220,784 research outputs found
The organization of transactions research with the Trust and Tracing Game
This paper presents empirical results of research on the influence of social aspects on the organization of transactions in the domain of chains and networks. The research method used was a gaming simulation called the Trust and Tracing game in which participants trade commodity goods with a hidden quality attribute. Previous sessions of this gaming simulation identified a list of variables for further investigation (Meijer et al., 2006). The use of gaming simulation as data gathering tool for quantitative research in supply chains and networks is a proof-of-principle. This paper shows results from 27 newly conducted sessions and previously unused data from 3 older sessions. Tests confirmed the use of network and market modes of organization. Pre-existing social relations influenced the course of the action in the sessions. Being socially embedded was not beneficial for the score on the performance indicators money and points. The hypothesized reduction in measurable transaction costs when there was high trust between the participants could not be found. Further analysis revealed that participants are able to suspect cheats in a session based on other factors than tracing. Testing hypotheses with data gathered in a gaming simulation proved feasible. Experiences with the methodology used are discusse
A New Analysis Method for Simulations Using Node Categorizations
Most research concerning the influence of network structure on phenomena
taking place on the network focus on relationships between global statistics of
the network structure and characteristic properties of those phenomena, even
though local structure has a significant effect on the dynamics of some
phenomena. In the present paper, we propose a new analysis method for phenomena
on networks based on a categorization of nodes. First, local statistics such as
the average path length and the clustering coefficient for a node are
calculated and assigned to the respective node. Then, the nodes are categorized
using the self-organizing map (SOM) algorithm. Characteristic properties of the
phenomena of interest are visualized for each category of nodes. The validity
of our method is demonstrated using the results of two simulation models. The
proposed method is useful as a research tool to understand the behavior of
networks, in particular, for the large-scale networks that existing
visualization techniques cannot work well.Comment: 9 pages, 8 figures. This paper will be published in Social Network
Analysis and Mining(www.springerlink.com
A matrix product algorithm for stochastic dynamics on networks, applied to non-equilibrium Glauber dynamics
We introduce and apply a novel efficient method for the precise simulation of
stochastic dynamical processes on locally tree-like graphs. Networks with
cycles are treated in the framework of the cavity method. Such models
correspond, for example, to spin-glass systems, Boolean networks, neural
networks, or other technological, biological, and social networks. Building
upon ideas from quantum many-body theory, the new approach is based on a matrix
product approximation of the so-called edge messages -- conditional
probabilities of vertex variable trajectories. Computation costs and accuracy
can be tuned by controlling the matrix dimensions of the matrix product edge
messages (MPEM) in truncations. In contrast to Monte Carlo simulations, the
algorithm has a better error scaling and works for both, single instances as
well as the thermodynamic limit. We employ it to examine prototypical
non-equilibrium Glauber dynamics in the kinetic Ising model. Because of the
absence of cancellation effects, observables with small expectation values can
be evaluated accurately, allowing for the study of decay processes and temporal
correlations.Comment: 5 pages, 3 figures; minor improvements, published versio
Information dynamics algorithm for detecting communities in networks
The problem of community detection is relevant in many scientific
disciplines, from social science to statistical physics. Given the impact of
community detection in many areas, such as psychology and social sciences, we
have addressed the issue of modifying existing well performing algorithms by
incorporating elements of the domain application fields, i.e. domain-inspired.
We have focused on a psychology and social network - inspired approach which
may be useful for further strengthening the link between social network studies
and mathematics of community detection. Here we introduce a community-detection
algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method
by considering networks' nodes as agents capable to take decisions. In this
framework we have introduced a memory factor to mimic a typical human behavior
such as the oblivion effect. The method is based on information diffusion and
it includes a non-linear processing phase. We test our method on two classical
community benchmark and on computer generated networks with known community
structure. Our approach has three important features: the capacity of detecting
overlapping communities, the capability of identifying communities from an
individual point of view and the fine tuning the community detectability with
respect to prior knowledge of the data. Finally we discuss how to use a Shannon
entropy measure for parameter estimation in complex networks.Comment: Submitted to "Communication in Nonlinear Science and Numerical
Simulation
Peer effects identified through social networks. Evidence from Uruguayan schools
This paper provides evidence on peer effects in educational achievement exploiting for the first time a unique data set on social networks within primary schools in Uruguay. The relevance of peer effects in education is still largely debated due to the identification challenges that the study of social interactions poses. I adopt a recently developed identification method that exploits detailed information on social networks, i.e. individual-specific peer groups. This method enables me to disentangle endogenous effects from contextual effects via instrumental variables that emerge naturally from the network structure. Correlated effects are controlled, to some extent, by classroom fixed effects. I find significant endogenous effects in standardized tests for reading and math. A one standard deviation increase in peers’ test score increases the individual’s test score by 40% of a standard deviation. This magnitude is comparable to the effect of having a mother that completed college. By means of a simulation I illustrate that when schools are stratified by socioeconomic status peer effects may operate as amplifiers of educational inequalities.
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
Stochastic Sampling Simulation for Pedestrian Trajectory Prediction
Urban environments pose a significant challenge for autonomous vehicles (AVs)
as they must safely navigate while in close proximity to many pedestrians. It
is crucial for the AV to correctly understand and predict the future
trajectories of pedestrians to avoid collision and plan a safe path. Deep
neural networks (DNNs) have shown promising results in accurately predicting
pedestrian trajectories, relying on large amounts of annotated real-world data
to learn pedestrian behavior. However, collecting and annotating these large
real-world pedestrian datasets is costly in both time and labor. This paper
describes a novel method using a stochastic sampling-based simulation to train
DNNs for pedestrian trajectory prediction with social interaction. Our novel
simulation method can generate vast amounts of automatically-annotated,
realistic, and naturalistic synthetic pedestrian trajectories based on small
amounts of real annotation. We then use such synthetic trajectories to train an
off-the-shelf state-of-the-art deep learning approach Social GAN (Generative
Adversarial Network) to perform pedestrian trajectory prediction. Our proposed
architecture, trained only using synthetic trajectories, achieves better
prediction results compared to those trained on human-annotated real-world data
using the same network. Our work demonstrates the effectiveness and potential
of using simulation as a substitution for human annotation efforts to train
high-performing prediction algorithms such as the DNNs.Comment: 8 pages, 6 figures and 2 table
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