Oswaldo Terán (2004)
Understanding MABS and Social Simulation: Switching Between Languages in a Hierarchy of Levels
Journal of Artificial Societies and Social Simulation
vol. 7, no. 4
<https://www.jasss.org/7/4/5.html>
To cite articles published in the Journal of Artificial Societies and Social Simulation, reference the above information and include paragraph numbers if necessary
Received: 28-May-2032 Accepted: 29-Jun-2004 Published: 31-Oct-2004
It is not a surprise to find simulation models built up from or taking elements from more than one social theory. Typically, a simulation model does not follow a social theory strictly, the important point is that the model does not contradict the fundamental assumptions of a (some) social theory(ies). This is important in order to make the computational model applicable to a wide range of contexts, as usually social theories are narrow and usually contradicted in the real world - what is understandable in case of any approach trying to understand complex systems like society.
Other aspects of the context of a model such as the goals of the study, the experimental frame (as defined in Zeigler 1976), and the descriptive model, might be identified at this level of languages.
At this level, the model validation and experimental design are described and justified in terms of: a) theories, observation and expert domain knowledge used to build up the model; b) the modelling and simulation perspective; and, c) the goals of the study.
The main purpose of this paper is to demonstrate an empirical approach to social simulation. The systems and the behaviour of middle-level managers of a real company are modelled. The managers' cognition is represented by problem space architectures drawn from cognitive science and an endorsements mechanism adapted from the literature on conflict resolution in rule based systems. Both aspects of the representation of cognition are based on information provided by domain experts. Qualitative and numerical results accord with the views of domain experts.
Figure 1. Agents' interaction in Moss' model |
token | value |
noEffect | -1 |
newModel | 0 |
reducedEvents | 1 |
eliminatedAllEvents | 2 |
reportedModel | 2 |
specializedModel | 3 |
(1) |
where b is an arbitrary number base not less than 1, and ei are the tokens.
Figure 2. Switching between languages for interpreting simulation results. Languages might be, e.g., at the Modelling Language level |
Figure 3. Influence of the Hierarchy of Levels of Language on the Whole Simulation Process |
Modelling Case | Case 1 | Case 2 |
Language Level | ||
Second Language Level: Theoretical and Modelling Paradigm | - Emphasises individual's decision-making process and behaviour; - Considers individuals rationally bounded; - Recognises that the individual's surrounding and the organization's environment are complex; - Rejects theoretical-biased approaches; - The rules of behaviour of each agent and the population of agents are allowed to evolve in the model. | - Emphasises emergent properties of the system as a result of the agents' interaction without caring too much about the agents' decision-making mechanism. Aspects such as the haziness of the agent's decision situations are not an important issue. - Only the population of agents is considered evolving. |
Third Language Level: Modelling Language | Ideas come from: - Simon partners and followers; - Behavioural theory of organisations; - Artificial intelligence and symbolic logic; - Knowledge representation such as the notion of space of problems; - Implementations of the idea of bounded rationality. | Neither social nor cognitive theories have an important impact in the elaboration of the model. Social theories simply inform about emergent properties of the target system and about general aspects of the agent's interaction. Ideas from other disciplines (especially from Biology and Physics) have an important impact. |
Fourth Language Level: Simulation programming language | Simulation programming languages should permit to implement the agents' structure and social interaction, as well as the agent's cognitive mechanism as suggested by Simon, Newell, Cohen, and others. | Simulation programming languages are more theoretical free (e.g., Java), as the modelling language usually requires fewer assumptions in relation to theoretical social and organisational constrains. |
2 The idea of language here includes not only words and grammar, but also meaning.
3 Here and in the next sections, the word “guideline” means a constraint from the assumed paradigm.
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