Papers by William G. Kennedy
Cognitive Science, 2016
Simulations of many people’s decisions are used in public health and safety as well as to support... more Simulations of many people’s decisions are used in public health and safety as well as to support policymaking. These simulations rely on creditable models of individual decision-making. An obvious approach is to develop a list of plausible actions and to then evaluate the benefits of each in the current situation to make the decision. However, such evaluations can be implausible, e.g., zero-intelligence traders in economics, or impracticable because the approach is computationally intensive for large-scale simulations. As a result, a commonly used approach is to select randomly from the plausible actions. Without data on how people would actually chose, a random number from a uniform distribution over the plausible options is often used to represent the unknown cognition. However, we claim that substituting a uniform random distribution for how people make decisions is making very strong claims about the process and we will present data demonstrating it is simply wrong.
This paper investigates the causality in the decision making of movie recommendations through the... more This paper investigates the causality in the decision making of movie recommendations through the users' affective profiles. We advocate a method of assigning emotional tags to a movie by the auto-detection of the affective features in the movie's overview. We apply a text-based Emotion Detection and Recognition model, which trained by tweets short messages and transfers the learned model to detect movie overviews' implicit affective features. We vectorize the affective movie tags to represent the mood embeddings of the movie. We obtain the user's emotional features by taking the average of all the movies' affective vectors the user has watched. We apply five-distance metrics to rank the Top-N movie recommendations against the user's emotion profile. We found Cosine Similarity distance metrics performed better than other distance metrics measures. We conclude that by replacing the top-N recommendations generated by the Recommender with the reranked recommendations list made by the Cosine Similarity distance metrics, the user will effectively get affective aware top-N recommendations while making the Recommender feels like an Emotion Aware Recommender.
What are the characteristics of long-term learning? We investigated the characteristics of long-t... more What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used, and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-R's computational performance problems and settings that appear to avoid the performance problems in ACT-R.
What are the characteristics of long-term learning? We investigated the characteristics of long-t... more What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used, and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-R's computational performance problems and settings that appear to avoid the performance problems in ACT-R.
Social Science Research Network, 2019
The authors used a decision tree classifier to reduce neuropsychological, behavioral and laborato... more The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment. Method: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADT's were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. Results: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. Interpretation: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying b 7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment.
Flocking with Only Two Parameters
Springer proceedings in complexity, Dec 31, 2022
Investigating Emergency Responders’ Roles in a Dirty Bomb Event with an Agent-Based Model
Springer proceedings in complexity, Dec 31, 2022
Capturing the Effects of Gentrification on Property Values: An Agent-Based Modeling Approach
Springer proceedings in complexity, 2021
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.
Integrating social networks into large-scale urban simulations for disaster responses
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 and Mathematical Organization Theory, Sep 1, 2011
The BRiMS Society and Conference (Behavioral Representation in Modeling and Simulation (BRiMS, br... more The BRiMS Society and Conference (Behavioral Representation in Modeling and Simulation (BRiMS, brimsconference.org) promote cross-disciplinary communication for basic and applied scientific research in the realm of modeling and simulation of human behavior, with a particular emphasis on defense government-related tasks and behavior. Thus, the BRiMS conference brings together scientists, engineers, practitioners, and application users to discuss modeling behavior ranging from that of individuals to the behavior of whole societies, their interactions, and their implications. For a few days each year, we get to meet to share ideas and experiences, identify gaps in current capabilities, discuss new directions, highlight promising technologies, and showcase applications. This special issue is similar to our previous special issue ( ) in that it includes four papers based on the award winning conference papers of the 2010 annual conference, reviewed and extended to journal article length. The papers include a new model integrating top-down and bottom-up factors affecting visual target acquisition (Jungkunz and Darken 2011), the application of a statistical methodology W.G. Kennedy ( )
Topics in Cognitive Science, 2018
Cognitive modeling is the effort to understand the mind by implementing theories of the mind in c... more Cognitive modeling is the effort to understand the mind by implementing theories of the mind in computer code, producing measures comparable to human behavior and mental activity. The community of cognitive modelers has traditionally met twice every 3 years at the International Conference on Cognitive Modeling (ICCM). In this special issue of topiCS, we present the best papers from the ICCM meeting. (The full proceedings are available on the ICCM website.) These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling.
Zenodo (CERN European Organization for Nuclear Research), Mar 16, 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 making recommendations.
Agent-Based Models and Ethnography: Combining Qualitative and Computational Techniques with Complexity Theory
Practicing anthropology, Dec 31, 2012
Increased interaction, adaptability, diversity, and emergence are all hallmarks of complexity (Mi... more Increased interaction, adaptability, diversity, and emergence are all hallmarks of complexity (Miller and Page 2007; see Simon 1996). While anthropologists may not use these specific complexity theory terms, they have long been interested in how diverse people interact and adapt in their negotiation of identity and society and what sorts of social phenomena emerge from these interactions. A complexity theory perspective can interpret culture or cultural practices as either the base rules from which identity emerges (consider Appadurai 1996) or the emergent system itself, the "webs of significance" in which humans are embedded (see Geertz 1973).
Winter Simulation Conference, Dec 11, 2016
Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provi... more Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provided to increase chances of surviving active shooter scenarios, usually emphasizing "Run, Hide, Fight." Evidence from prior mass shootings suggests that casualties may be limited should the shooter encounter unarmed resistance prior to the arrival of law enforcement officers (LEOs). An agent-based model (ABM) explored the potential for limiting casualties should a small proportion of potential victims swarm a gunman, as occurred on a train from Amsterdam to Paris in 2015. Results suggest that even with a miniscule probability of overcoming a shooter, fighters may save lives but put themselves at increased risk. While not intended to prescribe a course of action, the model suggests the potential for a reduction in casualties in active shooter scenarios.
Practicing anthropology, Dec 31, 2012
ACM transactions on interactive intelligent systems, Oct 10, 2019
A proposal for a unified theory of learned trust implemented in a cognitive architecture is prese... more A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. The theory is instantiated as a computational cognitive model of learned trust that integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The model relies on a combination of learning mechanisms to explain a variety of phenomena such as trust asymmetry, the higher impact of early trust breaches, the black-hat/white-hat effect, the correlation between trust and cognitive ability, and the higher resilience of interpersonal as compared to human-machine trust. In addition, the model predicts that trust decays in the absence of evidence of trustworthiness or untrustworthiness. The implications of the model for the advancement of the theory on trust are discussed. Specifically, this work suggests two more trust antecedents on the trustor's side: perceived trust necessity and cognitive ability to detect cues of trustworthiness. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms;
International Journal of Social Robotics, Feb 24, 2009
We present a successful design approach for social robotics based on a computational cognitive ar... more We present a successful design approach for social robotics based on a computational cognitive architecture and mental simulation. We discuss an approach to a Theory of Mind known as a "like-me" simulation in which the agent uses its own knowledge and capabilities as a model of another agent to predict that agent's actions. We present three examples of a "like-me" mental simulation in a social context implemented in the embodied version of the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, ACT-R/E (for ACT-R Embodied). Our examples show the efficacy of a simulation approach in modeling perspective taking (identifying another's left or right hand), teamwork (simulating a teammate for better team performance), and dominant-submissive social behavior (primate social experiments). We conclude with a discussion of the cognitive plausibility of this approach and our conclusions.
PsycEXTRA Dataset, 2012
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.
Social Science Research Network, Apr 26, 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 review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.
Active Shooter: An Agent-Based Model of Unarmed Resistance (Version 2)
CoMSES Computational Model Library, Dec 17, 2016
Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provi... more Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provided to increase chances of surviving active shooter scenarios, usually emphasizing "Run, Hide, Fight." Evidence from prior mass shootings suggests that casualties may be limited should the shooter encounter unarmed resistance prior to the arrival of law enforcement officers (LEOs). An agent-based model (ABM) explored the potential for limiting casualties should a small proportion of potential victims swarm a gunman, as occurred on a train from Amsterdam to Paris in 2015. Results suggest that even with a miniscule probability of overcoming a shooter, fighters may save lives but put themselves at increased risk. While not intended to prescribe a course of action, the model suggests the potential for a reduction in casualties in active shooter scenarios.
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Papers by William G. Kennedy