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Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa

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Title Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa
 
Creator Ermon, Stefano
 
Contributor Xue, Yexiang
Toth, Russell
Dilkina, Bistra
Bernstein, Richard
Damoulas, Theodoros
Clark, Patrick E.
De Gloria, Steve
Mude, Andrew
Barrett, Christopher
Gomes, Carla P.
 
Subject agropastoral
 
Description Understanding spatio-temporal resource preferences is
paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred
from data. In this paper we consider the problem of
inferring agents’ preferences from observed movement
trajectories, and formulate it as an Inverse Reinforcement Learning (IRL) problem . With the goal of informing policy-making, we take a probabilistic approach
and consider generative models that can be used to
simulate behavior under new circumstances such as
changes in resource availability, access policies, or climate. We study the Dynamic Discrete Choice (DDC)
models from econometrics and prove that they generalize the Max-Entropy IRL model, a widely used probabilistic approach from the machine learning literature.
Furthermore, we develop SPL-GD, a new learning algorithm for DDC models that is considerably faster than
the state of the art and scales to very large datasets.
We consider an application in the context of pastoralism in the arid and semi-arid regions of Africa, where
migratory pastoralists face regular risks due to resource
availability, droughts, and resource degradation from
climate change and development. We show how our approach based on satellite and survey data can accurately
model migratory pastoralism in East Africa and that it
considerably outperforms other approaches on a largescale real-world dataset of pastoralists’ movements in
Ethiopia collected over 3 years.
 
Date 2016-05-15T08:12:44Z
2016-05-15T08:12:44Z
 
Type Report
 
Identifier https://mel.cgiar.org/reporting/download/hash/vpumJBkn
Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick E. Clark, Steve De Gloria, Andrew Mude, Christopher Barrett, Carla P. Gomes. (1/9/2015). Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. Palo Alto, United States of America: Association for the Advancement of Artificial Intelligence.
https://hdl.handle.net/20.500.11766/4800
Open access
 
Language en
 
Rights CC-BY-NC-4.0
 
Format PDF
 
Publisher Association for the Advancement of Artificial Intelligence