In this paper, we seek to identity how mathematical and economic analysis can be used to gain ins... more In this paper, we seek to identity how mathematical and economic analysis can be used to gain insights about the mutation of social groups. Group mutability has been studied in multiple domains, with insights generated on significant factors at differing scales. Mathematical modeling enables the simultaneous study of such phenomena, understanding interactions and generating hypotheses for experiments. In particular, we focus on group fracture, where individuals leave groups of which they are members. For example, this can be due to perceived differences with other group members due to norm related conflict (such as extreme actions by some members). Our aim is to consider simple mathematical models incorporating a selection of social and psychological theory which describes these phenomena as a way to understand their interplay, and describe the trade-offs and challenges. This will help a federation model the behavior of extremist groups, and determine not only when an intervention is necessary, but also the best course of action to take to induce the fracture of such groups. This paper is an exploratory investigation into methods of achieving this goal and evaluating the usefulness of the outputs to federations.
Generative modelling of inter-group relations enables probabilistic forecasting of possible confl... more Generative modelling of inter-group relations enables probabilistic forecasting of possible conflict for scenarios where real-world data is sparse. In order for such models to have relevance and integrity, it is important to ensure that real-world data is used to parameterise the model and verify its characteristics. In this paper we investigate how real-world datasets can be mapped into generative model parameters concerning group structures and behaviours. We highlight the issues involved and present a framework for classifying potential data based on three attributes: (i) inter-group structure, (ii) inter-group actions and (iii) impact of actions. We argue that these attributes are fundamental for benchmarking and developing generative models in the context of limited existing data on inter-group interaction.
In this paper, we present a mathematical model for the mutation of social groups. Group mutabilit... more In this paper, we present a mathematical model for the mutation of social groups. Group mutability has been studied in multiple domains, with insights generated on significant factors at differing scales. Mathematical modeling enables the simultaneous study of such phenomena, understanding interactions and generating hypotheses for experiments. In particular, we focus on group fracture, where individuals leave groups of which they are members. For example, this can be due to perceived differences with other group members due to norm related conflict (such as extreme actions by some members). Our aim is to consider simple mathematical models incorporating a selection of social and psychological theory which describes these phenomena as a way to understand their interplay, and describe the trade-offs and challenges.
Objective: Recent years have seen an increasing number of studies using smartphones to sample par... more Objective: Recent years have seen an increasing number of studies using smartphones to sample participants' mood states. Moods are usually collected by asking participants for their current mood or for a recollection of their mood states over a specific period of time. The current study investigates the reasons to favour collecting mood through current or daily mood surveys and outlines design recommendations for mood sampling using smartphones based on these findings. These recommendations are also relevant to more general smartphone sampling procedures. Results: N=64 participants completed a series of surveys at the beginning and end of the study providing information such as gender, personality, or smartphone addiction score. Through a smartphone application, they reported their current mood 3 times and daily mood once per day for 8 weeks. We found that none of the examined intrinsic individual qualities had an effect on matches of current and daily mood reports. However timing played a significant role: the last followed by the first reported current mood of the day were more likely to match the daily mood. Current mood surveys should be preferred for a higher sampling accuracy, while daily mood surveys are more suitable if compliance is more important.
Psychological theories of inter-group behaviour offer justified representations for interaction, ... more Psychological theories of inter-group behaviour offer justified representations for interaction, influence, and motivation for coalescence. Agent-based modelling of this behaviour, using evolutionary approaches, further provides a powerful tool to examine the implications of these theories in a dynamic context. In particular, this can enhance our understanding of the escalation of hostility and warfare, and its mitigation, contributing to policy and interventions. In this paper we propose a framework through which social psychology can be embedded in computation for the examination of inter-group behaviour. We examine how various social-psychological theories can be embedded in evolutionary models, and identify ways in which visualisation can support the objective assessment of emergent behaviour. We also discuss how real-world data can be used to parameterise scenarios on which modelling is conducted.
Within networks one can identify motifs that are significant recurring patterns of interaction be... more Within networks one can identify motifs that are significant recurring patterns of interaction between nodes. Here motifs are sub-graphs that occur more frequently than would be explained by random connections. Graphs can be used to model internal network structures of human groups, or links between groups, with group dynamics being governed by these structures. Graphs can also model behavior in engineered systems, and internal network structures can significantly affect dynamic behavior. A graph may only be partially visible (such as in hostile or coalition environments), however detectable network motifs may in some cases be reflective of the entire graph. We outline a research plan and describe basic network motifs and their properties, along with current analytic techniques for static and dynamic settings. We offer suggestions as to how network motif techniques can be applied to intra-or inter-group behavior, for example to detect whether multiple groups behave as a cooperative alliance, or whether coalition networks inter-operate in positive ways. As an example, we examine a complex time-series graph dataset relevant to coalition focused aspects of the class of networks under study, specifically related to the social network resulting from the authorship of academic papers within a coalition. We provide details of the basic analysis of this network over time and outline how this can be used as one of the datasets for our planned network motif research activities, especially with regards to the temporal and evolutionary aspects.
6th International Conference on Cross-Cultural Decision Making, 2017
Group behavior is an important feature of conflict scenarios. Often such groups are chaotically o... more Group behavior is an important feature of conflict scenarios. Often such groups are chaotically organized, but their ideals are sociologically embedded across members such that the group has expected behavior that can represent a major threat. Therefore being able to model the evolution of groups on a generative basis, to anticipate their possible mutation, is valuable. However this is complex due to the diverse nature of human behavior and scenarios. In this paper we present an innovative approach to modeling these issues. Group identities are represented in terms of the behaviors (social norms) that members are expected to carry out towards other groups. Individuals predominantly compose their identity from the identity of the groups to which they belong, which is known to occur in situations of heightened conflict. The model introduced enables exploration of tensions associated with affiliation to multiple groups and the influence on inclusion and exclusion of individuals.
Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We p... more Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that predictive models can be built with >80% precision for most users. However we note discrepancies in performance across labelling, training, and evaluation methods, creating design considerations for future systems.
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015
When should a machine attempt to communicate with a user? This is a historical problem that has b... more When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology.
Proceedings of the 6th International Workshop on Human Behavior Understanding, 2015
Smartphone notifications are often delivered without considering user interruptibility, potential... more Smartphone notifications are often delivered without considering user interruptibility, potentially causing frustration for the recipient. Therefore research in this area has concerned finding contexts where interruptions are better received. The typical convention for monitoring interruption behaviour assumes binary actions, where a response is either completed or not at all. However, in reality a user may partially respond to an interruption, such as reacting to an audible alert or exploring which application caused it. Consequently we present a multi-step model of interruptibility that allows assessment of both partial and complete notification responses. Through a 6-month in-the-wild case study of 11,346 to-do list reminders from 93 users, we find support for reducing false-negative classification of interruptibility. Additionally, we find that different response behaviour is correlated with different contexts and that these behaviours are predictable with similar accuracy to complete responses.
In this paper, we seek to identity how mathematical and economic analysis can be used to gain ins... more In this paper, we seek to identity how mathematical and economic analysis can be used to gain insights about the mutation of social groups. Group mutability has been studied in multiple domains, with insights generated on significant factors at differing scales. Mathematical modeling enables the simultaneous study of such phenomena, understanding interactions and generating hypotheses for experiments. In particular, we focus on group fracture, where individuals leave groups of which they are members. For example, this can be due to perceived differences with other group members due to norm related conflict (such as extreme actions by some members). Our aim is to consider simple mathematical models incorporating a selection of social and psychological theory which describes these phenomena as a way to understand their interplay, and describe the trade-offs and challenges. This will help a federation model the behavior of extremist groups, and determine not only when an intervention is necessary, but also the best course of action to take to induce the fracture of such groups. This paper is an exploratory investigation into methods of achieving this goal and evaluating the usefulness of the outputs to federations.
Generative modelling of inter-group relations enables probabilistic forecasting of possible confl... more Generative modelling of inter-group relations enables probabilistic forecasting of possible conflict for scenarios where real-world data is sparse. In order for such models to have relevance and integrity, it is important to ensure that real-world data is used to parameterise the model and verify its characteristics. In this paper we investigate how real-world datasets can be mapped into generative model parameters concerning group structures and behaviours. We highlight the issues involved and present a framework for classifying potential data based on three attributes: (i) inter-group structure, (ii) inter-group actions and (iii) impact of actions. We argue that these attributes are fundamental for benchmarking and developing generative models in the context of limited existing data on inter-group interaction.
In this paper, we present a mathematical model for the mutation of social groups. Group mutabilit... more In this paper, we present a mathematical model for the mutation of social groups. Group mutability has been studied in multiple domains, with insights generated on significant factors at differing scales. Mathematical modeling enables the simultaneous study of such phenomena, understanding interactions and generating hypotheses for experiments. In particular, we focus on group fracture, where individuals leave groups of which they are members. For example, this can be due to perceived differences with other group members due to norm related conflict (such as extreme actions by some members). Our aim is to consider simple mathematical models incorporating a selection of social and psychological theory which describes these phenomena as a way to understand their interplay, and describe the trade-offs and challenges.
Objective: Recent years have seen an increasing number of studies using smartphones to sample par... more Objective: Recent years have seen an increasing number of studies using smartphones to sample participants' mood states. Moods are usually collected by asking participants for their current mood or for a recollection of their mood states over a specific period of time. The current study investigates the reasons to favour collecting mood through current or daily mood surveys and outlines design recommendations for mood sampling using smartphones based on these findings. These recommendations are also relevant to more general smartphone sampling procedures. Results: N=64 participants completed a series of surveys at the beginning and end of the study providing information such as gender, personality, or smartphone addiction score. Through a smartphone application, they reported their current mood 3 times and daily mood once per day for 8 weeks. We found that none of the examined intrinsic individual qualities had an effect on matches of current and daily mood reports. However timing played a significant role: the last followed by the first reported current mood of the day were more likely to match the daily mood. Current mood surveys should be preferred for a higher sampling accuracy, while daily mood surveys are more suitable if compliance is more important.
Psychological theories of inter-group behaviour offer justified representations for interaction, ... more Psychological theories of inter-group behaviour offer justified representations for interaction, influence, and motivation for coalescence. Agent-based modelling of this behaviour, using evolutionary approaches, further provides a powerful tool to examine the implications of these theories in a dynamic context. In particular, this can enhance our understanding of the escalation of hostility and warfare, and its mitigation, contributing to policy and interventions. In this paper we propose a framework through which social psychology can be embedded in computation for the examination of inter-group behaviour. We examine how various social-psychological theories can be embedded in evolutionary models, and identify ways in which visualisation can support the objective assessment of emergent behaviour. We also discuss how real-world data can be used to parameterise scenarios on which modelling is conducted.
Within networks one can identify motifs that are significant recurring patterns of interaction be... more Within networks one can identify motifs that are significant recurring patterns of interaction between nodes. Here motifs are sub-graphs that occur more frequently than would be explained by random connections. Graphs can be used to model internal network structures of human groups, or links between groups, with group dynamics being governed by these structures. Graphs can also model behavior in engineered systems, and internal network structures can significantly affect dynamic behavior. A graph may only be partially visible (such as in hostile or coalition environments), however detectable network motifs may in some cases be reflective of the entire graph. We outline a research plan and describe basic network motifs and their properties, along with current analytic techniques for static and dynamic settings. We offer suggestions as to how network motif techniques can be applied to intra-or inter-group behavior, for example to detect whether multiple groups behave as a cooperative alliance, or whether coalition networks inter-operate in positive ways. As an example, we examine a complex time-series graph dataset relevant to coalition focused aspects of the class of networks under study, specifically related to the social network resulting from the authorship of academic papers within a coalition. We provide details of the basic analysis of this network over time and outline how this can be used as one of the datasets for our planned network motif research activities, especially with regards to the temporal and evolutionary aspects.
6th International Conference on Cross-Cultural Decision Making, 2017
Group behavior is an important feature of conflict scenarios. Often such groups are chaotically o... more Group behavior is an important feature of conflict scenarios. Often such groups are chaotically organized, but their ideals are sociologically embedded across members such that the group has expected behavior that can represent a major threat. Therefore being able to model the evolution of groups on a generative basis, to anticipate their possible mutation, is valuable. However this is complex due to the diverse nature of human behavior and scenarios. In this paper we present an innovative approach to modeling these issues. Group identities are represented in terms of the behaviors (social norms) that members are expected to carry out towards other groups. Individuals predominantly compose their identity from the identity of the groups to which they belong, which is known to occur in situations of heightened conflict. The model introduced enables exploration of tensions associated with affiliation to multiple groups and the influence on inclusion and exclusion of individuals.
Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We p... more Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that predictive models can be built with >80% precision for most users. However we note discrepancies in performance across labelling, training, and evaluation methods, creating design considerations for future systems.
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015
When should a machine attempt to communicate with a user? This is a historical problem that has b... more When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology.
Proceedings of the 6th International Workshop on Human Behavior Understanding, 2015
Smartphone notifications are often delivered without considering user interruptibility, potential... more Smartphone notifications are often delivered without considering user interruptibility, potentially causing frustration for the recipient. Therefore research in this area has concerned finding contexts where interruptions are better received. The typical convention for monitoring interruption behaviour assumes binary actions, where a response is either completed or not at all. However, in reality a user may partially respond to an interruption, such as reacting to an audible alert or exploring which application caused it. Consequently we present a multi-step model of interruptibility that allows assessment of both partial and complete notification responses. Through a 6-month in-the-wild case study of 11,346 to-do list reminders from 93 users, we find support for reducing false-negative classification of interruptibility. Additionally, we find that different response behaviour is correlated with different contexts and that these behaviours are predictable with similar accuracy to complete responses.
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