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Agent-based Modelling in Agricultural Economics
Perspectives and Challenges
Alfons Balmann
JRSS 2012, Toulouse, December 14, 2012
View Video: http://ut-capitole.ubicast.tv/videos/agent_based-modelling/
2
Structure
• Motivation:Challenges of understanding structural change• topical• conceptual
• Agent-based modeling• Idea• Case study I: AgriPoliS • Case study II: SpAbCoM
• Conclusions
3
Challenges of Structural Change
General trends• New markets
• Food, fuel, fibres …• Rapid growth of emerging countries
• Globalization• Verticalization of supply chains• New technologies: GMO, …• Increasing knowledge intensity of agricultural production• Policy changes
Can structural change keep pace? How do society and policy respond?
4
Number of farms with livestock production in Germany2000=100%
Source: Statistisches Bundesamt, own calculations
2000 2003 2005 2007 20100%
20%
40%
60%
80%
100%
Dairy farms Hog farms
Challenges of Structural Change
- 41 %
- 58 %
5
Challenges of Structural Change
Size distribution of hog farms (Germany 2010)
Source: Statistisches Bundesamt, own calculations
1-99 100-499 500-999 1000-1999 2000-4999 > 50000%
10%
20%
30%
40%
50%
Anteil Betriebe Anteil SchweineShare of farms Share of hogs
6
Herausforderungen des Agrarstrukturwandels
Verteilung Größenklassen Schweine haltender Betriebe (2010)
Quelle: Statistisches Bundesamt, eigene Berechnungen
7
Structural change in poultry production (North-west Germany)
Source:Quelle: www.noz.de
8
Challenges of Structural Change
9
Challenges of Structural Change
Stakeholder analysis dairy production
Source: Ostermeyer 2011
FarmersExpertsPublic stakeholders
Altmark (Saxony Anhalt)
Abhängigkeit der Milchproduktion im Ostallgäu von dauerhafter
staatlicher Unterstützung
gering sehr hoch
Bed
eutu
ng d
er
Milc
hpro
dukt
ion
im
Ost
allg
äuse
hr h
och
gerin
g
Ostallgäu (Bavaria)
dependence of dairy farms on permanent subsidies
dependence of dairy farms on permanent subsidies
regi
onal
impo
rtanc
e of
dai
ry p
rodu
ctio
n low high low high
low
h
igh
regi
onal
impo
rtanc
e of
dai
ry p
rodu
ctio
n
low
h
igh
10
Agricultural structures as complex adaptive systems
manifold dimensions manifold levels:
individuals, enterprises, institutions, sectors, regions,… subjective perceptions, bounded rationality dynamics, non-linearities, discontinouities
Evolutionary process with limited foresights! Analysis requires specific (also heterodox) approaches!
Agent-Based Modelling (ABM)
interaction
time space
Challenges of Structural Change
11
(computer-)models,consisting of
artificial entities (agents), which
communicate and interactin an environment
(Ferber, 1999)interactions
Agent-based Models are …
12
Agents are …
sub-systems which perceive
parts of theirenvironmentand respond
autonomously
???
(Ferber, 1999)
13
Agent-based Modelling
Bottom-up approach allows flexible assumptions on individual level
(e.g. heterogenous agents, bounded rationality) allows for flexible frameworks
(e.g. non-convex functions, imperfect markets)
Self-organization spontanous order endogenous change
Particular perspectives for the analysis of emergence and change of structures organization and coordination problems
Discovery of "islands in the chaos"
14
Agent-based Modelling
Several predecessors in economics and social sciences Recursive Programming and Production Response (Day 1963)
Micromotives and Macrobehavior (Schelling 1978)
Nonlinear dynamics, chaos, erratic behavior (Benhabib & Day 1981)
The Evolution of Cooperation (Axelrod 1984)
Positive Feedbacks in the Economy (Arthur 1990)
Computational Economics driven by curiosity on complex social and economic processes
driven by increasing power and availability of computers
15
Agent-based Modelling
Clock rate
3,8 GHz
60 MHz
0,7 MHz
x 1000 000x 5000
16
Agent-based Modelling
ABM examples from social sciences and economics Santa Fe Artificial Stock Markets (Palmer et al. 1993)
Genetic Algorithm and the Cobweb Model (Arifovic 1994)
Growing Artificial Societies (Sugarscape) (Epstein and Axtell 1996)
The Complexity of Cooperation (Axelrod 1997)
…
Understanding complexity of markets and society Study complex games and economics Relaxing micro-economic assumptions (convexity, rationality)
17
Agent-based Modelling
In agricultural economics used since some 20 years CORMAS
rather a modeling platform with focus on common pool resource management developed at CIRAD by Francios Bousquet et al. since early 1990s participatory approach (compagnion modelling)
linking ABM with role-playing games broad international community of users
AgriPoliS developed in Göttingen, Berlin and at IAMO since 1991 somehow in the tradition of recursive programming models (Day 1963) focus on analysis of structural change several clones and extensions exist, e.g. MAS-MP (Berger 2001)
18
AgriPoliS
Policy
Environment
Markets (labor, capital, land, quotas)
Actions
Maximizationof profit/
household income
Perceptions
Resources
Interactions
Leave?
Invest?
Grow?
19
Agricultural Policy Simulator• agent-based (bottom-up)• spatial (land market)• dynamic (farm growth, exits, investments)
Farm level adjustments are considered! Endogenous structural change!
AgriPoliS
20
Effects of capping direct paymentson the Altmark region• ~ 270.000 ha UAA• ~ 980 farms > 10 ha• Ø farm size ~ 280 ha• > 45 % of land in farms > 1000 ha• 1,4 WU / 100 ha
AgriPoliSCase study: EU CAP after 2013
21
ScenarioREF
CAPPING
DescriptionNo modulation after 2013Devision of direct payments (352 €/ha) in base payment (70%) and greening component (30%)
Like REF, but:Capping base payment after deduction of wage costs (20,000 €/WU):150.000-200.000 € : 20%200.000-250.000 € : 40%250.000-300.000 € : 70%>300.000 € : 100%
AgriPoliSCase study: EU CAP after 2013
22
150
200
250
300
350
€/ha
2013 2015 2017 2019 2021 2023 2025year
payment REF CAPPINGeconomic land rent REF CAPPINGprofit REF CAPPING
Development of direct payments, profits and land rents
AgriPoliSCase study: EU CAP after 2013
23
0
10
20
30
%
farms not affected farms affected
0-30 h
a
31-100 h
a
101-200
201-500 h
a
501-1,0
00 ha
>1,000 h
a
201-500 h
a
501-1,0
00 ha
>1,000 h
a
Share of UAA in 2025 by farm size class of 2025
REF CAPPING
AgriPoliSCase study: EU CAP after 2013
Distribution of land in 2025 according to farm size classes
24
AgriPoliSCase study: EU CAP after 2013
Effects of capping on the Altmark region• Only a few large farms affected
• Adjustments allow to avoid capping• Adjustments cause negative long-term effects on efficiency and profitability• Some large farms even benefit
• Hardly any benefits for small and medium-sized farms (10-200ha)• Regional effects
• Just marginal losses of direct payments!• Losses in efficiency and profitability higher!
25
AgriPoliSConclusions
Contribution to understanding of structural change and policies• Powerful opportunities and broad scope for scenario analyses
• on structural change• on distributional issues (sizes, incomes, rents)• on productivity and efficiency Opportunity to use it also for participatory stakeholder interaction!
• Use is very demanding!• programming (AgriPoliS 3.0)• adaptation and calibration (regions, scenarios, maintainance, updates)• validation• analysis of results (not just pushing a button – apply theory and statistics!)• communication of assumptions and results
26
SpAbCom
• agricultural products (raw milk) are spatially distributed
• transport is costly many producers face few but also
spatially distributed processors
Location of milk processors in Germany
27
SpAbCom
Location of milk processors in Germany
milk market: uniform delivered pricing (udp) (Alvarez et al. 2000)
farmers receive the same price irrespective of location to dairy
price discrimination
What determines different spatial price strategies in agricultural markets?
28
SpAbCom Spatial price theory (monopsony)
local price p(r):• mill price m less a portion
of the transport costs tr• = (m, ) is the spatial
pricing strategy of a firmdistance r
price p(r)
odp
fob
udp
trmrp
Rfob,udp Rodp
zpl… zero profit liner ... distance to processors location fob ... free-on-board pricingudp ... uniform delivered pricingodp ... optimal dicriminatory pricingR ... market radius of the processor t ... transport rate
1
(=1)
(=0)
(=1/2)
zpl
= local prices differ by transport costs
= farmers receive same price irrespective of distance= local prices differ by less than transport costs
29
SpAbCom Spatial competition (duopsony)
price p(r)
odp
fob
udp
1zpl
distance rA B
zplA zplB
odp
fob
udp
price p(r)
standard assumptions (Espinosa 1992, Zhang/Sexton 2001):
distance AB=1 linear supply at each location
q(r)=p(r) price of the finished good is 1 linear transport rate t
What are the optimal strategies in terms of m and under spatial
competition?
1
30
SpAbCom Spatial competition
normalized transport costs (t)0 0.5 1.0 1.5 2.0
Perfect competition
Perfect competition
distance rA B
t=0
31
Local Monopsony
t>2
SpAbCom Spatial competition
normalized transport costs (t)0 0.5 1.0 1.5 2.0
Perfect competition
distance rA B2.0
Local Monopsony
32
0<t≤1
SpAbCom Spatial competition
normalized transport costs (t)0 0.5 1.0 1.5 2.0
Spatial competition Local MonopsonPerfect competition
distance rA B2.0
Local Monopsony
Spatial Competition
33
1<t≤2
SpAbCom Spatial competition
normalized transport costs (t)0 0.5 1.0 1.5 2.0
Spatial competition Local MonopsonPerfect competition
distance rA B2.0
Local Monopsony
Spatial Competition
34
SpAbCom Prior studies
Perfect com-
petitionSpatial competition Local
Monopson
t 0 0.4 0.6 1.1 4/3 5/3 2
ZS fob/fob udp/fob fob/udp udp/udp udp
or fobany combination
of fob and udp
ZS = Zhang and Sexton (2001) J IND ECON
Spatial competitionPerfect competition
Local Monopsony
35ZS = Zhang and Sexton (2001) J IND ECON
SpAbCom Prior studies
Perfect com-
petitionSpatial competition Local
Monopson
t 0 0.4 0.6 1.1 4/3 5/3 2
ZS fob/fob udp/fob fob/udp udp/udp udp
or fobany combination
of fob and udp
Prior studies only consider fob (α=1) and udp (α=0) as pricing
options but nothing in-between!!!
What comes out for 0 < α<1?
36
SpAbCom Methodology
Agent-based modeling farmers processors
Genetic algorithm (GA) one GA per agent selection of most profitable
strategies
1zplBzplA
distance rA B
F0
Agents of type „farmer“: max p(r)
F1 F3 F9 F10F6F5 F7F4F2
m
m
Generation 1 Generation 1
best in population
best in population
Agents of type „processor“: max PROFIT(ΓA, ΓB)
1
p(r)
p(r)
37
Generation 2 Generation 2
SpAbCom Methodology
Agent-based modeling farmers processors
Genetic algorithm (GA) one GA per agent selection of most profitable
strategies creation of new strategies
(recombination, mutation)
1zplBzplA
distance rA B
m
m
new strategy
new strategy
1
p(r)
p(r)
38
Generation n Generation n
SpAbCom Methodology
Agent-based modeling farmers processors
Genetic algorithm (GA) one GA per agent selection of most profitable
strategies creation of new strategies
(recombination, mutation)
1zplBzplA
distance rA B
m
m
optimum
optimum
1
p(r)
p(r)
39
fierce competition(low transport costs):
high price discrimination
less competition(high transport costs):
price (discrimination) increases (diminishes)
SpAbCom Results
0 0 . 5 1 . 1 . 5 2 .
0 . 2
0 . 4
0 . 6
0 . 8
1 .
normalized transport costs (t)
m
0 0.5 1.0 1.5 2.0
0.2
0.4
0.6
0.8
1.0
Spatial competition Local MonopsonPerfect competition
2.0
Local Monopsony
udp
partial freight absorption (FA)
m,
40
SpAbCom Compared to prior studies
Perfect com-
petitionSpatial competition Local
Monopson
t 0 0.4 0.6 1.1 4/3 5/3 2
ZS fob/fob udp/fob fob/udp udp/udp udp
or fobany combination (of
fob and udp)
GBS udp/udp partial FA* odp/odp
ZS = Zhang and Sexton (2001) J IND ECON GBS = Graubner, Balmann, and Sexton (2010)* FA = freight absorption (0<α<1/2)
41
Perfect com-
petitionSpatial competition Local
Monopson
t 0 0.4 0.6 1.1 4/3 5/3 2
SpAbCom Real world observations
Perfect com-
petitionSpatial competition Local
Monopson
t 0 0.4 0.6 1.1 4/3 5/3 2
ZS fob/fob udp/fob fob/udp udp/udp udp
or fobany combination (of
fob and udp)
GBS udp/udp partial FA* odp/odp
GBS = Graubner, Balmann, and Sexton (2010)* FA = freight absorption (0<α<1/2)
e.g., on markets of: raw milk, almonds, canning peaches and pears, rice, sugar beets (in Germany A and B Quota), processing tomatoes, wine grapes, corn for ethanol
e.g., on markets of sugar beets (in Germany C-Quota), milk market? And some markets were commonly fob-pricing is assumed?
42
SpAbCom Summary of findings
Spatial pricing in agricultural markets• pricing depends on the competitiveness of the market (distance,
measured by normalized transport costs)• prevalence of spatial price discrimination if production and
processing is spatially distributed ud pricing under fierce competition partial freight absorption if competition is less intense• results are consistent with observations on many agricultural
markets
43
Agent-based ModellingSpAbCom
Contribution to agricultural economics• Micro-economic approach for problems without "closed-form solutions"
• analyses on abstract level• spatial market power, spatial allocation,…
44
Agent-based ModellingSpAbCom
Contribution to agricultural economics• Micro-economic approach for problems without "closed-form solutions"
• analyses on abstract level• spatial market power, location strategies,…• further applications and extensions
• land markets (large farms, land funds)• repeated games for analysis of collusion• real options, auctions
• Challenges• very high computational needs,
particularly if many dimensions considered (space, time, interactions)• design of experiments demanding: parametrization• validation and communication of model and results
45
Summary
Agent-based models provide broad scope for interesting analyses• Micro-economic approaches for problems without "closed-form solutions"
• behavioral foundation of agents based on computational intelligence (e.g. GA) interesting perspectives for combination with human experiments to study games
• Scenario analyses for understanding systems and for decision support• policies, prices, technologies, institutions, …• behavioral foundation of agents usually based on
• optimization • rules
calibration, scenario definition and validation can be linked to participatory analyses• e.g. Compagnion Modelling linking ABM and role-playing games (Bousquet et al. 1999)
Use is demanding! Convincing addressees of results is demanding!