Data-driven modeling of leading-following behavior in Bechstein’s bats

AbstractLeading-following behaviour in Bechstein’s bats transfers information about suitable roost sites from experienced to inexperienced individuals, and thus ensures communal roosting. We analyze 9 empirical data sets about individualized leading-following (L/F) events, to infer rules that likely determine the formation of L/F pairs. To test these rules, we propose five models that differ regarding the empirical information taken into account to form L/F pairs: activity of a bat in exploring possible roosts, tendency to lead and to follow. The comparison with empirical data was done by constructing social networks from the observed L/F events, on which centralities were calculated to quantify the importance of individuals in these L/F networks. The centralities from the empirical network are then compared for statistical differences with the model-generated centralities obtained from 105 model realizations. We find that two models perform well in comparison with the empirical data: One model assumes an individual tendency to lead, but chooses followers at random. The other model assumes an individual tendency to follow and chooses leaders according to their overall activity. We note that neither individual preferences for specific individuals, nor other influences such as kinship or reciprocity, are taken into account to reproduce the empirical findings.

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A Novel Bayesian Framework for Uncertainty Management in Physics-Based Reliability Models

Volume 14: Safety Engineering, Risk Analysis and Reliability Methods ◽

10.1115/imece2007-41333 ◽

2007 ◽

Cited By ~ 11

Author(s):

M. Azarkhail ◽

M. Modarres

Keyword(s):

Bayesian Approach ◽

Empirical Data ◽

Probabilistic Approach ◽

Bayesian Framework ◽

Uncertainty Management ◽

Least Square ◽

The Other ◽

Point Estimate ◽

Data Sets ◽

The Bayesian Approach

The physics-of-failure (POF) modeling approach is a proven and powerful method to predict the reliability of mechanical components and systems. Most of POF models have been originally developed based upon empirical data from a wide range of applications (e.g. fracture mechanics approach to the fatigue life). Available curve fitting methods such as least square for example, calculate the best estimate of parameters by minimizing the distance function. Such point estimate approaches, basically overlook the other possibilities for the parameters and fail to incorporate the real uncertainty of empirical data into the process. The other important issue with traditional methods is when new data points become available. In such conditions, the best estimate methods need to be recalculated using the new and old data sets all together. But the original data sets, used to develop POF models may be no longer available to be combined with new data in a point estimate framework. In this research, for efficient uncertainty management in POF models, a powerful Bayesian framework is proposed. Bayesian approach provides many practical features such as a fair coverage of uncertainty and the updating concept that provide a powerful means for knowledge management, meaning that the Bayesian models allow the available information to be stored in a probability density format over the model parameters. These distributions may be considered as prior to be updated in the light of new data when they become available. At the first part of this article a brief review of classical and probabilistic approach to regression is presented. In this part the accuracy of traditional normal distribution assumption for error is examined and a new flexible likelihood function is proposed. The Bayesian approach to regression and its bonds with classical and probabilistic methods are explained next. In Bayesian section we shall discuss how the likelihood functions introduced in probabilistic approach, can be combined with prior information using the conditional probability concept. In order to highlight the advantages, the Bayesian approach is further clarified with case studies in which the result of calculation is compared with other traditional methods such as least square and maximum likelihood estimation (MLE) method. In this research, the mathematical complexity of Bayesian inference equations was overcome utilizing Markov Chain Monte Carlo simulation technique.

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Rewiring Strategy Based on Directed Betweenness to Mitigate Disruptions of Large-Scale Supply Chain Networks

Mathematical Problems in Engineering ◽

10.1155/2021/5550837 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Hui Xia

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Network Connectivity ◽

The Other ◽

Network Robustness ◽

Data Driven ◽

Supply Chain Network ◽

Supply Chain Networks ◽

The Impact

In current large-scale supply chain networks, unexpected disruptions degrade the supply availability and network connectivity for modern enterprises. How to improve the robustness of supply chain networks is very important for modern enterprises. In this paper, we explore how to improve the robustness of supply chain networks from a topological perspective. Firstly, through the empirical data-driven study, we show that the directed betweenness metric is more suitable than the other topological metrics in evaluating the robustness of supply chain networks. Then, we propose a rewiring algorithm based on directed betweenness to improve network robustness under the impact of disruptions. The experimental results in the large-scale supply chain network show that the rewiring algorithm based on directed betweenness effectively improves the network robustness.

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Empirical Data-Driven Modeling for Dependability Enhancement

2009 Fourth Latin-American Symposium on Dependable Computing ◽

10.1109/ladc.2009.31 ◽

2009 ◽

Author(s):

Miroslaw Malek

Keyword(s):

Empirical Data ◽

Data Driven ◽

Data Driven Modeling

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Data-Driven Modeling and Analysis of Online Social Networks

Web-Age Information Management - Lecture Notes in Computer Science ◽

10.1007/978-3-642-23535-1_3 ◽

2011 ◽

pp. 3-17 ◽

Cited By ~ 3

Author(s):

Divyakant Agrawal ◽

Bassam Bamieh ◽

Ceren Budak ◽

Amr El Abbadi ◽

Andrew Flanagin ◽

...

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Data Driven ◽

Modeling And Analysis ◽

Data Driven Modeling

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Data Driven Modeling of Continuous Time Information Diffusion in Social Networks

2017 IEEE Second International Conference on Data Science in Cyberspace (DSC) ◽

10.1109/dsc.2017.103 ◽

2017 ◽

Cited By ~ 2

Author(s):

Liang Liu ◽

Bin Chen ◽

Bo Qu ◽

Lingnan He ◽

Xiaogang Qiu

Keyword(s):

Social Networks ◽

Continuous Time ◽

Information Diffusion ◽

Data Driven ◽

Data Driven Modeling ◽

Time Information

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The (Co-)Location Sharing Game

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2019-0017 ◽

2019 ◽

Vol 2019 (2) ◽

pp. 5-25 ◽

Cited By ~ 1

Author(s):

Alexandra-Mihaela Olteanu ◽

Mathias Humbert ◽

Kévin Huguenin ◽

Jean-Pierre Hubaux

Keyword(s):

Social Networks ◽

Data Driven ◽

Social Benefits ◽

Location Information ◽

Individual Preferences ◽

Mobility Data ◽

Location Sharing ◽

Strategic Behaviors ◽

Game Theoretic ◽

Location Based Social Networks

Abstract Most popular location-based social networks, such as Facebook and Foursquare, let their (mobile) users post location and co-location (involving other users) information. Such posts bring social benefits to the users who post them but also to their friends who view them. Yet, they also represent a severe threat to the users’ privacy, as co-location information introduces interdependences between users. We propose the first game-theoretic framework for analyzing the strategic behaviors, in terms of information sharing, of users of OSNs. To design parametric utility functions that are representative of the users’ actual preferences, we also conduct a survey of 250 Facebook users and use conjoint analysis to quantify the users’ benefits o f sharing vs. viewing (co)-location information and their preference for privacy vs. benefits. Our survey findings expose the fact that, among the users, there is a large variation, in terms of these preferences. We extensively evaluate our framework through data-driven numerical simulations. We study how users’ individual preferences influence each other’s decisions, we identify several factors that significantly affect these decisions (among which, the mobility data of the users), and we determine situations where dangerous patterns can emerge (e.g., a vicious circle of sharing, or an incentive to over-share) – even when the users share similar preferences.

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The evolution and structure of social networks

Network Science ◽

10.1017/nws.2014.24 ◽

2014 ◽

Vol 2 (3) ◽

pp. 326-340

Author(s):

WHITMAN RICHARDS ◽

NICHOLAS WORMALD

Keyword(s):

Social Networks ◽

Social Network ◽

Empirical Data ◽

Large Scale ◽

Preferential Attachment ◽

The Other ◽

Local Patterns ◽

Global Patterns ◽

Scale Properties ◽

Empirical Networks

AbstractAs social networks evolve, new nodes are linked to the large-scale organization already in place. We show that the combination of two simple algorithms, one the Barabasi-Albert preferential attachment proposal and the other a neighbor attachment rule, successfully generate networks exhibiting both the local and global characteristics of empirical data on social network structures. Ideally, one might hope that some coarse features of this linking process and the form of the local patterns might enable the prediction of large-scale properties. We show that this is generally not the case. This might help explain the variety of local and global patterns in empirical networks.

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Cumulative Stress and Cumulative Inertia: A Behavioral Model of the Decision to Move

Environment and Planning A Economy and Space ◽

10.1068/a101101 ◽

1978 ◽

Vol 10 (10) ◽

pp. 1101-1119 ◽

Cited By ~ 87

Author(s):

J O Huff ◽

W A V Clark

Keyword(s):

Life Cycle ◽

Empirical Data ◽

Behavioral Model ◽

The Other ◽

Data Sets ◽

Change Over Time ◽

Cumulative Stress ◽

The One ◽

Over Time ◽

Independent Trials

A model of the probability of moving which incorporates aspects of the independent-trials process, the stage in the life cycle, and the concept of cumulative inertia is formulated. The model is based on the interaction of two forces. On the one hand there is a certain resistance to moving (cumulative inertia) and on the other the household may be dissatisfied with certain attributes of the present dwelling and its surroundings (residential stress). The probability of moving is a function of the resultant of these two conflicting forces. The model is designed not only to predict who will move (those individuals with high residential stress relative to their resistance to moving), but also to predict how an individual's probability of moving is likely to change over time. Some simple and limited simulations suggest that the model will capture rather well the different kinds of mobility rates which are observed from empirical data sets.

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