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The reductionist dream (and its extreme form - physicalism) accompanied science from its very birth. The hope to reduce all reality to simple microscopic fundamental laws had its great moments in the physical sciences. In the last decades, the reductionist ambitions got rekindled by a series of methodological and technological new tools. Some of the new developments highlighted the role of the Multi-Agents Complex Paradigm in describing and explaining the macroscopic complex phenomena in terms of a recurrent hierarchy of scales. In a Multi-Agent Hierarchy, the properties at each scale level are explained in terms of the underlying finer level immediately below it.

The main operations involved are:

• the identification of the relevant objects (agents) appropriate for describing the dynamics at each scale.
• expressing iteratively the emergence of the coarser scale objects as the collective features of a finer scale.

The identification of the hierarchy of scales involves sophisticated theoretical techniques (from mathematics and physics) as well as computer simulation, visualization and interactive animation tools bordering artificial reality.

In this interactive research process, the models, analytical techniques, computer programs and visualization methods become often the very expression and communication vehicle of the scientific understanding which they helped to uncover.

In exchange, for this cognitive and language shift from the classical reductionist position, the multi-agent multi-level complexity approach yielded new important results in systems spreading over a wide range of subjects: phases of physical systems, fractality in reaction-diffusion chemical systems, Boolean 'hard problems', proteomics, psychophysics, peer-to-peer organizations, vision, image processing and understanding, inventive thinking, cognition, economics, social planning and even esthetics and stories structure. The Multi-Agent Complexity Paradigm develops lately into a framework for defining, identifying and studying the salient collective macroscopic features of complex systems in amost general context. As such it constitutes a strong conceptual and methodological unifying factor over a very wide range of scientific, technological (and other) domains. Newton, Maxwell, Planck, Einstein, Gell-Mann) but it suffered bitter discrediting early defeats in explaining life (Descartes), conscience (La Metterie), thought (Russel) and socio-economical phenomena (Marx).

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