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Social Constraint

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Simulating Social Complexity

Part of the book series: Understanding Complex Systems ((UCS))

Why Read This Chapter?

To understand social norms and their complexities, including: how they can operate, how they can effectively constrain action and how such processes have been represented within simulations. The chapter also helps the reader to acquire an integrated view of norms and become aware of some of the relevant work simulating them using this framework.

Abstract

This chapter examines how a specific type of social constraint operates in Artificial Societies. The investigation concentrates on bottom-up behaviour regulation. Freedom of individual action selection is constraint by some kind of obligations that become operative in the individual decision making process. This is the concept of norms. The two-way dynamics of norms is investigated in two main sections of the chapter: the effect of norms on a social macro-scale and the operation of social constraints in the individual agent. While normative modelling is becoming useful for a number of practical purposes, this chapter specifically addresses the benefits of this expanding research field to understand the dynamics of human societies. For this reason, both sections begin with an elaboration of the problem situation, derived from the empirical sciences. This enables to specify questions to agent-based modelling. Both sections then proceed with an evaluation of the state of the art in agent-based modelling. In the first case, sociology is consulted. Agent-based modelling promises an integrated view on the conception of norms in role theoretic and individualistic theories of society. A sample of existing models is examined. In the second case, socialisation research is consulted. In the process of socialisation the obligatory force of norms become internalised by the individuals. A simulation of the feedback loop back into the mind of agents is only in the beginning. Research is predominantly on the level of the development of architectures. For this reason, a sample of architectures is evaluated.

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Notes

  1. 1.

    For a more in-depth discussion of this model the interested reader is referred to the chapter on reputation (Giardini et al. 2013).

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Authors and Affiliations

  1. Institute of information systems in Business and Public Administration, University of Koblenz-Landau, Universitaetsstr. 1, 56070, Koblenz, Germany

    Martin Neumann

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  1. Martin Neumann
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Correspondence to Martin Neumann .

Editor information

Editors and Affiliations

  1. CPM - Centre for Policy Modelling, Manchester Metropolitan University, Manchester, M1 3GH, United Kingdom

    Bruce Edmonds

  2. , Center for Policy Modelling, Manchester Metropolitan University, Aytoun Street, Manchester, M1 3GH, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

Even though they are quite old and some of their findings are out of date by now, it is still a good start (and not too much effort) to study the following two models to become familiar with the research field of normative agent-based models: Axelrod’s (1986) evolutionary approach to norms, and Conte and Castelfranchi’s (1995b) paper on understanding the functions of norms in social groups (using simulation).

As an introduction into the design and logical foundations of normative architectures the following anthologies are suggested: (Boella et al. 2005; Boella et al. 2007).

The relation of modelling and theory is particularly highlighted in the two anthologies (Conte and Dellarocas 2001; Lindemann et al. 2004). Here the reader will also find hints for further readings about the empirical and theoretical background.

For an overview of the theoretical background and developments in theorising norms it is suggested to refer to (Conte and Castelfranchi 1995a; Therborn 2002).

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Neumann, M. (2013). Social Constraint. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_14

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