scholarly journals The Good, the Bad and the Stochastic: How Living in Groups Innately Supports Cooperation

2021 ◽  
Author(s):  
Klaus F. Steiner
Keyword(s):  
Computer Simulations ◽  
Simulation Models ◽  
Individual Selection ◽  
Agent Based Simulation ◽  
Stochastic Effects ◽  
Selection Pressures ◽  
Agent Based ◽  
Complex Agent ◽  
Theoretical Considerations ◽  
Dynamic Equilibria

AbstractBased on theoretical considerations and computer simulations, I show that living in groups brings advantages for cooperative traits through purely stochastic effects that result from the division of a population into groups. These advantages can be sufficient to compensate individual selection pressures that may be associated with the cooperative traits. In more complex agent-based simulation models, this effect combined with some migration between the groups leads to stable dynamic equilibria between cooperative and defective replicators in the population.

Agent-Based Simulation Modeling

2016 ◽  
pp. 128-139
Author(s):  
Marco Valente
Keyword(s):  
Computer Simulations ◽  
Methodological Approach ◽  
Scientific Research ◽  
Simulation Models ◽  
Basic Unit ◽  
Full Potential ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Investigative Tool ◽  
Simulation Results

Computer simulations are a powerful tool for scientific research, but lack an accepted methodology for their use, and consequently their results are generally received with skepticisms. This chapter proposes a methodological approach allowing to formally unify the treatment of “traditional” quantitative phenomena with that of phenomena from economics or biology that prevent a universal adoption of data-centered methods. We propose to adopt the explanation as the basic unit of knowledge, which is able to cover all possible cases. From this assumption, we can derive the conclusion that simulation models fail to deliver their full potential as scientific investigative tool because their implementations lack crucial details on the intermediate steps producing simulation results.

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.

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.

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