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Mobilität von morgen
(2016)
Die Studie wurde von Studierenden des Kurses »Die Zukunft der Mobilität« am Institut für angewandte Forschung Urbane Zukunft der Fachhochschule Potsdam entwickelt und durchgeführt. Über einen Fragebogen wurden realistische Mobilitätsszenarien auf Basis von Expert_innenmeinungen erfasst und ausgewertet. Im Gegensatz zu ähnlich gelagerten Studien erfolgte eine Beschränkung auf Berlin und Brandenburg. Lokale Probleme, Entwicklungen und Lösungsmöglichkeiten standen somit im Zentrum der Untersuchung. Über die Auswahl der Teilnehmer_innen konnten praxisnahe und inhaltlich konkrete Antworten auf wichtige Zukunftsfragen unserer Region gewonnen werden.
The diffusion of electric vehicles (EVs) is considered an effective policy strategy to meet greenhouse gas reduction targets. For large-scale adoption, however, demand-side oriented policy measures are required, based on consumers' transport needs, values and social norms. We introduce an empirically grounded, spatially explicit, agent-based model, InnoMind (Innovation diffusion driven by changing Minds), to simulate the effects of policy interventions and social influence on consumers' transport mode preferences. The agents in this model represent individual consumers. They are calibrated based on empirically derived attributes and characteristics of survey respondents. We model agent decision-making with artificial neural networks that account for the role of emotions in information processing. We present simulations of 4 scenarios for the diffusion of EVs in the city of Berlin, Germany (3 policy scenarios and 1 base case). The results illustrate the varying effectiveness of measures in different market segments and the need for appropriate policies tailored to the heterogeneous needs of different travelers. Moreover, the simulations suggest that introducing an exclusive zone for EVs in the city would accelerate the early-phase diffusion of EVs more effectively than financial incentives only.
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.
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.
Emosaic – Visualisierung von Emotionen in Texten durch Farbumwandlung zur Analyse und Exploration
(2016)
Das computergestützte Extrahieren und Visualisieren von Emotionen in Texten ist eine etablierte Technik des “Distant Reading”. Die generelle Stimmung eines Textes kann schnell erfasst werden ohne den gesamten Text lesen zu müssen. Da Emotionen sehr komplex und die Eigenschaften zwischen verschiedenen Emotionen fließend sind, ist die visuelle Charakterisierung von Emotionen schwierig. Wir stellen Emosaic vor, ein Online-Tool welches Emotionen aus benutzerdefinierten Texten filtert und durch systematische und nachvollziehbare Farbumwandlung zur Exploration und Analyse innerhalb einer interaktiven Visualisierung bereitstellt. Die durch drei Dimensionen beschreibbaren Emotionen werden dabei in klar definierte Farbparameter übersetzt. Ein von uns entwickelter öffentlich zugänglicher Web- Prototyp ( vgl. Geuder et al. 2014-) zeigt anhand interaktiver Visualisierungen erste Analyse- und Explorationsmöglichkeiten dieser Methode.
We describe and illustrate a new method of graphically diagramming disputants’ points of view called cognitive-affective mapping. The products of this method—cognitive-affective maps (CAMs)—represent an individual’s concepts and beliefs about a particular subject, such as another individual or group or an issue in dispute. Each of these concepts and beliefs has its own emotional value. The result is a detailed image of a disputant’s complex belief system that can assist in-depth analysis of the ideational sources of the dispute and thereby aid its resolution. We illustrate the method with representations of the beliefs of typical individuals involved in four contemporary disputes of markedly different type: a clash over German housing policy, disagreements between Israelis over the meaning of the Western Wall, contention surrounding exploitation of Canada’s bitumen resources, and the deep dispute between people advocating action on climate change and those skeptical about the reality of the problem.
We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is shown by a model that simulates psychologically important cases of intention.
Priming
(2014)
Priming influences holistic representations of social situations and subsequent actions through interactive competition among relevant concepts such as the prime, the self, a partner, or other features of the environment. The constraints among these representations stem from culturally shared affective meanings of concepts acquired in socialization. Our theory is implemented in a localist connectionist model, which in simulations reproduced major experimental results on priming. The neural plausibility of our proposal comes from semantic pointers, a neural mechanism that integrates symbolic concepts with underlying emotional and sensorimotor processes. The compositional nature of semantic pointers also explains the interaction of priming with more deliberate and intentional forms of social cognition.
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.
We describe how we use social simulation as a core method in a new master’s program designed to teach future leaders of urban change to deal with the complexity inherent in current societal transformations. We start by depicting the challenges with regard to cross-disciplinary knowledge integration and overcoming value-based, rigid thinking styles that inevitably arise in the process of solving ecological, technological, or social problems in cities new and old. Next, we describe a course based on urban modeling and participatory approaches, designed to meet those challenges. We reflect on our first experience with this approach and discuss future developments and research needs.