The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Ruin problems under IBNR Dynamics

In this paper, we consider a discrete-time risk process allowing for delay in claim settlement, which introduces a certain type of dependence in the process. From martingale theory, an expression for the ultimate ruin probability is obtained, and Lundberg-type inequalities are derived. The impact of delay in claim settlement is then investigated. To this end, a convex order comparison of the aggregate claim amounts is performed with the corresponding non-delayed risk model, and numerical simulations are carried out with Belgian market data.

Effects of anxiety on visuospatial pattern discrimination and decision-making processes: what can we learn from brain dynamics ?

Detection and discrimination of visuospatial input involve at least extracting, selecting and encoding relevant information and decision-making processes allowing selecting a response. These two operations are altered, respectively, by attentional mechanisms that change discrimination capacities, and by beliefs concerning the likelihood of uncertain events. Information processing is tuned by the attentional level that acts like a filter on perception, while decision-making processes are weighed by subjective probability of risk. In addition, it has been shown that anxiety could affect the detection of unexpected events through the modification of the level of arousal. Consequently, purpose of this study concerns whether and how decision-making and brain dynamics are affected by anxiety. To investigate these questions, the performance of women with either a high (12) or a low (12) STAI-T (State-Trait Anxiety Inventory, Spielberger, 1983) was examined in a decision-making visuospatial task where subjects have to recognize a target visual pattern from non-target patterns. The target pattern was a schematic image of furniture arranged in such a way as to give the impression of a living room. Non-target patterns were created by either the compression or the dilatation of the distances between objects. Target and non-target patterns were always presented in the same configuration. Preliminary behavioral results show no group difference in reaction time. In addition, visuo-spatial abilities were analyzed trough the signal detection theory for quantifying perceptual decisions in the presence of uncertainty (Green and Swets, 1966). This theory treats detection of a stimulus as a decision-making process determined by the nature of the stimulus and cognitive factors. Astonishingly, no difference in d' (corresponding to the distance between means of the distributions) and c (corresponds to the likelihood ratio) indexes was observed. Comparison of Event-related potentials (ERP) reveals that brain dynamics differ according to anxiety. It shows differences in component latencies, particularly a delay in anxious subjects over posterior electrode sites. However, these differences are compensated during later components by shorter latencies in anxious subjects compared to non-anxious one. These inverted effects seem indicate that the absence of difference in reaction time rely on a compensation of attentional level that tunes cortical activation in anxious subjects, but they have to hammer away to maintain performance.

Stochastic time-concentration activity models for cytotoxicity in 3D brain cell cultures.

BACKGROUND: In vitro aggregating brain cell cultures containing all types of brain cells have been shown to be useful for neurotoxicological investigations. The cultures are used for the detection of nervous system-specific effects of compounds by measuring multiple endpoints, including changes in enzyme activities. Concentration-dependent neurotoxicity is determined at several time points. METHODS: A Markov model was set up to describe the dynamics of brain cell populations exposed to potentially neurotoxic compounds. Brain cells were assumed to be either in a healthy or stressed state, with only stressed cells being susceptible to cell death. Cells may have switched between these states or died with concentration-dependent transition rates. Since cell numbers were not directly measurable, intracellular lactate dehydrogenase (LDH) activity was used as a surrogate. Assuming that changes in cell numbers are proportional to changes in intracellular LDH activity, stochastic enzyme activity models were derived. Maximum likelihood and least squares regression techniques were applied for estimation of the transition rates. Likelihood ratio tests were performed to test hypotheses about the transition rates. Simulation studies were used to investigate the performance of the transition rate estimators and to analyze the error rates of the likelihood ratio tests. The stochastic time-concentration activity model was applied to intracellular LDH activity measurements after 7 and 14 days of continuous exposure to propofol. The model describes transitions from healthy to stressed cells and from stressed cells to death. RESULTS: The model predicted that propofol would affect stressed cells more than healthy cells. Increasing propofol concentration from 10 to 100 μM reduced the mean waiting time for transition to the stressed state by 50%, from 14 to 7 days, whereas the mean duration to cellular death reduced more dramatically from 2.7 days to 6.5 hours. CONCLUSION: The proposed stochastic modeling approach can be used to discriminate between different biological hypotheses regarding the effect of a compound on the transition rates. The effects of different compounds on the transition rate estimates can be quantitatively compared. Data can be extrapolated at late measurement time points to investigate whether costs and time-consuming long-term experiments could possibly be eliminated.

Long time behaviour of diffusing particles in constrained geometries; application to chromatin motion

Inspired by experiments that use single-particle tracking to measure the regions of confinement of selected chromosomal regions within cell nuclei, we have developed an analytical approach that takes into account various possible positions and shapes of the confinement regions. We show, in particular, that confinement of a particle into a subregion that is entirely enclosed within a spherical volume can lead to a higher limit of the mean radial square displacement value than the one associated with a particle that can explore the entire spherical volume. Finally, we apply the theory to analyse the motion of extrachromosomal chromatin rings within nuclei of living yeast.

Lifelong dynamics of human CD4+CD25+ regulatory T cells: insights from in vivo data and mathematical modeling.

Despite their limited proliferation capacity, regulatory T cells (T(regs)) constitute a population maintained over the entire lifetime of a human organism. The means by which T(regs) sustain a stable pool in vivo are controversial. Using a mathematical model, we address this issue by evaluating several biological scenarios of the origins and the proliferation capacity of two subsets of T(regs): precursor CD4(+)CD25(+)CD45RO(-) and mature CD4(+)CD25(+)CD45RO(+) cells. The lifelong dynamics of T(regs) are described by a set of ordinary differential equations, driven by a stochastic process representing the major immune reactions involving these cells. The model dynamics are validated using data from human donors of different ages. Analysis of the data led to the identification of two properties of the dynamics: (1) the equilibrium in the CD4(+)CD25(+)FoxP3(+)T(regs) population is maintained over both precursor and mature T(regs) pools together, and (2) the ratio between precursor and mature T(regs) is inverted in the early years of adulthood. Then, using the model, we identified three biologically relevant scenarios that have the above properties: (1) the unique source of mature T(regs) is the antigen-driven differentiation of precursors that acquire the mature profile in the periphery and the proliferation of T(regs) is essential for the development and the maintenance of the pool; there exist other sources of mature T(regs), such as (2) a homeostatic density-dependent regulation or (3) thymus- or effector-derived T(regs), and in both cases, antigen-induced proliferation is not necessary for the development of a stable pool of T(regs). This is the first time that a mathematical model built to describe the in vivo dynamics of regulatory T cells is validated using human data. The application of this model provides an invaluable tool in estimating the amount of regulatory T cells as a function of time in the blood of patients that received a solid organ transplant or are suffering from an autoimmune disease.

Dynamics of IS Adaptation in Multinational Corporations: A New Theoretical Lens

The specificities of multinational corporations (MNCs) have to date not been a focus area of IS research. Extant literature mostly proposes IS configurations for specific types of MNCs, following a static and prescriptive approach. Our research seeks to explain the dynamics of global IS design. It suggests a new theoretical lens for studying global IS design by applying the structural adjustment paradigm from organizational change theories. Relying on archetype theory, we conduct a longitudinal case study to theorize the dynamics of IS adaptation. We find that global IS design emerges as an organizational adaptation process to balance interpretative schemes (i.e. the organization's values and beliefs) and structural arrangements (i.e. strategic, organizational, and IS configurations). The resulting insights can be used as a basis to further explore alternative global IS designs and movements between them.

Social Dynamics in Paper Wasps: The case of nest-drifting in Polistes canadensis

Le passage de la vie solitaire à la vie sociale représente une des principales transitions évolutives. La socialité a évolué au sein de plusieurs taxons du règne animal et notamment chez les insectes sociaux qui ont atteint son niveau le plus élevé : l'eusocialité. Les colonies d'insectes sociaux se composent d'une reine, qui monopolise la reproduction, et d'ouvrières, non-reproductrices ou parfois stériles, qui aident à élever la descendance de la reine. Selon la théorie de la sélection de parentèle, les ouvrières augmentent leur fitness (succès reproducteur) non pas à travers leur propre progéniture, mais en aidant des individus apparentés (leur reine) à produire davantage de descendants. Cette théorie prédit ainsi que les ouvrières ont un intérêt à rester fidèles à leur nid natal. Toutefois, chez la guêpe tropicale Polistes canadensis, de nombreuse ouvrières visitent d'autres nids que leur nid natal : un phénomène appelé « dérive des ouvrières ». Le but de ce doctorat est ainsi de mieux comprendre les mécanismes impliqués dans ce comportement particulier des ouvrières ainsi que ces implications pour la théorie de la sélection de parentèle. Nous avons examiné le comportement de dérive des ouvrières à travers une étude des dynamiques sociales chez la guêpe tropicale P. canadensis. Mes résultats montrent que les populations de P. canadensis se composent en différentes agrégations de nids. Malgré de précédentes suggestions, on n'observe qu'une faible viscosité génétique au sein des populations de P. canadensis étudiées. On retrouve toutefois un degré d'apparentement entre nids d'une même agrégation. Ceci suggère que les ouvrières dériveuses sont susceptibles de bénéficier de fitness indirect en aidant les nids proches géographiquement. De plus, ces échanges d'ouvrières ne semblent pas accidentels puisque l'on constate des variations de taux de dérive et puisque les déplacements observés entre nids persistent sur plusieurs périodes de temps. La charge de travail, qui correspond aux différences d'effort de fourragement entre nid visités et natals, est décrite dans notre étude comme potentiel facteur expliquant le comportement de dérive des ouvrières chez P. canadensis. Nos expériences de retrait d'ouvrières et de couvain ont révélées que les dériveuses ne semblent pas répondre aux changements de besoins en aide des nids visités. Les ouvrières dériveuses biaisent leur effort en aidant leur propre nid, par lequel elles bénéficient le plus en termes de fitness indirect, avant de se consacrer à tout autre nid. Dans l'ensemble, ces résultats sur la dérive des ouvrières chez P. canadensis sont cohérents et suggèrent que ce comportement est une importante stratégie de reproduction alternative chez cette espèce qui contribue à la fitness indirecte de ces ouvrières non-reproductrices. De plus, ce doctorat apporte des informations sur la structure génétique des populations de guêpes Polistes et décrit le rôle des ouvrières inactives. Celles-ci semblent servir de réserve en ouvrières apportant du support à la colonie dans l'éventualité d'une perte d'individus. Plus généralement, ce travail met l'accent sur l'organisation complexe et l'adaptabilité des individus dans les sociétés d'insectes. - One major transition in evolution is the shift from solitary to social life. Sociality has evolved in a few taxa of the animal kingdom, most notably in the social insects which have achieved the highest level of sociality: eusociality. Colonies of social insects are formed by a reproductive queen, and many non-reproductive or sterile workers who help raise their mother queen's offspring. Kin selection theory explains worker behaviour in terms of the indirect fitness they gain from raising non-offspring kin. It therefore predicts that workers should stay faithful to their natal nests, to which they are the more related. However, in the tropical paper wasps Polistes canadensis, high levels of nest-drifting, whereby workers spend time on other neighbouring nests, has been reported. This PhD aimed at understanding the mechanisms involved in this peculiar behaviour as well as its implications for kin selection theory. I examined nest-drifting through the study of the social dynamics of the tropical paper wasp P. canadensis. My results showed that populations of this species of paper wasps are composed of different aggregations of nests. The studied populations showed little limited dispersal (viscosity), despite previous suggestion, but nests within these aggregations were more related to each other than nests outside of aggregations. This suggested that drifters may benefit from indirect fitness when helping on neighbouring nests. Drifting was unlikely to be accidental since we found drifting patterns at various rates and consistently over several time periods during monitoring. Workload (differences in colony-level foraging effort) was also a potential factor explaining nest-drifting in P. canadensis. Worker and brood removal experiments revealed that drifters do not respond to any changes in the need for help in the non-natal nests they visit. Drifters thus bias their help in their natal nests, from which they may benefit the most in terms of indirect fitness, before investing in others. Altogether, these results on nest-drifting in P. canadensis are consistent and suggest that nest-drifting is an important alternative reproductive strategy, contributing to the indirect fitness benefits gained by non-reproductive wasps. Additionally, this PhD provides information on the genetic structure of paper wasps' populations and demonstrates the role of inactive or lazy wasps as a "reserve worker force", which provides resilience to the colony in the event of worker mortality. More generally, this work further highlights the complex organization and adaptability of individuals in insect societies.

War Signals: A Theory of Trade, Trust and Conflict

We construct a dynamic theory of civil conflict hinging on inter-ethnic trust and trade. The model economy is inhabitated by two ethnic groups. Inter-ethnic trade requires imperfectly observed bilateral investments and one group has to form beliefs on the average propensity to trade of the other group. Since conflict disrupts trade, the onset of a conflict signals that the aggressor has a low propensity to trade. Agents observe the history of conflicts and update their beliefs over time, transmitting them to the next generation. The theory bears a set of testable predictions. First, war is a stochastic process whose frequency depends on the state of endogenous beliefs. Second, the probability of future conflicts increases after each conflict episode. Third, "accidental" conflicts that do not reflect economic fundamentals can lead to a permanent breakdown of trust, plunging a society into a vicious cycle of recurrent conflicts (a war trap). The incidence of conflict can be reduced by policies abating cultural barriers, fostering inter-ethnic trade and human capital, and shifting beliefs. Coercive peace policies such as peacekeeping forces or externally imposed regime changes have instead no persistent effects.

Rates of cultural change and patterns of cultural accumulation in stochastic models of social transmission.

Cultural variation in a population is affected by the rate of occurrence of cultural innovations, whether such innovations are preferred or eschewed, how they are transmitted between individuals in the population, and the size of the population. An innovation, such as a modification in an attribute of a handaxe, may be lost or may become a property of all handaxes, which we call "fixation of the innovation." Alternatively, several innovations may attain appreciable frequencies, in which case properties of the frequency distribution-for example, of handaxe measurements-is important. Here we apply the Moran model from the stochastic theory of population genetics to study the evolution of cultural innovations. We obtain the probability that an initially rare innovation becomes fixed, and the expected time this takes. When variation in cultural traits is due to recurrent innovation, copy error, and sampling from generation to generation, we describe properties of this variation, such as the level of heterogeneity expected in the population. For all of these, we determine the effect of the mode of social transmission: conformist, where there is a tendency for each naïve newborn to copy the most popular variant; pro-novelty bias, where the newborn prefers a specific variant if it exists among those it samples; one-to-many transmission, where the variant one individual carries is copied by all newborns while that individual remains alive. We compare our findings with those predicted by prevailing theories for rates of cultural change and the distribution of cultural variation.

The contribution of patch topology and demographic parameters to PVA predictions: the case of the European tree frog

Population viability analyses (PVA) are increasingly used in metapopulation conservation plans. Two major types of models are commonly used to assess vulnerability and to rank management options: population-based stochastic simulation models (PSM such as RAMAS or VORTEX) and stochastic patch occupancy models (SPOM). While the first set of models relies on explicit intrapatch dynamics and interpatch dispersal to predict population levels in space and time, the latter is based on spatially explicit metapopulation theory where the probability of patch occupation is predicted given the patch area and isolation (patch topology). We applied both approaches to a European tree frog (Hyla arborea) metapopulation in western Switzerland in order to evaluate the concordances of both models and their applications to conservation. Although some quantitative discrepancies appeared in terms of network occupancy and equilibrium population size, the two approaches were largely concordant regarding the ranking of patch values and sensitivities to parameters, which is encouraging given the differences in the underlying paradigms and input data.

Causal explanations and scientific realism in particle physics

Particle physics studies highly complex processes which cannot be directly observed. Scientific realism claims that we are nevertheless warranted in believing that these processes really occur and that the objects involved in them really exist. This dissertation defends a version of scientific realism, called causal realism, in the context of particle physics. I start by introducing the central theses and arguments in the recent philosophical debate on scientific realism (chapter 1), with a special focus on an important presupposition of the debate, namely common sense realism. Chapter 2 then discusses entity realism, which introduces a crucial element into the debate by emphasizing the importance of experiments in defending scientific realism. Most of the chapter is concerned with Ian Hacking's position, but I also argue that Nancy Cartwright's version of entity realism is ultimately preferable as a basis for further development. In chapter 3,1 take a step back and consider the question whether the realism debate is worth pursuing at all. Arthur Fine has given a negative answer to that question, proposing his natural ontologica! attitude as an alternative to both realism and antirealism. I argue that the debate (in particular the realist side of it) is in fact less vicious than Fine presents it. The second part of my work (chapters 4-6) develops, illustrates and defends causal realism. The key idea is that inference to the best explanation is reliable in some cases, but not in others. Chapter 4 characterizes the difference between these two kinds of cases in terms of three criteria which distinguish causal from theoretical warrant. In order to flesh out this distinction, chapter 5 then applies it to a concrete case from the history of particle physics, the discovery of the neutrino. This case study shows that the distinction between causal and theoretical warrant is crucial for understanding what it means to "directly detect" a new particle. But the distinction is also an effective tool against what I take to be the presently most powerful objection to scientific realism: Kyle Stanford's argument from unconceived alternatives. I respond to this argument in chapter 6, and I illustrate my response with a discussion of Jean Perrin's experimental work concerning the atomic hypothesis. In the final part of the dissertation, I turn to the specific challenges posed to realism by quantum theories. One of these challenges comes from the experimental violations of Bell's inequalities, which indicate a failure of locality in the quantum domain. I show in chapter 7 how causal realism can further our understanding of quantum non-locality by taking account of some recent experimental results. Another challenge to realism in quantum mechanics comes from delayed-choice experiments, which seem to imply that certain aspects of what happens in an experiment can be influenced by later choices of the experimenter. Chapter 8 analyzes these experiments and argues that they do not warrant the antirealist conclusions which some commentators draw from them. It pays particular attention to the case of delayed-choice entanglement swapping and the corresponding question whether entanglement is a real physical relation. In chapter 9,1 finally address relativistic quantum theories. It is often claimed that these theories are incompatible with a particle ontology, and this calls into question causal realism's commitment to localizable and countable entities. I defend the commitments of causal realism against these objections, and I conclude with some remarks connecting the interpretation of quantum field theory to more general metaphysical issues confronting causal realism.

Age-specific fitness components and their temporal variation in the barn owl.

Theory predicts that temporal variability plays an important role in the evolution of life histories, but empirical studies evaluating this prediction are rare. In constant environments, fitness can be measured by the population growth rate lambda, and the sensitivity of lambda to changes in fitness components estimates selection on these traits. In variable environments, fitness is measured by the stochastic growth rate lambda(S), and stochastic sensitivities estimate selection pressure. Here we examine age-specific schedules for reproduction and survival in a barn owl population (Tyto alba). We estimated how temporal variability affected fitness and selection, accounting for sampling variance. Despite large sample sizes of old individuals, we found no strong evidence for senescence. The most variable fitness components were associated with reproduction. Survival was less variable. Stochastic simulations showed that the observed variation decreased fitness by about 30%, but the sensitivities of lambda and lambda(S) to changes in all fitness components were almost equal, suggesting that temporal variation had negligible effects on selection. We obtained these results despite high observed variability in the fitness components and relatively short generation time of the study organism, a situation in which temporal variability should be particularly important for natural selection and early senescence is expected.

Cumulative cultural dynamics and the coevolution of cultural innovation and transmission: an ESS model for panmictic and structured populations.

When individuals in a population can acquire traits through learning, each individual may express a certain number of distinct cultural traits. These traits may have been either invented by the individual himself or acquired from others in the population. Here, we develop a game theoretic model for the accumulation of cultural traits through individual and social learning. We explore how the rates of innovation, decay, and transmission of cultural traits affect the evolutionary stable (ES) levels of individual and social learning and the number of cultural traits expressed by an individual when cultural dynamics are at a steady-state. We explore the evolution of these phenotypes in both panmictic and structured population settings. Our results suggest that in panmictic populations, the ES level of learning and number of traits tend to be independent of the social transmission rate of cultural traits and is mainly affected by the innovation and decay rates. By contrast, in structured populations, where interactions occur between relatives, the ES level of learning and the number of traits per individual can be increased (relative to the panmictic case) and may then markedly depend on the transmission rate of cultural traits. This suggests that kin selection may be one additional solution to Rogers's paradox of nonadaptive culture.