Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa

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

Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.Comment: AAAI 201

Spatially explicit, individual-based modelling of pastoralists' mobility in the rangelands of east Africa

An agent based-model of mobility of pastoralists was developed and applied to the semi-arid rangeland region extending from southern Ethiopia to northern Kenya. This model was used to investigate temporal adaptation of pastoralists to the spatial heterogeneity of their environment. This dissertation describes the development, structure, and corroboration process of the simulation model, Pastoral Livestock Movement Model (PLMMO). PLMMO is a spatially explicit, individual-based pastoralists-animal foraging and movement model. It simultaneously simulates the foraging and movement behavior of individual pastoralists and their livestock in a rangeland ecosystem. Pastoralists?? herd mobility patterns and other measures of movement were compared to data from field studies. Predictions of the model correspond to observed mobility patterns across seasons. The distances moved were found to be significantly correlated (r2 = 0.927 to 0.977, p<0.0001) to drought and non-drought climatic regimes. The PLMMO model therefore proved to be a useful tool for simulating general movement patterns of pastoralists relative to movement range sizes in the pastoral rangelands of southern Ethiopia and northern Kenya. We then used the PLMMO model to explore the impact of emerging changes in rangeland use in the study area. The ways in which pastoralists?? mobility patterns adapt to emerging challenges in the study area were explored by simulating the following four scenarios: 1) climate change with concomitant reduction in forage yield, 2) climate change with concomitant improvement and higher variability in forage yield, 3) increased livestock population densities and 4) improved access to water. The climate induced change scenario with increased and more variable forage production resulted in the shortest distances moved by pastoralists in comparison to all other scenarios. The total search distances under this scenario were only 20% of normal season distances. The improved water access scenario also returned a significant (p=0.017) drop in distances moved. There was, however, no significant impact on either increase in livestock numbers or reduction in available forage on mobility. We judged the agent-based model PLMMO developed here as a robust system for emulating pastoral mobility in the rangelands of eastern Africa and for exploring the consequences of climate change and adaptive management scenarios

Modelling and Simulation of Human-Environment Interactions

Computational models provide intelligent environmental decision support systems to understand how human decisions are shaped by, and contribute to changes in, the environment. These models provide essential tools to tackle the important issues raised by climate change, including migrations and conflicts due to resource scarcity (e.g., water resources), while accounting for the necessity of co-managing ecosystems across a population of stakeholders with diverse goals. Such socio-environmental systems are characterized by their complexity, which is reflected by an abundance of open questions. This book explores several of these open questions, based on the contributions from over 50 authors. While several books account for methodological developments in modeling socio-environmental systems, our book is unique in combining case studies, methodological innovations, and a holistic approach to training the next generation of modelers. One chapter covers the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation. In another chapter, we show that the benefits of simulations are not limited to managing complex eco-systems, as they can also serve an educational mission in teaching essential rules and thus improve systems thinking competencies in the broader population

Interactions between Climate, Vegetation and People in East African Savannas: a Kenyan Case Study through the Post-Colonial Era.

The interactions between biotic and abiotic factors driving savanna vegetation structure are complex that a combination of resource-based and disturbance-based theories are used to explain the coexistence between trees and grasses. Human impact further complicates these interactions and consequently, the structure of wildlife populations. As human development is linked to environmental sustainability, understanding the impact of the interactions between changing climates and land use patterns on savanna ecology requires an interdisciplinary approach that integrates social and natural factors. In this thesis, the importance of rainfall variability in driving woody vegetation biomass, production and turnover across Kenyan savannas is first assessed. It is established that woody biomass and production increases with rainfall while turnover rates decrease with rainfall. Secondly, to explore the history of land use changes, perceptions from community elders in two savanna ecosystems in southern Kenya (Amboseli and Mara) are collated using a semi-structured questionnaire. The elders from Amboseli regarded rainfall variability as key in shaping land use change decisions while those in Mara regarded socio-economic factors and conservation initiatives as important determinants of land use types. Thirdly, to explore the impact of climate and land use change, an agent based model that used grass biomass data, simulated by a dynamic global vegetation model, as input data is developed. Development of the model incorporated natural and social factors by using insights from the vegetation survey and from the community elders. The model showed that provision of conservation subsidies, up to 200 $ yr-1 for 1 km2 grazing land, is key in driving livestock and wildlife densities and further increases in conservation subsidies maintains the density of livestock and wildlife. The interdisciplinary nature of this thesis highlights the value of integrating local community perspectives and science-based interventions to address the sustainability of savannas, particularly sub-arable savannas. It also highlights the value of conservation subsidies in promoting wildlife numbers and pastoral well-being