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

A non-technical introduction to the ANEMMarket model of the Australian National Electricity Market (NEM)

In this paper, we provide an accessible introduction to our agent-based ANEMMarket simulation model of the Australian National Electricity Market. This model has been purpose built to assess the impacts of emissions trading schemes, carbon taxes and the introduction of significant new suppliers of electricity generated from low or zero carbon emitting generators. We provide an illustrative example that involves the simulation of the impacts of a range of carbon prices on the dispatch of power from different types of generators in different regional locations. From these we compute the resultant carbon reduction effects. However, these remain only illustrative simulations because they do not include a range of operative constraints that exist in reality.

Simulating Evolutionary Games: A Python-Based Introduction

This paper is an introduction to agent-based simulation using the Python programming language. The core objective of the paper is to enable students, teachers, and researchers immediately to begin social-science simulation projects in a general purpose programming language. This objective is facilitated by design features of the Python programming language, which we very briefly discuss. The paper has a 'tutorial' component, in that it is enablement-focused and therefore strongly application-oriented. As our illustrative application, we choose a classic agent-based simulation model: the evolutionary iterated prisoner's dilemma. We show how to simulate the iterated prisoner's dilemma with code that is simple and readable yet flexible and easily extensible. Despite the simplicity of the code, it constitutes a useful and easily extended simulation toolkit. We offer three examples of this extensibility: we explore the classic result that topology matters for evolutionary outcomes, we show how player type evolution is affected by payoff cardinality, and we show that strategy evaluation procedures can affect strategy persistence. Social science students and instructors should find that this paper provides adequate background to immediately begin their own simulation projects. Social science researchers will additionally be able to compare the simplicity, readability, and extensibility of the Python code with comparable simulations in other languages.Agent-Based Simulation, Python, Prisoner's Dilemma

Agent Based Onboard Firefighting System

This paper presents a model of agent-based architecture for fighting fires on ships. The introduction of agent technology in firefighting decision-making is a step towards safe autonomous vessels. The human factor can be excluded through the introduction of agent-based technology for the detection and extinguishing of fires onboard ships. The aim is to reduce the number of injuries and deaths, and minimize loss of ships and cargo. Another advantage of agent-based technology is its easy interoperability with other automated onboard systems. The presented model was implemented on a prototype in a simulation environment. The results of the experiment conducted on the implemented prototype are also presented

Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird

An Introduction to Agent-Based Simulation as a Decision-Support Tool

Agent-based simulation (ABS) has been used widely in various application areas. This paper provides a tutorial on ABS. In addition to the theoretical foundations, such as the definitions of key ABS concepts and the structure of an ABS model, this tutorial also explains the technical aspect, such as how an ABS model is implemented using a software tool and how ABS is used to solve a problem. The software tool used in this tutorial is Repast. The last part of the tutorial discusses the challenges that need to be addressed in order to increase the confidence of decision-makers, who may not be familiar with computer programming, when using ABS to help them make decisions. The challenges include conceptual model representation, validation and participatory modelling

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

[Note] An Introduction to Agent Based Simulation

本稿では、最近社会科学の分野でも注目されているマルチエージェントモデルによるシミュレーションを扱う。社会科学におけるシミュレーションについて歴史やその必要性を概観するとともに、マルチエージェントシミュレーションのプラットホームとして開発されたいくつかのシミュレータを紹介する。とくに、構造計画研究所により開発されたKK-Multi Agent Simulator (KK-MAS) を取り上げるとともに、シミュレーション事例についても議論する