scholarly journals Compliance and confirmation bias on the emergence of cultural conventions

2021 ◽  
Author(s):  
José Segovia-Martín
Keyword(s):  
Learning Algorithm ◽  
Initial Conditions ◽  
Confirmation Bias ◽  
Evolutionary Model ◽  
Alpha Diversity ◽  
Value Systems ◽  
Institutional Values ◽  
Institutional Influence ◽  
Population Sizes ◽  
Different Levels

In the present study we develop a co-evolutionary model of cardinal preferences and institutions to explore how the dynamics of cultural diversity in populations with different levels of compliance and confirmation bias evolve. This is the first attempt to formalise these two types of bias in a single learning algorithm for agents learning in iterative chains without access to completeinformation. Results show that, in some regions of the parameter space, institutional influence facilitates the emergence of shared cultural conventions when compliance biases increase. In general, a compliance bias pushes diversity up when institutions are diverse, and pushes diversity down when institutions convey value systems with strong dominance of one or few cultural variants. Interestingly, in some scenarios, a decrease in institutional influence and compliance bias allows theemergence of cultural conventions from the mutual reinforcement of local interactions and institutional values. We asses the robustness of these results by examining how sensitively they depend on different initial conditions of variant assignment, population sizes and alpha diversity indexes.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

A novel methodology to explore the periodic gait of a biped walker under uncertainty using a machine learning algorithm

Robotica ◽  
2021 ◽  
pp. 1-16
Author(s):  
Namjung Kim ◽  
Bongwon Jeong ◽  
Kiwon Park
Keyword(s):  
Limit Cycle ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Angular Displacement ◽  
External Disturbance ◽  
Rehabilitation Robotics ◽  
Stable Region ◽  
Gait Rehabilitation ◽  
External Disturbances

Abstract In this paper, we present a systematic approach to improve the understanding of stability and robustness of stability against the external disturbances of a passive biped walker. First, a multi-objective, multi-modal particle swarm optimization (MOMM-PSO) algorithm was employed to suggest the appropriate initial conditions for a given biped walker model to be stable. The MOMM-PSO with ring topology and special crowding distance (SCD) used in this study can find multiple local minima under multiple objective functions by limiting each agent’s search area properly without determining a large number of parameters. Second, the robustness of stability under external disturbances was studied, considering an impact in the angular displacement sampled from the probabilistic distribution. The proposed systematic approach based on MOMM-PSO can find multiple initial conditions that lead the biped walker in the periodic gait, which could not be found by heuristic approaches in previous literature. In addition, the results from the proposed study showed that the robustness of stability might change depending on the location on a limit cycle where immediate angular displacement perturbation occurs. The observations of this study imply that the symmetry of the stable region about the limit cycle will break depending on the accelerating direction of inertia. We believe that the systematic approach developed in this study significantly increased the efficiency of finding the appropriate initial conditions of a given biped walker and the understanding of robustness of stability under the unexpected external disturbance. Furthermore, a novel methodology proposed for biped walkers in the present study may expand our understanding of human locomotion, which in turn may suggest clinical strategies for gait rehabilitation and help develop gait rehabilitation robotics.

scholarly journals Polygenic local adaptation in metapopulations: a stochastic eco-evolutionary model

2020 ◽  
Author(s):  
Enikő Szép ◽  
Himani Sachdeva ◽  
Nick Barton
Keyword(s):  
Population Size ◽  
Local Adaptation ◽  
Genetic Drift ◽  
Diffusion Approximation ◽  
Joint Distribution ◽  
Evolutionary Model ◽  
Ecological Niches ◽  
Demographic Stochasticity ◽  
Linkage Disequilibria ◽  
Population Sizes

AbstractThis paper analyses the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments.

The Road to Chaos by Time-Asymmetric Hebbian Learning in Recurrent Neural Networks

2007 ◽  
Vol 19 (1) ◽  
pp. 80-110 ◽  
Author(s):  
Colin Molter ◽  
Utku Salihoglu ◽  
Hugues Bersini
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
A Priori ◽  
Computational Cost ◽  
Chaotic Regime ◽  
Dynamical Regime ◽  
New Form ◽  
The Impact

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides “on its own” the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more “respectful” of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as “frustrated chaos.”

scholarly journals Formation sites of Population III star formation: The effects of different levels of rotation and turbulence on the fragmentation behaviour of primordial gas

2020 ◽  
Vol 494 (2) ◽  
pp. 1871-1893 ◽  
Author(s):  
Katharina M J Wollenberg ◽  
Simon C O Glover ◽  
Paul C Clark ◽  
Ralf S Klessen
Keyword(s):  
Star Formation ◽  
Initial Conditions ◽  
Turbulent Energy ◽  
Star Forming ◽  
Wide Range ◽  
Population Iii Stars ◽  
The Individual ◽  
The Masses ◽  
Different Levels ◽  
Population Iii

ABSTRACT We use the moving-mesh code arepo to investigate the effects of different levels of rotation and turbulence on the fragmentation of primordial gas and the formation of Population III stars. We consider nine different combinations of turbulence and rotation and carry out five different realizations of each setup, yielding one of the largest sets of simulations of Population III star formation ever performed. We find that fragmentation in Population III star-forming systems is a highly chaotic process and show that the outcomes of individual realizations of the same initial conditions often vary significantly. However, some general trends are apparent. Increasing the turbulent energy promotes fragmentation, while increasing the rotational energy inhibits fragmentation. Within the ∼1000 yr period that we simulate, runs including turbulence yield flat protostellar mass functions while purely rotational runs show a more top-heavy distribution. The masses of the individual protostars are distributed over a wide range from a few $10^{-3} \, {\rm M_{\odot }}$ to several tens of M⊙. The total mass growth rate of the stellar systems remains high throughout the simulations and depends only weakly on the degree of rotation and turbulence. Mergers between protostars are common, but predictions of the merger fraction are highly sensitive to the criterion used to decide whether two protostars should merge. Previous studies of Population III star formation have often considered only one realization per set of initial conditions. However, our results demonstrate that robust trends can only be reliably identified by considering averages over a larger sample of runs.

Multiple steady states in exchange flows across faults and the dissolution of

2015 ◽  
Vol 769 ◽  
pp. 229-241 ◽  
Author(s):  
Andrew W. Woods ◽  
Marc Hesse ◽  
Rachel Berkowitz ◽  
Kyung Won Chang
Keyword(s):  
Initial Conditions ◽  
Storage Systems ◽  
Exchange Flow ◽  
Cross Contamination ◽  
Volume Flux ◽  
Geological Storage ◽  
Deep Aquifer ◽  
Exchange Flows ◽  
Equilibrium Exchange ◽  
Different Levels

We develop a model of the steady exchange flows which may develop between two aquifers at different levels in the geological strata and across which there is an unstable density stratification, as a result of their connection through a series of fractures. We show that in general there are multiple steady exchange flows which can develop, depending on the initial conditions, and which may involve a net upwards or downwards volume flux. We also show that there is a family of equilibrium exchange flows with zero net volume flux, each characterised by a different interlayer flux of buoyancy. We present experiments which confirm our simplified model of the exchange flow. Such exchange flows may supply unsaturated water from a deep aquifer to drive dissolution of a structurally trapped pool of geologically stored $\text{CO}_{2}$, once the water in the aquifer containing the trapped pool of $\text{CO}_{2}$ has become saturated in $\text{CO}_{2}$, and hence relatively dense. Such exchange flows may also lead to cross-contamination of aquifer fluids, which may be of relevance in assessing risks of geological storage systems.

scholarly journals Synchronising institutions and value systems: a model of opinion dynamics mediated by proportional representation

2021 ◽  
Author(s):  
José Segovia-Martín ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations' information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

scholarly journals Synchronising institutions and value systems: A model of opinion dynamics mediated by proportional representation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257525
Author(s):  
Jose Segovia-Martin ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations’ information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

scholarly journals Collective narratives catalyse cooperation

2020 ◽  
Author(s):  
Chaitanya Gokhale ◽  
Joseph Bulbulia ◽  
Marcus Frean
Keyword(s):  
Fundamental Problem ◽  
Evolutionary Model ◽  
Human Sciences ◽  
Finite Populations ◽  
Hunter Gatherers ◽  
Moral Exemplars ◽  
Cultural Selection ◽  
Evolutionary Theories ◽  
Collective Narratives ◽  
Population Sizes

Humans invest in fantastic stories -- mythologies.Recent evolutionary theories suggest that cultural selection may favour moralising stories that motivate prosocial behaviours.A key challenge is to explain the emergence of mythologies that lack explicit moral exemplars or directives. Here, we resolve this puzzle with an evolutionary model in which arbitrary mythologies transform a collection of egoistic individuals into a cooperative. Importantly, in finite populations, reflecting relative to contemporary population sizes of hunter-gatherers, the model is robust to the cognitive costs in adopting fictions. This approach resolves a fundamental problem across the human sciences by explaining the evolution of otherwise puzzling amoral, nonsensical, and fictional narratives as exquisitely functional coordination devices.

scholarly journals Optimizing genomic reference populations to improve crossbred performance

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Yvonne C. J. Wientjes ◽  
Piter Bijma ◽  
Mario P. L. Calus
Keyword(s):  
Genomic Prediction ◽  
Genetic Relatedness ◽  
Reference Population ◽  
Breeding Goal ◽  
Poultry Breeding ◽  
Population Sizes ◽  
Estimated Breeding Values ◽  
Production Animals ◽  
Chromosome Segments ◽  
Different Levels

Abstract Background In pig and poultry breeding, the objective is to improve the performance of crossbred production animals, while selection takes place in the purebred parent lines. One way to achieve this is to use genomic prediction with a crossbred reference population. A crossbred reference population benefits from expressing the breeding goal trait but suffers from a lower genetic relatedness with the purebred selection candidates than a purebred reference population. Our aim was to investigate the benefit of using a crossbred reference population for genomic prediction of crossbred performance for: (1) different levels of relatedness between the crossbred reference population and purebred selection candidates, (2) different levels of the purebred-crossbred correlation, and (3) different reference population sizes. We simulated a crossbred breeding program with 0, 1 or 2 multiplication steps to generate the crossbreds, and compared the accuracy of genomic prediction of crossbred performance in one generation using either a purebred or a crossbred reference population. For each scenario, we investigated the empirical accuracy based on simulation and the predicted accuracy based on the estimated effective number of independent chromosome segments between the reference animals and selection candidates. Results When the purebred-crossbred correlation was 0.75, the accuracy was highest for a two-way crossbred reference population but similar for purebred and four-way crossbred reference populations, for all reference population sizes. When the purebred-crossbred correlation was 0.5, a purebred reference population always resulted in the lowest accuracy. Among the different crossbred reference populations, the accuracy was slightly lower when more multiplication steps were used to create the crossbreds. In general, the benefit of crossbred reference populations increased when the size of the reference population increased. All predicted accuracies overestimated their corresponding empirical accuracies, but the different scenarios were ranked accurately when the reference population was large. Conclusions The benefit of a crossbred reference population becomes larger when the crossbred population is more related to the purebred selection candidates, when the purebred-crossbred correlation is lower, and when the reference population is larger. The purebred-crossbred correlation and reference population size interact with each other with respect to their impact on the accuracy of genomic estimated breeding values.

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