36,825 research outputs found
Stay by thy neighbor? Social organization determines the efficiency of biodiversity markets with spatial incentives
Market-based conservation instruments, such as payments, auctions or tradable
permits, are environmental policies that create financial incentives for
landowners to engage in voluntary conservation on their land. But what if
ecological processes operate across property boundaries and land use decisions
on one property influence ecosystem functions on neighboring sites? This paper
examines how to account for such spatial externalities when designing
market-based conservation instruments. We use an agent-based model to analyze
different spatial metrics and their implications on land use decisions in a
dynamic cost environment. The model contains a number of alternative submodels
which differ in incentive design and social interactions of agents, the latter
including coordinating as well as cooperating behavior of agents. We find that
incentive design and social interactions have a strong influence on the spatial
allocation and the costs of the conservation market.Comment: 11 pages, 6 figure
Decentralized dynamic task allocation for UAVs with limited communication range
We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem
Learning and coordinating in a multilayer network
We introduce a two layer network model for social coordination incorporating
two relevant ingredients: a) different networks of interaction to learn and to
obtain a payoff , and b) decision making processes based both on social and
strategic motivations. Two populations of agents are distributed in two layers
with intralayer learning processes and playing interlayer a coordination game.
We find that the skepticism about the wisdom of crowd and the local
connectivity are the driving forces to accomplish full coordination of the two
populations, while polarized coordinated layers are only possible for
all-to-all interactions. Local interactions also allow for full coordination in
the socially efficient Pareto-dominant strategy in spite of being the riskier
one
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