John J. Nay

A review of decision-support models for adaptation to climate change in the context of development

In order to increase adaptive capacity and empower people to cope with their changing environment, it is imperative to develop decision-support tools that help people understand and respond to challenges and opportunities. Some such tools have emerged in response to social and economic shifts in light of anticipated climatic change. Climate change will play out at the local level, and adaptive behaviours will be influenced by local resources and knowledge. Community-based insights are essential building blocks for effective planning. However, in order to mainstream and scale up adaptation, it is useful to have mechanisms for evaluating the benefits and costs of candidate adaptation strategies. This article reviews relevant literature and presents an argument in favour of using various modelling tools directed at these considerations. The authors also provide evidence for the balancing of qualitative and quantitative elements in assessments of programme proposals considered for financing through mechanisms that have the potential to scale up effective adaptation, such as the Adaptation Fund under the Kyoto Protocol. The article concludes that it is important that researchers and practitioners maintain flexibility in their analyses, so that they are themselves adaptable, to allow communities to best manage the emerging challenges of climate change and the long-standing challenges of development.

Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text.

We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a continuous vector space. We apply our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and official summaries of Congressional bills. The model discerns meaningful differences between government branches. We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses. The similarities between learned representations of Congresses over time and sitting Presidents are negatively correlated with the bill veto rate, and the temporal ordering of Presidents and Congresses was implicitly learned from only text. With the resulting vectors we answer questions such as: how does Obama and the 113th House differ in addressing climate change and how does this vary from environmental or economic perspectives? Our work illustrates vector-arithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map.

A machine-learning approach to forecasting remotely sensed vegetation health

ABSTRACT Drought threatens food and water security around the world, and this threat is likely to become more severe under climate change. High-resolution predictive information can help farmers, water managers, and others to manage the effects of drought. We have created an open-source tool to produce short-term forecasts of vegetation health at high spatial resolution, using data that are global in coverage. The tool automates downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) data sets and training gradient-boosted machine models on hundreds of millions of observations to predict future values of the enhanced vegetation index. We compared the predictive power of different sets of variables (MODIS surface reflectance data and Level-3 MODIS products) in two regions with distinct agro-ecological systems, climates, and cloud coverage: Sri Lanka and California. Performance in California is higher because of more cloud-free days and less missing data. In both regions, the correlation between the actual and model predicted vegetation health values in agricultural areas is above 0.75. Predictive power more than doubles in agricultural areas compared to a baseline model.

Data-driven dynamic decision models

This article outlines a method for automatically generating models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. This is useful for designing empirically grounded agent-based simulations and for gaining direct insight into observed dynamic processes. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple approximations that explain most of the structure of complex stochastic processes. This method, implemented in C++ and R, scales well to large data sets. We apply our methods to empirical data from human subjects game experiments and international relations. We also demonstrate the methods ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.

Urban Water Conservation Policies in the United States

Urban water supply systems in the United States are increasingly stressed as economic and population growth confront limited water resources. Demand management, through conservation and improved efficiency, has long been promoted as a practical alternative to building Promethean energy-intensive water-supply infrastructure. Some cities are making great progress at managing their demand, but study of conservation policies has been limited and often regionally focused. We present a hierarchical Bayesian analysis of a new measure of urban water conservation policy, the Vanderbilt Water Conservation Index (VWCI), for 195 cities in 45 states in the contiguous United States. This study does not attempt to establish causal relationships, but does observe that cities in states with arid climates tend to adopt more conservation measures. Within a state, cities with more Democratic-leaning voting preferences and large and rapidly growing populations tend to adopt more conservation measures. Economic factors and climatic differences between cities do not correlate with the number of measures adopted, but they do correlate with the character of the measures, with arid cities favoring mandatory conservation actions and cities in states with lower real personal income favoring rebates for voluntary actions. Understanding relationships between environmental and societal factors and cities’ support for water conservation measures can help planners and policy-makers identify obstacles and opportunities to increase the role of conservation and efficiency in making urban water supply systems sustainable.

Predicting and understanding law-making with word vectors and an ensemble model

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill’s sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

Predicting Human Cooperation.

The Prisoner’s Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner’s Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner’s Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner’s Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner’s Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.

Participatory simulations of urban flooding for learning and decision support

Flood-control measures, such as levees and floodwalls, can backfire and increase risks of disastrous floods by giving the public a false sense of security and thus encouraging people to build valuable property in high-risk locations. More generally, nonlinear interactions between human land-use and natural processes can produce unexpected emergent phenomena in coupled human-natural systems (CHNS). We describe a participatory agent-based simulation of coupled urban development and flood risks and discuss the potential of this simulation to help educate a wide range of the public-from middle- and high-school students to public officials-about emergence in CHNS and present results from two pilot studies.