Ermon, Stefano, Xue, Yexiang, Toth, Russell, Dilkina, Bistra, Bernstein, Richard, Damoulas, Theodoros, Mude, Andrew G., Clark, Patrick, DeGloria, Steve, Barrett, Christopher and Gomes, Carla P. (2015) Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa. In: 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 25–30 Jan 2015. Published in: AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence pp. 644-650. ISBN 0262511290.

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Abstract

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 large-scale real-world dataset of pastoralists' movements in Ethiopia collected over 3 years.

Item Type:	Conference Item (Paper)
Subjects:	H Social Sciences > HC Economic History and Conditions
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Sustainable development
Journal or Publication Title:	AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher:	ACM
ISBN:	0262511290
Official Date:	January 2015
Dates:	Date Event January 2015 Available
Date of first compliant deposit:	18 April 2016
Page Range:	pp. 644-650
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	National Science Foundation (U.S.) (NSF), Australia. Department of Foreign Affairs and Trade, David R. Atkinson Center for a Sustainable Future
Grant number:	0832782 (NSF), 1059284 (NSF), ADRAS 66138 (DoFAaT),
Conference Paper Type:	Paper
Title of Event:	29th AAAI Conference on Artificial Intelligence
Type of Event:	Conference
Location of Event:	Austin, Texas, USA
Date(s) of Event:	25–30 Jan 2015

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