Agent-based Modelling of Systemic Risk: A Big-data Approach
• Model individual agents and their individual decisions - decentralized decision making
• Emergent patterns from micro-processes to macro level
• Account for local interaction networks between agents - parallel computing can be used
• Based on micro-foundations - big-data can be included
• Very large models that incorporate low level details possible - need for supercomputing
Sebastian Poledna1,3 Michael Gregor Miess1,2, Stefan Schmelzer1,2, Elena Rovenskaya1,6, Stefan Hochrainer-Stigler1, and Stefan Thurner1,3,4,5 1International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria 2Institute for Advanced Studies, Josefstädter Straße 39, 1080 Vienna, Austria 3Section for Science of Complex Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria 4Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA 5Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria and 6Faculty of Computational Mathematics and Cybernetics,Lomonosov Moscow State University (MSU), Moscow, Russia
• The quality and quantity of economic data is expanding rapidly
• Shift from small-sample surveys to datasets with universal or near-universal population coverage
• Enables empirical calibration of agent-based models
Agent-based Models (ABMs)
Big-data
Local Interactions and Networks
Parallel Computing and Supercomputing
IIASA’s ABM of a national economy • Models the complete national economy of Austria with all institutional sectors
(households, non-financial corporations, financial corporations, and a general government)
• Includes all economic activities (producing and distributive transactions) as classified by the European system of accounts (ESA)
• Includes all economic entities: all juridical and natural persons are represented by agents (at a scale of 1:10)
• Empirically calibrated to actual macro and micro data (national accounting, census and firm-level data)
• Simultaneously fits observed macroeconomic variables, stylized facts, and (some) observed distributions between agents on the micro-level
Application: Systemic Risk Triggered by Natural Disasters
1 2 3 4 5 6 7 8 9 10year
-5
-4
-3
-2
-1
0
1
cum
ulativ
e ch
ange
in G
DP g
rowt
h ra
te [p
p]
1 2 3 4 5 6 7 8 9 10year
0
2
4
6
8
10
12
14
16
18
20
chan
ge in
gov
ernm
ent d
ebt-t
o-G
DP ra
tio [p
p]
• An economy is a network of interacting firms, households, banks, etc.
• Out-of-equilibrium dynamics of economic networks can be explicitly modeled using ABMs
Banking network of Austria in 2008 – based on data provided by the Austrian Central Bank (OeNB)
Ownership network of 170.000 Austrian firms in 2013 – based on data from the company register of Austria
Firm size distribution by output for model simulations
Comparison of output (gross value added) and inflation for model simulations and observed data of Austria
Macroeconomic effect on GDP and debt of flooding affecting only productive capital of a 1500 year event in Austria – based on the ABM simulations
• Economic ABMs are intrinsically massively parallel computational systems
• Very large populations of agents perform complicated local computations (decentralized decision making)
• Agents have precise positions on physical and conceptual networks (local interactions)
• Natural and necessary to run large-scale ABMs on supercomputers
A01A02
A03 B
C10-C12
C13-C15C16
C17C18
C19C20
C21C22
C23C24
C25C26
C27C28
C29C30
C31_C32C33 DE36
E37-E39 FG45
G46G47
H49H50
H51H52
H53 IJ58
J59_J60J61
J62_J63K64
K65K66 L
M69_M70M71
M72M73
M74_M75N77
N78N79
N80-N82 O PQ86
Q87_Q88
R90-R92R93
S94S95
S96
Sector
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Outpu
t [Euro
]
×1010
intermediate consumptionwagessocial contributionstaxes less subsidiescapital consumptionoperating surplus
Distribution of output and cost structure by sector used as initial values for model simulations from observed data of Austria
106 107 108 109 1010
Firm size (output)
10-5
10-4
10-3
10-2
10-1
100
Prob
abilit
y2.6
2.8
3
3.2 ×105 Gross value added (GVA), million euro
modeldata
2010 2011 2012 2013 2014 2015100
105
110 Inflation (implicit GVA deflator), 2010=100
Systemic Risk and Network Dynamics An EEP-ASA-RISK crosscutting project
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