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  1. Free, publicly-accessible full text available June 2, 2024
  2. Abstract

    The exploitation of ecosystem services, through processes like agricultural production, is associated with myriad negative environmental impacts, which are felt by stakeholders on local, regional, and global scales. The varying type and scale of impacts leads naturally to fragmented and siloed approaches to mitigating externalities by diverse governmental and non-governmental institutions. However, policies designed to address a single impact may worsen other negative impacts. As a result, even when groups have the expertise to design policy solutions in one dimension, policies addressing single issues may conflict and result in less than ideal outcomes in combination. In this paper, we present a conceptual framework and examples of this kind of ‘policy collision,’ where policies produce mutual negative interference so that policies designed independently may fail to achieve their goals. We argue that an integrated systems perspective is needed to overcome this problem and present several positive examples where this has been put into practice. Policy collision provides a useful framework for determining how each colliding policy should be modified in improve outcomes.

     
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  3. In this paper, we generalize the recently studied stochastic matching problem to more accurately model a significant medical process, kidney exchange, and several other applications. Up until now the stochastic matching problem that has been studied was as follows: given a graph G= (V,E), each edge is included in the realized sub-graph of G independently with probability pe, and the goal is to find a degree-bounded sub-graph Q of G that has an expected maximum matching that approximates the expected maximum matching of G. This model does not account for possibilities of vertex dropouts, which can be found in several applications, e.g. in kidney exchange when donors or patients opt out of the exchange process as well as in online freelancing and online dating when online profiles are found to be faked. Thus, we will study a more generalized model of stochastic matching in which vertices and edges are both realized independently with some probabilities pv, pe, respectively, which more accurately fits important applications than the previously studied model. We will discuss the first algorithms and analysis for this generalization of the stochastic matching model and prove that they achieve good approximation ratios. In particular, we show that the approximation factor of a natural algorithm for this problem is at least 0.6568 in unweighted graphs, and 1/2+ε in weighted graphs for some constant ε >0. We further improve our result for unweighted graphs to 2/3 using edge degree constrained sub-graphs (EDCS). 
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