In view of the environmental impact of high levels of car use, stimulating people to choose susta... more In view of the environmental impact of high levels of car use, stimulating people to choose sustainable transport modes instead is of importance. Transport mode choice has proven to be resilient to policy measures. Previous studies suggest qualitative methods do more justice to the complex nature of mobility systems, however the use of such research to policymakers is highly debated. Literature suggests that policymakers prefer quantitative results, and that qualitative studies are needed to investigate the way these results are interpreted and employed. As predictive models become increasingly sophisticated, they can include more aspects of human behavior. Currently, there is only a limited amount of literature on the value of agent-based modeling of mobility systems to policymakers and social scientists. This dissertation investigates the theoretical tangents between complexity theory and the field of transport geography. The starting point for this discussion is the new mobilities paradigm that adopts a view of mobility systems as context dependent, dynamic systems. In addition, this dissertation explores the application of agent-based modeling in the analysis of mode choice behavior. This agent-based model includes socio-economic and behavioral variables. The results suggest that applying complexity theory to transport geography offers promising ways of looking at mobility systems and that concepts of the former field are consistent with recent insights in the latter. In addition, the results suggest that agent-based modeling can be a viable alternative to traditional multinomial logit models. These findings imply that complexity theoretic methods are potentially valuable to policymakers to develop, test and inspire policy measures that contribute to achieving a more sustainable modal split
In view of the environmental impact of high levels of car use, stimulating people to choose susta... more In view of the environmental impact of high levels of car use, stimulating people to choose sustainable transport modes instead is of importance. Transport mode choice has proven to be resilient to policy measures. Previous studies suggest qualitative methods do more justice to the complex nature of mobility systems, however the use of such research to policymakers is highly debated. Literature suggests that policymakers prefer quantitative results, and that qualitative studies are needed to investigate the way these results are interpreted and employed. As predictive models become increasingly sophisticated, they can include more aspects of human behavior. Currently, there is only a limited amount of literature on the value of agent-based modeling of mobility systems to policymakers and social scientists. This dissertation investigates the theoretical tangents between complexity theory and the field of transport geography. The starting point for this discussion is the new mobilities paradigm that adopts a view of mobility systems as context dependent, dynamic systems. In addition, this dissertation explores the application of agent-based modeling in the analysis of mode choice behavior. This agent-based model includes socio-economic and behavioral variables. The results suggest that applying complexity theory to transport geography offers promising ways of looking at mobility systems and that concepts of the former field are consistent with recent insights in the latter. In addition, the results suggest that agent-based modeling can be a viable alternative to traditional multinomial logit models. These findings imply that complexity theoretic methods are potentially valuable to policymakers to develop, test and inspire policy measures that contribute to achieving a more sustainable modal split
Social sensing provides many opportunities for observing human behavior utilizing objective (sens... more Social sensing provides many opportunities for observing human behavior utilizing objective (sensor) measurements. This paper describes an approach for analyzing organizational social networks capturing face-to-face contacts between individuals. Furthermore, we outline perspectives and scenarios for an extended analysis in order to estimate happiness in the context of organizational social networks.
The A-level grading fiasco in the UK led to public outrage over algorithmic bias. This is a well-... more The A-level grading fiasco in the UK led to public outrage over algorithmic bias. This is a well-established problem that data professionals have sought to address through making their algorithms more explainable. However, Dr Daan Kolkman argues that the emergence of a “critical audience” in the A-level grading fiasco poses a model for a more effective means of countering bias and intellectual lock-in in the development of algorithms
This thesis inquires into how models are used to inform policy making and what makes models usefu... more This thesis inquires into how models are used to inform policy making and what makes models useful to those informing policy. A model is considered a formal representation of a target system that can be used to answer questions about that target system. Moreover, it is defined as the computer implementation of a mental model or conceptual model, written in algorithms or equations. This research draws on insights from existing literature on policy making, policy analysis, models and model use, which are then used to develop an analytical framework founded on science and technology studies. This framework is employed to guide an empirical investigation of models that have been used to inform policy making. Fieldwork was conducted over a period of two years at government departments, private companies and research institutes in the United Kingdom and the Netherlands. This fieldwork consisted of 38 interviews with model users, observations of model use and archival research. The data we...
Despite algorithms becoming an increasingly important tool for policymakers, little is known abou... more Despite algorithms becoming an increasingly important tool for policymakers, little is known about how they are used in practice and how they work, even amongst the experts tasked with using them. Drawing on research into the use of algorithmic models in the UK and Dutch governments, Daan Kolkman argues that the inherent complexity of algorithms renders attempts to make them transparent difficult and that to achieve public accountability for the role they play in society a dedicated watchdog is required.
Governments increasingly use algorithmic models to inform their policy making process. Many sugge... more Governments increasingly use algorithmic models to inform their policy making process. Many suggest that employing such quantifications will lead to more efficient, more effective or otherwise better quality policy making. Yet, it remains unclear to what extent these benefits materialize and if so, how they are brought about. This paper draws on the sociology and policy science literature to study how algorithmic models, a particular type of quantification, are used in policy analysis. It presents the outcomes of 38 unstructured interviews with data scientists, policy analysts, and policy makers that work with algorithmic models in government. Based on an in-depth analysis of these interviews, I conclude that the usefulness of algorithmic models in policy analysis is best understood in terms of the commensurability of these quantifications. However, these broad communicative and organizational benefits can only be brought about if algorithmic models are handled with care. Otherwise, they may propagate bias, exclude particular social groups, and will entrench existing worldviews.
In view of the environmental impact of high levels of car use, stimulating people to choose susta... more In view of the environmental impact of high levels of car use, stimulating people to choose sustainable transport modes instead is of importance. Transport mode choice has proven to be resilient to policy measures. Previous studies suggest qualitative methods do more justice to the complex nature of mobility systems, however the use of such research to policymakers is highly debated. Literature suggests that policymakers prefer quantitative results, and that qualitative studies are needed to investigate the way these results are interpreted and employed. As predictive models become increasingly sophisticated, they can include more aspects of human behavior. Currently, there is only a limited amount of literature on the value of agent-based modeling of mobility systems to policymakers and social scientists. This dissertation investigates the theoretical tangents between complexity theory and the field of transport geography. The starting point for this discussion is the new mobilities paradigm that adopts a view of mobility systems as context dependent, dynamic systems. In addition, this dissertation explores the application of agent-based modeling in the analysis of mode choice behavior. This agent-based model includes socio-economic and behavioral variables. The results suggest that applying complexity theory to transport geography offers promising ways of looking at mobility systems and that concepts of the former field are consistent with recent insights in the latter. In addition, the results suggest that agent-based modeling can be a viable alternative to traditional multinomial logit models. These findings imply that complexity theoretic methods are potentially valuable to policymakers to develop, test and inspire policy measures that contribute to achieving a more sustainable modal split
In view of the environmental impact of high levels of car use, stimulating people to choose susta... more In view of the environmental impact of high levels of car use, stimulating people to choose sustainable transport modes instead is of importance. Transport mode choice has proven to be resilient to policy measures. Previous studies suggest qualitative methods do more justice to the complex nature of mobility systems, however the use of such research to policymakers is highly debated. Literature suggests that policymakers prefer quantitative results, and that qualitative studies are needed to investigate the way these results are interpreted and employed. As predictive models become increasingly sophisticated, they can include more aspects of human behavior. Currently, there is only a limited amount of literature on the value of agent-based modeling of mobility systems to policymakers and social scientists. This dissertation investigates the theoretical tangents between complexity theory and the field of transport geography. The starting point for this discussion is the new mobilities paradigm that adopts a view of mobility systems as context dependent, dynamic systems. In addition, this dissertation explores the application of agent-based modeling in the analysis of mode choice behavior. This agent-based model includes socio-economic and behavioral variables. The results suggest that applying complexity theory to transport geography offers promising ways of looking at mobility systems and that concepts of the former field are consistent with recent insights in the latter. In addition, the results suggest that agent-based modeling can be a viable alternative to traditional multinomial logit models. These findings imply that complexity theoretic methods are potentially valuable to policymakers to develop, test and inspire policy measures that contribute to achieving a more sustainable modal split
Social sensing provides many opportunities for observing human behavior utilizing objective (sens... more Social sensing provides many opportunities for observing human behavior utilizing objective (sensor) measurements. This paper describes an approach for analyzing organizational social networks capturing face-to-face contacts between individuals. Furthermore, we outline perspectives and scenarios for an extended analysis in order to estimate happiness in the context of organizational social networks.
The A-level grading fiasco in the UK led to public outrage over algorithmic bias. This is a well-... more The A-level grading fiasco in the UK led to public outrage over algorithmic bias. This is a well-established problem that data professionals have sought to address through making their algorithms more explainable. However, Dr Daan Kolkman argues that the emergence of a “critical audience” in the A-level grading fiasco poses a model for a more effective means of countering bias and intellectual lock-in in the development of algorithms
This thesis inquires into how models are used to inform policy making and what makes models usefu... more This thesis inquires into how models are used to inform policy making and what makes models useful to those informing policy. A model is considered a formal representation of a target system that can be used to answer questions about that target system. Moreover, it is defined as the computer implementation of a mental model or conceptual model, written in algorithms or equations. This research draws on insights from existing literature on policy making, policy analysis, models and model use, which are then used to develop an analytical framework founded on science and technology studies. This framework is employed to guide an empirical investigation of models that have been used to inform policy making. Fieldwork was conducted over a period of two years at government departments, private companies and research institutes in the United Kingdom and the Netherlands. This fieldwork consisted of 38 interviews with model users, observations of model use and archival research. The data we...
Despite algorithms becoming an increasingly important tool for policymakers, little is known abou... more Despite algorithms becoming an increasingly important tool for policymakers, little is known about how they are used in practice and how they work, even amongst the experts tasked with using them. Drawing on research into the use of algorithmic models in the UK and Dutch governments, Daan Kolkman argues that the inherent complexity of algorithms renders attempts to make them transparent difficult and that to achieve public accountability for the role they play in society a dedicated watchdog is required.
Governments increasingly use algorithmic models to inform their policy making process. Many sugge... more Governments increasingly use algorithmic models to inform their policy making process. Many suggest that employing such quantifications will lead to more efficient, more effective or otherwise better quality policy making. Yet, it remains unclear to what extent these benefits materialize and if so, how they are brought about. This paper draws on the sociology and policy science literature to study how algorithmic models, a particular type of quantification, are used in policy analysis. It presents the outcomes of 38 unstructured interviews with data scientists, policy analysts, and policy makers that work with algorithmic models in government. Based on an in-depth analysis of these interviews, I conclude that the usefulness of algorithmic models in policy analysis is best understood in terms of the commensurability of these quantifications. However, these broad communicative and organizational benefits can only be brought about if algorithmic models are handled with care. Otherwise, they may propagate bias, exclude particular social groups, and will entrench existing worldviews.
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