1. 1
The role of ambiguity in the evaluation of the net benefits of the MOSE system in the Venice lagoon
Fulvio Fontini
Department of Economics, University of Padua (Italy)
Georg Umgiesser
ISMAR-CNR, Venice (Italy)
Lucia Vergano
ECCET, IPTS,JRC, European Commission, Sevilla (Spain)

2. Structure of the paper 2
Introduction to decision making under ambiguity
The Neo-additive capacities framework: introduction and graphical representation
3. The Venice lagoon case study: analysis of the
problem and policy implications

3. Decision making under ambiguity I3
Policy decisions impacting on the environment → Ambiguity
Ecological system Scenarios whose likelihood → can not be inferred from any
Economic system probability distribution
No clear definition of the problem
→ Incomplete states of the world (Mukerji, 1997)
Use of any a priori distribution not justified
(Chichilnisky, 2000)

4. Decision making under ambiguity II4
Decision criteria:
Maximin decision criterion (Wald, 1950) - complete
ignorance (Rawls, 1971): evaluation of the worst possible
consequence
- Arrow-Hurwicz criterion (1971): evaluation of a linear
combination of the best and worst possible consequences
→ strong interpretation of the Precautionary Principle (PP): only the worst possible consequence(s) is (are) to be taken into account unless it can be proved “beyond any reasonable doubt” that some other consequences will occur

5. Capacity as a measure of ambiguity I5
Capacity = normalized monotone measure of ambiguity
ν: E → ℜ
→ generalisation of the subjective utility theory as a decision criterion through the Choquet Integral
where f: S → X ⊂ ℜ is a utility function
→ the integral w.r.t. a capacity identifies the so called Choquet Expected Utility (CEU)

6. Capacity as a measure of ambiguity II6
Wakker, 2001
Concave capacity → Overweight good outcomes → Optimistic attitude towards ambiguity
Convex capacity → Overweight bad outcomes → Pessimistic attitude towards ambiguity

7. NEO-capacity as a measure of ambiguity I7
Chateauneuf et al., 2007: NEO-additive capacity = a specific type of capacity, additive on non-extreme outcomes, reflecting pessimism for some events and optimism for some other events
where: π = finitely additive probability distribution
μαN (A) = Hurwicz capacity

8. NEO-capacity as a measure of ambiguity II8 8
→ The CEU calculated w.r.t. a NEO-additive capacity:
where: Eπ = expected value calculated w.r.t. π
f = act function

9. Special cases of the CEU9
Several decision criteria may be interpreted as special cases of the Choquet Expected Utility framework:
1. δ=0: the Expected Value approach 2. N={Ø}, δ>0, α=0: pure pessimism
N={Ø}, δ=1, α=0: Maxmin criterion 3. N={Ø}, δ>0, α=1: pure optimism N={Ø}, δ=1, α=1: Maxmax criterion
4. N={Ø}, δ=1, α=(0,1): Hurwicz criterion
The parameters δ(1-α) and δα capture the impact of pessimism and optimism

10. Generalization of the functional form10
Renaming
1. λ=δ(1- α)
2. γ=δα 3. 4.
the CEU function can be expressed in the following way:

11. Graphical representation I11
the simplex
constraints the set of the admissible ranges for γ and λ.
The space of the CEU graphically corresponds to the side of the triangle shown in Fig. 1

12. Graphical representation II12
Figure 1

13. Interpretation of the graphical representation I13
1. γ=λ=0: the Expected Value approach 2. γ=0: pure pessimism
3. λ=0: pure optimism
4. λ+γ=1: Hurwicz criterion

14. Interpretation of the graphical representation II14
Expected Value Equivalent set:
(γ,λ) s.t. CEU=Eπ and the Hurwicz criterion holds
→ it defines the implicit values of the ambiguity attitude that a decision maker has in mind when taking the decision on the basis of the expected value only, i.e. if having an optimistic or pessimistic attitude towards it

15. The ‘acqua alta’ phenomenon in the Venice lagoon15
‘Acqua alta’ = the periodical high water event causing partial flooding of Venice, corresponding to +80 cm above the ‘Punta della Salute’ tidal datum
During last decades: increased frequency and intensity (Fig. 2) Forecasts for the next century (IPCC, 2007: Fig. 3): further worsening of the phenomenon, due to the global sea level rise induced by climate change
→ Mitigation and prevention measures: hydraulic pumps, ‘vasche’, ‘paratie’, rising of pavements, MOSE

16. The ‘acqua alta’ phenomenon I16
Figure 2

17. The ‘acqua alta’ phenomenon II17
Figure 3

18. The ‘acqua alta’ phenomenon: the frequency18
Figure 4 – Yearly distribution of tidal  +110 cm in Venice,

19. The IPCC forecasts 19 Figure 5 – Global sea level rise
Sources: IPCC, 2007

20. The MOSE functioning 20
MOSE = system of mobile barriers installed on the sea floor of the inlets (‘Chioggia’, ‘Lido’ and ‘Malamocco’)
→ Separate from a hydraulic point of view the lagoon from the Adriatic Sea whenever the forecasted water level exceeds the safeguarding level (+100 or +110 cm above ‘Punta della Salute’)

21. The MOSE impact: benefits I21
avoided damages to buildings:
damages affecting the real estate stock, depending on the intensity of flooding;
2) avoided damages to individuals:
damages to the flow of (touristic and personal) services depending on the frequency of flooding
MOSE → flooding intensity and frequency↓ → costs saved = benefits

22. The MOSE impact: benefits II21
avoided damages for buildings:
costs of renovation after the highest tide experienced in
- plastering costs of the walls’ flooded surface
- lead plate introduction costs (historical buildings)

23. The MOSE impact: benefits I21
2) avoided damages for individuals:
costs due to displacement problems
- costs of children (27% of children in schooling age) and
elderly people (10% of people aged 75-84) caring
- loss of touristic expenses (daily tourist flow x average daily
expenses)

24. The MOSE impact: benefits II22
Table 1 – Avoided costs and expenses for category
Buildings
Plastering: internal walls
2.77 €/1m 1cm high
external walls
5.44 €/1m 1cm high
Artistic buildings
Lead plate: internal walls
20.11 €/m
61.87 €/m
Aged people
Caring
12.37 €/hour
Children in age of schooling
8.25 €/hour
Tourists
Tourist expense
85.96 €/day

25. The MOSE impact: costs I23
→ Operational and maintenance costs (11,136 thousands of €/year)
→ Direct costs due to the interferences with port activities (Tables 2-3):
additional time to getting in and out of the lagoon (charter costs)
longer period staying in a wharf/quay (mooring costs)
Safeguarding level → Frequency of mobile barriers closure → Amount of the additional charter and mooring costs
→ The choice between a or a cm safeguarding level = example of decision under ambiguity: the likelihood of the environmental parameter (sea level rise) can not be inferred on the basis of any probability distribution

26. The MOSE impact: costs II24
Table 2 – Charter and mooring costs for ship category
Charter costs
Mooring costs
Tons
€/m/Hour
€/Hour
Crude oil tanker x
0.03
1,333.33
Other oils tanker
x>1,000
0.13
1,000>=x<1,500
0.12
x>=1,500
0.11
LNG tanker
x<7,000
0.29
7,000<=x<20,000
0.19
x>=20,000
0.14
Container
x<15,000
1,000.00
x>=15,000
0.02
Cargo
x>=4,500
0.01
Carrier
x<4,500

27. The MOSE impact: costs III25
Table 3 – Charter and mooring costs for ship category
Charter costs
Mooring costs
€/Pass/Hour
€/m/Hour
Passenger ship
1.60
0.17
Yacht
46.77
0.45

28. MOSE direct impacts economic assessment: the hydrodynamic model26
Hydrodynamic model (Umgiesser et al., 2004):
- using water level measurements at the inlets and wind over the lagoon → simulates water levels and barotropic currents inside the lagoon → computes how often the water level exceeds the safeguarding level and the time of mobile barriers closure
- using ship traffic data for (Venice harbour office)
→ computes how often and for how long the ship traffic is interrupted due to MOSE functioning (Umgiesser and Matticchio, 2006)

29. Direct impacts economic assessment: scenarios27
Hydrodynamic model hypotheses: - Adriatic Sea level rise (0, +30 cm, +50 cm) - safeguarding level (+100 cm, +110 cm) - security increment (0, +10 cm)
Table 4 – The twelve scenarios
+100 cm
+110 cm
+30 cm
+50 cm A E I B F J
+10 cm C G K D H L

30. Direct impacts economic assessment: the estimates28
Table 5 – Total costs and benefits (millions of €/year) for each
scenarios
Scenarios
Total costs
Total benefits
Net benefits
(A) 00_100_00
31.838
(B) 00_110_00
31.934
25.444
-6.490
(C) 00_100_10
31.669
(D) 00_110_10
31.862
(E) 30_100_00
33.588
(F) 30_110_00
32.080
(G) 30_100_10
35.485
(H) 30_110_10
33.024
(I) 50_100_00
41.383
(J) 50_110_00
36.277
(K) 50_100_10
45.084
(L) 50_110_10
39.522

31. Policy implications: decision variables29
Environmental variable: Adriatic Sea level rise
Decision making variables: safeguarding level
security increment
Safeguarding level → Frequency of mobile barriers closure → MOSE net benefits
The choice between a safeguarding level of or cm = example of decision under ambiguity: the likelihood of the environmental parameter (sea level rise) can not be inferred on the basis of any probability distribution
→ choice based on the estimated costs associated to
each scenario, weighted according to the decision criterion
adopted by policy makers

32. Policy implications: decision criteria I30
No ambiguity
→ Expected value criterion (benchmark):
states of the world are equally weighted according to
a subjective probability measure pi =1/12, i=A, …, L
ci, pi i=A; …,L
Ambiguity
→ application of the CEU framework under different values of the
subjective parameters

33. Policy implications: decision criteria II31
1. Max-Min criterion (pessimistic attitude – C1)
The option corresponding to the less bad outcome (B) among
the worst outcomes associated with each possible decision
(B,D,A,C) is taken
2. Max-Max criterion (optimistic attitude – C2)
The option corresponding to the best outcome (K) among
the best outcomes associated with each possible decision
(K,L,I,J) is taken
3. Hurwicz criterion
The best (B) and the worst (K) outcomes are equally
weighted

34. Implicit ambiguity attitude: λhut = 0.81: γhut = 0.19
Estimated net benefits: a comparison between different decision criteria 32
Table 6 – Estimated net benefits (millions of €/year)
Decision criterion
Net benefits EU
Max-Min
Max-Max
CEUp
λ ( )
CEUo
γ ( )
CEUH
γ ( )
Implicit ambiguity attitude: λhut = 0.81: γhut = 0.19

35. Conclusions 33 Cost estimates vary substantially according to the
attitude decision makers have towards ambiguity
(pessimism vs optimism)
2. Even assuming a symmetric attitude of decision
makers towards ambiguity, estimated costs substantially
differ from the ones calculated following the expected
value criterion
3. Decision makers have implicitly shown pessimism, i.e. overevaluation of the scenarios providing the lowest benefits and underevaluation of those inducing the highest ones (precautionary approach)