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 Poledna
1,3Michael Gregor Miess
1,2, Stefan Schmelzer
1,2, Elena Rovenskaya
1,6, Stefan Hochrainer-Stigler
1, and Stefan Thurner
1,3,4,51International 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 10
year -5
-4 -3 -2 -1 0 1
cumulative change in GDP growth rate [pp]
1 2 3 4 5 6 7 8 9 10
year 0
2 4 6 8 10 12 14 16 18 20
change in government debt-to-GDP ratio [pp]
• 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
A01A02A03 B C10-C12C13-C15
C16C17C18C19C20C21C22C23C24C25C26C27C28C29C30 C31_C32
C33 D E36 E37-E39
FG45G46G47H49H50H51H52H53 I J58 J59_J60
J61 J62_J63
K64K65K66 L M69_M70
M71M72M73 M74_M75
N77N78N79 N80-N82
O PQ86 Q87_Q88R90-R92
R93S94S95S96 Sector
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Output [Euro]
×1010
intermediate consumption wages
social contributions taxes less subsidies capital consumption operating 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
Probability
2.6 2.8 3
3.2 ×105 Gross value added (GVA), million euro
model data
2010 2011 2012 2013 2014 2015
100 105
110 Inflation (implicit GVA deflator), 2010=100
Systemic Risk and Network Dynamics
An EEP-ASA-RISK crosscutting project
