Magda Fontana (2006)
Simulation in Economics: Evidence on Diffusion and Communication
Journal of Artificial Societies and Social Simulation
vol. 9, no. 2
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Received: 05-Dec-2005 Accepted: 16-Jan-2006 Published: 31-Mar-2006
|Table 1: Description of the 'theoretical' simulation techniques|
|Technique||Descriptive Ability||Analytical treatment||Simulation vs. Analytical Solutions||Interaction among agents||Agents'|
|Number of agents|
|System Dynamics||High||Differential Equations||Discrete Time;|
Discontinuous and non-differentiable functions
|Microsimulation||Low||Mathematical solutions exist only for the simplest cases||Covers the entire range of probability distributions.||No||High||Many|
|Discrete Events||Low||Mathematical solutions exist only for the simplest cases||Covers the entire range of probability distributions.||No||Low||Many|
|Multilevel Simulation||High||Mathematical solutions exist only for the simplest cases||Deals with individual probabilities of transitions.||Yes||Low||Many|
|Agent-based models||High||(so far) explored only within computer||Yes||High or Low||Many|
|Learning models||High||(so far) explored only within computer||Possible||High||Many|
|Figure 1. Trend of publications (1969 -2004)|
|Figure 2. Trend of publications (1969 - 2004) disaggregated according to the kind of simulation|
|Figure 3. Diffusion of techniques across sectors|
The sectors with the highest density of simulation are public economics, industrial economics, and finance, while the sectors with the lowest density are economic growth and international economics.
|Table 2: Theoretical simulations in the population|
|Table 3: Trend of unexplained (1969- 2004)|
|Table 4: Degree of explained works and relative importance of sectors|
|Sectors||Explained||Number of Publications||Percentage|
|Fiscal and Monetary Policy||47%||617||8%|
|Urban and Regional||43%||175||2%|
|Table 5: Population sorted by outlet|
|Outlet||Population ||Explained items||Sample without |
|Table 6: Population sorted by generality of sources|
|Table 7: Percentage of articles appearing in the first position of the ranking|
|Ranking||Percentage of articles|
2Book reviews and references to simulation conducted in other publications are excluded.
3For a thorough discussion about the epistemology of simulation in social science see David et al. (2005)
4In this representation econometric and statistical techniques are not included.
5For instance, an anonymous referee suggested that authors could be more interested in the problems they are analysing than in clarifying the methods they are using.
6It might be the case that a work that - from the title or the abstract - appears to be as purely methodological contains an application to a given sector. This would imply a bias in the relative weight of the sectors listed in table 4. However, since there is no reason to think that the bias is systematic, the shares of explained works across sectors should not be dramatically affected by it. In any case, ambiguity generated by the lack of details is precisely what repeatedly emerges from this analysis.
7As stressed above, in the population there are no journals devoted to simulation while there are some field journals especially in environmental economics, agricultural and health economics.
8On the impossibility of interpreting economics as an experimental science see Debreu (1991, p. 2).
9Hahn (1991) believes that simulation will become more common in economics as the complexity view gains more consensus. However, he complains about the loss of generality of results and of elegance with respect to the axiomatic approach.
10On the approach of physicists to complex systems see Goldenfeld and Kadanoff (1999)
11For the a survey of the most recent areas of interest in this ambit see Markose (2005) and the other papers appeared on the special issue of The Economic Journal, vol. 115, published in June 2005.
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