Team

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.

Seminar: Simulation in Social Sciences - ENTFÄLLT

Dozent:innen: Prof. Dr. Petra Ahrweiler; Blanca Luque Capellas
Kurzname: S Techniksoziologie
Kurs-Nr.: 02.149.16911
Kurstyp: Seminar

Inhalt

Social Simulation is a type of modelling for social scientists and computer scientists which has been gaining interest as a result of extremely affordable computing power and a rising interest in complex systems methods and approaches to understanding the world. Many approaches to simulation are now accessible to social scientists with some programming skills who seek to develop a deeper understanding of social and economic issues and problems, centering on “complexity” ideas: evolution, adaptation, nonlinear behavior, emergence and self-organization. These approaches have become essential tools for social scientists in a wide range of fields, sociology, economics, cognitive psychology, organizational theory, political science and geography, and are now also part of the toolbox for computer scientists interested in distributed artificial intelligence and multi-agent technologies. Of primary concern are multi-level simulation models that allow simultaneous consideration of multiple levels of systems and processes of interest (social, economic, environmental), and have users alternate between views on macro and micro behaviors, and possibly levels in between. The most common approach to multi-level simulation, agent-based modelling, allows for hetero­geneity in simulated behaviors of the “agents” on the lower levels (representing, for instance, individuals, groups, organizations or networks). This provides to scientists the opportunity for (much) higher degrees of detail when observing “in silico” complex evolutionary phenomena, as opposed to some very abstract mathematical models.

This seminar offers an introduction to common approaches in Social Simulation. It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each simulation method (on its own, but without disregarding the projects that seek to develop “hybrid” modelling techniques across strands of simulation research, building first and foremost on agent-based approaches). It will also assist in developing appreciation for the common elements of Social Simulation workflows, for instance: the theoretical discussion of a model’s conceptual framework, the data-to-model links, and communication of results of simulation research.