A model for a simple Luhmann economy
                Authored by A Fleischmann
                
                    Date Published: 2005-03
                
                
                
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                    Platforms:
                    
                        Microsoft Excel
                        
                        Microsoft Visual Basic
                        
                
                
                    Model Documentation:
                    
                        Other Narrative
                        
                
                
                    Model Code URLs:
                    
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                Abstract
                The core of this work is the definition of an agent-based model for a simple Luhmann economy based on publications of Niklas Luhmann. Using an implementation on a default personal computer the behaviour of the model is studied when assumptions regarding initial conditions are made. Fuzzy-c-means clustering is used as visualisation aid. The impact of the observation horizon (a model parameter determining how far agents can see) is studied interactively. Solution paths of the Luhmann economy originating from an initial endowment to equilibrium (when the economy settles down) are studied. The impact of model parameters determining the unevenness regarding the initial distribution of wealth is studied by Monte Carlo simulation. Niklas Luhmann's hypothesis, that the economy starts from and produces further inequality in order to continue (see Luhmann 1988., p. 112) could be reproduced by computer simulation. The main characteristic of the approach is the consideration of the cohesive structure of communication (i.e. one communicative act - many understanding observers) also prominent in Dunbar (1996, pp. 192-207). The model gives directions how to model further aspects of Niklas Luhmann's theory.
                
Tags
                
                    Agent-based modelling
                
                    Luhmann economy
                
                    fuzzy clustering