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Intelligent Social Learning

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Abstract

One of the cognitive processes responsible for social propagation is social learning, broadly meant as the process by means of which agents' acquisition of new information is caused or favoured by their being exposed to one another in a common environment. Social learning results from one or other of a number of social phenomena, the most important of which are social facilitation and imitation. In this paper, a general notion of social learning will be defined and the main processes that are responsible for it, namely social facilitation and imitation, will be analysed in terms of the social mental processes they require. A brief analysis of classical definitions of social learning is carried on, showing that a systematic and consistent treatment of this notion is still missing. A general notion of social learning is then introduced and the two main processes that may lead to it, social facilitation and imitation, will be defined as different steps on a continuum of cognitive complexity. Finally, the utility of the present approach is discussed. The analysis presented in this paper draws upon a cognitive model of social action (cf. Conte & Castelfranchi 1995; Conte 1999). The agent model that will be referred to throughout the paper is a cognitive model, endowed with mental properties for pursuing goals and intentions, and for knowledge-based action. To be noted, a cognitive agent is not to be necessarily meant as a natural system, although many examples examined in the paper are drawn from the real social life of humans. Cognitive agents may also be artificial systems endowed with the capacity for reasoning, planning, and decision-making about both world and mental states. Finally, some advantages of intelligent social learning in agent systems applications are discussed. Keywords:

Suggested Citation

  • Rosaria Conte & Mario Paolucci, 2001. "Intelligent Social Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(1), pages 1-3.
  • Handle: RePEc:jas:jasssj:2000-11-1
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    File URL: http://jasss.soc.surrey.ac.uk/4/1/3.html
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    Cited by:

    1. András Németh & Károly Takács, 2007. "The Evolution of Altruism in Spatially Structured Populations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-4.
    2. Oswaldo Terán & Johanna Alvarez & Magdiel Ablan & Manuel Jaimes, 2007. "Characterising Land Holding Size Distributions in a Forest Reserve," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-6.
    3. Paola Tubaro, 2009. "Agent-based Computational Economics: a Methodological Appraisal," EconomiX Working Papers 2009-42, University of Paris Nanterre, EconomiX.
    4. David Chavalarias, 2006. "Metamimetic Games : Modeling Metadynamics in Social Cognition," Post-Print hal-00007743, HAL.
    5. David Chavalarias, 2006. "Metamimetic Games: Modeling Metadynamics in Social Cognition," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-5.
    6. Caterina Cruciani & Anna Moretti & Paolo Pellizzari, 2012. "Sense making and information in an agent-based model of cooperation," Working Papers 14, Department of Management, Università Ca' Foscari Venezia.
    7. Paola Tubaro, 2011. "Computational Economics," Chapters,in: The Elgar Companion to Recent Economic Methodology, chapter 10 Edward Elgar Publishing.

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