The Experts below are selected from a list of 179802 Experts worldwide ranked by ideXlab platform
Nele Russwinkel - One of the best experts on this subject based on the ideXlab platform.
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a cognitive modeling approach to Strategy Formation in dynamic decision making
Frontiers in Psychology, 2017Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele RusswinkelAbstract:Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional inFormation about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
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A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
Frontiers Media S.A., 2017Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele RusswinkelAbstract:Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional inFormation about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks
Patsy Healey - One of the best experts on this subject based on the ideXlab platform.
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the communicative turn in planning theory and its implications for spatial Strategy Formation
Environment and Planning B-planning & Design, 1996Co-Authors: Patsy HealeyAbstract:There is an increasing contemporary interest, particularly in Europe, in the spatial organization of urban regions and in spatial Strategy. But there is a general loss of confidence in political systems as mechanisms for conflict mediation and the strategic management of collective affairs. This raises questions about how stakeholders in spatial change in urban regions get to understand the complex dynamics of urban regions, how they get to agree on strategies and actions, and how this may be translated into influence on events. In this paper I explore the potential of the new ideas about public argumentation and communicative policy practice developing in the field of planning theory for addressing the task of strategic spatial Strategy-making. I first outline the ideas, and then develop them into an approach focused around questions about the forums and arenas where spatial Strategy-making takes place, and who gets access to them; the style of discussion, the way issues are identified and filtered; how new policy discourses emerge, and how agreements are reached and monitored. Throughout, I emphasise the locally contingent ways in which policy processes are invented by political communities in relation to their particular economic, social, environmental, and political circumstances.
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the communicative turn in planning theory and its implications for spatial Strategy Formation
Environment and Planning B-planning & Design, 1996Co-Authors: Patsy HealeyAbstract:There is an increasing contemporary interest, particularly in Europe, in the spatial organization of urban regions and in spatial Strategy. But there is a general loss of confidence in political systems as mechanisms for conflict mediation and the strategic management of collective affairs. This raises questions about how stakeholders in spatial change in urban regions get to understand the complex dynamics of urban regions, how they get to agree on strategies and actions, and how this may be translated into influence on events. In this paper I explore the potential of the new ideas about public argumentation and communicative policy practice developing in the field of planning theory for addressing the task of strategic spatial Strategy-making. I first outline the ideas, and then develop them into an approach focused around questions about the forums and arenas where spatial Strategy-making takes place, and who gets access to them; the style of discussion, the way issues are identified and filtered; how ...
Ilan Oshri - One of the best experts on this subject based on the ideXlab platform.
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Business model renewal and ambidexterity: Structural alteration and Strategy Formation process during transition to a Cloud business model
R and D Management, 2014Co-Authors: Saeed Khanagha, Henk Volberda, Ilan OshriAbstract:This paper presents the findings of a longitudinal study of a large corporation's transition to a new business model in the face of a major transFormation in the ICT industry brought about by Cloud computing. We build theory on the process of business model innovation through a qualitative study that investigates how an established firm organizes for an emerging business model. Contrary to previous findings that presented spatial separation as the optimal structural approach for dealing with two competing business models, our findings indicate a need for recursive iterations between different modes of separated and integrated structures in line with the emergent nature of strategic intent toward the new business models. Our analyses reveal Strategy Formation to be a collective experimental learning process revolving around a number of alternative strategic intentions ranging from incremental evolution and transFormation to complete replacement of the existing business model. Given the fundamental differences in the nature and requirements of those alternative intents, iterations between different structural modes and differing combinations proved to be crucial in enabling the organization to make transition to the new business model.
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business model renewal and ambidexterity structural alteration and Strategy Formation process during transition to a cloud business model
ERIM Top-Core Articles, 2014Co-Authors: Saeed Khanagha, Henk Volberda, Ilan OshriAbstract:textabstractThis paper presents the findings of a longitudinal study of a large corporation's transition to a new business model in the face of a major transFormation in the ICT industry brought about by Cloud computing. We build theory on the process of business model innovation through a qualitative study that investigates how an established firm organizes for an emerging business model. Contrary to previous findings that presented spatial separation as the optimal structural approach for dealing with two competing business models, our findings indicate a need for recursive iterations between different modes of separated and integrated structures in line with the emergent nature of strategic intent toward the new business models. Our analyses reveal Strategy Formation to be a collective experimental learning process revolving around a number of alternative strategic intentions ranging from incremental evolution and transFormation to complete replacement of the existing business model. Given the fundamental differences in the nature and requirements of those alternative intents, iterations between different structural modes and differing combinations proved to be crucial in enabling the organization to make transition to the new business model.
Jonas Meckling - One of the best experts on this subject based on the ideXlab platform.
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oppose support or hedge distributional effects regulatory pressure and business Strategy in environmental politics
Global Environmental Politics, 2015Co-Authors: Jonas MecklingAbstract:What explains the choice of corporate political Strategy in environmental politics? Drawing on recent models of actor Strategy Formation in political economy, this article argues that basic material interests of firms are translated into strategies in the context of institutional environments. I advance a typological model that posits how distributional effects—positive versus negative—and perceived regulatory pressure—low versus high—interact in leading firms to adopt one of four ideal-type strategies: opposition, hedging, support, and non-participation. This article examines the model through the case of corporate strategies in the making of the European Union’s Emission Trading Scheme. The article contributes to theory-building on business Strategy in environmental politics by offering a probabilistic explanatory model, and it flags hedging strategies as an increasingly prevalent form of business behavior.
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oppose support or hedge distributional effects regulatory pressure and business Strategy in environmental politics
Social Science Research Network, 2014Co-Authors: Jonas MecklingAbstract:What explains the choice of corporate political Strategy in environmental politics? Drawing on recent models of actor Strategy Formation in Political Economy, this paper argues that basic material interests of firms are translated into strategies in the context of institutional environments. The paper advances a typological model, which posits how distributional effects — positive versus negative — and perceived regulatory pressure — low versus high — interact in leading firms to adopt one of four ideal-type strategies: opposition, hedging, support, and non-participation. The paper examines the model in the case of corporate strategies in the making of the European Union Emission Trading Scheme. The contribution is twofold: first, the article contributes to theory-building on business Strategy in environmental politics by offering a probabilistic explanatory model, and, second, it flags hedging strategies as an increasingly prevalent form of business behavior.
Sabine Prezenski - One of the best experts on this subject based on the ideXlab platform.
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a cognitive modeling approach to Strategy Formation in dynamic decision making
Frontiers in Psychology, 2017Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele RusswinkelAbstract:Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional inFormation about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
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A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
Frontiers Media S.A., 2017Co-Authors: Sabine Prezenski, Susann Wolff, André Brechmann, Nele RusswinkelAbstract:Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional inFormation about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks
