Agent Based Modeling - Science topic

Thankyou very much Shima Shafiee Ill take a look. it looks very helpful

Hi,

you can read this paper, an example of using agent-based approach in construction.

"Resource Allocation in Construction Scheduling based on Multi- Agent Negotiation"

I hope it may help you.

Jiří Kroc: Greetings Prof. Kroc. In neurology the cutting-edge research is on 1) neurodegeneration, 2) neuroprotection, 3) the unification/entanglement between the nervous system and the immune system and 4) disorders of consciousness. thanks, Mustafa.

Dear Mr. Jansen!

May I kindly argue that a financial program in a supply chain network evolves via the context, the objectives set for a green supply chain integration. The performance of actors - the focal company and the 3 tiers - can be measured by the social exchange theory:

Taiwen Feng, et al. 2022. Sustainable supply chain finance adoption and firm performance: Is green supply chain integration a missing link? 28 March 2022, Sustainable Development Early View, Available at:

Yours sincerely, Bulcsu Szekely

If you have a little spare time, I'd suggest you try and imllent a *really* rudimentary analogue of your planned model in several of the major frameworks -- focus on the most significant elements -- it might take a week or so for eah, but piing the wrong horse may make for a much slower journey overall.

Good question, I'm trying to follow the same issue. Maybe you can have a look about this interested courses:

Best Regards,

Luis Cruz

As promised, reactions to the sceptical points raised in the previous post.

Computers do not need feelings. Computer based models can have knowledge of the preconditions for the appearance of eg anger or guilt, in an agent and knowledge of the corresponding behaviour that typically follows.

All models are pitched at a level of abstraction/simplification which must be such that the model is both tractable as regardes its complexity and effective as regards its usefulness. There may not exist such a level of abstraction, but one cannot knew in advance. To a considerable degree, one must "suck it and see".

The danger of weaponisation of such a project is very real I feel (compare war-gaming). Key are independent funding, full publicity and openness at all stages, and an unswerving comitment to global neutrality. United Nations??

As ever, all comments welcomed, even those of the "Total Garbage!" variety.

James

As was clearly described by Eugene Veniaminovich Lutsenko, material world is only part of the universe. Not the whole. Science is studying material things. The rest is expected to be non-existent.

The fact is that there is a piling evidence on the existence of the after-life or before-life where energies that we call spirits resides. When spirits enter the material world, they get accompanied by atoms.

Spirit can live without a body a.k.a. matter. Matter cannot become alive without a spirit that is making it alive.

Here we come to the important point: "What we spirits/souls?" Those are quantum fields that we are unable to detect by the current technology. Spirits are the oscillating specks within all encompassing field of the whole Universe.

After-life is a serious research subject that is working usually with deep hypnosis with regression of subjects into their past lives. Then researchers go and check all details and in the most cases find relatives of deceased who lived exactly same life as described by a person in regression. Such research was started by Dorothy Carron. There are followers like Newman and co-workers.

Dear Martin Barrenechea

Maybe this will help you. Take a look:

Is there any agent based software which can be coupled with COMSOL? and isn't COMSOL capable of solving agent based models itself?

An agent-based model is using agents -- entities having certain variables and rules behavior encoded within them -- that interact with each other and create the system's higher-level properties. Many systems express self-organization and/or emergence. Very useful processes to know.

Your goal is to design agents and their interactions in such a way that they create observed systems phenomena. In most of the cases, this is very hard to achieve.

That is the moment when evolutionary design kicks in. You make many different systems, let them evolve, and somehow classify the agreement of their outputs with observed phenomena of the observed system.

The best solutions are chosen and allowed to cross their building blocks. They produce offsprings that again goes through the cycle of evaluation, competition, and creation of a new generation of offsprings. Evolution stops when you either fail to find a solution or the found best solution is sufficiently fulfilling your criteria of a successful solution.

I recommend you to go into my project "Complexity Digest..." there are provided citations and links to Illachinski's book on Artificial war and NetLogo software. Those are two starting points in your search. There is more about the subject, which is very interesting.

You should be careful. There is a methodological issue (and I know I am in a minority on this) that calibration of an ABM by "fitting" has the potential to undermine the value of the approach. This is because most ABM have lots of parameters and if they were statistical models, we would already know that too many parameters and not enough data = rubbish. The trouble is, unlike for statistics, we don't have a "formal" way of deciding how many parameters we are "allowed" not to know the values of (but fit) relative to the "amount" of data. I suspect that for ABM we will have to rely on some sort of operational procedure (analogous to sensitivity analysis) to understand this issue. But I do not want to overstate the case. Some fitting could sometimes be legitimate depending on the data and the model - but at the moment we are not really sure when or why IMO, we just do it. (But also, ask yourself, is it a bad design principle to specify a model where you cannot see how to collect the data "for real" and only fit? Sometimes the problem is only practical I agree but sometimes it is definitely bad design.) I have tried to develop these arguments more rigorously here: http://methods.sagepub.com/foundations/agent-based-models. I have also shown that calibration/validation without fitting does not have to be an impossible goal: (though I know this article has many weaknesses.)

What is really fascinating about agent-based models is the nature of flexible design? They can involve a physical model of movements (mechanics with differential equations), a psychological model (a state machine), and discrete variables like we are used to using in cellular automata.

Let us think about an ABM of a drone. You definitely need to incorporate the model of physics: gravity & equations of motion. Otherwise, the drone will crash. The other point is that you need to define its orientation on the map and terrain. GPS related evaluation is involved.

Additionally, you need to incorporate sensors and effectors, avoiding obstacles. You need to implement the administration of some tasks too.

Do you see, where is it going? Various models can be incorporated into one agent. When you want to simulate millions of them, it is advisable to get rid of the unnecessary complicatedness of all models. Some can be really simple, others not. Hence some of them must use differential equations.

A very good intro to ABM is the book of Andrew Illachinski: Artificial War.

It describes the very principles of ABM with a detailed description of the internal functioning of agents. You can start to program your own agents after reading this book.

This chapter might be useful, maybe you have it already in your library:

Hi, I recommend:

Wilensky, U. and W. Rand (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo, MIT Press.

Hello, sorry i am jus seeing your chat. I dont know where the next symposium would be held, i dod like to attend too. With this COVID19, the world is on a stand still. Thanks for asking anyway.

Lock down strategy difficult to implement because our people so disobedient.

We are luck, they closed the airport and banned public transport.

Maybe Uganda has low cases less than 60 cases due to those strategies.

Python has more powerful libraries than 'R' for Data Analysis. So i will suggest Python.

Christian Bauckhage

Hi . I will definitely try to read these slides. Thanks a lot

Thank you

It's been a while, but I always modelled as households. One should strictly allow for household formation behaviour eg children leaving home and forming new households, divorce, group households splitting and re-assembling. Depending on what you are trying to do I would not expect the propensity to move to vary too much.

If you are doing logit/probit modelling - it should not be too hard to add in propensity to split, followed by propensity to move, in a hierarchical framework.

Having good demographic data is an excellent start, but you also need data on how households may change their strategies in response to changes in policy, household economics (wages, prices and other socio-economic influences). Even without the more complex data, you can construct a reasonable example model. At a national scale there will be significant overhead modelling at the household level, and you may need to pay attention to local interhousehold influences ("Our neighbour has just .... and it seems like a good idea") This is particularly the case if programmes rely on community involvement.

I would think that any of the prevalent approaches for constricting ABMs would serve, it seems likely that you could work with a fixed timestep (something between daily and every two weeks?).

Personally, I'd start with a dynamic array or list of agents and just run through it at each timestep, unless the agents actually influence each other (IN which case you'd need to randomise the order at the start of each pass).

Good luck!

What about reflecting boundaries? That's what I used in case of the confined CTRW,

Recommendations on mobile APPs.

Access to such APPs for more info.

please look at the class by

Leonard

from

Stanford university

on

YouTube

Hi,

you can get NUTS 1 level shapefile by using ARCview program. If you have province (NUTS 3) level shapefile, you can merge provinces and get NUTS 1 level shapefile.

Best...

Myself and a colleague have published some scripts about this. Here's his first one. http://resources.modelling4all.org/guides/running-experiments-on-a-cluster-of-computers

However, to my knowledge there isn't a parallel distributed version in the works. REPAST HPC is likely your closest best bet if you're looking for that.

Hi,

First of, You may try to combine reinforcement learning algorithm for reward and game theory for punishment together.

On the other hand, Anylogic is a nice tool for Agent based modeling and system analysis.

Thank you all for the feedback. I am using python and it's basically follows selection-reproduction process, and I was using lotka-volterra function to control the reproduction probability of each agent. I realized right now I can have multiple agents on the same grid, which can be coded into one agent each grid. In that way, I may not need to add in the density dependent control for reproduction, since in high density areas the parents won't be lay offspring in occupied space.

The reason I am asking the question is in the book "individual-based modeling and ecology", it says the individual should not be able to sense the population density, so anything that contains a density-dependent controlling factor will not be considered as "agent based modeling", rather we can only code behavior of each individual and observe the emergent property on the population level.

For more details about the model, it is a 100*100 grid, starting with 100 each of two types of agents. Their fitness and reproduction probability are determined by a fluctuating environment parameter. They also mutate at each step. I am not sure if there would be a difference between competing for space or competing for some resource that can be consumed and generated in each grid. I feel the first one is more easily to run.

Dear Laurent,

Maybe the following papers will help you on the subject:

Badland H, White M, Macaulay G, Eagleson S, Mavoa S, Pettit C, Giles-Corti B. Using simple agent-based modeling to inform and enhance neighborhood walkability. Int J Health Geogr 2013;12:58. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874648/pdf/1476-072X-12-58.pdf

Yang Y, Diez-Roux A, Evenson KR, Colabianchi N. Examining the impact of the walking school bus with an agent-based model. Am J Public Health 2014;104(7):1196-203. https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.2014.301896

Lemoine PD, Cordovez JM, Zambrano JM, Sarmiento OL, Meisel JD, Valdivia JA, Zarama R. Using agent based modeling to assess the effect of increased Bus Rapid Transit system infrastructure on walking for transportation. Prev Med 2016;88:39-45. https://www.sciencedirect.com/science/article/pii/S009174351630038X?via%3Dihub

Garcia LMT, Diez Roux AV, Martins ACR, Yang Y, Florindo AA. Development of a dynamic framework to explain population patterns of leisure-time physical activity through agent-based modeling. Int J Behav Nutr Phys Act 2017;14(1):111. https://ijbnpa.biomedcentral.com/track/pdf/10.1186/s12966-017-0553-4

Garcia LMT, Diez Roux AV, Martins ACR, Yang Y, Florindo AA. Exploring the emergence and evolution of population patterns of leisure-time physical activity through agent-based modelling. Int J Behav Nutr Phys Act 2018;15(1):112. https://ijbnpa.biomedcentral.com/track/pdf/10.1186/s12966-018-0750-9

Hulme A, Thompson J, Nielsen RO, Read GJM, Salmon PM. Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. Br J Sports Med. 2018 Jun 18. pii: bjsports-2017-098871. doi: 10.1136/bjsports-2017-098871. [Epub ahead of print]. https://bjsm.bmj.com/content/early/2018/06/18/bjsports-2017-098871.long

Best wishes from Germany,

Martin

Sustainable development can be achieved by various methods. Which method will be selected is determined by many factors of the environment, the environment of specific economic processes as well as the specifics of economic enterprises and the national economy. However, in the absence of a key development factor, a key raw material such as water can be a significant cost barrier for the implementation of the process of achieving sustainable development. Sustainable development can not be excluded in the context of continuous irrigation of agriculture, but it will generate high costs. In such a situation, profitable ventures should be developed in the domestic economy, which will allow to finance the costs of the said irrigation. It can not be ruled out that irrigated poor soil with proper fertilization and use will produce high yields, high production of agricultural produce will be generated and after a few or more years, it will be possible to create more complex flora ecosystems, including forests next to arable fields, and the way can be improved. Then, sustainable development will be analyzed, implemented and improved in terms of the national economy.

I invite you to the discussion

Dear Badr Elkhalyly,

Container Technology and Docker Containerization allow the installation of multiple Containers with different Virtualized Functions (VFs) on a Docker (like on a Ship). Because VFs provide certain services in Containers, they are also called Container-based Services. These Services are often referred to as Microservices.

Implementing in the IoT Microservices very often provide Network-specific Functions. They are referred to as VNFs (VNF: Virtualized Network Function). In IoT, VNFs are therefore often provided by Microservices. For example, in an IoT Cloud, a Docker (like a Ship) can be installed with multiple Containers (Cloud Computing Containerization). Each Container can contain a variety of Microservices. From VNFs (Microservices), correspondingly isolated VNF Group scan be formed. An isolated Group can be an intelligent Virtual Private Network. This means Network Slicing.

For more information see:

Martin Fowler: Microservices Resource Guide

Cisco: Cloud-Native Network Functions (CNFs) , White Paper, Jun 2018; Document ID:1529344804993194

Azhar Sayeed, Dejan Leskaroski: „Cloud Native Applications in a Telco World - How Micro Do You Go?“

5G-PPP Software Network Working Group: From Webscale to Telco, the Cloud Native Journey; July 2018

Lu Ma1, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp: „An SDN/NFV Based Framework for Management and Deployment of Service Based 5G Core Network“; China Communications, Vol. 15(10), Oct. 2018; DOI: 10.1109/CC.2018.8485472

Best regards und much success

Anatol Badach

Hi Eric,

For this purpose, I would recommend you to look into a toolkit called "Multi-agent transport simulation" or MATSim (https://www.matsim.org/). It is an agent-based modelling toolkit designed for large scale transportation scenarios, and you can naturally use OSM networks with it. Although it is based on Java, but if you are just going to use it (and not code against it) I guess you will only need to work with its .xml files which are not much difficult to learn. There is also a good user guide for MATSim if you decided to learn more about it in here : https://www.matsim.org/docs/userguide/

Best,

Afshin

Dear Jim,

My own (current) views on these kinds of endeavours is that perhaps it's useful to take a step back to ask what kind of answer / model / result would be of use and to whom? Suppose you could wind back history and be there in 1945 knowing everything you know now. What would you do? Suppose you had a model that was very persuasive and said "this intervention" would prevent a nuclear war. What if that intervention was that rich countries should redistribute 50% of their wealth to poor countries. How would you make this intervention?

Please tell me if the attempt to open the zip file prospered. Regards

I agree with Ervin regarding the North and Macal text.

North, M. & Macal, C. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling. New York: Oxford University Press.

You may also want to review:

Railsback, S. & Grimm, V. (2012). Agent-based and individual-based modeling: A practical introduction. Princeton University Press. New Jersey.

Hi Javed! A recently published paper I've co-authored might be of interest:

Dear Ali,

The answer provided by Dr. Lafifi is recommendable. Please follow.

Dear Ali Kamali,

In ABM you do not define the relations or the global behavior of the system, but the behavior of the individual entities, the relations emerges (is a nice example of emergent modelling). Other think is if you want to do an hybrid approach (as is done commonly) and define, over the behavior of the agents, some relations.

Both approaches are powerful, depending for what; at least, a model is just a tool useful to solve an specific problem.

Best,

Conceptually: You can start by creating a system of agents, each with different rules for thematic analysis. Assign them individual fitness ("automatically evaluate what is the achieved measurable quality of the result, that could be a problem) and connect the arsenal of evolutionary processes (replication, selection, mutation), then let the agents evolve on the data- text substrate. Try to change slightly substrate with time (new inputs, new texts - means variable environment). Look at the best fitness agents. What rules they achieved.... How stable they are ... etc...

Agent-based modelling (ABM) is a powerful tool for representing the complexities of energy demand, such as social interactions and spatial constraints. Unlike other approaches for modelling energy demand, ABM is not limited to studying perfectly rational agents or to abstracting micro details into system-level equations. Instead, ABM provides the ability to represent behaviours of energy consumers — such as individual households — using a range of theories, and to examine how the interaction of heterogeneous agents at the micro-level produces macro outcomes of importance to the global climate, such as the adoption of low-carbon behaviours and technologies over space and time. We provide an overview of ABM work in the area of consumer energy choices, with a focus on identifying specific ways in which ABM can improve understanding of both fundamental scientific and applied aspects of the demand side of energy to aid the design of better policies and programmes.

Hi there, this answer is exactly one paragraph of a text I am just writing (a chapter in a policy-based ABM). What I have so far:

According to Gilbert & Troitzsch, 2005, p.8 in Hamill & Gilbert, p. 4

"Microsimulation takes a set of data about a population – of people, households or firms – and applies rules to reflect changes, enabling the modeller to look at the overall impact. Such an approach is particularly useful for modelling policy changes, for example, to see who is made better or worse off by tax changes. However, although allowing for heterogeneity, microsimulation does not allow interaction. Only with the arrival of ABM did modelling interaction between agents become possible".

Crooks & Heppenstall, 2012, p. 92 argue that:

"However, crucially in contrast to ABM, MSM only models one-direction interactions: the impact of the policy on the individuals, but not the impact of individuals on the policy and interactions between individuals are not simulated. Furthermore MSM models do not have the behavioural modelling capability of ABM".

More importantly, I think, is the difference between ABM and Systems Dynamics (which for me are greater). And I was upset to find a text (by a famous author) that uses deterministic differential equations (one direction only) but calls it a ABM...

I hope it helps.

Best,

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 

thanks both of u.....really many thanks

No simulation software, however sophisticated it is, can be compared with the unlimited possibilities when you just write a program to do that simulation exactly the way you want it to be done. That being said, the choice of programming language used is also crucial.

Personally, since this is all about number crunching, I believe that the best option is by far Fortran (version 95, 2003, or 2008). A popular choice is C/C++, but those languages are just not made for what you are trying to do. The primitive (essentially non-existant) matrix support in C, and the awful matrix support in C++ should be enough justification for rejecting them as the programming language of choice for anything involving Mathematics. However, i could add a long list of other reasons too. It is extremely easy to write a memory-leaking program in C/C++ (the language lets you do things with pointers that you shouldn't be able to do, and it's not even easy to figure out where the problem is after that). Furthermore, Fortran is module- or object-oriented; it's up to you which way you will go, and you can even mix them if you need to. So things that you can easily do in Fortran are often hard to do in C/C++, unless you use tricks to dodge the unecessarily complicated and badly designed core of the language. Not to mention the much better compiler optimization in Fortran. The list goes on, and it is a long one.

Honestly, I don't understand how people prefer to do simulations in C/C++. In any case, I shouldn't be surprised. Nowadays, many people even use... Java or Python or whatever similar, for writing mathematical applications. It sounds crazy, but it is true.

Have you considered http://area9learning.com/ ?

Or are you looking for a more open source tool?

Please refer to the article at:

CAs and ABMs are quite different yet similar!  CAs have fixed neighbourhoods (grid) while the nearest neighbours in an ABM vary with time, as the agents are free to move and interact with each other and their environment.  Generally, both methods' agents have state, and basically can be represented as FSM (finite state machines) -- however, ABM are possibly more powerful, as the AI capabilities of each agent can be very complex, see e.g. the BDI architecture, and the possibilities of interactions, communication as well as interaction with environment contributes to more complex and realistic situations.   CAs usually follow only a few rules to update state depending on neighbourhood states, so this has limited application.  In my humble opinion, ABM will give more realistic simulations, especially when dealing with social phenomena and complex adaptive systems.  The best introduction to ABM simulation is the NetLogo suite, very easy to program and with an extensive library of demonstration models;  there are many other platforms that usually require more programming ability.  In computer science, ABM are usually referred to as multi-agent systems (MAS) -- in my opinion, the emphasis is then more on platform development, not the eventual models that are implemented on the platform -- there are very many MAS platforms.

The main purpose of ABM is to simulate the emergent and/or self-organizing properties of systems of agents, which cannot be determined from the rules governing the individual agents.  It is a fascinating and still emerging field of study.

The book on ABM by Rand & Wilensky is highly recommended to give you an overview of ABM in NetLogo.  Prof Rand also gave a great MOOC on ABM recently at the Santa Fe Institute (www.complexityexplorer.org) and you can find the course lectures there and on YouTube.  SFI's Complexity Explorer portal also has a great resource listing about complex systems, highly recommended.

Since arrival of vehicles is a random process, it can be represented by generating random numbers in the simulation process. By knowing the statistical distribution which fits well with the field observed headway of vehicles, vehicle arrivals can be generated theoretically by generating random numbers as per the chosen statistical distribution. Once a vehicle is generated as per the applicable theoretical distribution, the type of vehicle is identified based on the percentage composition of traffic.

Please refer the below link for further details

There is more general research on the spread of information

in (social) networks.

Regards,

Joachim

You may find the following publication helpful, it's an approach for using Metamorphic testing to validate agent-based models in this precise type of situation.

What is your mean about BDI? see also anylogic.

Also you can use screen recorder software.

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

What is the purpose of your agents?

If you are interested in service system and social simulation perhaps my lecture slides can help you to answer some questions.

I have also published a paper on how to build a multi agent system from scratch:

If your goal is to build a model that includes both (1) climate changes over time, as well as (2) adaptation due to social aspects, then either (A) a multi-paradigm approach of both SD and ABM could be helpful or (B) an ABM model only could accomplish this. Some general comments for either approach follow. Selecting the type of model that you want to build will likely depend on the temporal granularity that you want to capture for both the climate and the social adaptation responses. If the climate needs to be captured continuously but the adaptation only needs to occur following certain events, then (A) is the approach that I would probably take. Otherwise, I would go with (B) and explore the interactions of climate (as agents) and people (as agents) that contribute to adaptation. The attached paper provides a framework for identifying which paradigms can be used to address a problem within a multi-paradigm modeling approach.

(A) For a multi-paradigm approach:

On the SD side, you can model the pieces of the climate that you want to capture over time.

On the ABM side, you can model the social aspects that you want and how this affects the decisions that pertain to agents adapting to the environment.

(B) For a ABM only approach:

Climate components can be modeled as events to cause triggers within the population that require adaptation. Alternatively, climate components could be modeled as agents that interact with agents based on co-location with the population within the environment (assuming that the climate components don't just cover 100% of the environment).

I think that if the comparison between two computational models of emotions (whether the PIA expressed or not faithfully emotions of humans), as we are dealing with in this dialectic interaction may be based, or emanate from the "Conceptual Science Systems" under the agreement that can provide a theoretical framework for understanding, or provide knowledge on the development of a theory or a mental model using rigorous methodologies and a common programming language artificial intelligence "emotional" (PIAe)

A number of factors are relevant in this regard.Particular insurance packages, simplicity of procedure and various security related issues are very much relevant.

I suggest that you use uncertainty to measure the quality of your model.  As you noted, you can estimate the uncertainty of your model by verifying and validating it but these two activities tell you different things about your model's uncertainties.

In order to validate a model, you must compare its representation to something to determine its uncertainties (i.e., the referent) and decide if the model meets your (or others) needs.  That something can be (1) observations of the real thing that you're modeling, (2) other credible models or (3) the opinions of one or more credible people about how the model should behave.  Observations (aka, empirical data ... sometimes measurements, other times not) are often held as the most credible standards for model validation but observations have uncertainties (often ignored) that can be greater than you can tolerate.  It is also possible to compare models to other models as long as those other models have known uncertainties as well as credibility in your field.  The weakest form of comparison is subject matter expert opinion (even from credible experts) because the uncertainties are often poorly known or completely unknown.  There are ways to collect expert knowledge that portray its uncertainties but they seem to require more work than most want to exert.  In the end, the uncertainties of your model will always be greater than the uncertainties in the knowledge that you use for comparison independent of the particular source of that knowledge.

Verification generally tells you how internally consistent the model's design and implementation are.  A verified model is a necessary, but not sufficient, condition for a valid model.  Verification can directly support model validity only when a model's implementation is compared with a validated design but the uncertainty of this comparison can be high because most designs are very abstract representations.

The uncertainty of any evaluation of a model depends directly upon the evaluation's coverage of the model's behavior space (i.e., the phase space defined by the model's range).  The coverage of any validation depends upon the coverage of the referent.  Most of the model domains that I've seen face with very sparse referents.  You can overcome this by combining referent sources and by combining verification evidence with validation evidence but all this takes time and effort.

I would suggest to align the design of your ontology on the ODD protocol of Grimm et al. 2010 (http://www.sciencedirect.com/science/article/pii/S030438001000414X). ODD stands "Overview, Design concepts, and Details" and was published in 2006 with the objective to standardize published descriptions of individual-based and agent-based models. Therefore, it may act as a feasible starting point for you ...

Best, Mario

I would recommend to distinguish between modelling and simulation.

If we look at modelling we can take two world views: process oriented and object oriented (individual based). In a process oriented model the decisions are embedded in the process model; if we take an object oriented world view the decisions are made by the objects (actors). These actors also often have a memory. At least in Operations Research & Management Science (which is my area of expertise) process oriented models (often queuing system) are related to DES, while object oriented models (often service or social systems) are related to ABS.

With regards to simulation process oriented models mostly use event driven (asynchronous) simulation execution algorithms while object oriented models mostly use time sliced (synchronous) simulation execution algorithms.

You can find more information in my lecture slides, e.g.

Agent based modelling is also used in economics. See for example this article and its list of literature: http://www2.econ.iastate.edu/tesfatsi/acewp1.pdf You can also read this article and find more references: https://en.wikipedia.org/wiki/Agent-based_computational_economics

I think that programming is always quantitative. But it is also true that some quantitative economic variables (like utility, fitness) do not correspond to physically measurable variables, and thus quantities represent virtual variables with their evolution.

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

Ich habe einen Optimierungsansatz gewählt (--> The Impact of Different Flexibility Options on Future Electricity Spot Prices in Germany). Ich könnte mir vorstellen, dass es spannend sein könnte, diese Ergebnisse mit denen aus einem agentenbasierten Ansatz zu vergleichen.

Let me suggest you FLAME.  It has been used in economics modelling like:

For more info you can visit:

Dear Prof Doran,

I do not have a concrete answer, but I have done a cursory reading of the "holonic agents" article of Gerber et al in 1999 titled "Holonic Multi-Agent Systems":

Perhaps this article is of interest to you.   All the best with your research.

Best regards

Check this:

Look at the work on complexity and evolution at the Sta Fe institute. 

The project based on LCA is conducted in the same as manner as in the past, and as you rightly point out it has no joined up thinking across  sciences and ideas. 

I have been aware of this for a long time and have come to the conclusion that we must make fundamental reforms in the area of taxation primarily so as to make a mind shift change in all  our thinking. Instead of isolating individual sciences , the earths life sciences are intrinsically linked to each other  and we must understand that for every action taken in one area,  there is a direct and indirect effect on all the other areas of earth's systems.

For example , The fossil fuel industry has just woken up to the damage that its products are doing , not just in carbon release but all the side consequences of the other products it spawns from plastics to chemicals , and the effects on marine life and soil and insect micro flora.

This is why we must link consumerism directly to natural resources by way of a Natural Resource Tax , and Ideally this would replace almost all other taxes, so taxation is then the driver to reduce climate change, environmental and ecosystem damage. This would then empower every individual to make the right choices, as damaging and polluting technologies would soon become redundant as new cleaner methods would be found.

This is the greatest challenge to mankind, how to save ourselves, the earth will continue without us, so it is entirely in our own hands to make the right choices.

As you say we need an holistic approach in all  what we do, not just in the scientific community , but at home, our lifestyles, it is here we desperately need research and forward thinking. 

At present we have in the UK a n election looming . and so far not one word hardly has been spoken about climate change. Nobody is taking this seriously!

We should be looking at a seismic shift in politics to account for the necessary change that have to happen , but not a dickie bird is to be seen!

Will mankind have the guts to stand up and be counted for the damage they are doing?

This is where the scientific community has to challenge politicians and corporations alike in their wisdom of maintaining the status quo.

Did know JaCaMo?  Take a look:

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

It depends on your goals and the characteristics of their systems, ie,

tools ETL (Extract Transform Loads) are more suitable for

Data synchronization (batch or real time), this

when a large amount of data must be extracted

an application transformed (usually transformations

SQL or XML), and then loaded into another application. solutions

EAI (Enterprise Application Integration) are more suitable when

workflow and process management is required, which usually

involves a large number of small transactions and messages

low level of transformation.

A colleague of mine with a background in evolutionary computation and myself with a background at self-organising systems have looked at this kind of problem before and have produced a number of ideas. A former Ph.D. student of his has taken these ideas and implemented some, albeit in a slightly less general form than we originally anticipated. I am not sure if the following papers are helpful to you, but they might at least serve as related work. You find the references to Abbas Sarraf's work in the first one.

These papers all pertain to abstraction of repeated interaction patterns. The systems we had in mind are mostly of chemical or biological nature. If a body, e.g., has to be simulated, the level of observation is usually limited. We wanted to be able to simulate a body at all levels of observation, starting from molecular interaction, to cells, to tissue, to organs. Of course, this is utterly impossible if all interactions on all levels have to be regarded. Fortunately, most of these interactions are recurring and always exhibit the same side effects. If we can detect those interactions and find a suitable abstraction for them (i.e., check if the preconditions are there and then immediately instantiate the side effects without going through the complex interactions), we can save a lot of computational power. At the same time, these abstractions must be dissolved again if the preconditions are no longer valid so as to not lose any information.

We have had several versions of the algorithm running on much smaller systems than a body, but with good success. Unfortunately, time and money constraints have not allowed us to pursue our ideas further. Maybe they are useful to you now.

Perhaps something along the lines of swarm intelligence such as the three links given below?