arXiv subjects

Yan Leng

Publications and source records attributed to Yan Leng.

24 records · Page 2Linked to original sources

arXiv2019

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning. Implicit in many algorithms that attempt to solve Deep Reinforcement Learning (DRL) tasks is the network of processors along which parameter values are shared. So far, existing approaches have implicitly utilized fully-connected networks, in which all processors are connected. However, the scientific literature on human collective intelligence suggests that complete networks may not always be the most effective information network structures for distributed search through complex spaces. Here we show that alternative topologies can improve deep neural network training: we find that sparser networks learn higher rewards faster, leading to learning improvements at lower communication costs.

cs.AI↗

arXiv2018

Familiar Strangers: the Collective Regularity in Human Behaviors

The social phenomenon of familiar strangers was identified by Stanley Milgram in 1972 with a small-scale experiment. However, there has been limited research focusing on uncovering the phenomenon at a societal scale and simultaneously investigating the social relationships between familiar strangers. With the help of the large-scale mobile phone records, we empirically show the existence of the relationship in the country of Andorra. Built upon the temporal and spatial distributions, we investigate the mechanisms, especially collective temporal regularity and spatial structure that trigger this phenomenon. Moreover, we explore the relationship between social distances on the communication network and the number of encounters and show that larger number of encounters indicates shorter social distances in a social network. The understanding of the physical encounter network could have important implications to understand the phenomena such as epidemics spreading and information diffusion.

cs.SI↗

arXiv2017

Large-Scale Experiment on the Importance of Social Learning and Unimodality in the Wisdom of the Crowd

In this study, we build on previous research to understand the conditions within which the Wisdom of the Crowd (WoC) improves or worsens as a result of showing individuals the predictions of their peers. Our main novel contributions are: 1) a dataset of unprecedented size and detail; 2) we observe the novel effect of the importance of the unimodality of the social information shown to individuals: if one does not see only one clear peak in the distribution of the crowd's predictions, the WoC is worsened after social exposure; and 3) we estimate social learning weights that we use to show that there exists individuals who are much better at learning from the crowd and can be filtered to improve collective accuracy.

cs.SI↗

arXiv2017

Social Bayesian Learning in the Wisdom of the Crowd

Being able to correctly aggregate the beliefs of many people into a single belief is a problem fundamental to many important social, economic and political processes such as policy making, market pricing and voting. Although there exist many models and mechanisms for aggregation, there is a lack of methods and literature regarding the aggregation of opinions when influence and learning between individuals exist. This is in part because there are not many models of how people update their belief when exposed to the beliefs of others, and so it is hard to quantify the dependencies between people's mental models which is essential to minimizing redundancies in the aggregation. In this paper, we explore many models of how users influence and learn from each other, and we benchmark our models against the well-known DeGroot model. Our main contributions are: 1) we collect a new dataset of unprecedented size and detail to be posted online; 2) we develop a new Social Bayesian model of how people update their mental models, 3) we compare of our model to other well-known social learning models. Specifically, we show that our new Social Bayesian model is superior to the other models tested.

cs.SI↗

arXiv2016

Analysis of Tourism Dynamics and Special Events through Mobile Phone Metadata

Tourism has been an increasingly important factor in global economy, society and environment, accounting for a significant share of GDP and labor force. Policy and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at low spatial and temporal granularity. Anonymous call detail records (CDRs) are a novel source of data, showing enormous potential in areas of high societal value: such as epidemics, poverty, and urban development. This study demonstrates the added value of using CDRs for the formulation, analysis and evaluation of tourism strategies, at the national and local levels. In the context of the European country of Andorra, we use CDRs to evaluate marketing strategies in tourism, understand tourists' experiences, and evaluate revenues and externalities generated by touristic events. We do this by extracting novel indicators in high spatial and temporal resolutions, such as tourist flows per country of origin, flows of new tourists, tourist revisits, tourist externalities on transportation congestion, spatial distribution, economic impact, and profiling of tourist interests. We exemplify the use of these indicators for the planning and evaluation of high impact touristic events, such as cultural festivals and sports competitions.

cs.CY↗

arXiv2016

Managing travel demand: Location recommendation for system efficiency based on mobile phone data

Growth in leisure travel has become increasingly significant economically, socially, and environmentally. However, flexible but uncoordinated travel behaviors exacerbate traffic congestion. Mobile phone records not only reveal human mobility patterns, but also enable us to manage travel demand for system efficiency. In this paper, we propose a location recommendation system that infers personal preferences while accounting for constraints imposed by road capacity in order to manage travel demand. We first infer unobserved preferences using a machine learning technique from phone records. We then formulate an optimization method to improve system efficiency. Coupling mobile phone data with traffic counts and road network infrastructures collected in Andorra, this study shows that uncoordinated travel behaviors lead to longer average travel delay, implying the opportunities in managing travel demand by collective decisions. The interplay between congestion relief and overall satisfied location preferences observed in extensive simulations indicate that moderate sacrifices of individual utility lead to significant travel time savings. Specifically, the results show that under full compliance rate, travel delay fell by 52% at a cost of 31% less satisfaction. Under 60% compliance rate, 41% travel delay is saved with a 17% reduction in satisfaction. This paper highlights the effectiveness of the synergy among collective behaviors in increasing system efficiency.

cs.CY↗