Socio-ecological systems and the distributional effect of collective conditionality constraints in rural policies: A case study in Emilia-Romagna

Agricultural policy makers are increasingly interested in the role of collective action to improve the effectiveness of natural resource management in rural areas. Analyses of socio-ecological systems highlight how distribution of benefits/cost is crucial for the success of cooperation among actors and hence it seems an element to take into account for the design of policies that focus on collective action. In this paper we use the Shapley value to ex-ante assess the distributional effect of collective conditionality constraints embedded in the policy, and their interaction with asymmetry in the access to the resource and with the social environment. We parameterize a model to a collective reservoir located in Emilia-Romagna (Italy), modelling the reservoir and the infrastructure connecting the farms by using a network. The results show that distributional effect of the asymmetry in the access to the resource can be counteracted by properly setting Minimum Participation Rules. However, the results highlight the potential difficulties in designing agricultural policies dealing with collective action

A Mean-Risk Mixed Integer Nonlinear Program for Network Protection

Many of the infrastructure sectors that are considered to be crucial by the Department of Homeland Security include networked systems (physical and temporal) that function to move some commodity like electricity, people, or even communication from one location of importance to another. The costs associated with these flows make up the price of the network\u27s normal functionality. These networks have limited capacities, which cause the marginal cost of a unit of flow across an edge to increase as congestion builds. In order to limit the expense of a network\u27s normal demand we aim to increase the resilience of the system and specifically the resilience of the arc capacities. Divisions of critical infrastructure have faced difficulties in recent years as inadequate resources have been available for needed upgrades and repairs. Without being able to determine future factors that cause damage both minor and extreme to the networks, officials must decide how to best allocate the limited funds now so that these essential systems can withstand the heavy weight of society\u27s reliance. We model these resource allocation decisions using a two-stage stochastic program (SP) for the purpose of network protection. Starting with a general form for a basic two-stage SP, we enforce assumptions that specify characteristics key to this type of decision model. The second stage objective---which represents the price of the network\u27s routine functionality---is nonlinear, as it reflects the increasing marginal cost per unit of additional flow across an arc. After the model has been designed properly to reflect the network protection problem, we are left with a nonconvex, nonlinear, nonseparable risk-neutral program. This research focuses on key reformulation techniques that transform the problematic model into one that is convex, separable, and much more solvable. Our approach focuses on using perspective functions to convexify the feasibility set of the second stage and second order conic constraints to represent nonlinear constraints in a form that better allows the use of computational solvers. Once these methods have been applied to the risk-neutral model we introduce a risk measure into the first stage that allows us to control the balance between an efficient, solvable model and the need to hedge against extreme events. Using Benders cuts that exploit linear separability, we give a decomposition and solution algorithm for the general network model. The innovations included in this formulation are then implemented on a transportation network with given flow demand