Re: [Swarm-Modelling] Re: [Swarm-Support] Re: Agent communication and grid models

[Kanagaraj Krishna]

Hi,
   Can i know in detail what is meant by a "soup" model. Any such examples 
available (java preferably)?
It would be nice if I could get your codes too, for reference.  

I've seen most examples of SWARM with GUI simulation (bugs etc..) where grids 
were used. Can a "soup" model be presented in a GUI simulation too?

Another one question .........would it be possible for an agent to release a 
few other slave agents (sub agents) to go and get the best offers from 
different traders and then compare&decide. Is this posibble in SWARM.......i 
mean any specific functions available.  

Thanks.

Kana  
--

--------- Original Message ---------

DATE: Mon, 26 May 2003 15:48:31
From: Darren Schreiber <address@hidden>
To: address@hidden
Cc: address@hidden

>I'm following this up on the modelling list only, so reply there.  My 
>code is in the Objective-C version of Swarm, but let me know if you'd 
>like to look at it.
>
>What I am reading from your description of the society you want to 
>model indicates that you really aren't looking for a "topological" 
>solution.  Rather, I would guess that you are wanting what is sometimes 
>called a "soup" model, where all agents are capable of interaction with 
>any other agent.
>
>What I would do is this:
>
>Creation Phase:
>       1.  As all traderAgents are created, you place them onto a 
>traderAllList.
>
>       2.  All buyerAgents are put onto a buyerAllList
>
>       3.   Each buyerAgent will be created with its own traderList of known 
>other traders (initially empty) and its own friendsList of other agents 
>that it trusts (also empty.)
>
>       4,  Each traderAgent is created with its own buyerList and its own 
>friendsList (both empty.)
>
>Simulation Phase:
>
>       1.  As each buyerAgent is called, it either randomly sorts its 
>traderList or it sorts the list by trust rating.  Then the buyer 
>iterates through the traderList to see if it can trade with each agent.
>
>       2.  If unsuccessful with its traderList, it sorts its friendsList 
>(either randomly or according to trust) and iterates through the 
>friendsList, asking each friendAgent to recommend a traderAgent.  If I 
>were designing it, I would make the askAFriend method recursive, so 
>that I can ask a friend to ask his friends to ask his friends (etc.).   
>You may initially only want to ask your close friends, but I could 
>imagine it would be worth parameterizing the friendsDepth option (0: my 
>friends, 1: friends of my friends, 2: friends of my friends' friends, 
>etc) to explore the importance of the social network depth.
>
>       3.  If unable to find a suitable trading partner from either my 
>traderList or the lists of my friends, then my buyerAgent would 
>randomly sample from the traderAllList.
>
>So, since you not really using any physical construct of distance, I 
>would definitely avoid using a grid.  In fact, you will probably want 
>to visualize the model with just a series of graphs and maybe a 
>probeable display of agents (so you can study the performance of a 
>particular agent.)
>
>       Darren
>
>
>On Monday, May 26, 2003, at 11:16 AM, Kanagaraj Krishna wrote:
>
>> Hi,
>> Thanks for the input. Are you're simulation codes  available for me to 
>> have a look at. I prefer refering to java examples of SWARM, so that 
>> it would be easier for me to integate into my work.
>>
>> Regarding how my model works. This models will simulate the management 
>> of trust among agents in a multi agent trading environment (more 
>> applicable to internet.....e-commerce etc). I'll play around with the 
>> parameters to study the behaviour of my new model.
>>
>> I'll give a brief outline of how the agents in the society works.
>>
>> -buyer agents gives priority to known trader agents first, when 
>> sending slave agents to do trading. (experience based trust ratings)
>> -next if there hasn't been any experience with a trader agent 
>> before........the buyer agents will ask its trusted friends for 
>> support. (trust based on reputation/recommendation)
>> -and the last choice is to explore and take risk with the unknown 
>> trader agent.
>> -the same vice versa for the trader agents in choosing a buyer.
>>
>> Any comments.
>>
>> Thanks again,
>> Kana
>> --
>>
>> --------- Original Message ---------
>>
>> DATE: Mon, 26 May 2003 00:57:15
>> From: Darren Schreiber <address@hidden>
>> To: address@hidden, address@hidden
>> Cc:
>>
>>> (I am cross posting this to the modeling list, since my discussion is
>>> more on the theory than code related.  If your thoughts are on the
>>> modeling side, you'd get more interesting replies by following it up
>>> there.)
>>>
>>> There are a lot of possible topologies that you can use, not just
>>> grids.  If I were you, I would think more about the substantive 
>>> problem
>>> and what kind of model topology you think would best describe the real
>>> world problems you are interested in studying.
>>>
>>> Grids are nice because they are easy to visualize and it is pretty 
>>> easy
>>> to manage proximity and distance.  Plus, there are pre-existing
>>> examples you can work from.  But, you might also specify a hex (six
>>> sided) grid since you don't have the "corner" problem of square grids.
>>>
>>> In some of my work, I have used both square grids and continuous
>>> spaces.  My political party model envisions agents with positions on
>>> diverse number of political issues.  Each issue dimension runs from 0
>>> to 100, and since I use floating points to represent the position,
>>> agents can be anywhere along that continuum (i.e. they are not
>>> constrained to position 1, 5, or 10, but could be 1.20348 or 
>>> 5.294842).
>>>
>>> But, because of the nature of the political world I am interested in
>>> modeling, I did not want to constrain my agents to a single issue
>>> dimension.  So, I set the model up to handle an arbitrary number of
>>> issues.  In later versions of this model (coming soon (like 6 months 
>>> or
>>> so)), this model will be used to illustrate how we have one 
>>> ideological
>>> dimension (left to right) emerge out of a space containing many issue
>>> dimensions.  Thus, the space that my agents traverse is a
>>> multidimensional one.
>>>
>>> Furthermore, because not all political issues are equally important, I
>>> have altered the geometry of the space so that distance is not the
>>> typical Euclidean measure, but is instead a weighted Euclidean 
>>> distance
>>> where some dimensions are viewed as more important than others.
>>> Similarly, since a bundle of political issues might be related to each
>>> other (not independent) I allow the separability of the dimensions to
>>> be changed.  Both of these tricks are accomplished by sticking a 
>>> little
>>> matrix of weights into the standard Euclidean distance measure.  And,
>>> since not everyone agrees about the salience or separability of 
>>> issues,
>>> I allow every agent in my model to have its own perceptions of the
>>> topology of the political universe.  I may thus view our political
>>> positions as very similar, but you might view them as very different.
>>> This creates a relativistic geometry quite different than the 
>>> Cartesian
>>> grids that we learn in high school.
>>>
>>> Rather than starting with coding constraints, I started with the
>>> substantive problem, worked towards a plausible model, sketched some
>>> concepts, and then got into the coding.  Because my design is heavily
>>> parameterized, I can represent a simple two dimensional space with
>>> integer values like a grid or a high dimensional relativistic geometry
>>> using the same code.
>>>
>>> To manage communication and agent interaction, I put all of the agents
>>> onto a list as they were created.  All agents have the ability to send
>>> and get messages.  To find nearby agents, they look through the list 
>>> of
>>> all agents and calculate the distance to each agent.  My agents also
>>> have the ability to form hierarchical networks, so they have a list of
>>> agents below them and above them in the hierarchy.
>>>
>>> Now, the method of searching a list of all agents is not optimized in
>>> terms of coding, but I am willing to sacrifice that for the 
>>> flexibility
>>> that my model has in representing my substantive beliefs.  
>>> Furthermore,
>>> it isn't hard to think of some ways of creating subdivided the agent
>>> lists to make it faster to find adjacent agents.
>>>
>>> So, what I would do is think about the substantive problem you are
>>> representing.  When your buyer sends out his representatives, is he
>>> asking them to walk down the street to talk to other traders?  Are 
>>> they
>>> getting in planes and flying to another city or country?  Or, are they
>>> staying behind the desk and working the phone lines cold calling
>>> perspective traders?  Or, perhaps the representatives exist in a 
>>> social
>>> network of other agents who know other agents?
>>>
>>> In my view, the programming issues here are much simpler than than
>>> modeling issues.  And, you are more likely to useful help on the
>>> programming once you know what you want.  As far as Swarm is 
>>> concerned,
>>> it is incredibly flexible (good enough to handle my relativistic
>>> hyper-dimensional hierarchically networked topology).
>>>
>>>     Darren
>>>
>>>
>>> On Sunday, May 25, 2003, at 10:57  PM, Kanagaraj Krishna wrote:
>>>
>>>> Hi,
>>>>
>>>> -Are there any SWARM examples that involves interaction between 
>>>> agents
>>>> (commuication) that can help me in designing my model (java ones).
>>>> Example: one buyer agent release few slave agents to interact with 
>>>> few
>>>> trader agents to do business and then come back to report.
>>>>
>>>> -Most of the examples used the grid/lattice way of designing
>>>> models......are this the only way?
>>>>
>>>> -Are there any specific functions for an agent to look for another
>>>> specific agent in a world? how can this be done?
>>>>
>>>> I was looking into the CharlesJavaTutorial examples on bugs looking
>>>> for food. It is more straight forward and there is no interaction
>>>> between agents.Thanks.
>>>>
>>>> Regards,
>>>> Kana
>>>>
>>>>
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>>
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