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Suppose we have a system designed to deliver services to customers arriving during weekdays. The arrival process is modeled as a Poisson process with an Arrival rate of λ, also we use agent-based modeling with NetLogo to study the behavior of customers. After multiple Observation and replication of the model, the first 8 hours was selected as the warm-up period and the remaining time as the steady-state. If we consider the average length of stay (ALOS) as the crucial output data, how should we handle the initialization bias in this case?
As a workaround, is removing those data sufficient if we take into account the effects of the warm-up period on the ALOS?
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Kindly read this work may you can get a strong base about what you are looking for.
If you cannot download it, please let me know. I will try to download it from my side.
Regards
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Basically I would like to know if the FEM solution obtained by Comsol could be used as input that could guide agents to behave in a certain way in the same common mesh. For example stem cells on a scaffold with specific material properties that undergoes a certain type of deformation. What is the most appropriate ABM Software? Any guidance or tip on the procedure that must be followed in order to interface it with Comsol Multiphysics 4 would be valuable
Regards,
Filippos
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Is there any agent based software which can be coupled with COMSOL? and isn't COMSOL capable of solving agent based models itself?
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I'm currently developing my first lateral flow immunoassay and I have come accross some publications like when they optimize they lateral flow by computer simulation. I would like to dive deeper into such a branch of simulations and I wonder where to start, what should I try? Maybe you know some other publications with lateral flow device simulations.
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You might want to start with lattice-boltzman schemes, as I think they offer an an easy to understand introduction into numerical simulations. There is this python library pylbm ( https://pylbm.readthedocs.io/en/latest/). You could check out their example gallery to see if it's sufficient for your purposes..
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By definition, Public Good (PG) and Common Pool Resource (CPR) are both non-excludable. The main difference is their rivalry property: PG can be consumed without reducing availability for others, while consuming CPR will decrease the available resources for others. PG has free-riders problem (lack of contributions); CPR has "tragedy of the commons problem" (overuse).
I have 3 questions:
1. So in experimental economics, how do you set up a experiment that distinguishes the differences between two games? For example, if it is a CPR game, will you tell participants that 'the resource is limited and you cannot play anymore when it is depleted'?
2. In a paper by Pahl-Wostl and Ebenhöh (2004), they developed a CPR simulation, in which data is from a PG experiment of Fehr and Gächter (2002). How the data of PGG can be used in CPR? Is there any modification required?
3. The difference between the experiment setup in 2 works above is their utility function. For Fehr and Gächter, the_return = total_investment x 0.4. For Pahl-Wostl and Ebenhöh, the_return = total_investment x 0.6 / 4 = total_investment x 0.15. Is it the difference between experiment's description of CPR and PGG?
References:
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Davor Mance I really like the differentiation by Elinor Ostrom that you stated in the end. Do you, by any chance, have the reference for this distinction?
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Hello,
Some agent-based simulation frameworks support the implementation of Belief–Desire–Intention (BDI) agent models directly or through libraries.
I would like to ask what is the best agent-based simulation framework to implement BDI agents?
Thanks & Regards,
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I think this always depends on the technology ecosystem one wants or has to develop in. Most popular BDI agent frameworks are Java-based (Jason, JACK). From an academic point of view, Jason is a natural top candidate, but from a practical software engineering perspective I would select based on the following criteria:
  • Actively maintained
  • Does not require adopting a new language (which is a downside of Jason: in practice, most organizations will refuse adopting a language like AgentSpeak)
  • Integrates seamlessly into the technology ecosystem that you work in
  • Scales well enough for use case
For web-based BDI agent development, let me shamelessly plug my own new library, JS-son: https://github.com/TimKam/JS-son (JavaScript-based).
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The list of simulation software is available at
Please visit the corresponding website if you do not understand Russian.
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The list will be helpful, especially if they are categorized by the type and application. If there is an overarching organization for simulation software (government or commercial), a link to that organization or main points of contact can be useful.
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Hi everyone, I have to simulate a container terminal on Anylogic, and the agent that enter in the terminal is truck. Trucks can enter empty, loaded with one container or loaded with two container, so the maximum capacity of the trucks is two containers.
How can I tell the program that they can enter empty, loaded with one or two container randomly? thank in advance
#Anylogic #logistics #TerminalContainer #simulation
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I came across papers by Sigglekow, Knudsen, Gavetti, etc and they use agent based simulation to generate NK based complex models. From the few paper that I read, none of them mention the software that they use. So, I am looking for recommendations - as a beginner, what would be the best software to learn to be able to simulate such complex models in strategy?
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Please tell me if the attempt to open the zip file prospered. Regards
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I am looking for tool/s or game/s that may be actual or simulation of agent-based decisions/behavior to test or model decision making patterns.
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Monica, 
This depends on different parameters, your programming skills, the problem domain and how complex it is, etc. 
AnyLogic is a simple tool that can be utilized with minimal programming skills. And as Mahmoud just stated, it is not only dedicated to ABM, but also to SD. Its not free, and not as efficient as the other models below. 
Repast <https://repast.github.io/> is an open-source ABM toolkit that runs on C++ and Java. 
MASON <http://cs.gmu.edu/~eclab/projects/mason/> is another open-source ABM toolkit that runs on java. It is one of the fastest and most efficient toolkits, but requires advanced programming skills. I carried out my PhD research on MASON, and can't recommend it enough.
I'd recommend this article to have more "unbiased" comparison of the different ABM toolkits: Agent-based Simulation Platforms: Review and Development Recommendations. Steven F. Railsback Steven L. Lytinen Stephen K. Jackson (2006)<http://journals.sagepub.com/doi/abs/10.1177/0037549706073695>
Best of luck, and hope this is useful. 
Mohamed 
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Hi everybody,
I want to calculate reputation and trust based on direct information and witness information in my simulation. I was wondering if anyone has an experience about these two subjects. Or, if you know any samples\ software regarding to these areas let me know.
Thanks in advance,
Regards,
Ameneh
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Hey Ameneh,
there is a whole host of papers out there that deals with the calculation of trust values by applying a trust metric on the experiences gathered by the agents. Depending on your concrete systems, the meaning of these trust values and the way to get to them can vary greatly. The seminal work in that area is Steve Marsh's Ph.D. thesis in which he discusses the most important issues. It was written 26 years ago, but it is still valid and I know that he is working on an updated version to be published as a book next year. You can find the thesis at https://dspace.stir.ac.uk/bitstream/1893/2010/1/Formalising%20trust%20as%20a%20computational%20concept.pdf
Based on this work, many people thought about specific trust models and how to make use of the generated values in decision procedures. The list of authors provided by Houcine is a good starting point, but of course there are many others. I am not aware of software solutions that you can use out of the box. This might be due to the fact that each system seems to have a very specific notion of trust that is not easily generalisable. The basic process is always the same: collect expectations about the behaviour of others, collect evidence about their actual behaviour, compare the two and derive a value through a metric, exchange this information (that would be the reputation part), and use the value in a decision procedure. But what the value means and how to get to it (the metric) differs greatly. The other problem is that there is very little standardisation in multi-agent system platforms, so you can't just have a package that can be universally applied.
Having said that, implementing an actual trust system yourself if you know your implementation platform, your metric, and the purpose of the trust values is not very difficult. Let me know if you need more pointers for that.
Best,
Jan-Philipp
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To describe the individual decision-making of an ABS, is recommended that should include the implementation details of subjects and objects of decisions and the multiple levels of decisions making. Decisions rules and uncertainty could be relevant to the problem rationality and, therefore, necessary for the decision-making system.
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1.Come up with a taxonomy/ hierarchy of heuristics which is simultaneously mapped to facets  of your problem/area of application. This is where multilevel decision making is addressed. The complexity of your problem dictates the complexity of the taxonomy, though not necessarily in a proportionate manner. As you could exploit the power of recursion to attain to different categories of the taxonomy.This will give you an idea of the kind of decision rules that are contextualized for your application. Recursive agent based simulation can be used to define or identify useful heuristics in the problem space.
2. Determine what kind of decisions are possible/ decision rules are applicable from the availability of data. Using principles from data-driven decision support systems/decision-making (DDDM).
3. An outline of possible decisions can then be inferred through a combination of 1&2. Point 1 is mainly generative. Point 2 focuses on implementation and constraints to the set of possible heuristics. May need to take a trial and error approach and reverse the sequence -point 2 then point 1, at multiple junctures in the design flow.
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The agents are in a simplistic trade environment were an agent can buy a resource from potential customers (having various prices). However, the aim is to train the agent on when to buy (optimizing his payoffs) given the limits of time, energy and competition among other agents.
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Obinna,
I would suggest that you look into publicly available sources such as Bloomberg APi's for obtaining data or yahoo finance (it has export functionality). What is important is that you get both the historical movement as well as volume of trading from whichever source that you target. Volume measurements will help you establish easy and reasonable points on which to measure trading exchanges as well as big movements(or minimal) in the market.
Based on this information and the strategy that you choose for each agent, then divide it into sell, no sell. What can be done afterwards is to let the agents do decisions on non tagged data and see if the predict the uptick or not and then quantify the loss.
If you have specific questions you can send me a direct message and we can work the details.
Regards
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I would like to have different opinions on how to create a human behavior model for an agent-based simulation, starting from a survey.
In particular, I would like to have more references on how to behavior matrices starting from questions that are asked during the surveying activity.
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For example you could simulate humans as points
in the plane. Objects that have x,y coordinates.
In order to accelerate computation, store
references to these objects in an array where
each cell can hold a list of several humans.
Behavior: normally they avoid approaching
each other too close. Under normal circumstances
humans walk with a certain speed toward diverse
goals, avoiding collisions. In panic, they
try to reach one of several exit doors.
They try to follow distinct individuals
which appear to be on the right track.
Panic can appear in different levels which
leads to wrong decisions, whatever that
may be.
Literature:
Regards,
Joachim
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I am interested in the processes of diffusion and sustainability of innovations and finding the connections between actions that enable and inhibit further adoption beyond the first wave of early adopters of proven elearning innovations in universities?
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I've looked at the Fishbone; and adored it as highly comprehensive. The only thing I really saw as missing was HR structure and process to recruit the right people for innovation/adoption. But I have a broad quibble as to whether the fishbone structure  isn't a generally applicable one, around technology adoption, rather than specific to Higher Education, an observation I feel highly relevant. The point is, in HE, I've seen ineffective technology as a barrier to adoption and I've seen resistance to change, but HE has a particular enabling factor for resistance to change, that you won't find elsewhere - Academic Freedom
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Is there any precise definition for the agent-based simulation? If a discrete event simulation (DES) is load-driven, i.e., decisions are made from a load (e.g., product, customer, etc.) prospective, can we consider it as agent-based?
There is some paper suggest that DES and agent-based simulation are separate categories. Why?
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First of all, just to mention that exist a great confusion on this because there is a great confussion regarding on what is a model and what is an implementation of a model.
To perform this classification is needed to be focused on the model and not in its implementation, that can be diverse.
Having said that, a DES model is a model where the EVENTS rules the dynamic behavior of the processes that  pictures our system understanding, the EVENTS are the elements that modifies the state's variables. Whit this idea, the models can be represented easily with DEVS, Petri Nets or Specification and Description Language (among other formal languages). In these formalisms is quite easy to detect that the EVENTS are the key element that rules the behavior, hence here we are talking of DES models.
In contraposition to this we can define a models where the EVENTS do not rule the dynamics, as an example, those models defined by Forrester diagrams (system dynamics) where a continuum modification of the state variables takes place. In that case the paradigm used to define the models is different (here we are not talking of DES models but continuous models).
MAS systems  (AB simulations) are those simulations where the entities (i.e. agents) can own a great complexity, can learn, and can modify his behavior depending on his knowledge and the information they obtain from the environment. The definition of this kind of models are based on the description of EVENTS that impact on the agents modifying its states. Hence MAS is an specific DES model where exists specific elements that are more complex, but, the dynamic modification of the states are mainly based on EVENTS, hence MAS are a DES models.
If you are interested on see a formal representation of a MAS system see the papers I attach. On they are clearly detailed why (although is not the purpose of the papers) a MAS model is a DES model.
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Intelligent  agents  are  one  of  the  most promising  future  emerging  fields.  The  more  intelligent  they become  the  more  useful  they  are ! However, intelligent agents without ethical behaviours may turn  out  to  dramatic consequences. How can we define (unformally and formally) an "unethical intelligent agent" in a cooperative multi agent environment ?
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De Kleer is a good start - some additional pointers:
Coordination for ATM
You may also wish to develop a variation on Chang et al.’s. (1993) research on distributed reasoning supporting multi-auditor cooperation. The auditors can be represented as agents. The Chang et al. presentation addresses the support for a team of cooperating auditors.
During the cooperation stage, the auditors must ensure that their default assumptions are not contradicted by the empirical evidence of other team members. The cooperation process requires a narrowing down of the areas of conflict, identifying the areas where further testing is required and the development of the explanation for the consensus opinion that emerges from the process (Chang et al.1993, p. 347).
As part of their analysis, auditors develop propositions or beliefs, based on their assumptions . These propositions may then be communicated to a more experienced auditor who may judge them as true, false or unknown (this triplet may be seen as a component of the linguistic). The judgments of the more experienced auditor are subsequently communicated back to the originating auditor who negotiates until a consensus is reached.
Chang, A., A. Bailey Jr. and A. Whinston (1993). "Multi-Auditor Cooperation: A Model of Distributed Reasoning". IEEE Transactions on Engineering Management, 20(4): 346-59.
[1]These assumptions are by nature default assumptions, which hold that in
   the absence of evidence to the contrary, the item under review is sound.
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I just began using the platform Janus for the development of MAS and it seems interesting. Can the users of this platform give us their feedback about it?
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Did know JaCaMo?  Take a look:
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I am writing a computer program that implements an abstract social network of inter-communicating individuals (so a multiple agent system) and I want to be able to compute for each agent in the network of computational agents its individual POWER. I mean actual power, not e.g. power attributed by reputation or constitution. Thus does the mayor of the city of Metropolis have more or less power than the person about to detonate a bomb that will collapse a dam and flood the city? In the UK does the Prime Minister David Cameron have more or less power than Queen Elizabeth or Ian Hislop, editor of the famous satirical magazine Private Eye? By how much?
TO CLARIFY, although the suggestive examples I have given involve human beings [OK, maybe there is some slight doubt about Ian Hislop...] I am looking for (and not yet finding) an ALGORITHMIC means of calculating the "size" of some dynamic attribute reasonably called "power" for a COMPUTATIONAL agent that is a member of a dynamic network of COMPUTATIONAL agents.within a computer. All help much appreciated and duly acknowledged in any consequent publication(s)!
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Hi James!
I would say that the concept of (social) power is basically linked to the idea of the ability to control the flow of "scarce" ressources in a given network. Hence, persons occupying strategically important positions - sometimes also called "brokers" (R. Burt refers to "structural holes" as the structural complement; see also his book on "brokerage and closure") might exercise power. Such actors possess instrumental social capital; they provide scarce ressources to others and receive obedience in return.
I published an article in Connections (that you can find on reserach gate) on a way to concepzualize different forms of social capital and, hence, to identify powerful broker roles in social networks based on a distinct concept of "betweenness":
"Measuring the Social Capital of Brokerage Roles"
Kind regrads, Volker
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My research is related to innovation processes.  Currently I'm studying the process by which startups develop while residing within the environment of a business incubator. I am developing a model that I would like to test using agent-based simulation, but my previous experience in simulation programming goes too far back to be useful. Anybody out there with updated skills on agent-based simulation programming - using AnyLogic or any other tool? 
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You should have a look to teh journal Economic Dynamics and Control, with a special attention to Giovanni Dosi's articles.
Regards,
MV
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Otherwise what is the most used methodology (based on OOSE) in AOSE ?
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There is a new standard for designing and reporting ABMs. A group of modelers that includes two leading authors of ABM books, Volker Grimm and Steven Railsback, published the ODD Protocol, then followed up with an update in 2010 in the journal Ecological Modelling.
doi:10.1016/j.ecolmodel.2010.08.019 
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Apart from early models of Sugarscape (local market), hash and beans (by Tesfatsion, incomplete), Litghttower (at EURACE project, also incomplete), and "A Generic Framework for a Combined Agent-based Market and Production Model" by Straatman et. al. What other microeconomic models of markets (goods, land and labor) are there? I have seen some of labor, a lot spatially focused, but not a single one that reproduces basic models.
Note that there are a lot of macroeconomic models, especially of financial markets. That is not what I am searching for. I want a detailed, step-by-step design of consumers, labor, land market that I can build upon.
Thank you.
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@Roman and Bernardo: to directly answer the question, there's an overview of ABM micro/macro models in an upcoming Handbook chapter (see attached):
"Section 2. Recent Developments in Agent-Based Macroeconomic Modelling"
@Roman: Concerning complexity, I have the feeling that the Eurace@Unibi Model is perceived to be more complex than it actually is. (Disclaimer: I have a personal interest in the model.) Yes, there's a lot of details, and yes, there's a lot of code. But it also models 5 different types of markets! At least we tried really hard to make everything open, and you can now also download the entire code and run it in a virtual machine, something not many models have allowed you to do so far, in fact.
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Computer simulation of biological evolution are largely determined by the objective.
What is the evolution searching for? Does it try to increase the biomass of the system? Does it try to increase the complexity of biological structures? Nothing at all? Just follow its own rules. And if so, are these rules the only possible or are they just contingent?
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Evolution is a process that has no goal or aim. It's not a teleological process.
When we run a simulation with a determined goal, it's our aim, not evolution's that is being pursued.
Aleksandar suggestion is confusing an a posteriori observation with an a priori goal. It is like saying that the goal of evolution is produce biodiversity. Simply wrong.
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I am modelling a repeated Public Goods Game using Agent Based Modelling, and thinking of a scenario where one player can skip rounds. In a skipped round, that player will not receive any benefit (and of course not use any resource and not contribute as well). Do you know any works on a scenario similar to this situation?
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Check the work of Susan Spierre Clark. She has done some interesting game design similar to ABM but not quite ABM.
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Im currently working on evaluating ABMS Toolkits for Complex social systems. I wish to know the key components that form the evaluation metrics for ABMS Toolkits.
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This really depends on what you are after. From a technical perspective, you have to determine if your system allows you to scale you model. Some tools work well for a few hundreds agents but will fail to scale for thousands. Can the tool be parallelized for use on clusters/clouds.
What language do you need to use to express your ABMS will determine the availability of other tools, such as graph or statistics libraries. You certainly want to be able to use efficient data structures as complex social systems often are very large.
Another aspect is that of data representation and visualization. Does the toolkit allow you to easily interpret the results? What visualization capabilities do you have available? Can you generate file formats that easily integrate into existing visualization systems?
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Alternatively you can use some simple methods. For example you can rank variables based on the utility. Is that true? Sorry if the question seems simple for some of you. I don't have basic knowledge about GA. I just tried to solve an optimization problem in my agent-based model and couldn't figure out why most researchers use GA when alternatively they can have a true answers with simple and traditional methods. For example, in my model I can calculate the utility with all the variable combinations and choose the best result. Is there anything in GA that I cannot see?
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Main Advantage is following – GA, as all other random-search oriented optimization algorithms (Simulated Annealing, Cross-Entropy, etc.) doesn't require any information about structure of the function to be optimized and uses it as Black Box. Classical optimization methods should use some information. Consider, for example, function FMINS from MATLAB. To search minimum of the Rozenbrock function of the two variables, it should use initial point. If initial point is "good" (e.g., [-1; 1]) - will be success for optimization. But if you don't know, how to select appropriate initial point, search will be wrong with very large error. Random-search oriented optimization algorithms don't require information about initial point and successfully find optimal solution for Rozenbrock function even for 10 variables.
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Comparison between Multi-agent system platform development.
Is there difference between agent-based modeling platform and multi-agent modeling platform?
Which multi-agent platform is suitable for system of systems modeling?
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NetLogo look very good for beginners, but this is "dead end".
It have unique, strange language and biased philosophy of model design. From any JAVA based environment you can switch quite easily to any other based on JAVA, C# or even C++ or CUDA (when you need higher efficiency and scalability), from NetLogo you have to learn almost from scratch.
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I am interested in better understanding Non-Player Character (NPC) design in games in order to apply the ideas to social and socio-economic agent-based simulation. I was wondering if there are any object oriented design patterns that are commonly used for the design of NPCs. I am currently using hierarchical finite state machines for defining my agents. As an example one pattern I came across is the actor-role design pattern which seems to be quite useful for my purpose.
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Part of my doctoral reseach on NPCs and PCs are presented as game design patterns: Lankoski, 2010, Character-driven game design. https://www.taik.fi/kirjakauppa/images/4463b7dc09f925da01eaf73cbc72eed9.pdf
Patterns are in Appendix 2 (pages 61-74). More detailed analyses are presented on pages 116-155 & 162-181.
Staffan Björk has been extending some of these patterns in Gameplay design patterns 2.0 wiki http://gdp2.tii.se/index.php/Main_Page
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the DGS is an adaptation of Holland's Learning Classifier System with Tagged Urns and some additional structure. I'm interested in using it to model socio-technical systems related to information security, privacy, confidentiality, homeland defense, etc. Please share references to published papers or working papers, if possible. I will be coding in Mathematica for prototype, then in Java (or maybe Python) for production.
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My only direct knowledge of Holland's DGS' is from this 1.5-page review by Adami:
Are you applying the DGS to security? ... Or better yet, what aspect of security are you applying it to ? The problems of, say, spear phishing are likely to be very different from those of, say, airport security -- would you agree?
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To initiate the debate, I would like to share with you my views on the question I expressed during a recent TEDx Talk. Simply follow the link: http://www.youtube.com/watch?v=qNF4SgHxKVQ&feature=share&list=PLsRNoUx8w3rNYPACQ4oxdeAcq8mjtpaFP
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actually liveability is one of the main elements which indicate a good city. So if it consider in the first step of an urban planning, I think that lead to a good urban designing and also architecture. it highly depends on the act of the city in its regional and its attraction.
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Controlled laboratory experiments are used (i) to explore individual behavior and (ii) to test theories about individual behavior. A lot of anomalies (endowment effect, context dependence, influence of irrelevant alternatives or framing) are found, not to mention non-material incentives and social preferences. In most agent-based macro-model, the focus is on fluctuations at the macro-level (cyclical behavior of prices, wage-profit-cycles, wealth distribution in econophysics models) which emerge from individual interactions governed by simple rules of thumb. I would like to know more about how the empirical insights from behavioral economics can be taken into account for modeling the behavior of individual agents in agent-based macro-models. Given the variety and complexity of individual behavior found in controlled laboratory experiments, how should the individual behavior of agents be modeled?
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Since a few years we have been starting an endeavor which is based on the interplay between Experimental Economics and Agent-based Economics to the study of how to model artificial agents using human behaviors. You will find the paper on:
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I am looking for examples where multi-agent system frameworks (software agent templates) have been used to define the agents of social simulation models.
To give you an example of what I am looking for here is a presentation that I recently gave to my colleagues from the NIBS Network (mainly Economists):
Many thanks to everyone who answered so far :).
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You can check the work of Giuseppe Vizzari from Milan:
He works on agent-based simulations of crowded places, in particular he exploits agents to simulate people behaviours.
Hope this helps,
Giacomo
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I’m involved in a research project looking for different simulation tools for logistic management (in particular infrastructural resource dislocation “optimization”) in military environment.
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You could try some of the generic discrete event simulation tools to model this (such as arena, plant simulation or extend)
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We are attempting to develop a novel agent-based simulation modelling framework based on principles adopted from software engineering (object oriented analysis and design) to help studying the behaviour of elephants (or any other types of animals) in captivity.
Unlike current models that use an agent based approach for defining the agents and their interactions we want to use UML (use case diagrams, sequence diagrams, class diagrams, and state machine diagrams) for defining our agents and their interaction. But we also want to embed some theoretical knowledge about animal behaviour in our agent definitions. So in the end it will be a mixture between software agents and social simulation agents usually used in the field.
To give you an idea what I am talking about here is a link to a presentation I recently gave to some of my colleagues from the economics department. Although it does not feature animal behaviour the problem we are approaching is similar to that described above – trying out a novel approach to defining agents in a field where UML is relatively unknown.
Do you have any tips for us? Any references we should look at? Any similar projects you know of?
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Very interesting! Swarm particle optimization method is often used to solve the problem of finding the global optimum of a dimensional function. The basis of method are the swarm intelligence algorithms, which concern with the design of intelligent multi-agent systems based on the collective behavior of insects (ants, termites, bees, and wasps) or other animal societies (flocks of birds and schools of fish).
Below I am giving some references:
[1] H. Schmeck and J. Branke, Designing evolutionary algorithms for dynamic optimization problems. Theory and application of evoluacionary computation: recent trends, S. Tsutsui and A. Ghosh, Eds., 2002, pp. 239-262.
[2] J. Kennedy, R. Eberhart and Y. Shi, Swarm Intelligence, Los Altos, CA: Morgan Kaufmann, 2001.
[3] K. Parsopoulos and M. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Natural Computing, pp. 235-306, 2002.
[4] T. Blackwell, R. Poli and J. Kennedy, "Particle swarm optimization. An Overview," Swarm Intell., vol. 1, p. 33–57, 2007.
There exist many technical aspects of the algorithms, which recently I have been interested too in a different area of application. So if you have any questions we can discuss it.
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These last years, I have developed many models to tackle real problems, but until now none of them have been used in real situations by decision-makers.
I am curious to know if some of you have heard about examples of agent-based models that are actually used by decision-makers (city-planners, environmental health and safety manager, etc.) and not only by researchers/modelers.
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The GLOWA Danube project is also a real example of use of agent-based models by decision-makers.
Here are some links to the project:
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I am working in Computer Science. Here, we commonly use the Unified Modeling Language (UML) for software engineering project (mainly for problem analysis and design). I am surprised it is not more commonly used in Economics or Social Science Agent-Based Modelling.
My questions are:
(1) Why is UML not used more frequently in those fields?
(2) Can we use the UML instead of (or besides) equations to describe (better conceptualise) agent based models in those fields?
(3) Are there any (obvious) rules to translate equations into agents?
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Dear Peer-Olaf,
I think we might have met in EASSS 2009 in Turin... but maybe I'm wrong.
Anyway, from my perspective, it all comes down to your first question... and the answer is probably "because most people actively working in this area do not really come from Computer Science"...
From my understanding, moreover, it is relatively simple to use a class diagram to capture static aspects of a domain, which in this context would mean to represent agents, relevant aggregated abstractions, relevant 'objects'... it would also help giving an explicit account of additional "things" (in a better English abstractions) you put in the model to manage some mechanisms... but you'd need additional diagrams to capture dynamic aspects of the model, and this would make things more complex (you probably need to be quite good at UML modeling to really capture all the necessary aspects)... and frankly I think it would be difficult to achieve the level of formalism you can get with equations.
I guess you might be interested, on one hand, in all the systematic efforts carried out in the AOSE community about meta-models and modeling methodologies. On the other hand, I think that the idea you propose of defining a systematic way of translating equations into agents is original, and you might actually exploit work carried out in the AOSE community.
Sincerely
Giuseppe
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I am currently working on evacuation of people facing natural hazards (more especially volcanic hazards and tsunamis). In my fieldwork (Indonesia), people combine walking/motorcycles/trucks to be evacuated. Models such as SIMWALK/Evacuation Root Tools/Route Finder do not fit with this kind of context.
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If you want to know the whole story about tsunami related evacuation simulation with matsim you may look into my dissertation:
or the newer work by Christoph Dobler:
For the multi-modal evacuation you can check this tutorial:
If you planning to develop a hybrid model like Anh et al. at al did, the work of Bourghout might also interesting for you (see, e.g.,
Burghout, W. and J. Wahlstedt, Hybrid Traffic Simulation with Adaptive Signal Control. Transportation Research Record, Vol. 1999, 2007, pp. 191–197.)
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We have written (what we think is) a high quality article in the field of Systems Biology with the title "Comparing Stochastic Differential Equations and Agent-Based Simulation for Studying Early-Stage Cancer" and we are looking for a high quality outlet in the field of Systems Biology or a related field. We have previously published an article in BMC Bioinformatics (Impact Factor 3.03) and we aim to publish this paper in a journal with a higher impact factor. Do you have any suggestions?
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The highest IP score rests with Molecular Systems Biology. It is now back to eleven, probably since they started including reviews as well. From you title you may however fare better with a journal that is a bit more more open to methods-focused papers. BMC Systems Biology might be a bit lower in impact but is well read in the field. Submitting to Bioinformatics (5.3) might also be an option if your article fits into their strict page limits/layouts. PloS Comp Biol is at 4.9 now and is also a good target. If your study contains experimental/clinical data that is linked to your modelling definitely try to give those a go. If it is a purely theoretical study it will definitely be more difficult.
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The consumer should be able to buy only one good, choosing between the one provided by the various companies. The parameters that I want to consider are: the price, the company's visibility in the market, the quality of the product and the quality perceived by the consumer.
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This may help "Comparing Cartel Behavior: A Simulation Analysis with
the System of Cartel Markers (SCM)"
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I would be interested by references to papers dealing with "agile modeling" for complex systems simulation (esp. agent-based simulation). Although I'm aware of methodological proposals that more or less "import" or mimic this concept from software engineering, I've actually found very few papers about real applications, or software environments that can enable a continuous loop between modeling and simulation, allow an interactive design of models through the interaction of modelers with a simulation, etc.
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We have just started looking at the topic of "Test Driven Object Oriented Simulation Modelling" and are planning to extend this to "Test Driven Agent-Based Simulation Modelling" in the near future.
Here is a list of papers we found useful for our studies:
- Sawyer and Brann (2009) How to test your models more effectively - Applying agile and automated techniques to simulation testing
- Dietrich et al (2010) Validating UML simulation models with model-level unit tests
- Nguyen et al (2011) Testing in multi-agent systems
- Djanatliev et al (2011) Veritas - A versatile modeling environment for test-driven agile simulation
- Coyne et al (2008) A methodology for unit testing actors in proprietary discrete event based simulations
- Pitt-Francis et al (2009) Chaste - A test-driven approach to software development for biological modelling
- Guercan et al (2011) Towards a generic testing framework for agent-based simulation models
- Collier et al (2007) Test-driven simulation development using Repast Simphony
- Kleijnen (1995) Verification and validation of simulation models
- Kleijnen et al (1998) Validation of trace-driven simulation models - A novel regression test
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Check my papers, see ARL theory, this is what I'm proposing what do you think ?
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I´ve had the same feeling just by doing some preliminary analysis of trends in scientific databases (Scopus, WoK), were one is going down, the other is coming up both in publication rate and in citations. However, I am not so sure about industry yet.
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W.r.t agent based model
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Thanks a lot for answer, but a bit of elaboration on the same would have helped a lot
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What is a suitable ABM framework for learning? That is, something where you can quickly get up to speed and play around with it, to get a feel for how ABM works.
It has to be suited for biological simulations, since that is what I would like to use it for in the end.
I have seen about Flame, Breve, Spade, MASON, Swarm etc, but it is hard to know in beforehand what it is like to work in the respective tools, what is the learning curve etc.
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Dear Samuel, I think that the NetLogo is a very good modelling tool for the biology. Maybe, it looks like a toy :-), but it is possible to create useful MAS models. Try to look on the wepage http://ccl.northwestern.edu/netlogo/models/ containing models created in the NetLogo. The next alternative can be Repast. It is free as a NetLogo. It offers different programming languages: Groovy, Java, state-charts and Relogo (similar syntax as in NetLogo). The GAMA generic multi-agent environment can be also useful (http://code.google.com/p/gama-platform/). There are good tutorials and examples. It uses quite untypical language GAML, but I think that it is not hard for learn as in case of NetLogo. The epidemiological model SIR was created with the aid of GAMA (for example). I hope that these advices will help to you :-). Best regards, Martina Husáková
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Does anyone know of any clear and simple problem for assessing an algorithm proposed for organizational structures like hierarchies in Multi-agent Systems?
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Hi Patrick,
Many thanks for your answer. I am working on an algorithm for dynamically building an organizational structure in MAS. I am looking for a proper test bed or application to assess the algorithm I propose and probably compare it with the algorithms alike. The applications that you suggested would be good matches; however, they have their own complexities that I don't like to involve with, at least at this stage of my research. I wonder whether there is any simple handy problem.
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Has anyone done any work in this area? I only found this so far:
Sharpanskykh, A and Stroeve, S H (2011) An agent-based approach for structured modeling, analysis and improvement of safety culture. Computational and Mathematical Organization Theory, 17(1), 77-117. Any other suggestions?
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I currently work on the subject, I inforler yours.
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For a lecture, I'm looking for well-documented systems or questions, in any scientific domain, that could be well represented using agent-based models and lead to the design of "mid-size" models. By "mid-size", I mean something between the "small-size" toy models found in the NetLogo library, for instance, and the large ABMs that take months to calibrate and analyze. A student would ideally be able to read the paper(s) describing the system and the associated question(s), and design then code a possible model in 2-3 weeks of work. Any suggestions? A link to the corresponding literature would also be much appreciated. Thanks in advance!
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@all : what great answers ! Thanks so much.
@ Franziska : traffic simulation could be a good idea (and we have the infrastructure to do that), but I'm always afraid, in this kind of models, about the amount of details in which you have to dive to obtain something satisfying. Anyway, your pointer to the work of Dornhaus is just perfect (didn't know her work).
@ Arend : well, I did not think about evolutionary models, but you hit a point there. This is indeed a great way of showing the interest of ABM vs. other techniques.
@ Stefan : hope the other answers will contribute to your lecture as well :) Daisy World is also a nice model I totally overlooked, probably because I was focused on "real" problems. But, in a sense, since I have six groups of students, it could be a good idea to introduce one "abstract" model in case…
@ Desi : one other work I did not know. And a great discovery. Thanks !
@ Derek : I totally agree with the — highly pedagogical — idea of building models of incremental complexity. That's what we use to build our tutorials for GAMA, for instance. My only concern with this course is that I have at most 4 weeks left with the students, which makes it quite difficult to implement a nice progression (for instance, if I want them to present their intermediary findings or implementations). However, I might very well reuse your two first models, given the fact that I already worked with them on a "simple" Schelling model of segregation.
Meanwhile, I have also worked a bit (and not only waited for your answers :) ), and found two interesting ideas :
- the different papers of Charlotte Hemelrijk about the DomWorld models (dominance hierarchies among primates). The ABMs (or IBMs) are more or less given in them, but since each paper deals with a different trait or behavior, the interesting question is how to couple them in order to obtain a quite complex artificial society.
- This (new) paper : http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044501?imageURI=info:doi/10.1371/journal.pone.0044501.t001, about the importance of the "negative feedback" in ant foraging. Combined with some already classic toy ant models, it could lead to some nice models.
Plus things I already had under the hood (water flow modeling like the one depicted in the RIVAGE project, plant hoppers invasions modeling based on the life-cycle of individual insects, and a few other ones).
By the way, while looking for all this, I thought it would be great, somehow, to maintain this type of resources somewhere (as I'm probably not the only one scratching his head to find new and not so obvious models to implement with students). I know the domain quite well, but I was very surprised to see how difficult it was to find something else than simple toy models. And sites like openABM, for example, are not of a great help in that respect. Any volunteer :) ?