David Newth

Understanding Human Mobility from Twitter.

Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short- and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers’ movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

Australia is ‘free to choose’ economic growth and falling environmental pressures

Over two centuries of economic growth have put undeniable pressure on the ecological systems that underpin human well-being. While it is agreed that these pressures are increasing, views divide on how they may be alleviated. Some suggest technological advances will automatically keep us from transgressing key environmental thresholds; others that policy reform can reconcile economic and ecological goals; while a third school argues that only a fundamental shift in societal values can keep human demands within the Earth’s ecological limits. Here we use novel integrated analysis of the energy–water–food nexus, rural land use (including biodiversity), material flows and climate change to explore whether mounting ecological pressures in Australia can be reversed, while the population grows and living standards improve. We show that, in the right circumstances, economic and environmental outcomes can be decoupled. Although economic growth is strong across all scenarios, environmental performance varies widely: pressures are projected to more than double, stabilize or fall markedly by 2050. However, we find no evidence that decoupling will occur automatically. Nor do we find that a shift in societal values is required. Rather, extensions of current policies that mobilize technology and incentivize reduced pressure account for the majority of differences in environmental performance. Our results show that Australia can make great progress towards sustainable prosperity, if it chooses to do so.

Emergence and Self-Organization in Chemistry and Biology

Complex systems display two key properties that distinguish them from systems that are merely very, very complicated: emergence and self-organization. Emergence is the appearance of behaviour at system level that is not implicit in the properties of the system’s components; self-organization implies the increase of a system’s internal order without the imposition of external control. Competing definitions of emergence and self-organization have led to confusion. Here, we follow the idea proposed by Anderson, that emergence and self-organization are signalled by symmetry-breaking. In general, a steady-state configuration of matter must exhibit the same symmetries as the equations that govern its dynamics. However, while this might apply to the component parts of a system in isolation, the whole system might display less symmetry because of the interactions between its individual parts. Here, we will explore several systems where microscopic symmetry is broken by the interaction between the component parts of the system. These examples show that macroscopic symmetry-breaking is an important factor in the formation of system level order from chemical reactions through to the organization of ecosystems.

Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution

In a globalised world, land use change outlooks are influenced by both locally heterogeneous land attributes and world markets. We demonstrate the importance of high resolution land heterogeneity representation in understanding local impacts of future global scenarios with carbon markets and land competition influencing food prices. A methodologically unique Australian continental model is presented with bottom-up parcel scale granularity in land use change, food, carbon, water, and biodiversity ecosystem service supply determination, and partial equilibrium food price impacts of land competition. We show that food price feedbacks produce modest aggregate national land use and ecosystem service supply changes. However, high resolution results show amplified land use change and ecosystem service impact in some places and muted impacts in other areas relative to national averages. We conclude that fine granularity modelling of geographic diversity produces local land use change and ecosystem service impact insights not discernible with other approaches. We modeled Australian land use change and ecosystem service responses to global scenarios.The model features a novel approach to very high resolution land heterogeneity representation.To demonstrate, we model how food price feedbacks of land competition differ spatially.Modest land use change and ecosystem service impacts are observed in aggregate for Australia.High resolution impacts vary from large to minuscule depending on local land heterogeneity.

Asynchronous spatial evolutionary games

Over the past 50 years, much attention has been given to the Prisoners Dilemma as a metaphor for problems surrounding the evolution and maintenance of cooperative and altruistic behavior. The bulk of this work has dealt with the successfulness and robustness of various strategies. Nowak and May (1992) considered an alternative approach to studying evolutionary games. They assumed that players were distributed across a two-dimensional (2D) lattice, interactions between players occurred locally, rather than at long range as in the well mixed situation. The resulting spatial evolutionary games display dynamics not seen in their well-mixed counterparts. An assumption underlying much of the work on spatial evolutionary games is that the state of all players is updated in unison or in synchrony. Using the framework outlined in Nowak and May (1992), we examine the effect of various asynchronous updating schemes on the dynamics of spatial evolutionary games. There are potential implications for the dynamics of a wide variety of spatially extended systems in biology, physics and chemistry.

Trends of news diffusion in social media based on crowd phenomena

Information spreads across social media, bringing heterogeneous social networks interconnected and diffusion patterns varied in different topics of information. Studying such cross-population diffusion in various context helps us understand trends of information diffusion in a more accurate and consistent way. In this study, we focus on real-world news diffusion across online social systems such as mainstream news (News), social networking sites (SNS), and blogs (Blog), and we analyze behavioral patterns of the systems in terms of activity, reactivity, and heterogeneity. We found that News is the most active, SNS is the most reactive, and Blog is the most persistent, which governs time-evolving heterogeneity of these systems. Finally, we interpret the discovered crowd phenomena from various angles using our previous model-free and model-driven approaches, showing that the strength and directionality of influence reflect the behavioral patterns of the systems in news diffusion.

Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media

Diverse online social networks are becoming increasingly interconnected by sharing information. Accordingly, emergent macro-level phenomena have been observed, such as the synchronous spread of information across different types of social media. Attempting to analyze the emergent global behavior is impossible from the examination of a single social platform, and dynamic influences between different social networks are not negligible. Furthermore, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both the heterogeneity and structural connectivity of social networks. As real-world phenomena, we explore instances of news diffusion across different social media platforms from a dataset that contains over 386 million web documents covering a one-month period in early 2011. We find that influence between different media types is varied by the context of information. News media are the most influential in the arts and economy categories, while social networking sites (SNS) and blog media are in the politics and culture categories, respectively. Furthermore, controversial topics, such as political protests and multiculturalism failure, tend to spread concurrently across social media, while entertainment topics, such as film releases and celebrities, are more likely driven by interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields and that it provides a way of interpreting the dynamics of diffusion in terms of the strength and directionality of influences among populations.

Strategic Choice of Preferences: The Persona Model

Recent work in several fields has established that humans can adopt binding “behavioral” preferences and convincingly signal those preferences to other humans, either via their behavior or via their body language / tone of voice. In this paper, we model the strategic implications of this ability. Our thesis is that through a persons lifetime they (perhaps subconsciously) learn what such signaled, binding behavioral preferences result in the highest value of their actual preferences, given the resultant behavior of other players. We argue that this “persona” model may explain why many interpersonal preferences have the particular form they do. As an illustration, we use the persona model to explain cooperation in non-repeated versions of the Prisoners Dilemma (PD). We also provide quantitative predictions to distinguish this explanation of cooperation from simply assuming people have actual preferences biased towards cooperation. In particular, we show that the persona model predicts a “crowding out” phenomenon in the PD, in which introducing incentives to cooperate causes players to stop cooperating instead. We also use the persona model to predict a tradeoff between the robustness of cooperation in the PD and the benefit of that cooperation.

Diffusion and social networks: revisiting medical innovation with agents

Introduction : In this chapter we reanalyze Medical Innovation by Coleman, Katz and Menzel (1966), the classic study on diffusion of Tetracycline, which at that time was a newly introduced antibiotic. Their pioneering study elaborated on how different patterns of interpersonal communications can influence the diffusion of a medical innovation in four medical communities in Illinois. The motivation for our reanalysis is to capture the complex interactions involved in the diffusion process by combining Agent-based Modeling (ABM) and network analysis. Based on the findings in Medical Innovation, we develop a diffusion model called Gammanym. The topology of networks generated in Gammanym, and its evolution, are analyzed to evaluate the network structure influencing the diffusion process. We describe the original study and the rationale for our study in the following section. Section 1.3 describes the modeling framework, modeling sequences and methods. Simulation results under different scenarios are analyzed in Section 1.4. The structure of the social networks depicted in our model, and its evolution are analyzed in Section 1.5. We explore the significance of types of network integration after normalizing adoption curves in terms of numbers for professionally integrated and isolated doctors in Section 1.6. The paper concludes with discussions and a review of the implications of the simulation results.

Modeling direct and indirect influence across heterogeneous social networks

Real-world diffusion phenomena are governed by collective behaviors of individuals, and their underlying connections are not limited to single social networks but are extended to globally interconnected heterogeneous social networks. Different levels of heterogeneity of networks in such global diffusion may also reflect different diffusion processes. In this regard, we focus on uncovering mechanisms of information diffusion across different types of social networks by considering hidden interaction patterns between them. For this study, we propose dual representations of heterogeneous social networks in terms of direct and indirect influence at a macro level. Accordingly, we propose two macro-level diffusion models by extending the Bass model with a probabilistic approach. By conducting experiments on both synthetic and real datasets, we show the feasibility of the proposed models. We find that real-world news diffusion in social media can be better explained by direct than indirect diffusion between different types of social media, such as News, social networking sites (SNS), and Blog media. In addition, we investigate different diffusion patterns across topics. The topics of Politics and Disasters tend to exhibit concurrent and synchronous diffusion by direct influence across social media, leading to high relative entropy of diverse media participation. The Arts and Sports topics show strong interactions within homogeneous networks, while interactions with other social networks are unbalanced and relatively weak, which likely drives lower relative entropy. We expect that the proposed models can provide a way of interpreting strength, directionality, and direct/indirectness of influence between heterogeneous social networks at a macro level.