scholarly journals Network-based diffusion analysis: a new method for detecting social learning

2009 ◽  
Vol 276 (1663) ◽  
pp. 1829-1836 ◽  
Author(s):  
Mathias Franz ◽  
Charles L. Nunn
Keyword(s):  
Social Learning ◽  
Likelihood Estimation ◽  
Information Criterion ◽  
Learning Mechanisms ◽  
Agent Based ◽  
Social Network Structure ◽  
Diffusion Analysis ◽  
Wide Range ◽  
Diffusion Curve ◽  
Group Members

Social learning has been documented in a wide diversity of animals. In free-living animals, however, it has been difficult to discern whether animals learn socially by observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyses the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using maximum-likelihood estimation. The underlying learning mechanism can then be identified using model selection based on the Akaike information criterion. We tested our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison with diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.

On the Interplay between Extortion and Punishment. An Agent Based Model of "Camorra"

2013 ◽  
pp. 65-77
Author(s):  
Barbara Sonzogni ◽  
Federico Cecconi ◽  
Rosaria Conte
Keyword(s):  
Social Learning ◽  
Agent Based Model ◽  
Learning Mechanisms ◽  
Italian Region ◽  
Agent Based ◽  
Preliminary Results ◽  
Highly Active

This paper presents an Agent-Based Model aimed to reproduce the demographics, economic and employment variables of a Southern Italian region (Campania) where one specific variant of Extortion Racketeering Systems (Erss), camorra, is highly active and prosperous. Preliminary results of a set of simulations show the effects of varying levels of extortion and punishment on the rates of inactivity, employment, etc. of a population of agents endowed with social learning mechanisms

Application and Simulation Study of Stress-Dependent Weibull Lifetime Models

2016 ◽  
Vol 23 (02) ◽  
pp. 1650008 ◽  
Author(s):  
Jochen Juskowiak ◽  
Bernd Bertsche
Keyword(s):  
Test Data ◽  
Simulation Study ◽  
Model Evaluation ◽  
Likelihood Estimation ◽  
Information Criterion ◽  
Accelerated Life Test ◽  
Ratio Test ◽  
Plausibility Check ◽  
Wide Range ◽  
Stress Dependent

Different Weibull lifetime models are presented whose scale, shape and minimum lifetime parameters are stress-dependent. This allows describing and predicting the lifetime of products with a Weibull distribution more accurately wherever stress-dependence applies to the failure mechanism. For instance, this is the case for failures due to fatigue, on which this paper focusses. The proposed procedure encompasses a two-step maximum likelihood estimation and a Fisher matrix (FM) confidence bounds calculation, followed by a model evaluation. This model evaluation is conducted by means of a general plausibility check (PC), likelihood ratio test (LRT) and Bayesian information criterion (BIC). Their applicability to accelerated life test data is discussed and validated using test data. Finally, a simulation study confirms a wide range of applicability.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST

2020 ◽  
Vol 70 (1) ◽  
pp. 181-189
Author(s):  
Guy Baele ◽  
Mandev S Gill ◽  
Paul Bastide ◽  
Philippe Lemey ◽  
Marc A Suchard
Keyword(s):  
High Performance ◽  
Markov Models ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Phylogenetic Inference ◽  
Time Variability ◽  
Substitution Process ◽  
Wide Range ◽  
Markov Modulated ◽  
Over Time

Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

Low Self-Control, Substance-Using Peers and Intimate Partners, Pro-Drug Use Definitions, and Inhalant Use Among Convicted Offenders in South Korea

2020 ◽  
Vol 51 (1) ◽  
pp. 128-142 ◽  
Author(s):  
Jaeyong Choi ◽  
Nathan E. Kruis
Keyword(s):  
Social Learning ◽  
South Korea ◽  
Strong Support ◽  
The United States ◽  
Self Control ◽  
Learning Mechanisms ◽  
Mediation Effects ◽  
Mediation Hypothesis ◽  
Low Self Control ◽  
The Relationship

Hirschi has repeatedly argued that the relationship between social learning variables and crime is a product of “self-selection” driven by low self-control (LSC). Akers’ has suggested that social learning mechanisms, such as affiliations with deviant individuals and acceptance of criminal definitions, can mediate the effects of LSC on crime. Interestingly, there has been little comparative work done to explore this mediation hypothesis in the realm of substance use for offender populations outside of the United States. This study helps fill these gaps in the literature by exploring the potential mediation effects of social learning variables on the relationship between LSC and inhalant use among a sample of 739 male offenders in South Korea. Our results provide strong support for the mediation hypothesis that LSC indirectly influences self-reported inhalant use through social learning mechanisms.

scholarly journals Evolution of social learning when high expected payoffs are associated with high risk of failure

2011 ◽  
Vol 8 (64) ◽  
pp. 1604-1615 ◽  
Author(s):  
Michal Arbilly ◽  
Uzi Motro ◽  
Marcus W. Feldman ◽  
Arnon Lotem
Keyword(s):  
High Risk ◽  
Social Learning ◽  
Risk Aversion ◽  
Individual Risk ◽  
Individual Experience ◽  
Agent Based ◽  
Risk Of Failure ◽  
Expected Payoffs ◽  
Maladaptive Behaviours ◽  
Optimal Behaviour

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.

scholarly journals The evolution of social learning mechanisms and cultural phenomena in group foragers

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Daniel J. van der Post ◽  
Mathias Franz ◽  
Kevin N. Laland
Keyword(s):  
Social Learning ◽  
Learning Mechanisms

scholarly journals An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences

Birds ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 250-260
Author(s):  
Christoph Randler
Keyword(s):  
Cluster Analysis ◽  
Optimal Solution ◽  
Social Bonding ◽  
Cluster Solution ◽  
Novice Group ◽  
Wide Range ◽  
German Speaking ◽  
Group Members ◽  
Bird Group ◽  
And Gender

The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.

Copy the In-group: Group Membership Trumps Perceived Reliability, Warmth, and Competence in a Social-Learning Task

2021 ◽  
pp. 095679762110322
Author(s):  
Marcel Montrey ◽  
Thomas R. Shultz
Keyword(s):  
Social Learning ◽  
Cultural Evolution ◽  
Learning Strategy ◽  
Group Membership ◽  
Social Groups ◽  
Learning Task ◽  
Minimal Group Paradigm ◽  
Learning Game ◽  
Group Members ◽  
Cumulative Cultural Evolution

Surprisingly little is known about how social groups influence social learning. Although several studies have shown that people prefer to copy in-group members, these studies have failed to resolve whether group membership genuinely affects who is copied or whether group membership merely correlates with other known factors, such as similarity and familiarity. Using the minimal-group paradigm, we disentangled these effects in an online social-learning game. In a sample of 540 adults, we found a robust in-group-copying bias that (a) was bolstered by a preference for observing in-group members; (b) overrode perceived reliability, warmth, and competence; (c) grew stronger when social information was scarce; and (d) even caused cultural divergence between intermixed groups. These results suggest that people genuinely employ a copy-the-in-group social-learning strategy, which could help explain how inefficient behaviors spread through social learning and how humans maintain the cultural diversity needed for cumulative cultural evolution.

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