1. Computer and ontology in social sciences: Multi-agent simulation par Pierre LIVET Schelling’s model of segregation between ethnic territories Growth of cities... etc. Simulation as a way of testing ontologies (what are the social entities?) Segregation could be the emergent result of a population of Schelling-type reactive agents (reacting to their immediate neighbours). It could also be the result of a population of cognitive agents, reacting to their representations of the disconnected but dense distribution of a community. Problem: similarity of simulation to social reality is itself a product of our perceptive capacities and of the social categories we are sensitive to. How can the validity of simulation be tested?

2. Simulation as remedy for simulation? Detecting emergent collective forms depends on: - our perceptive ways of isolating forms; and - our social interpretation of such forms. Do we need AI models of our perceptive functioning and social classification to test the relevance of simulations? At least models of certain perceptive discrimination mechanisms would make us aware of the biases due to our sensitivity to specific forms. We cannot escape these biases, but we can build (and have built) devices that generate these kinds of forms from data that do no show such pregnant structures at first. These devices are simulators in another sense. Simulation might be the remedy to simulation!

3. Network capabilities and knowledge as a revision process. Problems of phase coherence. A researcher now has access to a huge repository of past knowledge. (e.g. genetic databases: time needed to know that your project is too innovative to find something useful in them!) and to a huge flux of knowledge in progress. Knowledge in progress is a collective and collaborative mix of hypothetical knowledge, beliefs, research programs, data not confirmed yet, still in discussion, to be revised.

4. Hidden knowledge Data are normally the best items for triggering revision. There is still hidden knowledge (e.g. experiments results of which are not to be communicated because of competitive pressure). A researcher (rationally) communicates data when the time needed for other researchers to obtain and exploit them in order to arrive at an acceptable state is longer than the time remaining for the first researcher to have the paper published. Therefore data arrive too late to trigger useful revision of other works on the wrong track.

5. What can trigger revision? The difficulty is that if data cannot trigger revision at the right time, what triggers revision? When must we trigger revision, and how do we revise research using the said mix? Opinion of authoritative experts? Too conservative. Majority of opinions in the network? Too unstable. Result: discontinuity in research programs, programs abandoned too hastily, and periods when clones of one seminal research multiply.

6. When revision comes too early We tend to trust conclusions that have gone through the “three-step process”: 1) Submission and discussion; 2) Counter-arguments; 3) Convincing replies. If counter-discussion and reply are too hasty, maybe the revision process has to go on… but how to stop it (without confirmed data)? Network facilities require a kind of precautionary virtue: stop revising when the interest of discussion decreases but keep side-sensitivity to possible sources of revision.