Modeling Scientists as Agents. How Scientists Cope with the Challenges of the New Public Management of Science

The paper at hand applies agent-based modeling and simulations (ABMS) as a tool to reconstruct and to analyze how the science system works. A Luhmannian systems perspective is combined with a model of decision making of individual actors. Additionally, changes in the socio-political context of science, such as the introduction of „new public management\", are considered as factors affecting the functionality of the system as well as the decisions of individual scientists (e.g. where to publish their papers). Computer simulation helps to understand the complex interplay of developments at the macro (system) and the micro (actor) level.Systems Theory, Theory of Action and Decision Making, Academic Publication System, Science System, New Public Management, Agent-Based Modeling and Simulation

Explaining the Past with ABM: On Modelling Philosophy

This chapter discusses some of the conceptual issues surrounding the use of agent-based modelling in archaeology. Specifically, it addresses three questions: Why use agent-based simulation? Does specifically agent-based simulation imply a particular view of the world? How do we learn by simulating? First, however, it will be useful to provide a brief introduction to agent-based simulation and how it relates to archaeological simulation more generally. Some readers may prefer to return to this chapter after having read a more detailed account of an exemplar (Chap. 2) or of the technology (Chap. 3). Textbooks on agent-based modelling include Grimm and Railsback [(2005) Individual-based modeling and ecology, Princeton University Press, Princeton] and Railsback and Grimm [(2012) Agent-based and individual-based modeling: a practical introduction, Princeton University Press, Princeton], both aimed at ecologists, the rather briefer [Gilbert (2008) Agent-based models. Quantitative applications in the social sciences, Sage, Thousand Oaks, CA], aimed at sociologists, and [Ferber (1999) Multi-agent systems: an introduction to distributed artificial intelligence, English edn. Addison-Wesley, Harlow], which treats agent-based simulation from the perspective of artificial intelligence and computer science

Agent-based modeling and simulation of individual traffic as an environment for bus schedule simulation

To re-establish the regular driving operations of a tram network, which was disturbed significantly by unforeseen external events, traffic schedulers apply rescheduling and rerouting strategies. These strategies are usually multi-modal; they consider the interaction of trams, buses, even taxis. Thus, to evaluate the applicability of a given rescheduling or rerouting strategy prior to its implementation in the real-world system, a multi-modal simulation software is needed. In this article we present an agent-based model of individual traffic which will be applied as background to a planned simulation of bus traffic. These combined models are to be integrated with an existing tram schedule simulation; the resulting multi-modal model will then be applied to evaluate the usefulness of given rescheduling or rerouting strategies. After a short introduction to agent-based modeling and simulation, as well as to existing models of individual traffic, this paper proposes to model the behavior of individual traffic as an environment for agent-based bus schedule simulation. Finally, some experiments are conducted by modeling and simulating individual traffic in Cologne's highly frequented Barbarossaplatz area

Agent-based modeling and simulation of individual traffic as an environment for bus schedule simulation

To re-establish the regular driving operations of a tram network, which was disturbed significantly by unforeseen external events, traffic schedulers apply rescheduling and rerouting strategies. These strategies are usually multi-modal; they consider the interaction of trams, buses, even taxis. Thus, to evaluate the applicability of a given rescheduling or rerouting strategy prior to its implementation in the real-world system, a multi-modal simulation software is needed. In this article we present an agent-based model of individual traffic which will be applied as background to a planned simulation of bus traffic. These combined models are to be integrated with an existing tram schedule simulation; the resulting multi-modal model will then be applied to evaluate the usefulness of given rescheduling or rerouting strategies. After a short introduction to agent-based modeling and simulation, as well as to existing models of individual traffic, this paper proposes to model the behavior of individual traffic as an environment for agent-based bus schedule simulation. Finally, some experiments are conducted by modeling and simulating individual traffic in Cologne's highly frequented Barbarossaplatz area

The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling

Agent-based simulation: an application to the new electricity trading arrangements of England and Wales.

This paper presents a large-scale application of multiagent evolutionary modeling to the proposed new electricity trading arrangements (NETA) in the U.K. This is a detailed plant-by-plant model with an active specification of the demand side of the market. NETA involves a bilateral forward market followed by a balancing mechanism and then an imbalance settlement process. This agent-based simulation model was able to provide pricing and strategic insights, ahead of NETA's actual introduction

Agent-Based Modeling and Simulation of Earthmoving Operations

Simulation has been used in construction modeling for decades, especially in large scale operations, such as earth moving, where heavy and costly equipment is used. Simulation can be used as a planning tool to analyze the time and cost of earthmoving operations. Current methods used in simulating earthmoving operations are based on Discrete-Event Simulation (DES), with recent efforts to introduce System Dynamics (SD) in a hybrid DES-SD approach. However, due to the predetermined nature of Discrete-Event Simulation (DES) models, some inflexibility is experienced when modeling earthmoving operations, which translates into a higher degree of difficulty in regards to model creation and a reduced accuracy of outputs. Although the introduction of System Dynamics (SD) contributed significantly to accounting for qualitative factors and strategic aspects of earthmoving operations, there still exists a need for enhancing the accuracy of capturing the logistics of these operations in a smart and flexible manner. With the advancement of computational capabilities, Agent-Based Modeling and Simulation (ABMS) is rapidly replacing the conventional simulation techniques. This thesis introduces Agent-Based Modeling and Simulation (ABMS) as an effective tool for modeling earthmoving operations. First of all, it provides a generic methodology introduced for creating Agent-Based models for construction operations, based on a set of rules and criteria. Then, an Agent-Based (AB) model for earthmoving operations consisting of bulldozers, loaders, haulers and spotters is developed. The model in question governs the process logistics, information sharing, equipment properties as well as activity durations. Finally, a Java-Based software application (ABSEMO) is developed as an implementation of the proposed Agent-Based (AB) simulation model. Overall, the desired outcome is to create a smart system that has a flexible logic in addition to a good representation of model operations. A real-life case study of a riverbed excavation in a dam construction project is simulated using ABSEMO and the results are compared with those obtained from Discrete-Event Simulation (DES) models for verification. A percentage difference of 0.42% from the DES results was finally obtained, indicating that the model’s logic and flow of resources are indeed accurate. The proposed Agent-Based (AB) methodology and the developed model aim at enhancing current practices of modeling earthmoving operations by looking at these operations from an individual Agent-Based (AB) prospective. This allows the capturing of realistic behaviors, through crafting agents’ attributes, roles and interactions. The proposed methodology can be extended to general applications in construction management, where heterogeneity can be accounted for through replicating the different participants of construction projects in Agent-Based (AB) models as well as studying the emergent behavior of their interactions on the system

Multi-Scale Modeling and Simulation of Cell Signaling and Transport in Renal Collecting Duct Principal Cells

The response of cells to their environment is driven by a variety of proteins and messenger molecules. In eukaryotes, their distribution and location in the cell is regulated by the vesicular transport system. The transport of aquaporin 2 between membrane and storage region is a crucial part of the water reabsorption in renal principal cells, and its malfunction can lead to Diabetes insipidus. To understand the regulation of this system, I aggregated pathways and mechanisms from literature and derived models in a hypothesis-driven approach. Furthermore, I combined the models to a single multi-scale model to gain insight into key regulatory mechanisms of aquaporin 2 recycling. To achieve this, I developed a computational framework for the modeling and simulation of cellular signaling systems. The framework integrates reaction and difusion of biochemical entities on a microscopic scale with mobile vesicles, membranes, and compartments on a cellular level. The simulation uses an adaptive step-width approach that e ciently regulates the agent-based simulation of macroscopic components with the numerical integration of mass action kinetics and grid-based nite diference methods. A reaction network generation algorithm was designed, that, in combination with a highly-modular modeling approach, allows for fast model prototyping. The analysis of the aquaporin 2 model system rationalizes that the compartmentalization of cAMP in renal principal cells is a result of the protein kinase A signalosome and can only occur if speci c cellular components are observed in conjunction. Endocytotic and exocytotic processes are inherently connected and can be regulated by the same protein kinase A signal.:Abstract 1. Introduction 1.1. Eukaryotic Signaling 1.2. Modeling and Simulation of Cellular Processes 1.3. Aquaporin 2 recycling 1.4. Motivation and Aims 1.5. Outline I. Background 2. Modeling and Simulation of Complex Signaling Pathways 2.1. Multi-scale Modeling 2.1.1. Approaches to Multi-scale Modeling 2.1.2. Reduction of Computational Complexity 2.2. Models of Chemical Reaction Networks 2.2.1. Reactions and Reaction Rates 2.2.2. Numerical Solutions 2.2.3. Reaction Network Generation 2.3. Models of Intracellular Transport 2.3.1. Undirected Transport 2.3.2. Directed Transport 3. Aquaporin 2 Recycling in Renal Principal Cells 3.1. The Physiology of Water Homeostasis 3.2. Molecular Mechanisms of the Vasopressin Response 3.2.1. The Vasopressin Receptor 3.2.2. cAMP Regulation of Protein Kinase A 3.2.3. Endo- and Exocytosis 3.3. Models of Water Transport in Renal Principal Cells II. Results & Discussion 4. Multi-scale Simulation of Cellular Signaling Pathways 4.1. Scale Separation and Bridging 4.2. Micro-scale Simulation Approach 4.2.1. Difusion and Discretization of the Simulation Space 4.2.2. Reaction Kinetics 4.3. Rule-based Reaction Network Generation 4.3.1. Definition of the Data Model 4.3.2. Design of Rule Based Reactions 4.3.3. Automated Generation of Reaction Networks 4.4. Macro-scale Simulation Approach 4.4.1. Agent-based Simulation of Discrete Entities 4.4.2. Modules for Displacement-based Behavior 4.5. Modularization and Error Estimation 4.5.1. Determination of the Numerical Error 4.5.2. Modularization of Concentration-based Events 4.5.3. Determination of the Displacement-based Error 5. Aquaporin 2 Recycling Model and Simulation 5.1. Model of Allosteric PKA Phosphorylation 5.1.1. Model Design 5.1.2. Simulation Results and Discussion 5.1.3. Conclusions 5.2. cAMP Compartmentalization in the Vesicle Storage Region 5.2.1. Model Design 5.2.2. Simulation Results and Discussion 5.2.3. Conclusions 5.3. Clathrin-mediated Endocytosis 5.3.1. Model Design 5.3.2. Simulation Results and Discussion 5.3.3. Conclusions 5.4. Intracellular Transport and Recycling 5.4.1. Model Design 5.4.2. Simulation Results and Discussion 6. Conclusion 6.1. Modeling and simulation approach 6.2. Insights into the AQP2 recycling model III. Appendix A. Code Availability B. Module Overview Bibliograph