Riccardo Boero, Marco Castellani and Flaminio Squazzoni (2004)
Micro Behavioural Attitudes and Macro Technological Adaptation in Industrial Districts: an Agent-Based Prototype
Journal of Artificial Societies and Social Simulation
vol. 7, no. 2
To cite articles published in the Journal of Artificial Societies and Social Simulation, please reference the above information and include paragraph numbers if necessary
Received: 07-Feb-2004 Accepted: 21-Feb-2004 Published: 31-Mar-2004
|Table 1: "Change Matrix" shows costs needed to implement a new technology, that is to shift to a more complex technological paradigm, (first line) or to improve the techno-organisational asset, that is to say to change number/factors combination (second line). Along the column, there are all the three paradigms impacting ID firms over time. Costs gradually increase over time.|
|Technological Paradigm Change||200||400|
|Production Factors Combination Change||50||100||200|
|Table 2: "Info Matrix" shows costs that firms must pay in order to achieve different type of information. Information concerns both technological strategies (innovation and imitation), and partnership selection mechanisms. The second case refers to different information criteria by which final firms organise their production chains, aggregating a team of sub contracted firms. Final firms continuously need information about economic, technological and organisational features of sub contracted firms in order to choose between stabilising or destabilising their inter-organisational contexts (production chains).|
|Production Factor Imitation||30||20||10|
|Production Factor Innovation||80||50||30|
|Table 3: "Tech Matrix" shows data about costs and performance of firms in all the different learning steps undertaken by firms. As it is mentioned above, technology costs and economic performance gradually increase as well as market requests over time. Column A shows technology costs, B shows levels of achievable performance, and C shows decreasing costs for the usage of the same combination of number/factors for more than one simulation/production cycle. All costs and performance values are expressed by a continuum between the "worst" and the "best" techno-organisational levels, with an average on the degree of distance/nearness of the combination of number/factors implemented by firms with respect to the range just mentioned.|
|Figure 1. Information-Action Loop|
|Figure 2. From information to indexes, by means of an "approximation-abstraction-synthesis mechanism". In order to transform information data (the lightest solid), into rough indexes (the middle one) and then into macro indexes (the darkest solid), agent-based cognitive operations face a trade-off between the increase in the degree of the three dimensions (abstraction, synthesis, approximation) and the decrease of the volume of information to be considered.|
Ma = Wa1Ra1 + Wa2Ra2 + ... + WanRan,where Ma , Wa1 , Ra1 , etc.. ∈ [0,1] and Ma represents a macro index, Wa1 is the weight of the first rough index Ra1, and so on. ID firms are heterogeneous and hence they have different values for weighting rough indexes in the macro ones.
|Figure 3. Action Code of ID Firms (according to features of ID prototype, just final firms have the complete action code)|
|Table 4: Relations amongst operation fields, behavioural attitudes and action recipes|
|Action Recipes||Behaviour Attitudes||Operation Fields|
|look at the first agent with different technology/techno-organisational asset you meet||self centred||Technology
imitation in the sub-fields of technology and techno-organisation asset
|look at the first agent with different technology/techno-organisational asset you meet, which has sold its product|
|look at the agent with different technology/techno-organisational asset you meet, which has a percentage of extra-profit better than yours and the highest available|
|look at the agent with different technology/techno-organisational asset you meet, which has a behavioural attitude higher than yours and the highest available|
|look at the agent with different technology/techno-organisational asset you meet, which has a level of cost higher than yours and the highest available||social centred|
|look at the agent with different technology/techno-organisational asset you meet, which has a level of effectiveness of techno-organisational asset better than yours and the highest available|
|look at the agent with different technology/techno-organisational asset you meet, which has a level of investment on technology/techno-organisational asset better than yours and the highest available|
|look at the first agent with different technology/techno-organisational asset you meet, which has a level of performance better than yours and the highest available|
|keep your team of sub firms if time compression Δt, t-1 >= 0||self centred||Keep
strategy of partnership stabilization
|Keep your team of sub firms if profit Δt, t-1 >= 0|
|keep your team of sub firms if resources Δt, t-1 >= 0|
|keep your team of sub firms if you have sold your product||self centred & social centred|
|keep your team of sub firms if time compression Δt, t-5 >= 0|
|Keep your team of sub firms if profit Δt, t-5 >= 0|
|keep your team of sub firms if resources Δt, t-5 >= 0|
|search for a new team of sub firms randomly||self centred
strategy of partnership definition
|search for a new team of sub firms focusing on who has the highest investment on techno-organisational asset|
|search for a new team of sub firms focusing on who has the highest performance|
|search for a new team of sub firms focusing on who has the most similar technology and techno-organisational asset configuration|
|give to your partners the 0% extra-profit||self centred||Share
policy of chain extra- profit management and distribution
|give the 5% extra-profit to each partner|
|give the 10% extra-profit to each partner|
|give the 13.3% extra-profit to each partner|
|give the 23.3% extra-profit to each partner||social centred|
|give 25% extra-profit to each partner|
|give the 70% extra-profit to partners, distributed proportionally according to their needs|
|distribute proportionally the 100% extra-profit according to the needs of each member of the chain|
|Figure 4. Final firms matching market request over time (on the left set 1 and on the right set 2)|
|Figure 5. Evolution of macro indexes over time (on the left set 1, while on the right set 2). Pa depicts the partnership index, Te the technology one, Or the organisation one, En the environment one, and Ec the economic one|
|Figure 6. Dynamics of distribution of attention (on the left set 1, on the right set 2). Pa depicts the partnership index, Te the technology one, Or the organisation one, En the environment one, and Ec the economic one|
|Figure 7. Final firms matching market requests that reach the top level of techno-organisational asset, over time (on the left set 1, on the right set 2)|
2 The identification between agents (“computational units”) and firms is a quite strong assumption, even if it is a standard practice both in the literature on agent-based models and in evolutionary economics. But, in the case of IDs, such a reduction seems less strong, since ID firms exhibit a close identification amongst entrepreneurship, ownership and management (i.e., see other simulation models: Brenner 2001; Fioretti 2001; i.e., see behavioural analysis in Moran 1998).
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