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On a node a there is a price denoted Pa.

On a link ab, there is a flow denoted Xab and a transportation cost denoted Cab. On a node, we define excess as inflow minus outflow; in any way, excess is non negative; if price is positive, then excess is zero; there is complementarity between price and excess. On a link not ending in a constrained node; we define excess as destination price minus origin price and transport costs; in any way excess is non negative; if there is a flow, then excess is zero; there is complementarity between flow and excess. On a constrained node, we define excess by expressing the constraint; in any way excess is non negative; if excess is zero, there is a non negative shadow price; there is complementarity between the constraint and the shadow price.

On a link ending to this node, for the same reason as before, we still have a complementarity relationship between flow and the difference: price at destination minus price at origin minus shadow price minus transportation costs. We get a complementarity problem by putting all these conditions together. State vector V is made of prices on intermediate nodes, flows on links, shadow prices on constrained nodes. The resulting complementarity problem is quite large.

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The length of a state vector is about the number of nodes, plus the number of links, plus the number of constraints in the network. We have implemented and adapted the necessary algorithms for computing the network equilibrium when it exists. Above excess functions that constitute functional F V are expressed in a simple way according to parameters corresponding to network entities: transportation costs on links, production functions on nodes corresponding to fleets, price functions inverse demand functions on nodes corresponding to markets.

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The migration game in habitat network: the case of tuna

To calibrate the model, we have used data sets concerning catches and trade. They provide values characterizing states of the modeled network on nodes and links. We have assumed that the values corresponding to the period were defining a reference state of the system. To test the model and its realism, we built a reference simulation without any variation of parameters and checked that the results corresponded to the above reference state.

Then we proceeded to systematic sensitivity analysis on all input parameters, checking that they were having, one by one, the expected effect. Recall that our goal is not to predict exactly the future of the system but to provide a tool for envisaging it in all its components biology, economics, policy and that all conclusions we draw from a simulation experiment are hypothetical. A modeling experiment consists in setting a value to input parameters, to run the previous algorithm for 30 years from to and then to observe the effects on fish stocks, on catches, on fishing capacity, on farm production, on fish consumption, on import and export.

This allows representing the system with a network of intermediate size several hundred of nodes and links , the definition of which being based 1 on the objective of developing scenarios, 2 on the structure of existing datasets. This allows to calibrate the system with standard inverse modeling techniques. Let us recall that the methodological issue of building scenarios about exploited ecosystems lies in the involvement of stakeholders. It appears from the experience of the modeling of the North Atlantic basin that the proposed approach provides specific tools to avoid several well known pitfalls: to oversimplify versus to be too detailed whence the idea of a model of intermediate size , to draw a conclusion too rapidly versus to differ the conclusion whence the implementation of a normalized process of scenario building , neglect their knowledge versus be scared by their reactions whence the idea of a collaborative design of scenarios.

Thanks to Rachel Mullon for helpful comments. Accept and follow the intructions.

It takes about 10 minutes. The other times, downloading the code of the simulator takes less than a minute. To build a scenario, select values for control parameters. Running a scenario takes about 5 minutes. While running a scenario, you may observe the progress on the selected view. Sometimes, according to the coosen values for control parameters, the algorithm fails to find a network equilibrium.

Frontiers | The Challenge of Implementing the Marine Ecosystem Service Concept | Marine Science

In this case, plots represents the system at ulterior steps at the last equilibrium value that has been computed. To anticipate in a collaborative way the futures of exploited oceanic basins, that is 1 to deal with ecosystems under the double exposure, 2 to allow scenarios about the future of these ecosystems, 3 to provide integrated views on the dynamics of these systems, including both biological and economic processes, we suggest a modeling framework figure 1 , with the key features of using network economics principles, i.

The selected value is a percent of change. It affects one or several parameters of the model.

Week 3: Christian Susan on Economic valuation in a Large Marine Ecosystem

European demand: it affects the intercept of the demand function in all European countries Extra European demand: it affects the intercept of the demand function in all extra European countries TAC: it affects the constraint on catches; increasing TAC corresponds to a less severe policy.

Fishing efficiency: it affects the catachability coefficient q. Farming efficiency: it affects the conversion rate the need of fishmeal and fish oil for growing a cultivated fish in aquaculture.


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