'information le processus d'ap-prentissage ainsi que le risque dans les marchés finances. Elle se concentre d'abord sur les implications à l'équilibre de l'hétérogénéité des agents à travers un processus d'apprentissage comprtemental et de mise à jour de l'information. De plus, elle examine les effets du partage des risques dans un reseau entreprise-fournisseur. Le premier chapitre étudie les effets du biais de disponibili sur l'évaluation des actifs. Ce biais décrit le fait que les agents surestiment l'importance de l'information acquise via l'expérience personnelle. L'hétérogénéité restante des différentes perceptions individuelles amène à une volonté d'échanges. Conformé¬ment aux données empiriques, les jeunes agents échangent plus mais en même temps souffrent d'une performance inférieure. Le deuxième chapitre se penche sur l'impact qu'ont les différences de modelisation entre les agents sur leurs percevons individuelles du processus de prix, dans le contexte des projections de modèles. Les agents sujets à un biais de projection pensent être représentatifs et interprètent les opinions des autres agents comme du bruit. Les agents, avec des modèles plus persistants, perçoivent que les prix réagissent de façon excessive lors des périodes de turbulence. Le troisième chapitre analyse l'impact du partage des risques dans la relation entreprise-fournisseur sur la décision optimale de financement de l'entreprise. Il étudie l'impact sur l'optimisation de la structure du capital ainsi que sur le coût du capital. Les résultats indiquent en particulier qu'un fournisseur avec un effet de levier faible est utile pour le financement d'un nouveau projet d'investissement. Pour des projets très rentables et des fournisseurs à faible effet de levier, le coût des capitaux propres de l'entreprise peut diminuer.

From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination.

Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291ms in the left middle frontal gyrus; and iii) 512-545ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra-categorical auditory discrimination for untrained items follows the temporal hierarchy and transpires in a late stage of semantic processing. On the other hand, correct categorization of individually trained stimuli occurs earlier, during a period contemporaneous with human vs. animal vocalization discrimination, and involves a parallel semantic pathway requiring expertise.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

The motivation to learn as a self-presentation tool among Swiss high school students : The moderating role of mastery goals' perceived social value on learning

Although it has been assumed that the motivation to learn - or mastery goal endorsement - positively predicts learning achievement, most empirical findings fail to demonstrate this relationship. In the present research, conducted in a Swiss high school, we adopted a social value approach to test the hypothesis that adolescent students' mastery goals do in fact predict learning, but only if these goals are perceived as highly useful for scholarly success (high social utility), and are not endorsed as a means to be appreciated by the teachers (low social desirability), a finding that has previously been observed among college students and on teacher-graded achievement measures only. Results demonstrate that in spite of potential peculiarities of an adolescent population, individual differences in mastery goals' perceived social utility and desirability moderate the mastery goal endorsement-learning achievement relation. Findings are discussed with regard to both theory development and educational practice.

Literature report : a review of policy learning in five strands of political science research

This paper reviews the policy learning literature in political science. In recent years, the number of publications on learning in the political realm increased dramatically. Researchers have focused on how policymakers and administrators adapt policies based on learning processes or experiences. Thereby, learning has been discussed in very different ways. Authors have referred to learning in the context of ideas, understood as deeply held beliefs, and, as change and adaptation of policy instruments. Two other strands of literature have taken into consideration learning, namely the diffusion literature and research on transfer, which put forward learning to understand who learns from whom and what. Opposed to these views, political learning emphasizes the adaptation of new strategies by policymakers over the transfer of knowledge or broad ideas. In this approach, learning occurs due to the failure of existing policies, increasing problem pressure, scientific innovations, or a combination of these elements.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Labour market innovations and policy learning : report on Switzerland

This paper analyses learning and implementation of labour market reforms in Switzerland.

Best practice report on policy learning infrastructures in innovative labour market policies

This report synthesizes the findings of 11 country reports on policy learning in labour market and social policies that were conducted as part of WP5 of the INSPIRES project, which is funded by the 7th Framework Program of the EU-Commission. Notably, this report puts forward objectives of policy learning, discusses tools, processes and institutions of policy learning and presents the impacts of various tools and structures of the policy learning infrastructure for the actual policy learning process. The report defines three objectives of policy learning: evaluation and assessment of policy effectiveness, vision building and planning, and consensus building. In the 11 countries under consideration, the tools and processes of the policy learning, infrastructure can be classified into three broad groups: public bodies, expert councils, and parties, interest groups and the private sector. Finally, we develop four recommendations for policy learning: Firstly, learning processes should keep the balance between centralisation and plurality. Secondly, learning processes should be kept stable beyond the usual political business cycles. Thirdly, policy learning tools and infrastructures should be sufficiently independent from political influence or bias. Fourth, Policy learning tools and infrastructures should balance out mere effectiveness, evaluation and vision building.

Policy learning in Europe : social policy and labour market reforms in 11 countries

This report compares policy learning processes in 11 European countries. Based on the country reports that were produced by the national teams of the INSPIRES project, this paper develops an argument that connects problem pressure and politicization to learning in different labor market innovations. In short, we argue that learning efforts are most likely to impact on policy change if there is a certain problem pressure that clearly necessitates political action. On the other hand, if problem pressure is very low, or so high that governments need to react immediately, chances are low that learning impacts on policy change. The second part of our argument contends that learning impacts on policy change especially if a problem is not very politicized, i.e. there are no main conflicts concerning a reform, because then, solutions are wound up in the search for a compromise. Our results confirm our first hypothesis regarding the connection between problem pressure and policy learning. Governments learn indeed up to a certain degree of problem pressure. However, once political action becomes really urgent, i.e. in anti-crisis policies, there is no time and room for learning. On the other hand, learning occurred independently from the politicization of problem. In fact, in countries that have a consensual political system, learning occurred before the decision on a reform, whereas in majoritarian systems, learning happened after the adoption of a policy during the process of implementation.

The effect of learning on the evolution of new courtship behavior: a simulation model

The fact that individuals learn can change the relationship between genotype and phenotype in the population, and thus affect the evolutionary response to selection. Here we ask how male ability to learn from female response affects the evolution of a novel male behavioral courtship trait under pre-existing female preference (sensory drive). We assume a courtship trait which has both a genetic and a learned component, and a two-level female response to males. With individual-based simulations we show that, under this scenario, learning generally increases the strength of selection on the genetic component of the courtship trait, at least when the population genetic mean is still low. As a consequence, learning not only accelerates the evolution of the courtship trait, but also enables it when the trait is costly, which in the absence of learning results in an adaptive valley. Furthermore, learning can enable the evolution of the novel trait in the face of gene flow mediated by immigration of males that show superior attractiveness to females based on another, non-heritable trait. However, rather than increasing monotonically with the speed of learning, the effect of learning on evolution is maximized at intermediate learning rates. This model shows that, at least under some scenarios, the ability to learn can drive the evolution of mating behaviors through a process equivalent to Waddington's genetic assimilation.

Undergraduate palliative care teaching in Swiss medical faculties: a nationwide survey and improved learning objectives.

BACKGROUND: In 2007, a first survey on undergraduate palliative care teaching in Switzerland has revealed major heterogeneity of palliative care content, allocation of hours and distribution throughout the 6 year curriculum in Swiss medical faculties. This second survey in 2012/13 has been initiated as part of the current Swiss national strategy in palliative care (2010 - 2015) to serve as a longitudinal monitoring instrument and as a basis for redefinition of palliative care learning objectives and curriculum planning in our country. METHODS: As in 2007, a questionnaire was sent to the deans of all five medical faculties in Switzerland in 2012. It consisted of eight sections: basic background information, current content and hours in dedicated palliative care blocks, current palliative care content in other courses, topics related to palliative care presented in other courses, recent attempts at improving palliative care content, palliative care content in examinations, challenges, and overall summary. Content analysis was performed and the results matched with recommendations from the EAPC for undergraduate training in palliative medicine as well as with recommendations from overseas countries. RESULTS: There is a considerable increase in palliative care content, academic teaching staff and hours in all medical faculties compared to 2007. No Swiss medical faculty reaches the range of 40 h dedicated specifically to palliative care as recommended by the EAPC. Topics, teaching methods, distribution throughout different years and compulsory attendance still differ widely. Based on these results, the official Swiss Catalogue of Learning Objectives (SCLO) was complemented with 12 new learning objectives for palliative and end of life care (2013), and a national basic script for palliative care was published (2015). CONCLUSION: Performing periodic surveys of palliative care teaching at national medical faculties has proven to be a useful tool to adapt the national teaching framework and to improve the recognition of palliative medicine as an integral part of medical training.