# AAMAS09 MABS and emergent behaviour

This is my first 'normal session' in the AAMAS. Yesterday we had a very boring poster session: three hours standing up just to talk 10 mins. with 4 people :-( And today an stressing morning with 6 parallel sessions Impossible to attend the all the topics I'm interested in.

On the Significance of Synchroneity in Emergent Systems

I've arrive late, so I'm only trying to get a chair.

On Recursive Simulation
Latek Maciej, Rob Axtell, Bogumil Kaminski

Using something calles n-th order rationality (I haven't the foggiest idea about what it is). Oh!, it's about rationality in games (I don't like game theory and any utility-based solution). They have a tactical model (that represents the evolution of the environment, without decision making information from other agents). So only the 'trajectories' of the policies can be found, nor the policies themselves. A n-th order rationality is defined recursively and... ups, too fast... he's in the example now :-( playing Blotto (I don't know this game). He's talking about cognitive capabilities but, at the end, it's represented just by one equation. Can knowledge be reduce just to one equation?

Tomas Klos, Bart Nooteboom

Motivation: task allocation in networks of trading agents, with input/aoutput relations. IN classical economics, individual nodes are optimized, whereas transaction costr economics is focus on transactions (edges). Agents are rational and opportunistic. Buyers choose make or buy something and it's implemented using Gale-Shapley algorithm for matching (game theory again... maybe the title of this session is wrong), using preferences based on scores related with potential profit and some trust (loyalty) measure.

Some experimental results, that turn into something interesting when they begin to consider the network itself, at least the indegree and outdegree (4 max. with a random network I guess). So, they have a model to simulate organizations using agents to check hypothesis.

A Mathematical Analysis of Collective Cognitive Convergence
Van Parunak

The idea of CCC is intgeresting: how a 'closed' collective can 'corrupt' the knowledge being something 'endemic'. And they're using agents-based models to explain why this is happening. They have simulated that and now they have a formal model of all this stuff.

An interesting result in theorem 4: it detects when the system converges (non deseable), so it can be corrected. And this convergence depends on number of agents, the number of topics and the topic's density.

Emergent Service Provisioning and Demand Estimation through Self-Organizing Agent Communities
Mariusz Jacyno, Seth Bullock, Michael Luck, Terry Payne

A simulation model to match supply with demand in service based communities (coalitions and teams). Very difficult to synchronise choices in a centralised way and it leads to over-provisioning of services (againt a reason to create a distributed SF federation and, why not,  using a small world model ;-) This work is based on the emergent behaviour of insect colonies (OMG, ants again!) -> limited knowledge about peers and local behaviour (it sounds to me).

One interesting idea: to change the type of a provided service  has a cost, a penalty, so the system trends to keep the services unchanged as long as possible. Besides, resources are limited so a limited-size registry of known providers are maintained by customers, whereas providers have a limited-seize registry of user's requests.

The simulation considers the amount of memory and only two types of services are available. When you have too few or too much memory the performance is worse that in a medium case, where you know enough providers/customers to work locally without service changes.

Effective Tag Mechanisms for Evolving Cooperation
Matthew Matlock, Sandip Sen

The last one, about considering expertise in a agent network using tag mechanism. Tags are a useful mechanism to promote collaborative behavior and it allows to reuse knowledge (game theory again). They introduce a boolean function to tag matching and examines four models to extend the tags.

• matched:
• pay off:
• paired reproduction. solves both problems, but they need more research to check if it is robust enough.

(check the paper to complete)

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