TY - JOUR A1 - Schröder, Tobias A1 - Hoey, Jesse A1 - Rogers, Kimberly B. T1 - Modeling dynamic identities and uncertainty in social interaction BT - Bayesian affect control theory JF - American Sociological Review N2 - Drawing on Bayesian probability theory, we propose a generalization of affect control theory (BayesACT) that better accounts for the dynamic fluctuation of identity meanings for self and other during interactions, elucidates how people infer and adjust meanings through social experience, and shows how stable patterns of interaction can emerge from individuals’ uncertain perceptions of identities. Using simulations, we illustrate how this generalization offers a resolution to several issues of theoretical significance within sociology and social psychology by balancing cultural consensus with individual deviations from shared meanings, balancing meaning verification with the learning processes reflective of change, and accounting for noise in communicating identity. We also show how the model speaks to debates about core features of the self, which can be understood as stable and yet malleable, coherent and yet composed of multiple identities that may carry competing meanings. We discuss applications of the model in different areas of sociology, implications for understanding identity and social interaction, as well as the theoretical grounding of computational models of social behavior. KW - Sozialwesen KW - Zusammenarbeit KW - Bayes-Verfahren KW - Soziologie KW - Psychologie Y1 - 2016 U6 - https://doi.org/10.1177/0003122416650963 SN - 0003-1224 VL - 81 IS - 4 SP - 828 EP - 855 PB - Sage CY - Thousand Oaks ER - TY - CHAP A1 - Jung, Joshua A1 - Morgan, Jonathan A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Wolf, Ingo T1 - Grounding social interaction with affective intelligence T2 - Advances in Artificial Intelligence : 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings KW - Künstliche Intelligenz KW - Soziologie KW - Humanfaktor KW - Informationsverhalten Y1 - 2016 SN - 978-3-319-34111-8 U6 - https://doi.org/10.1007/978-3-319-34111-8_7 SP - 52 EP - 57 PB - Springer CY - Cham ER - TY - CHAP A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Bayesian affect control theory of self T2 - Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence and the Twenty-Seventh Innovative Applications of Artificial Intelligence Conference, 25 - 30 January, Austin, Texas, USA. Volume 1 N2 - Notions of identity and of the self have long been studied in social psychology and sociology as key guiding elements of social interaction and coordination. In the AI of the future, these notions will also play a role in producing natural, socially appropriate artificially intelligent agents that encompass subtle and complex human social and affective skills. We propose here a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents. This theory posits that each human maintains an internal model of his or her deep sense of "self" that captures their emotional, psychological, and socio-cultural sense of being in the world. The "self" is then externalised as an identity within any given interpersonal and institutional situation, and this situational identity is the person's local (in space and time) representation of the self. Situational identities govern the actions of humans according to affect control theory. Humans will seek situations that allow them to enact identities consistent with their sense of self. This consistency is cumulative over time: if some parts of a person's self are not actualized regularly, the person will have a growing feeling of inauthenticity that they will seek to resolve. In our present generalisation, the self is represented as a probability distribution, allowing it to be multi-modal (a person can maintain multiple different identities), uncertain (a person can be unsure about who they really are), and learnable (agents can learn the identities and selves of other agents). We show how the Bayesian affect control theory of self can underpin artificial agents that are socially intelligent. KW - Künstliche Intelligenz KW - Identität KW - Reasoning KW - Wahrscheinlichkeit KW - Affekt KW - Selbstkontrolle Y1 - 2015 SN - 978-1-57735-699-8 SP - 529 EP - 536 PB - AAAI Press CY - Palo Alto, Calif. ER - TY - CHAP A1 - Lin, Luyuan A1 - Czarnuch, Stephen A1 - Malhotra, Aarti A1 - Yu, Lifei A1 - Schröder, Tobias A1 - Hoey, Jesse T1 - Affectively Aligned Cognitive Assistance Using Bayesian Affect Control Theory T2 - Ambient Assisted Living and Daily Activities N2 - This paper describes a novel emotionally intelligent cognitive assistant to engage and help older adults with Alzheimer’s disease (AD) to complete activities of daily living (ADL) more independently. Our new system combines two research streams. First, the development of cognitive assistants with artificially intelligent controllers using partially observable Markov decision processes (POMDPs). Second, a model of the dynamics of emotion and identity called Affect Control Theory that arises from the sociological literature on culturally shared sentiments. We present background material on both of these research streams, and then demonstrate a prototype assistive technology that combines the two. We discuss the affective reasoning, the probabilistic and decision-theoretic reasoning, the computer-vision based activity monitoring, the embodied prompting, and we show results in proof-of-concept tests. KW - Dichte KW - Markov-Entscheidungsprozess KW - Alzheimerkrankheit Y1 - 2014 SN - 978-3-319-13105-4 U6 - https://doi.org/10.1007/978-3-319-13105-4_41 SN - 1611-3349 SP - 279 EP - 287 PB - Springer CY - Cham ER - TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Alhothali, Areej T1 - Affect control processes BT - Intelligent affective interaction using a partially observable Markov decision process JF - Artificial Intelligence N2 - This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and suppress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This “affect control principle” has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional “plug-in” for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability. KW - Emotionales Verhalten KW - Soziologie KW - Markov-Entscheidungsprozess KW - Intelligentes Tutorsystem KW - Mensch-Maschine-Kommunikation Y1 - 2015 U6 - https://doi.org/10.1016/j.artint.2015.09.004 SN - 0004-3702 VL - 230 SP - 134 EP - 172 PB - Elseviert CY - Amsterdam ER - TY - CHAP A1 - Malhotra, Aarti A1 - Yu, Lifei A1 - Schröder, Tobias A1 - Hoey, Jesse T1 - An exploratory study into the use of an emotionally aware cognitive assistant T2 - AAAI Workshop : Artificial Intelligence Applied to Assistive Technologies and Smart Environments N2 - This paper presents an exploratory study conducted to understand how audio-visual prompts are understood by people on an emotional level as a first step towards the more challenging task of designing emotionally aligned prompts for persons with cognitive disabilities such as Alzheimer’s disease and related dementias (ADRD). Persons with ADRD often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. This paper presents a set of prompt videos of a virtual human ‘Rachel’, wherein she expressively communicates prompts at each step of a simple hand washing task, with various human-like emotions and behaviors. A user study was conducted for 30 such videos with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results show that, while people generally agree on the evaluation (valence: good/bad) of a prompt, consensus about power and activity is not as socially homogeneous. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption. KW - Audiovisuelles Material KW - Alzheimerkrankheit KW - Demenz KW - Künstliche Intelligenz Y1 - 2015 UR - https://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/download/10147/10147 PB - Association for the Advancement of Artificial Intelligence CY - Palo Alto ER - TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Morgan, Jonathan Howard A1 - Rogers, Kimberly B. A1 - Rishi, Deepak A1 - Nagappan, Meiyappan T1 - Artificial Intelligence and Social Simulation BT - Studying Group Dynamics on a Massive Scale JF - Small Group Research N2 - Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams. KW - Künstliche Intelligenz KW - Gruppendynamik KW - Maschinelles Lernen KW - Virtuelles Team KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1177/1046496418802362 SN - 1552-8278 VL - 49 IS - 6 SP - 647 EP - 683 PB - Sage Publications CY - London ER - TY - JOUR A1 - Hoey, Jesse A1 - MacKinnon, Neil J. A1 - Schröder, Tobias T1 - Denotative and connotative management of uncertainty BT - A computational dual-process model JF - Judgment and Decision Making N2 - The interplay between intuitive and deliberative processing is known to be important for human decision making. As independent modes, intuitive processes can take on many forms from associative to constructive, while deliberative processes often rely on some notion of decision theoretic rationality or pattern matching. Dual process models attempt to unify these two modes based on parallel constraint networks or on socially or emotionally oriented adjustments to utility functions. This paper presents a new kind of dual process model that unifies decision theoretic deliberative reasoning with intuitive reasoning based on shared cultural affective meanings in a single Bayesian sequential model. Agents constructed according to this unified model are motivated by a combination of affective alignment (intuitive) and decision theoretic reasoning (deliberative), trading the two off as a function of the uncertainty or unpredictability of the situation. The model also provides a theoretical bridge between decision-making research and sociological symbolic interactionism. Starting with a high-level view of existing models, we advance Bayesian Affect Control Theory (BayesACT) as a promising new type of dual process model that explicitly and optimally (in the Bayesian sense) trades off motivation, action, beliefs and utility. We demonstrate a key component of the model as being sufficient to account for some aspects of classic cognitive biases about fairness and dissonance, and outline how this new theory relates to parallel constraint satisfaction models. KW - Zwei-Prozess-Modell KW - Gefühl KW - Affekt KW - Kognitive Dissonanz Y1 - 2021 UR - http://journal.sjdm.org/20/200104/jdm200104.pdf U6 - https://doi.org/10.1017/S1930297500008652 SN - 1930-2975 VL - 16 IS - 2 SP - 505 EP - 550 PB - Society for Judgment and Decision Making ER - TY - CHAP A1 - Morgan, Jonathan Howard A1 - Zhao, Jun A1 - Zöller, Nikolas A1 - Sedlacek, Andrea A1 - Chen, Lena A1 - Piper, Hayley A1 - Beck, Yliana A1 - Rogers, Kimberly B. A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Modeling the Culture of Online Collaborative Groups with Affect Control Theory T2 - Advances in Social Simulation N2 - We review Affect Control Theory (ACT) as a promising basis for equipping computational agents in social simulations with a sense of sociality. ACT is a computational theory that integrates sociological insights about the symbolic construction of the social order with psychological knowledge about cognitive-affective mechanisms. After explaining the theoretical foundations of ACT and applications of the theory at the dyadic and group level, we describe a case study applying the theory from an ongoing research project examining self-organized online collaboration in software development. KW - Culture KW - Affect KW - Gruppendynamik KW - Kollaboration Y1 - 2021 SN - 978-3-030-61503-1 U6 - https://doi.org/10.1007/978-3-030-61503-1_14 SP - 147 EP - 160 PB - Springer CY - Cham ER - TY - RPRT A1 - Hoey, Jesse A1 - Nagappan, Meiyappan A1 - Rogers, Kimberly B. A1 - Schröder, Tobias A1 - Dametto, Diego A1 - De Zoysa, Nalin A1 - Iyer, Rahul A1 - Morgan, Jonathan H. A1 - Rishi, Deepak A1 - Sirianni, Antonio D. A1 - Yun, Seonghu A1 - Zhao, Jun A1 - Zöller, Nikolas T1 - Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups N2 - Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups (THEMIS.COG) was an interdisciplinary research collaboration of computer scientists and social scientists from the University of Waterloo (Canada), Potsdam University of Applied Sciences (Germany), and Dartmouth College (USA). This white paper summarizes the results of our research at the end of the grant term. Funded by the Trans-Atlantic Platform’s Digging Into Data initiative, the project aimed at theoretical and empirical modeling of identity and sentiments in collaborative groups. Understanding the social forces behind self-organized collaboration is important because technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal organizational hierarchies or through market forces. Our work used a data-driven approach to explore the social psychological mechanisms that motivate such collaborations and determine their success or failure. We focused on the example of GitHub, the world’s current largest digital platform for open, collaborative software development. In contrast to most, purely inductive contemporary approaches leveraging computational techniques for social science, THEMIS.COG followed a deductive, theory-driven approach. We capitalized on affect control theory, a mathematically formalized theory of symbolic interaction originated by sociologist David R. Heise and further advanced in previous work by some of the THEMIS.COG collaborators, among others. Affect control theory states that people control their social behaviours by intuitively attempting to verify culturally shared feelings about identities, social roles, and behaviour settings. From this principle, implemented in computational simulation models, precise predictions about group dynamics can be derived. It was the goal of THEMIS.COG to adapt and apply this approach to study the GitHub collaboration ecosystem through a symbolic interactionist lens. The project contributed substantially to the novel endeavor of theory development in social science based on large amounts of naturally occurring digital data. KW - Computational social science KW - GitHub KW - Gruppendynamik KW - Sozialpsychologie Y1 - 2021 U6 - https://doi.org/10.31235/osf.io/4hukx SP - 1 EP - 36 ER - TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Disruption of Social Orders in Societal Transitions as Affective Control of Uncertainty JF - American behavioral scientist N2 - Bayesian affect control theory is a model of affect-driven social interaction underconditions of uncertainty. In this paper, we investigate how the operationalization of uncertainty in the model can be related to the disruption of social orders—societal pressures to adapt to ongoing environmental and technological change. First, we study the theoretical tradeoffs between three kinds of uncertainty as groups navigate external problems: validity (the predictability of the environment, including of other agents), coherence (the predictability of interpersonal affective dynamics), and dependence (the predictability of affective meanings). Second, we discuss how these uncertainty tradeoffs are related to contemporary political conflict and polarization in the context of societal transitions. To illustrate the potential of our model to analyze the socio-emotional consequences of uncertainty, we present a simulation of diverging individual affective meanings of occupational identities under uncertainty in a climate change mitigation scenario based on events in Germany. Finally, we sketch a possible research agenda to substantiate the novel, but yet mostly conjectural, ideas put forward in this paper. T2 - Störung sozialer Ordnungen in gesellschaftlichen Übergängen als affektive Kontrolle von Ungewissheit Y1 - 2023 U6 - https://doi.org/10.1177/00027642211066055 SN - 1552-3381 VL - 67 IS - 2 SP - 311 EP - 331 PB - Sage Publ. CY - Thousand Oaks ER -