Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

Increased costs reduce reciprocal helping behaviour of humans in a virtual evacuation experiment

Altruistic behaviour is widespread and highly developed in humans and can also be found in some animal species. It has been suggested that altruistic tendencies can depend on costs, benefits and context. Here, we investigate the changes in the occurrence of helping behaviour in a computer-based experiment that simulates an evacuation from a building exploring the effect of varying the cost to help. Our findings illuminate a number of key mechanistic aspects of human decision-making about whether to help or not. In a novel situation where it is difficult to assess the risks associated with higher costs, we reproduce the finding that increasing costs reduce helping and find that the reduction in the frequency of helping behaviour is gradual rather than a sudden transition for a threshold cost level. Interestingly, younger and male participants were more likely to help. We provide potential explanations for this result relating to the nature of our experiment. Finally, we find no evidence that participants in our experiment plan ahead over two consecutive, inter-dependent helping opportunities when conducting cost-benefit trade-offs in spontaneous decisions. We discuss potential applications of our findings to research into decision-making during evacuations

Scaling up Mean Field Games with Online Mirror Descent

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibrium under a natural and well-motivated set of monotonicity assumptions. This theoretical result nicely extends to multi-population games and to settings involving common noise. A thorough experimental investigation on various single and multi-population MFGs shows that OMD outperforms traditional algorithms such as Fictitious Play (FP). We empirically show that OMD scales up and converges significantly faster than FP by solving, for the first time to our knowledge, examples of MFGs with hundreds of billions states. This study establishes the state-of-the-art for learning in large-scale multi-agent and multi-population games

Evolutionary Game Dynamics for Crowd Behavior in Emergency Evacuations

This paper studies the problem of a large group of individuals that has to get to a safety exit in the context of high-stress emergency evacuations. We model this problem as a discrete-state continuous-time game, where the players update their strategies to reach the exit within a defined time horizon, whilst avoiding undesirable situations such as congestion and being trampled. The proposed model builds on crowd dynamics in a two-strategy game theoretic context, which we extend to include aspects of crowd behavior originating in sociology and psychology, and in the analogous studies performed in immersive virtual environments. The main contribution of this paper is threefold: i) we propose a novel game formulation of the model in terms of the population distribution across three strategies, and provide a link with prospect theory; ii) we study the equilibria of the system and their stability via Lyapunov stability theory of nonlinear systems; iii) we extend the model to a multi-population setting, where each population represents the group of players at a certain distance from the exit

The Heat is On: Exploring User Behaviour in a Multisensory Virtual Environment for Fire Evacuation

Understanding validity of user behaviour in Virtual Environments (VEs) is critical as they are increasingly being used for serious Health and Safety applications such as predicting human behaviour and training in hazardous situations. This paper presents a comparative study exploring user behaviour in VE-based fire evacuation and investigates whether this is affected by the addition of thermal and olfactory simulation. Participants (N=43) were exposed to a virtual fire in an office building. Quantitative and qualitative analyses of participant attitudes and behaviours found deviations from those we would expect in real life (e.g. pre-evacuation actions), but also valid behaviours like fire avoidance. Potentially important differences were found between multisensory and audiovisual-only conditions (e.g. perceived urgency). We conclude VEs have significant potential in safety-related applications, and that multimodality may afford additional uses in this context, but the identified limitations of behavioural validity must be carefully considered to avoid misapplication of the technology.Comment: CHI Conference on Human Factors in Computing System

SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Predictors of social vulnerability : a multilevel analysis.

Over the past three decades there has been a rapid increase in the number of disasters occurring worldwide that affect communities, households and individuals. The increase in disasters and the associated impacts are evident in our society. The impact of disasters can have more chronic impacts generating social and economic hardship, loss of employment, dissolution of personal relationships, and the long-term decline of physical and mental health. A study was undertaken to develop an understanding of the predictors of individual social vulnerability on individuals nested within communities. The Behavioural Risk Factor Surveillance System and 14 other community level data sources were used. The model investigated the influence of parish disaster history, operational resilience and socio-economic resilience on individual social vulnerability. Methods: The research design for the study was a multilevel repeated cross-sectional design with a three level nested structure. The software package MLwiN was used to conduct the multilevel analysis using empirical Bayes Markov chain Monte Carlo (MCMC) estimation. Using a representative sample of 34,685 individuals from 2004 to 2010, nested in 56 Louisiana parishes, the trend study allowed for an understanding of the subjective and objective factors that predict individual social vulnerability. Results: In each step, the model fit improved using the DIC statistic. Overall the results indicated that there were differences between parishes and their levels of individual social vulnerability; individual social vulnerability decreased from 2004 to 2010 and several statistically significant predictors of social vulnerability were identified. Statistically significant community level predictors of individual social vulnerability were lack of educational attainment, communities with less access to a household phone, community poverty and community unemployment. A trend was detected for age. Statistically significant two-way interactions were number of disasters and total population per square mile, and number of disasters and number of physicians per 100,000 population. A moderate trend was observed for the interaction effect of age and access to a household phone. Conclusions: With the significant increase of disasters worldwide it is imperative that factors causing social vulnerability are addressed. Results indicated that communities with lower levels of social vulnerability had higher levels of education, access to communication, and lower poverty and unemployment rates. Recommendations for future research are made, with policy and practice implications discussed

The simulation of social exchange: developing a multidimensional model of exchange rules in human interaction.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Social exchange underpins social structure and as such, social exchange theory has taken a central role in the field of social psychology. The study of exchange rules and how they interact with each other is an area within this theory that has not received much attention up until now. This study has aimed to study the exchange rules of fairness, reciprocity, self-interest, vicinity and ingroup favouritism within an interacting exchange network. Agent based computational modelling with a comparison to empirical data has been proposed as a novel method to uncover the process of exchange from the bottom up. The results of the study indicate that there exists no universal combination of exchange rules that can predict human behaviour in all settings. Exchange rules are adopted based on institutional norms as well as norms that emerge during interaction