This article provides an overview of the current state of agent-based modeling in managerial science. In particular, the aim is to illustrate major lines of development in agent-based modeling in the field and to highlight the opportunities and limitations of this research approach. The article employs a twofold approach: First, a survey on research efforts employing agent-based simulation models related to domains of managerial science is given which have benefited considerably from this research method. Second, an illustrative study is conducted in the area of management accounting research, a domain which, so far, has rarely seen agentbased modeling efforts. In particular, we introduce an agent-based model that allows to investigate the relation between intra-firm interdependencies, performance measures used in incentive schemes, and accounting accuracy. We compare this model to a study which uses both, a principal-agent model and an empirical analysis. We find that the three approaches come to similar major findings but that they suffer from rather different limitations and also provide different perspectives on the subject. In particular, it becomes obvious that agent-based modeling allows us to capture complex organizational structures and provides insights into the processual features of the system under investigation.

Agent-based computational economics (ACE) - while adopted comparably widely in other domains of managerial science - is a rather novel paradigm for management accounting research (MAR). This paper provides an overview of opportunities and difficulties that ACE may have for research in management accounting and, in particular, introduces a framework that researchers in management accounting may employ when considering ACE as a paradigm for their particular research endeavor. The framework builds on the two interrelated paradigmatic elements of ACE: a set of theoretical assumptions on economic agents and the approach of agent-based modeling. Particular focus is put on contrasting opportunities and difficulties of ACE in comparison to other research methods employed in MAR.

With global challenges like climate change and, of course, the crisis of capital markets in the recent past stakeholder oriented management receives enhanced attention whereas shareholder value management is increasingly criticized for its undesirable external effects on stakeholders other than owners. Regardless of whether these criticisms are well founded or not, the question arises how accounting-related techniques for supporting managerial decision-making differ in shareholder and stakeholder value management. Accounting information can affect managerial decision-making in two ways: directly as input to decisions or indirectly by influencing the behavior of managers. This article reviews the contributions and limitations of information that prominent accounting-related techniques of shareholder management and stakeholder management provide for managerial decisionmaking. In a comparative perspective we find that the approaches in shareholder value management are much more advanced. In particular the two roles of information in shareholder value management are manifest in accounting-related techniques which are focused on increasing firm value. The value driver models or residual income-based performance measures may serve as examples. In comparison, accounting-related techniques to support managerial decision-making in stakeholder management are not as well advanced. So far we have approaches which concentrate on selective stakeholder groups and only partially address the multi-dimensionality of stakeholder value creation. From a conceptual perspective our findings indicate that stakeholder value creation requires a more integrated approach for answering the question whether stakeholder value is created or

The hidden-action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed (Holmström in Bell J Econ 10(1):74, 1979). However, the hidden-action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included in the hidden-action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and about feasible actions for the agent. We follow an approach to transfer closed-form mathematical models into agent-based computational models and show that the extent of information about feasible options to carry out a task only has an impact on performance if decision makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, on the contrary, appears to be crucial in almost all situations.

Abstract-This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decisionmaking support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, midterm decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles.

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The paper analyses the effects of imperfect information on organisational performance under the regime of alternative organisational settings. The analysis is based on an agent-based model which is an extended variant of the NK model. In the simulations, fitness measurements are distorted with imperfections according to information asymmetries that are related to differentiation and delegation of decision-making. The results indicate that the effects of informational imperfections on organisational performance subtly interfere with coordination mode, incentives and intra-organisational interactions. The results might throw some new light on imperfect information as in some organisational settings rather insignificant performance losses compared to perfectly informed decision-makers occur, and in some settings imperfect information turn out to be beneficial.

This article studies how to dimension and control at the system level a fleet of autonomous aerial vehicles delivering goods from depots to customers. Customer requests (jobs) arrive according to a space-time stochastic process. We compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. It is shown that job assignment policies can exhibit a tipping point behavior: One vehicle makes the difference between almost optimal delivery time and instability. This phenomenon calls for careful dimensioning of the system. We thus demonstrate the trade-off between financial costs and service quality. We propose a policy that assigns each incoming job to the vehicle that will do the job faster than other ones, seeking to minimize the overall workload in the system in the long term. This policy is scalable with the number of depots and vehicles, performs optimal in low load, and works well up to high loads. Simulations suggest that it stabilizes the system for any load if the number of vehicles per depot is sufficient.