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88 Socially Intelligent Agentsstimuli evolves in relation to the resolution of some of the problems. As longas a task hasn’t been fulfilled, it can still be perceived as a task to be done by anagent passing by; if another agent does it, the stimulus disappears or decreases.Agents learn, for example by the use re-enforcement according to tasks theyhave done already, and thus according to what was not done by other entities.The system is completely socially regulated, although not a single agent has theperception of the existence of other agents.This is often considered as a radically opposed point of view to the precedingone. It has a great flexibility in terms of the openness in the system. On theother hand it is not easy to know how this approach can help to coordinateagents that need to exchange complex information or confidential informationthat cannot be abandoned in an open environment.None of these approaches exhibits agents which combine the two differentabilities: to be able to meet new entities with potentially different ways of communicating(openness) and integrate with them into a normal communicationnetwork so that they can exchange important information or consciously organisecommon work. This double social competence could be defined as theability to build trust (a "coherent" trust: which doesn’t affect the survival ofthe individual agents or the system) and is clearly hard to create with artificialentities.3. Social Intelligence And CreativityIt could be argued that because the kind of inputs that humans get from theirinteractions are of diverse forms, are more complex and carry more informationthan written messages, that this explains why they are more able to make inferencesabout unknown people. In the description of human interactions, not onlybody movements and positions are studied but also geographical relative positionand the use of time in relations - as can be seen with [15] and [16]. I claimthat this argument is not relevant, since the characteristics of human interactioncan be recognised in very artificial settings. Here the example of interactionsamong humans who use computer networks to communicate is relevant. Ahuman looking at a screen has necessarily less data coming from the interactionchannel than the computer itself, but he or she seems to be able do much moreabout it, and be able to turn this data into information. Two examples illustratethe social complexity that can emerge from exchanges over open networks:academic discussion lists, and communities of teenagers playing games on theInternet.Academics frequently exchange points of view via the Internet and do sopublicly in discussion lists. Watching the traffic on these lists, one can identifyunofficial reasons that lead people to participate. These include: the creationof their own reputation and the discovery of allies. Sometimes, considering the