Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis.In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results.An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis. In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results. An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis. In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results. An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

This volume presents recent advances in the dynamic field of Artificial Economics and its various applications. Artificial Economics provides a structured approach to model and investigate economic and social systems. In particular, this approach is based on the use of agent-based simulations and further computational techniques. The main aim is to analyze the outcomes at the overall systems’ level as results from the agents’ behavior at the micro-level. These emergent characteristics of complex economic and social systems can neither be foreseen nor are they intended. The emergence rather makes these systems function. Artificial Economics especially facilitates the investigation of this emergent systems’ behavior. ​

This volume presents recent advances in the dynamic field of Artificial Economics and its various applications. Artificial Economics provides a structured approach to model and investigate economic and social systems. In particular, this approach is based on the use of agent-based simulations and further computational techniques. The main aim is to analyze the outcomes at the overall systems’ level as results from the agents’ behavior at the micro-level. These emergent characteristics of complex economic and social systems can neither be foreseen nor are they intended. The emergence rather makes these systems function. Artificial Economics especially facilitates the investigation of this emergent systems’ behavior. ​

One of the main aims of management accounting is to provide managers with accurate information in order to provide a good basis for decision-making. There is evidence that the information provided by management accounting systems (MAS) is distorted and the occurrence of biases in accounting information is widely accepted among users of MAS. At the same time, the intensity and the frequency of use of MAS increase, too. Consequently, the quality of the provided information is critical. The focus of this simulation study is twofold. On the one hand, the impact of the sophistication of traditional costing systems on error propagation in the case of a set of input biases is investigated. On the other hand, the impact of single and multiple input biases on the quality of the information provided by traditional costing systems is focused. In order to investigate the research questions, a simulation approach is applied.

Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis.In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results.An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis. In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results. An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

Over the past few decades, agent-based modelling and agent-based computational technologies have spread into research on business and management. Agent-based modelling takes account of individual behavior alongside interactions within and across organizations and their competitive environment, seeking to explain complex, macro-level patterns emerging from micro-level behavior. These characteristics make an agent-based approach useful for studying a number of open issues in management, including adaptive behavior and dynamics in and across organizations, variable outcomes caused by management practices, and bridging micro-and macro-levels in organizational analysis. In The Oxford Handbook of Agent-based Computational Management Science, Friederike Wall, Shu-Heng Chen, and Stephan Leitner bring together a wide array of experts in the field to give a comprehensive overview of this novel approach in management science. In particular, the volume focuses on adaptive behavior and dynamics in and across organizations, non-linearities caused by management practices, and bridging micro-and macro-levels in organizational analysis. The Handbook addresses two purposes of agent-based approaches in management science. First, the volume shows how an agent-based approach advances theory-building in management. Second, it provides an overview of agent-based technologies employed for more practical issues in management. These comprise, for example, a normative understanding of systems design, such as determining which designs provide good results. An indispensable resource on agent-based modeling in management science, this Handbook introduces its current state from theoretical and operational perspectives and discusses its future opportunities.

This volume presents recent advances in the dynamic field of Artificial Economics and its various applications. Artificial Economics provides a structured approach to model and investigate economic and social systems. In particular, this approach is based on the use of agent-based simulations and further computational techniques. The main aim is to analyze the outcomes at the overall systems’ level as results from the agents’ behavior at the micro-level. These emergent characteristics of complex economic and social systems can neither be foreseen nor are they intended. The emergence rather makes these systems function. Artificial Economics especially facilitates the investigation of this emergent systems’ behavior. ​

This volume presents recent advances in the dynamic field of Artificial Economics and its various applications. Artificial Economics provides a structured approach to model and investigate economic and social systems. In particular, this approach is based on the use of agent-based simulations and further computational techniques. The main aim is to analyze the outcomes at the overall systems’ level as results from the agents’ behavior at the micro-level. These emergent characteristics of complex economic and social systems can neither be foreseen nor are they intended. The emergence rather makes these systems function. Artificial Economics especially facilitates the investigation of this emergent systems’ behavior. ​

One of the main aims of management accounting is to provide managers with accurate information in order to provide a good basis for decision-making. There is evidence that the information provided by management accounting systems (MAS) is distorted and the occurrence of biases in accounting information is widely accepted among users of MAS. At the same time, the intensity and the frequency of use of MAS increase, too. Consequently, the quality of the provided information is critical. The focus of this simulation study is twofold. On the one hand, the impact of the sophistication of traditional costing systems on error propagation in the case of a set of input biases is investigated. On the other hand, the impact of single and multiple input biases on the quality of the information provided by traditional costing systems is focused. In order to investigate the research questions, a simulation approach is applied.