Agent-Based Macroeconomic Modeling and Policy Analysis

2018 ◽  
Author(s):  
Herbert Dawid ◽  
Simon Gemkow ◽  
Philipp Harting ◽  
Sander van der Hoog ◽  
Michael Neugart
Keyword(s):  
Policy Analysis ◽  
Simulation Models ◽  
Proof Of Concept ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Modeling Approaches ◽  
Macroeconomic Modeling ◽  
Spatial Policy

This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

Scoping the Future with Theory-Driven Models—Where’s the Uncertainty?: Commentary on Lustick and Tetlock 2021

2021 ◽  
Author(s):  
David R. Mandel
Keyword(s):  
National Security ◽  
Policy Decision ◽  
Simulation Models ◽  
Insufficient Data ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Strategic Intelligence ◽  
The Future ◽  
Policy Decision Making ◽  
Rich Data

Lustick and Tetlock outline an intellectually ambitious approach to scoping the future. They are particularly interested in sectors of national security and foreign policy decision-making that require anticipatory strategic intelligence that is difficult to produce because there is insufficient data, even if relevant theories are available. They propose that in these theory-rich/data-impoverished cases, there can be great value in developing agent-based simulation models that incorporate probabilistic rules that cohere with postulates of the theory or theories that are brought to bear on the intelligence challenge. This is the gist of the “simulation manifesto.” The aim of this commentary is to focus on the assessment and representation of key uncertainties in such models and I outline several ways in which uncertainty may arise in the process of simulation model construction.

Microbial Fuel Cells Using Agent-Based Simulation

2017 ◽  
pp. 212-226
Author(s):  
Diogo Ortiz Machado ◽  
Diana Francisca Adamatti ◽  
Eder Mateus Nunes Gonçalves
Keyword(s):  
Wastewater Treatment ◽  
Fuel Cells ◽  
Microbial Fuel Cells ◽  
Electrical Energy ◽  
Analytical Models ◽  
Agent Based Model ◽  
Technological Opportunity ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Model And Simulation

Microbial Fuel Cells (MFC) could generate electrical energy combined with the wastewater treatment and they can be a promising technological opportunity. This chapter presents an agent-based model and simulation of MFC comparing it with analytical models, to show that this approach could model and simulate these problems with more abstraction and with excellent results.

scholarly journals BDI vs FSM Agents in Social Simulations for Raising Awareness in Disasters

2017 ◽  
Vol 9 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Carole Adam ◽  
Patrick Taillandier ◽  
Julie Dugdale ◽  
Benoit Gaudou
Keyword(s):  
Finite State Machine ◽  
Management Strategies ◽  
The Other ◽  
Simulated Population ◽  
Agent Architecture ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Raising Awareness ◽  
Finite State

Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool to test various management strategies on a simulated population, and to raise awareness of the actual population behaviour. But valid results depend on realistic underlying models. This article describes two simulations of the Australian population's behaviour during bushfires designed in previous work, one based on a finite-state machine architecture, the other based on a belief-desire-intention agent architecture. It then proposes several contributions towards more realistic agent-based models of human behaviour: a methodology and tool for easily designing BDI models; a number of objective and subjective criteria for comparing agent-based models; a comparison of our two models along these criteria, showing that BDI provides better explanability and understandability of behaviour, makes models easier to extend, and is therefore best adapted; and a discussion of possible extensions of BDI models to further improve their realism.

The Agent Object Relationship Simulation as a Business Process

2010 ◽  
pp. 348-370
Author(s):  
Emilian Pascalau ◽  
Adrian Giuca ◽  
Gerd Wagner
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Simulation Models ◽  
Use Case ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Process ◽  
Simulation Paradigm ◽  
Business Process Modeling Notation ◽  
Object Relationship

The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.

Agent Based Model to Analyze Consumer Behavior in Consuming the Electricity Energy

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
Erma Suryani ◽  
Rully Agus Hendrawan ◽  
Umi Salama ◽  
Lily Puspa Dewi
Keyword(s):  
Consumer Behavior ◽  
Simulation Model ◽  
Environmental Awareness ◽  
Electricity Consumption ◽  
Point Of View ◽  
Agent Based Model ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Save Energy ◽  
The Impact

Several studies have been conducted regarding save energy in consuming the electricity through the simple changes in routines and habits. In the case of electricity consumption, consumer behavior might influenced by several factors such as consumer profession, season, and environmental awareness. In this paper, we developed an Agent Based Model (ABM) to analyze the behavior of different agents in consuming the electricity energy for each type of profession (agent) as well as their interaction with the environment. This paper demonstrates a prototype agent based simulation model to estimate the electricity consumption based on the existing condition and some scenarios to reduce the electricity consumption from consumer point of view. From the scenario results, we analyzed the impact of the save energy to increase the electrification ratio. 

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