Trust in Crowds: probabilistic behaviour in anonymity protocols

The existing analysis of the Crowds anonymity protocol assumes that a participating member is either ‘honest’ or ‘corrupted’. This paper generalises this analysis so that each member is assumed to maliciously disclose the identity of other nodes with a probability determined by her vulnerability to corruption. Within this model, the trust in a principal is defined to be the probability that she behaves honestly. We investigate the effect of such a probabilistic behaviour on the anonymity of the principals participating in the protocol, and formulate the necessary conditions to achieve ‘probable innocence’. Using these conditions, we propose a generalised Crowds-Trust protocol which uses trust information to achieves ‘probable innocence’ for principals exhibiting probabilistic behaviour

From individual characters to large crowds: augmenting the believability of open-world games through exploring social emotion in pedestrian groups

Crowds of non-player characters improve the game-play experiences of open-world video-games. Grouping is a common phenomenon of crowds and plays an important role in crowd behaviour. Recent crowd simulation research focuses on group modelling in pedestrian crowds and game-designers have argued that the design of non-player characters should capture and exploit the relationship between characters. The concepts of social groups and inter-character relationships are not new in social psychology, and on-going work addresses the social life of emotions and its behavioural consequences on individuals and groups alike. The aim of this paper is to provide an overview of current research in social psychology, and to use the findings as a source of inspiration to design a social network of non-player characters, with application to the problem of group modelling in simulated crowds in computer games

Attack on the clones: managing player perceptions of visual variety and believability in video game crowds

Crowds of non-player characters are increasingly common in contemporary video games. It is often the case that individual models are re-used, lowering visual variety in the crowd and potentially affecting realism and believability. This paper explores a number of approaches to increase visual diversity in large game crowds, and discusses a procedural solution for generating diverse non-player character models. This is evaluated using mixed methods, including a “clone spotting” activity and measurement of impact on computational overheads, in order to present a multi-faceted and adjustable solution to increase believability and variety in video game crowds

System Issues in Multi-agent Simulation of Large Crowds

Crowd simulation is a complex and challenging domain. Crowds demonstrate many complex behaviours and are consequently difficult to model for realistic simulation systems. Analyzing crowd dynamics has been an active area of research and efforts have been made to develop models to explain crowd behaviour. In this paper we describe an agent based simulation of crowds, based on a continuous field force model. Our simulation can handle movement of crowds over complex terrains and we have been able to simulate scenarios like clogging of exits during emergency evacuation situations. The focus of this paper, however, is on the scalability issues for such a multi-agent based crowd simulation system. We believe that scalability is an important criterion for rescue simulation systems. To realistically model a disaster scenario for a large city, the system should ideally scale up to accommodate hundreds of thousands of agents. We discuss the attempts made so far to meet this challenge, and try to identify the architectural and system constraints that limit scalability. Thereafter we propose a novel technique which could be used to richly simulate huge crowds

Bars, Nightclubs, and Cancer Prevention: New Approaches to Reduce Young Adult Cigarette Smoking.

IntroductionTobacco contributes to multiple cancers, and it is largely preventable. As overall smoking prevalence in California declines, smoking has become concentrated among high-risk groups. Targeting social/cultural groups (i.e., "peer crowds") that share common values, aspirations, and activities in social venues like bars and nightclubs may reach high-risk young adult smokers. Lack of population data on young adult peer crowds limits the ability to assess the potential reach of such interventions.MethodsThis multimodal population-based household survey included young adults residing in San Francisco and Alameda counties. Data were collected in 2014 and analyzed in 2016. Multivariable logistic regressions assessed smoking by sociodemographic factors, attitudes, self-rated health, peer crowd affiliation, and bar/nightclub attendance.ResultsSmoking prevalence was 15.1% overall; 35.3% of respondents sometimes or frequently attended bars. In controlled analyses, bar attendance (AOR=2.13, 95% CI=1.00, 4.53) and binge drinking (AOR=3.17, 95% CI=1.59, 6.32) were associated with greater odds of smoking, as was affiliation with "Hip Hop" (AOR=4.32, 95% CI=1.48, 12.67) and "Country" (AOR=3.13, 95% CI=1.21, 8.09) peer crowds. Multivariable models controlling for demographics estimated a high probability of smoking among bar patrons affiliating with Hip Hop (47%) and Country (52%) peer crowds.ConclusionsBar attendance and affiliation with certain peer crowds confers significantly higher smoking risk. Interventions targeting Hip Hop and Country peer crowds could efficiently reach smokers, and peer crowd-tailored interventions have been associated with decreased smoking and binge drinking. Targeted interventions in bars and nightclubs may be an efficient way to address these cancer risks