William G Kennedy | George Mason University (original) (raw)

Papers by William G Kennedy

Frontiers in Robotics and AI

The development of AI that can socially engage with humans is exciting to imagine, but such advan... more The development of AI that can socially engage with humans is exciting to imagine, but such advanced algorithms might prove harmful if people are no longer able to detect when they are interacting with non-humans in online environments. Because we cannot fully predict how socially intelligent AI will be applied, it is important to conduct research into how sensitive humans are to behaviors of humans compared to those produced by AI. This paper presents results from a behavioral Turing Test, in which participants interacted with a human, or a simple or "social" AI within a complex videogame environment. Participants (66 total) played an open world, interactive videogame with one of these co-players and were instructed that they could interact non-verbally however they desired for 30 min, after which time they would indicate their beliefs about the agent, including three Likert measures of how much participants trusted and liked the co-player, the extent to which they perceived them as a "real person," and an interview about the overall perception and what cues participants used to determine humanness. T-tests, Analysis of Variance and Tukey's HSD was used to analyze quantitative data, and Cohen's Kappa and χ ² was used to analyze interview data. Our results suggest that it was difficult for participants to distinguish between humans and the social AI on the basis of behavior. An analysis of in-game behaviors, survey data and qualitative responses suggest that participants associated engagement in social interactions with humanness within the game.

Innovation in Aging, 2020

Research indicates that family caregivers of individuals living with dementia are at risk for hig... more Research indicates that family caregivers of individuals living with dementia are at risk for high levels of stress, depression, physical health declines, and illness. The health and well-being of family caregivers is critically important to a long-term care system that is dependent on them to continue their caregiving role. In-depth individual and focus group interviews of 16 dementia caregivers were conducted to explore the emotional experiences of caregiving stress during transitions of individuals living with dementia to a higher level of care. Data were transcribed verbatim, checked for accuracy, and analyzed by at least two members of the research team. Line-by-line coding, memo writing, and constant comparative analyses were conducted until redundancy, when no new themes were discovered. Caregivers described various levels of feeling overwhelmed and symptom progression leading to the move to a nursing facility. Social isolation featured prominently, with caregivers describing...

George Mason University, 2003

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, 2020

Social connections between people influence how they behave and where they go; however, such netw... more Social connections between people influence how they behave and where they go; however, such networks are rarely incorporated in agent-based models of disaster. To address this, we introduce a novel synthetic population method which specifically creates social relationships. This synthetic population is then used to instantiate a geographically explicit agent-based model for the New York megacity region which captures pre- and post- disaster behaviors. We demonstrate not only how social networks can be incorporated into models of disaster but also how such networks can impact decision making, opening up a variety of new application areas where network structures matter in urban settings.

Computational Urban Science, 2022

Geographically-explicit simulations have become crucial in understanding cities and are playing a... more Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of d...

Handbook of Research Methods in Complexity Science

Human services planners and evaluators require an increasing high level of flexibility and adapta... more Human services planners and evaluators require an increasing high level of flexibility and adaptability to remain effective in measuring the effectiveness of social interventions. Understanding the logic and assessing the impact behind the intervention can be difficult because commonly-used evaluative tools are based primarily on linear methods that assume that a set amount of input, throughput, and output will result in a set outcome. This chapter takes a complexity science approach and facilitates the use of agent-based modelling (ABM). It provides the requisite background for evaluators and researchers to frame their efforts as complex adaptive systems. These systems have several components that include agents having options, boundaries, self-organising behaviour, different options from which to choose, feedback to adapt, and an emergent behaviour. Complexity is viewed as a mathematical field where the relations between inputs and are better understood through simulations. Both qualitative and quantitative aspects of complexity are addressed through two applications of ABM that consider related social policy issues.

Urban Science, 2021

Increasingly urbanized populations and climate change have shifted the focus of decision makers f... more Increasingly urbanized populations and climate change have shifted the focus of decision makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: (1) the physical system, consisting of geological, ecological, and human-built systems; (2) the social system, consisting of informal and formal human collective behavior; and (3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just...

Interactions Between the Fast and Slow Mental Processes William Kennedy George Mason University M... more Interactions Between the Fast and Slow Mental Processes William Kennedy George Mason University Magdalena Bugajska Naval Research Laboratory Abstract: Our actions seem to be controlled by two separate types of mental processes: one fast, automatic, and unconscious and one slow, deliberate, and conscious. With the attention in the literature focused on the characteristics of the two processes and whether to include emotions, we do not find any discussion of how they interact. We present evidence that the slower process is not able to perceive the operation of faster process, but it can perceive the environmental stimulus common to both processes and the response of the faster process. It can then generate its own more deliberate response, possibly contrary to the faster process’s response. We also provide evidence that the slower process is sometimes able to inhibit the fast process’s response, but with effort. We present common experiences as well as cognitive theory and neurologica...

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2020

This paper introduces an ingenious text-based affective aware pseudo association method (AAPAM) t... more This paper introduces an ingenious text-based affective aware pseudo association method (AAPAM) to connect disjoint users and items across different information domains and leverage them to make crossdomain content-based and collaborative filtering recommendations. This paper demonstrates that the AAPAM method could seamlessly join different information domain datasets to act as one without additional cross-domain information retrieval protocols. Besides making cross-domain recommendations, the benefit of joining datasets from different information domains through AAPAM is that it eradicates cold start issues while making serendipitous recommendations.

2021 Annual Modeling and Simulation Conference (ANNSIM), 2021

Within agent-based models, agents interact with each other (e.g., social networks) and their envi... more Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems.

International Conference on Social Media and Society, 2020

Social media is a popular source for political communication and user engagement around social an... more Social media is a popular source for political communication and user engagement around social and political issues. While the diversity of the population participating in social and political events in person are often considered for social science research, measuring the diversity representation within online communities is not a common part of social media analysis. This paper attempts to fill that gap and presents a methodology for labeling and analyzing diversity in a social media sample based on emojis and keywords associated with gender, skin tone, sexual orientation, religion, and political ideology. We analyze the trends of diversity related themes and the diversity of users engaging in the online political community during the leadup to the 2018 U.S. midterm elections. Our results reveal patterns along diversity themes that otherwise would have been lost in the volume of content. Further, the diversity composition of our sample of online users rallying around political campaigns was similar to those measured in exit polls on election day. The diversity language model and methodology for diversity analysis presented in this paper can be adapted to other languages and applied to other research domains to provide social media researchers a valuable lens to identify the diversity of voices and topics of interest for the less-represented populations participating in an online social community.

Computer Science & Information Technology, 2020

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as bui... more We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.

Computational and Mathematical Organization Theory, 2019

We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and g... more We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. We review our thesis here, provide the current research context, address a set of issues with the thesis, and provide some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents unity of purpose, an interdependence of theory and methods, and a call for the careful development of new approaches for understanding human social systems, broadly construed.

Journal on Policy and Complex Systems, 2015

Caregivers providing support to family members with Alzheimer's disease often encounter high leve... more Caregivers providing support to family members with Alzheimer's disease often encounter high levels of stress within the fragmented long-term care system. To address this emerging issue affecting millions of families, we applied agent-based computational modeling methods to better understand the impacts of policy alternatives. Potential options include increased respite care, tax incentives, work place policies, and adult day services as alternatives to reduce caregiver stress. Experiments with our model demonstrate that policy options providing programs, services, and support for caregivers can reduce their stress by providing a minimum of 16 hours per week of respite care.

Future Internet, 2019

Disaster events and their economic impacts are trending, and climate projection studies suggest t... more Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief re...

International Journal on Natural Language Computing, 2021

Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class ... more Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user’s preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems’ capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top-N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision making. The method shows its adaptability to group's moods change when m...

We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative... more We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. In addition to explaining our thesis, we provide the current research context, a set of issues with the thesis and some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents some unity of purpose and interdependence of theory and methods.

Frontiers in Robotics and AI

The development of AI that can socially engage with humans is exciting to imagine, but such advan... more The development of AI that can socially engage with humans is exciting to imagine, but such advanced algorithms might prove harmful if people are no longer able to detect when they are interacting with non-humans in online environments. Because we cannot fully predict how socially intelligent AI will be applied, it is important to conduct research into how sensitive humans are to behaviors of humans compared to those produced by AI. This paper presents results from a behavioral Turing Test, in which participants interacted with a human, or a simple or "social" AI within a complex videogame environment. Participants (66 total) played an open world, interactive videogame with one of these co-players and were instructed that they could interact non-verbally however they desired for 30 min, after which time they would indicate their beliefs about the agent, including three Likert measures of how much participants trusted and liked the co-player, the extent to which they perceived them as a "real person," and an interview about the overall perception and what cues participants used to determine humanness. T-tests, Analysis of Variance and Tukey's HSD was used to analyze quantitative data, and Cohen's Kappa and χ ² was used to analyze interview data. Our results suggest that it was difficult for participants to distinguish between humans and the social AI on the basis of behavior. An analysis of in-game behaviors, survey data and qualitative responses suggest that participants associated engagement in social interactions with humanness within the game.

Innovation in Aging, 2020

Research indicates that family caregivers of individuals living with dementia are at risk for hig... more Research indicates that family caregivers of individuals living with dementia are at risk for high levels of stress, depression, physical health declines, and illness. The health and well-being of family caregivers is critically important to a long-term care system that is dependent on them to continue their caregiving role. In-depth individual and focus group interviews of 16 dementia caregivers were conducted to explore the emotional experiences of caregiving stress during transitions of individuals living with dementia to a higher level of care. Data were transcribed verbatim, checked for accuracy, and analyzed by at least two members of the research team. Line-by-line coding, memo writing, and constant comparative analyses were conducted until redundancy, when no new themes were discovered. Caregivers described various levels of feeling overwhelmed and symptom progression leading to the move to a nursing facility. Social isolation featured prominently, with caregivers describing...

George Mason University, 2003

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, 2020

Social connections between people influence how they behave and where they go; however, such netw... more Social connections between people influence how they behave and where they go; however, such networks are rarely incorporated in agent-based models of disaster. To address this, we introduce a novel synthetic population method which specifically creates social relationships. This synthetic population is then used to instantiate a geographically explicit agent-based model for the New York megacity region which captures pre- and post- disaster behaviors. We demonstrate not only how social networks can be incorporated into models of disaster but also how such networks can impact decision making, opening up a variety of new application areas where network structures matter in urban settings.

Computational Urban Science, 2022

Geographically-explicit simulations have become crucial in understanding cities and are playing a... more Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of d...

Handbook of Research Methods in Complexity Science

Human services planners and evaluators require an increasing high level of flexibility and adapta... more Human services planners and evaluators require an increasing high level of flexibility and adaptability to remain effective in measuring the effectiveness of social interventions. Understanding the logic and assessing the impact behind the intervention can be difficult because commonly-used evaluative tools are based primarily on linear methods that assume that a set amount of input, throughput, and output will result in a set outcome. This chapter takes a complexity science approach and facilitates the use of agent-based modelling (ABM). It provides the requisite background for evaluators and researchers to frame their efforts as complex adaptive systems. These systems have several components that include agents having options, boundaries, self-organising behaviour, different options from which to choose, feedback to adapt, and an emergent behaviour. Complexity is viewed as a mathematical field where the relations between inputs and are better understood through simulations. Both qualitative and quantitative aspects of complexity are addressed through two applications of ABM that consider related social policy issues.

Urban Science, 2021

Increasingly urbanized populations and climate change have shifted the focus of decision makers f... more Increasingly urbanized populations and climate change have shifted the focus of decision makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: (1) the physical system, consisting of geological, ecological, and human-built systems; (2) the social system, consisting of informal and formal human collective behavior; and (3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just...

Interactions Between the Fast and Slow Mental Processes William Kennedy George Mason University M... more Interactions Between the Fast and Slow Mental Processes William Kennedy George Mason University Magdalena Bugajska Naval Research Laboratory Abstract: Our actions seem to be controlled by two separate types of mental processes: one fast, automatic, and unconscious and one slow, deliberate, and conscious. With the attention in the literature focused on the characteristics of the two processes and whether to include emotions, we do not find any discussion of how they interact. We present evidence that the slower process is not able to perceive the operation of faster process, but it can perceive the environmental stimulus common to both processes and the response of the faster process. It can then generate its own more deliberate response, possibly contrary to the faster process’s response. We also provide evidence that the slower process is sometimes able to inhibit the fast process’s response, but with effort. We present common experiences as well as cognitive theory and neurologica...

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2020

This paper introduces an ingenious text-based affective aware pseudo association method (AAPAM) t... more This paper introduces an ingenious text-based affective aware pseudo association method (AAPAM) to connect disjoint users and items across different information domains and leverage them to make crossdomain content-based and collaborative filtering recommendations. This paper demonstrates that the AAPAM method could seamlessly join different information domain datasets to act as one without additional cross-domain information retrieval protocols. Besides making cross-domain recommendations, the benefit of joining datasets from different information domains through AAPAM is that it eradicates cold start issues while making serendipitous recommendations.

2021 Annual Modeling and Simulation Conference (ANNSIM), 2021

Within agent-based models, agents interact with each other (e.g., social networks) and their envi... more Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems.

International Conference on Social Media and Society, 2020

Social media is a popular source for political communication and user engagement around social an... more Social media is a popular source for political communication and user engagement around social and political issues. While the diversity of the population participating in social and political events in person are often considered for social science research, measuring the diversity representation within online communities is not a common part of social media analysis. This paper attempts to fill that gap and presents a methodology for labeling and analyzing diversity in a social media sample based on emojis and keywords associated with gender, skin tone, sexual orientation, religion, and political ideology. We analyze the trends of diversity related themes and the diversity of users engaging in the online political community during the leadup to the 2018 U.S. midterm elections. Our results reveal patterns along diversity themes that otherwise would have been lost in the volume of content. Further, the diversity composition of our sample of online users rallying around political campaigns was similar to those measured in exit polls on election day. The diversity language model and methodology for diversity analysis presented in this paper can be adapted to other languages and applied to other research domains to provide social media researchers a valuable lens to identify the diversity of voices and topics of interest for the less-represented populations participating in an online social community.

Computer Science & Information Technology, 2020

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as bui... more We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.

Computational and Mathematical Organization Theory, 2019

We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and g... more We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. We review our thesis here, provide the current research context, address a set of issues with the thesis, and provide some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents unity of purpose, an interdependence of theory and methods, and a call for the careful development of new approaches for understanding human social systems, broadly construed.

Journal on Policy and Complex Systems, 2015

Caregivers providing support to family members with Alzheimer's disease often encounter high leve... more Caregivers providing support to family members with Alzheimer's disease often encounter high levels of stress within the fragmented long-term care system. To address this emerging issue affecting millions of families, we applied agent-based computational modeling methods to better understand the impacts of policy alternatives. Potential options include increased respite care, tax incentives, work place policies, and adult day services as alternatives to reduce caregiver stress. Experiments with our model demonstrate that policy options providing programs, services, and support for caregivers can reduce their stress by providing a minimum of 16 hours per week of respite care.

Future Internet, 2019

Disaster events and their economic impacts are trending, and climate projection studies suggest t... more Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief re...

International Journal on Natural Language Computing, 2021

Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class ... more Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user’s preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems’ capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top-N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision making. The method shows its adaptability to group's moods change when m...

We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative... more We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. In addition to explaining our thesis, we provide the current research context, a set of issues with the thesis and some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents some unity of purpose and interdependence of theory and methods.

How can we facilitate human-robot teamwork? The teamwork literature has identified the need to kn... more How can we facilitate human-robot teamwork? The teamwork literature has identified the need to know the capabilities of teammates. How can we integrate the knowledge of another agent's capabilities for a justifiably intelligent teammate? This paper describes extensions to the cognitive architecture, ACT-R, and the use of artificial intelligence (AI) and cognitive science approaches to produce a more cognitively-plausible, autonomous robotic system that "mentally" simulates the decision-making of its teammate. The extensions to ACT-R added capabilities to interact with the real world through the robot's sensors and effectors and simulate the decision-making of its teammate. The AI applications provided visual sensor capabilities by methods clearly different than those used by humans. The integration of these approaches into intelligent team-based behavior is demonstrated on a mobile robot. Our "TeamBot" matches the descriptive work and theories on human teamwork. We illustrate our approach in a spatial, team-oriented task of a guard force responding appropriately to an alarm condition that requires the human and robot team to "man" two guard stations as soon as possible after the alarm.

We present an agent-based model of human-environment interaction and conflict in East Africa usin... more We present an agent-based model of human-environment interaction and conflict in East Africa using the MASON agent-based simulation environment. Our model
focuses on the complex interaction of pastoral groups with their environment and other emerging external actors. Our model supports the observation that increased seasonal rainfall variability and droughts create tremendous stress on pastoralists groups and challenges their long-term resilience and adaptive response mechanisms.

An agent-based model conflict between herdsmen in east Africa using the MASON agent-based simulat... more An agent-based model conflict between herdsmen in east Africa using the MASON agent-based simulation environment is presented. Herders struggle to keep their herds fed and watered in a GIS-based, spatially diverse environment with data-driven seasonal cycles. The model produces realistic carrying capacity dynamics and basically plausible conflict dynamics. With the rather basic set of behaviors, herders come into conflict over limited resources and one clan is eventually eliminated. We find that greater environmental scarcity leads to faster domination by a single group. At the same time, we note that there is tremendous variability from run to run in the rate and timing of the transition from a conflict-prone, multi-clan environment to hegemony of a single group.

Human reactions appear to be controlled by two separate types of mental processes: one fast, auto... more Human reactions appear to be controlled by two separate types of mental processes: one fast, automatic, and unconscious and the other slow, deliberate, and conscious. With the attention in the literature focused on the taxonomy of the two processes, there is little discussion of how they interact. In this paper, we focus on modeling the slower process’s ability to inhibit the fast process. We present computational cognitive models in which different strategies allow a human to consciously inhibit an undesirable fast response. These general strategies include (a) blocking sensory input, (b), blocking or interrupting the fast process’s response, and (c) slowing down or delaying processing by introducing additional task. Furthermore, we discuss an approach to learning such strategies based on the inference of the causes and effects of the fast process.

An agent-based model of conflict among herders and between herders and private farmers of east Af... more An agent-based model of conflict among herders and between herders and private farmers of east Africa is presented using the MASON agent-based simulation environment. Herders survive by keeping their herds fed and watered in the inhospitable environment with some of the land becoming unavailable due to privatization. Our model develops realistic population and conflict dynamics. With only basic behaviors, herders come into conflict with other herders and farmers over the limited resources. However, the introduction of farmers did not have severe effects on the herder carrying capacity and, when farmers are included in the overall carrying capacity, adding farmers actually increased the capacity by almost a factor of two. The introduction of farmers appears to have provided a separation between the two modeled herder clans reducing the trend toward hegemony seen without farmers.

The modeling of disasters will be a very important topic for computational social science. An app... more The modeling of disasters will be a very important topic for computational social science. An approach to modeling disasters is presented using a point source and exponential decay of the intensity of the disaster with space and time from the disaster’s origin. The representation is demonstrated using the modification of a well-known NetLogo model as well as its application to modeling a flooding disaster in East Africa.

Large-scale social simulations require a cognitively credible but computationally efficient cogni... more Large-scale social simulations require a cognitively credible but computationally efficient cognitive architecture to support simulations with thousands to tens of thousands agents. In previous work developing and experimenting with a large-scale social simulation, we successfully employed an ad hoc cognitive model, a formula-based decision function. We explain why we now believe that a “fast and frugal” cognitive architecture to be superior based on its indistinguishable computational efficiency and much better cognitive plausibility.

Trust is not simply the result of rational cognition but relies on rational and “beyond rational”... more Trust is not simply the result of rational cognition but relies on rational and “beyond rational” cognition. The concept of trust is discussed in terms of its biological, Maslow, and cognitive roots. The cognitive roots rely on the Dual Process Theory, i.e. that there are two types of cognition sometimes called rational and non-rational. Therefore, the roots of trust need a cognitive architecture that implements the Dual Process Theory and involves cognition both rational and beyond rational.

One mode of human decision-making is considered intuitive, i.e., unconscious situational pattern ... more One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition. Implicit
statistical learning, which involves the sampling of
invariances from the environment and is known to involve
procedural (i.e., non-declarative) memory, has been shown to
be a foundation of this mode of decision making. We present
an ACT-R model of implicit learning whose implementation
entailed a declarative memory-based learner of the
classification of example strings of an artificial grammar. The
model performed very well when compared to humans. The
fact that the simulation of implicit learning could not be
implemented in a straightforward way via a non-declarative
memory approach, but rather required a declarative memory based implementation, suggests that the conceptualization of
procedural memory in the ACT-R framework may need to be
expanded to include abstract representations of statistical
regularities. Our approach to the development and testing of
models in ACT-R can be used to predict the development of
intuitive decision-making in humans.

One mode of human decision-making is considered intuitive, i.e., unconscious situational pattern ... more One mode of human decision-making is considered intuitive,
i.e., unconscious situational pattern recognition. Implicit
statistical learning, which involves the sampling of
invariances from the environment and is known to involve
procedural (i.e., non-declarative) memory, has been shown to
be a foundation of this mode of decision making. We present
an ACT-R model of implicit learning whose implementation
entailed a declarative memory-based learner of the
classification of example strings of an artificial grammar. The
model performed very well when compared to humans. The
fact that the simulation of implicit learning could not be
implemented in a straightforward way via a non-declarative
memory approach, but rather required a declarative memory based implementation, suggests that the conceptualization of
procedural memory in the ACT-R framework may need to be
expanded to include abstract representations of statistical
regularities. Our approach to the development and testing of
models in ACT-R can be used to predict the development of
intuitive decision-making in humans.

AAAI Spring Symposium 2013

This paper describes work underway at the Krasnow Institute for Advanced Study on the topic of m... more This paper describes work underway at the Krasnow
Institute for Advanced Study on the topic of modeling social
trust. We have built and are testing an ACT-R model
intended to replicate human participants building and
maintaining social trust using an economic investment
game. We already have behavioral and fMRI imaging data
for subjects which we expect to generate comparable data
by having an ACT-R model read the same inputs the
humans did and decide whether to trust or not their partner.

Proceedings of the 2013 International Conference on Cognitive Modeling

Previous work comparing fMRI data for two participants participating in a trust game provides a ... more Previous work comparing fMRI data for two participants
participating in a trust game provides a unique source for the
development of an ACT-R model of trust. The model
replicates a large portion of the behavior data. Continuing
efforts expect to match more of the behavioral data and the
imaging data comparison is expected to identify architectural
needs.

This paper discusses the development of a model of the household migration behavior of a nation’s... more This paper discusses the development of a model of the household migration behavior of a nation’s population. From information synthesized from across available microdata sources which are each temporally, spatially, or topically inconsistent in coverage, we learned decision trees and instantiated agents in an agent-based model. The generative results of the whole-country simulation of this ABM mimicked the observed macro-level findings, engendering confidence in this method to develop agents and decision trees from microdata.

Computational cognitive modeling is normally thought of as rational cognition. However, there are... more Computational cognitive modeling is normally thought of as rational cognition. However, there are human behaviors that do not appear to be driven by rational cognition. The other, "beyond rational" cognition is also appropriate for computational models of cognition. The panel will discuss their efforts at modeling this form of cognition.

Post-experiment results showed that if sexually differentiated philopatry and dispersal were conj... more Post-experiment results showed that if sexually differentiated philopatry and dispersal were conjoined with attitude biased foraging; the distribution of genetic values within an aggregate and affected population would change.

Trust is a vital component of human society. Without trust, we would all have to continuously foc... more Trust is a vital component of human society. Without trust, we would all have to continuously focus on survival, i.e., our physical security and the daily finding of food and water. With trust, we developed the division of labor that allowed us to sleep safely and develop a complex society. However, the concept of trust is very defused and not described operationally, i.e., in terms of the mechanisms by which we build, maintain, and decide to trust. There is now evidence that trust has a biological foundation [1]. That brain imaging data shows that some of the regions of our brain associated with trust are part of our high-level reasoning but others are deep within the brain. Like emotions, deep brain processes are typically fast, unconscious, and unexplainable. My hypothesis is that trust is based on cognitive mechanisms, likely the brain's slow and fast processes [2]. This talk focuses on a computational cognitive modeling approach to developing and understanding the mechanism...

An agent-based model conflict between herdsmen in east Africa using the MASON agent-based simulat... more An agent-based model conflict between herdsmen in east Africa using the MASON agent-based simulation environment is presented. Herders struggle to keep their herds fed and watered in a GIS-based, spatially diverse environment with data-driven seasonal cycles. The model produces realistic carrying capacity dynamics and basically plausible conflict dynamics. With the rather basic set of behaviors, herders come into conflict over limited resources and one clan is eventually eliminated. We find that greater environmental scarcity leads to faster domination by a single group. At the same time, we note that there is tremendous variability from run to run in the rate and timing of the transition from a conflict-prone, multi-clan environment to hegemony of a single group.

HerderLand is an agent-based model of the people and environment in the Mandera Triangle area of ... more HerderLand is an agent-based model of the people and environment in the Mandera Triangle area of Eastern Africa developed to address the causes of conflict in the area. With it we have conducted three sets of experiments varying the major environmental parameters we believed would affect conflict in the region.

2019 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation,

Individual connections between human beings often dictate where people go and how they behave, ye... more Individual connections between human beings often dictate where people go and how they behave, yet their representation through social networks are rarely used as measures of human behavior in agent-based models. Social networks are increasingly used for study of human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and cowork-ers. Based on this assumption we have created a set of heuristics for modeling how agents behave in an emergency event and how the individual behavior aggregates into a variety of patterns of life. In this paper will present briefly our agent-based model being used to characterize the population's reaction to a Nuclear Weapon of Mass Destruction (NWMD) event in the New York City region. Agents are modeled commuting on workday schedules before the explosion of a small (10Kt) nuclear device. After the explosion, agents respond to signals in their environment and make decisions based on prioritization of safety for themselves and those in their networks. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making, and preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time.

The 2019 Computational Social Science Society of Americas Conference, 2019

Connections between human beings often influence where people go and how they behave, yet their r... more Connections between human beings often influence where people go and how they behave, yet their representation as social networks are rarely modeled as a factor of human behavior in agent-based models. Social networks are increasingly being used to study human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and coworkers. In this poster, we briefly present our agent-based model being used to characterize the New York City area population's reaction to a Nuclear Weapon of Mass Destruction (NWMD) event. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making during a disaster. Preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time.

The 2019 Computational Social Science Society of Americas Conference, 2019

Cities are complex systems which are constantly changing because of the interactions between the ... more Cities are complex systems which are constantly changing because of the interactions between the people and their environment. Such systems often go through several life cycles which are shaped by various processes. These may include urban growth, sprawl, shrinkage, and gentrification. These processes affect the urban land markets which in turn affect the formation of a city through feedback loops. Through models we can explore such dynamics, populations, and the environments in which people inhabit. The model proposed in this paper intends to simulate the aforementioned dynamics to capture the effect of agents' choices and actions on the city structure. Specifically, this model explores the effect of gentrification on population density and housing values. The proposed model is significant in its integration of ideas from complex systems theory which is operationalized within an agent-based model stylized on urban theories to study gentrification as a cause of increased in land values. The model is stylized on urban theories and results from the model show that the agents move to and reside in properties within their income range, neighboring agents that have similar economic status. The model also shows the role of gentrification by capturing both the supply and demand aspects of this process in the displacement and immobilization of agents with lower incomes. This is one of the first models that combines several processes to explore the life cycle of a city through agent-based modeling.