A decision-making Fokker-Planck model in computational neuroscience

Minimal models for the explanation of decision-making in computational neuroscience are based on the analysis of the evolution for the average firing rates of two interacting neuron populations. While these models typically lead to multi-stable scenario for the basic derived dynamical systems, noise is an important feature of the model taking into account finite-size effects and robustness of the decisions. These stochastic dynamical systems can be analyzed by studying carefully their associated Fokker-Planck partial differential equation. In particular, we discuss the existence, positivity and uniqueness for the solution of the stationary equation, as well as for the time evolving problem. Moreover, we prove convergence of the solution to the the stationary state representing the probability distribution of finding the neuron families in each of the decision states characterized by their average firing rates. Finally, we propose a numerical scheme allowing for simulations performed on the Fokker-Planck equation which are in agreement with those obtained recently by a moment method applied to the stochastic differential system. Our approach leads to a more detailed analytical and numerical study of this decision-making model in computational neuroscience.

Decision making in a human population living sustainably

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity--which includes omitted variables, omitted selection, simultaneity, common methods bias, and measurement error--renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation is confounded, including fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66 % and up to 90 % of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

Public particiation and the legitimacy of intergovernmental decision-making : a focus on the World Trade Organization

The legitimacy of the WTO's decision-making process has always been questioned, and many have advocated public participation mechanisms as a remedy. The present study considers the limits and potential of these mechanisms by advancing a conceptual framework, which distinguishes the four 'implementation parameters' of public participation: the goal, the object, the modalities, and the actors. It addresses the issue of legitimacy by considering to what extent, and by virtue of which legal developments, one can see implementing the democratic principle as a goal for public participation in the context of the WTO. By analyzing the institutional structure of the WTO and its different types of decisions, it then outlines how this goal should influence the object and modalities of public participation, which decision-making procedures should be opened to public participation, and how the mechanisms should be implemented in practice. Finally, it suggests speciflrc amendments to existing WTO affangements on public participation

Spatial predictions of phylogenetic diversity in conservation decision making.

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.

Revisiting the size-R&D productivity relation: introducing the mediating role of decision-making style on the scale and quality of innovative output

We develop a mediation model in which firm size is proposed to affect the scale and quality of innovative output through the adoption of different decision styles during the R&D process. The aim of this study is to understand how the internal changes that firms undergo as they evolve from small to larger organizations affect R&D productivity. In so doing, we illuminate the underlying theoretical mechanism affecting two different dimensions of R&D productivity, namely the scale and quality of innovative output which have not received much attention in previous literature. Using longitudinal data of Spanish manufacturing firms we explore the validity of this mediation model. Our results show that as firms evolve in size, they increasingly emphasize analytical decision making, and consequently, large-sized firms aim for higher-quality innovations while small firms aim for a larger scale of innovative output.

Patients' preferences on information and involvement in decision-making for gastrointestinal surgery

Objective: The relationship between physicians and patients has undergone¦important changes, and the current emancipation of patients has led to¦a real partnership in medical decision-making. This study aimed to assess¦patients' preferences on different aspects of decision-making during treatment¦and potential complications, as well as the amount and type of preoperative¦information before visceral surgery.¦Methods: Prospective non-randomized study based on a questionnaire given¦to 253 consecutive patients scheduled for elective GI surgery.¦Results: Concerning surgical complications or treatment in the intensive care¦unit, 64% of patients wished to take actively part in any medical decisions.¦The respective figures for cardiac resuscitation and treatment limitations were¦89% and 60%. About information, 73%, 77% and 47% of patients wish¦detailed information, information on a potential ICUhospitalization and cardiac¦resuscitation, respectively. Elderly and low-educated patients were significantly¦less interested in shared medical decision-making (p = 0·003 and 0·015) and in¦information receiving (p = 0·03 and 0·05). Similarly, involvement of the family¦in decision-making was significantly less important in elderly and male patients¦(p = 0·05 and 0·03 respectively). Neither the type of operation (minor or major)¦nor the severity of disease (malignancies vs. non-malignancies) was a significant¦factor for shared decision-making, information or family involvement.¦Conclusion: The vast majority of surgical patients clearly want to get adequate¦preoperative information about their disease and the planned treatment. They¦also consider it as crucial to be involved in any kind of decision-making for¦treatment and complications. The family's role is limited to support the treating¦physicians if the patient is unable to participate in taking decisions.

The de facto independence of regulatory agencies and its consequences for policy making and regulatory outcomes

This dissertation focuses on the practice of regulatory governance, throughout the study of the functioning of formally independent regulatory agencies (IRAs), with special attention to their de facto independence. The research goals are grounded on a "neo-positivist" (or "reconstructed positivist") position (Hawkesworth 1992; Radaelli 2000b; Sabatier 2000). This perspective starts from the ontological assumption that even if subjective perceptions are constitutive elements of political phenomena, a real world exists beyond any social construction and can, however imperfectly, become the object of scientific inquiry. Epistemologically, it follows that hypothetical-deductive theories with explanatory aims can be tested by employing a proper methodology and set of analytical techniques. It is thus possible to make scientific inferences and general conclusions to a certain extent, according to a Bayesian conception of knowledge, in order to update the prior scientific beliefs in the truth of the related hypotheses (Howson 1998), while acknowledging the fact that the conditions of truth are at least partially subjective and historically determined (Foucault 1988; Kuhn 1970). At the same time, a sceptical position is adopted towards the supposed disjunction between facts and values and the possibility of discovering abstract universal laws in social science. It has been observed that the current version of capitalism corresponds to the golden age of regulation, and that since the 1980s no government activity in OECD countries has grown faster than regulatory functions (Jacobs 1999). Following an apparent paradox, the ongoing dynamics of liberalisation, privatisation, decartelisation, internationalisation, and regional integration hardly led to the crumbling of the state, but instead promoted a wave of regulatory growth in the face of new risks and new opportunities (Vogel 1996). Accordingly, a new order of regulatory capitalism is rising, implying a new division of labour between state and society and entailing the expansion and intensification of regulation (Levi-Faur 2005). The previous order, relying on public ownership and public intervention and/or on sectoral self-regulation by private actors, is being replaced by a more formalised, expert-based, open, and independently regulated model of governance. Independent regulation agencies (IRAs), that is, formally independent administrative agencies with regulatory powers that benefit from public authority delegated from political decision makers, represent the main institutional feature of regulatory governance (Gilardi 2008). IRAs constitute a relatively new technology of regulation in western Europe, at least for certain domains, but they are increasingly widespread across countries and sectors. For instance, independent regulators have been set up for regulating very diverse issues, such as general competition, banking and finance, telecommunications, civil aviation, railway services, food safety, the pharmaceutical industry, electricity, environmental protection, and personal data privacy. Two attributes of IRAs deserve a special mention. On the one hand, they are formally separated from democratic institutions and elected politicians, thus raising normative and empirical concerns about their accountability and legitimacy. On the other hand, some hard questions about their role as political actors are still unaddressed, though, together with regulatory competencies, IRAs often accumulate executive, (quasi-)legislative, and adjudicatory functions, as well as about their performance.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.