The theory of social functions: challenges for computational social science and multi-agent learning

Action editor: Ron Sun
https://doi.org/10.1016/S1389-0417(01)00013-4Get rights and content

Abstract

A basic claim of this paper is that the foundational theoretical problem of the social sciences — the possibility of unconscious, unplanned forms of cooperation and intelligence among intentional agents (the very hard issue of the ‘invisible hand’, of the ‘spontaneous social order’ but also of ‘social functions’) — will eventually be clarified thanks to the contribution of AI (and, in particular, of cognitive Agent modelling, learning, and MAS) and its entering the social simulation domain. After introducing Multi-Agent-Based Social Simulation and its trends, the limits of the very popular notion of ‘emergence’ are discussed, Smith’s and Hayek’s view of ‘spontaneous social order’ are critically introduced, and serious contradictions in the theory of ‘social functions’ among intentional agents are pointed out. The problem is how to reconcile the ‘external’ teleology that orients the agent’s behaviour with the ‘internal’ teleology governing it. In order to account for the functional character of intentional action, we need a somewhat sophisticated model of intention, and a different view of layered cognitive architectures combining explicit beliefs and goals with association and conditioning. On such a basis we sketch a model of unknown functions impinging on intentional actions through a high level form of (MA) reinforcement learning. This model accounts for both eu-functions and dys-functions, autonomous and heteronomous functions. It is argued that, in order to reproduce some behaviour, its effects should not necessarily be ‘good’, i.e. useful for the goal of the agent or of some higher macro-system.

Introduction

The social paradigm is rapidly growing within AI because of the situated and interactive perspective (Bobrow, 1991) and of Agent-oriented computing and Multi-Agent Systems (MAS) (Gasser, 1991, Hunhs & Singh, 1998). Such a paradigm will strongly contribute — mainly thanks to Agent-Based Social Simulation — to the birth of the ‘Computational Social Sciences’ Carley, 2000, Müller et al., 1998, Castelfranchi, 1998d. Social sciences will contribute to the design and understanding of artificial societies, cyber-organisations and computer-mediated interaction, while the sciences of the artificial will transform the social sciences, providing experimental platforms, operational and formal conceptualisations, and new models of social phenomena. A significant inter-disciplinary fertilisation is expected like that which, in the 1960s and 1970s, gave birth to Cognitive Science.

The basic claims of this paper are as follows:

  • The main contribution of AI (and, in particular, of cognitive-Agent modelling and MAS) entering the social simulation domain will be an impressive advance in the theory of the micro–macro link. In particular, the foundational theoretical problem of the social sciences — the possibility of unconscious, unplanned emergent forms of cooperation, organisation and intelligence among intentional, planning agents (the ‘vexata quaestio’ of the ‘invisible hand’, of the ‘spontaneous social order’ but also of ‘social functions’) — will eventually be clarified.

  • A very serious problem for the theory (and architecture) of cognitive agents is how to reconcile the ‘external’ teleology of behaviour with the ‘internal’ teleology governing it; how to reconcile intentionality, deliberation, and planning with playing social functions and contributing to the social order.

  • To solve these foundational and architectural problems, complex models of learning are needed, where learning does not operate within a ‘reactive’ architecture made of simple rules, classifiers, associations, and stereotypic behaviours, but operates upon high level anticipatory cognitive representations (beliefs, goals) which govern intentional action. A theory of the relationships between individual intentional behaviour, reinforcement learning, and the feedback of collective emerging effects is needed.

I will present a critical characterisation of the problem of self-organising social phenomena and functions among intentional agents, discussing both unsatisfactory accounts in social theory and in MAS, and the hard theoretical problems to be solved.

I will also try to sketch a possible line of reconciliation between emergence and cognition, by building a notion of behavioural function of intentional action. To do this, I have to build on the unintended social effects of the agents’ behaviours, and on some sort of reinforcement learning dealing with beliefs and intentions.

Section snippets

MAS, agent-based social simulation and their promises

Computer simulation of behavioural and social phenomena is a successful and rapidly growing interdisciplinary area Conte & Gilbert, 1995, Troitzsch, 1997. Suffice to mention the renewed interest of sociologists and economists, testified by several workshops in the international conferences of sociology, economics, and game theory, several ‘social’ papers in the new area of Artificial Life (ALife), papers in the Journal of Mathematical Sociology, books on simulating organisations Masuch, 1995,

An emergent confusion

The triumphant notion of ‘emergence’ has a bad conceptual and epistemological status. Its different meanings exemplify the confusion and the need for a discussion. This is particularly important since, in my view, only computer simulation of emerging phenomena (including social ones) can finally provide some clear notions of ‘emergence’. My aim is also to stress which notion of emergence is really needed, and how to model it on the basis of selection processes (evolution or learning).

‘Emergent’

Social functions and cognition

The aim of this section is to analyse the crucial relationship between social ‘functions’ and cognitive agents’ mental representations. This relationship is crucial for at least two reasons:

  • (a) on the one hand, no theory of social functions is possible and tenable without clearly solving this problem (see Section 4.2);

  • (b) on the other hand, without a theory of emerging functions among cognitive agents social behaviour cannot be fully explained.6

Cognitive requirements for a theory of social functions

In order to account for the functional character of intentional actions, from the cognitive point of view, on the one hand we have an architectural problem, on the other we need a sophisticated model of intentional action.

How social functions are implemented through cognitive representations

After the previous characterisation of the critical points in the notion of social ‘function’, and the necessary specifications about intention and cognitive architecture, we can try to sketch the ‘internal’ mechanism(s) for external functions impinging on intentional actions.

I will first describe an abstract simplified model of ‘auto-functions’, be them either ‘kako-functions’ or ‘eufunctions’ relative to the goals or interests of the agents they emerge from. Second, I will exemplify this

Kako- or eu-functions: relative to whom or what?

In what sense is the ‘clean-street’ habit good and the ‘dirty-street’ habit bad? As we saw, ‘good’ (eu) and ‘bad’ (kakos) must be relative to the goals or interests of some system/agent (Miceli & Castelfranchi, 1989). In fact, so far we have referred the bad character of these functions to the involved agents’ goals or interests. So, the habits of dirtying streets is bad relative to B1 and its related goals or to the cleanliness and aesthetic interests of the agent. With respect to those goals

Why Elster and Hayek are wrong

On the basis of this cognitive characterisation of functions, let me now summarise the main points that Elster’s criticism and proposal about the notion of function do not take into account. I will also discuss the limits of Hayek’s view of spontaneous order as necessarily advantageous for the agents.

Concluding remarks

I hope that, after this long and tangled argumentation, it will be clearer why only Computational Social Science and, in particular, Multi-Agent-Based Social Simulation (SS) could probably deal with this kind of problem. Moreover, the task of SS is not only to predict emerging social effects or the experimentation of possible policies. I believe that the contribution of SS to the theoretical development of the cognitive and social sciences could be really remarkable. SS can provide not only an

Acknowledgements

This research forms part of a 20-year project pursued at the IP-CNR aimed at reconciling scientific teleological approaches and a theory of goal-governed agents with the theory of goal-oriented systems and of functional activities (Castelfranchi, 1982). This work on functions would not be possible without years of reflection and collaboration with Rosaria Conte and Maria Miceli on these topics. I would like to thank Rosaria, Maria, and Rafael Bordini for their precious comments. I am also in

References (93)

  • C Bicchieri

    Norms of cooperation

    Ethics

    (1990)
  • Birckhard, M. H. (2000). Autonomy, function, and representation....
  • D Bobrow

    Dimensions of interaction

    AI Magazine

    (1991)
  • R Boudon

    Effects pervers et ordre social

    (1977)
  • P Bourdieu et al.

    An invitation to reflexive sociology

    (1992)
  • D.J Buller

    Etiological theories of function: a geographical survey

  • D.T Campbell

    Evolutionary epistemology

  • K.M Carley

    Computational social science: agents, interaction, and dynamics

  • C Castelfranchi

    Scopi esterni (External ends)

    Rassegna Italiana di Sociologia

    (1982)
  • C Castelfranchi

    Social power: a missed point in DAI, MAS and HCI

  • C Castelfranchi

    Reasons: belief support and goal dynamics

    Mathware and Soft Computing

    (1996)
  • C Castelfranchi

    Individual social action

  • C Castelfranchi

    Challenges for agent-based social simulation. The theory of social functions

  • C Castelfranchi

    Through the minds of the agents

    Journal of Artificial Societies and Social Simulation

    (1998)
  • C Castelfranchi

    Simulating with cognitive agents: the importance of cognitive emergence

  • C Castelfranchi

    Emergence and cognition: towards a synthetic paradigm in AI and cognitive science

  • C Castelfranchi

    Per una teoria (pessimistica) della mano invisibile e dell’ordine spontaneo (For a pessimistic theory of the invisible hand and spontaneous social order)

  • C Castelfranchi

    Through the agents’ minds: cognitive mediators of social action

  • C Castelfranchi

    Affective appraisal vs. cognitive evaluation in social emotions and interactions

  • C Castelfranchi et al.

    Emerging functionalities among intelligent systems: co-operation within and without minds

    AI & Society

    (1992)
  • L Cavalli Sforza et al.

    Cultural transmission and evolution. A quantitative approach

    (1981)
  • E Chattoe

    Just how (un)realistic are evolutionary algoritms as representations of social processes?

    Journal of Artificial Societies and Social Simulation

    (1998)
  • R Cialdini

    Influence. The psychology of persuasion

    (1993)
  • P.R Cohen et al.

    Rational interaction as the basis for communication

  • Conte, R. (1985). Ancora sul funzionalismo nelle scienze sociali. Roma: IP-CNR, TR.3,...
  • R Conte

    The necessity of intelligent agents in social simulation

  • Conte, R. & Castelfranchi, C. (1995). Cognitive and Social Action, UCL Press,...
  • R Conte et al.

    Introduction: computer simulation for social theory

  • R Cummins

    Functional analysis

  • A.R Damasio

    Descartes’ error

    (1994)
  • A Drogoul et al.

    MANTA: new experimental results on the emergence of (artificial) ant societies

  • Edmonds, B., & Dautenhahn, K. (2000). Starting from society — the application of social analogies to computational...
  • M Egidi et al.

    Division of labour and social co-ordination modes: a simple simulation model

  • S.N Eisenstadt

    Functional analysis in anthropology and sociology: an interpretative essay

    Annual Review of Anthropology

    (1990)
  • J Elster

    Marxism, functionalism and game-theory: the case for methodological individualism

    Theory and Society

    (1982)
  • Cited by (93)

    • A Biased Inferential Naivety learning model for a network of agents

      2022, Cognitive Systems Research
      Citation Excerpt :

      The process of obtaining information and updating beliefs in the context of society is called social learning, which is an important subject of research in various disciplines. It is applied in the context of distributed signal processing in the wireless sensor networks for distributed detection, distributed estimation, and cognitive agent modeling (Borkar & Varaiya, 1982; Krishnamurthy & Poor, 2013; Tsitsiklis & Athans, 1984; Wang & Djuric, 2015; Castelfranchi, 2001). It is also applied to solve coordination and consensus problems in control theory (Blondel et al., 2009; Bullo et al., 2009).

    • Multi-agent-based modelling and simulation of high-speed train

      2020, Computers and Electrical Engineering
      Citation Excerpt :

      The agent has a flexible organisational framework and evolutionary mechanism. MABMS has been applied in many areas, such as the modelling of organisational psychology [5], engineering team work [6], social functions [7], consumer behaviour [8], friendship in social networks [9], traffic and transportation simula- tion [10], population oscillations [11], dynamics of contagious disease spread [12], and military applications [13]. With respect to the HST, most researchers have adopted the methodology of mechanical systems and structural dynamics simulation using the automatic dynamic analysis of mechanical systems (ADAMS) software [14].

    • A Theory of Tutelary Relationships

      2023, A Theory of Tutelary Relationships
    • A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art

      2023, Lecture Notes on Data Engineering and Communications Technologies
    View all citing articles on Scopus
    View full text