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 computational 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 extremely 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.Comment: Added references. New inline citation style. Added small portions of text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic changes to plot

Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.Comment: Forthcoming in Northwestern Journal of Technology and Intellectual Property, Volume 2

Betting and Belief: Prediction Markets and Attribution of Climate Change

Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics. We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the "true" climate model, suggesting that a climate market could be useful for building public consensus.Comment: All code and data for the model is available at http://johnjnay.com/predMarket/. Forthcoming in Proceedings of the 2016 Winter Simulation Conference. IEEE Pres

Topic Modeling the President: Conventional and Computational Methods

Legal and policy scholars modeling direct actions into substantive topic classifications thus far have not employed computational methods. To compare the results of their conventional modeling methods with the computational method, we generated computational topic models of all direct actions over time periods other scholars have studied using conventional methods, and did the same for a case study of environmental-policy direct actions. Our computational model of all direct actions closely matched one of the two comprehensive empirical models developed using conventional methods. By contrast, our environmental-case-study model differed markedly from the only empirical topic model of environmental-policy direct actions using conventional methods, revealing that the conventional methods model included trivial categories and omitted important alternative topics. Provided a sufficiently large corpus of documents is used, our findings support the assessment that computational topic modeling can reveal important insights for legal scholars in designing and validating their topic models of legal text. To be sure, computational topic modeling used alone has its limitations, some of which are evident in our models, but when used along with conventional methods, it opens doors towards reaching more confident conclusions about how to conceptualize topics in law. Drawing from these results, we offer several use cases for computational topic modeling in legal research. At the front end, researchers can use the method to generate better and more complete topic-model hypotheses. At the back end, the method can effectively be used, as we did, to validate existing topic models. And at a meta-scale, the method opens windows to test and challenge conventional legal theory. Legal scholars can do all of these without the machines, but there is good reason to believe we can do it better with them in the toolkit

Application of machine learning to prediction of vegetation health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern

Adapt, move, or die: how will tropical coral reef fishes cope with ocean warming?

Previous studies hailed thermal tolerance and the capacity for organisms to acclimate and adapt as the primary pathways for species survival under climate change. Here we challenge this theory. Over the past decade more than 365 tropical stenothermal fish species have been documented moving pole-ward, away from ocean warming hotspots where temperatures 2-3 °C above long-term annual means can compromise critical physiological processes. We examined the capacity of a model species - a thermally-sensitive coral reef fish, Chromis viridis (Pomacentridae) – to use preference behaviour to regulate its body temperature. Movement could potentially circumvent the physiological stress response associated with elevated temperatures and may be a strategy relied upon before genetic adaptation can be effectuated. Individuals were maintained at one of six temperatures (23, 25, 27, 29, 31 and 33 °C) for at least six weeks. We compared the relative importance of acclimation temperature to changes in upper critical thermal limits, aerobic metabolic scope, and thermal preference. While acclimation temperature positively affected the upper critical thermal limit, neither aerobic metabolic scope nor thermal preference exhibited such plasticity. Importantly, when given the choice to stay in a habitat reflecting their acclimation temperatures or relocate, fish acclimated to end-of-century predicted temperatures (i.e., 31 or 33 °C) preferentially sought out cooler temperatures, those equivalent to long-term summer averages in their natural habitats (~29 °C). This was also the temperature providing the greatest aerobic metabolic scope and body condition across all treatments. Consequently, acclimation can confer plasticity in some performance traits, but may be an unreliable indicator of the ultimate survival and distribution of mobile stenothermal species under global warming. Conversely, thermal preference can arise long before, and remain long after, the harmful effects of elevated ocean temperatures take hold and may be the primary driver of the escalating pole-ward migration of species

Longitudinal Stability of Genetic and Environmental Influences on Irritability: From Childhood to Young Adulthood.

OBJECTIVE: Little is known about genetic influences on juvenile irritability and whether such influences are developmentally stable and/or dynamic. This study examined the temporal pattern of genetic and environmental effects on irritability using data from a prospective, four-wave longitudinal twin study. METHOD: Parents and their twin children (N=2,620 children) from the Swedish Twin Study of Child and Adolescent Development reported on the children's irritability, defined using a previously identified scale from the Child Behavior Checklist. RESULTS: Genetic effects differed across the sexes, with males exhibiting increasing heritability from early childhood through young adulthood and females exhibiting decreasing heritability. Genetic innovation was also more prominent in males than in females, with new genetic risk factors affecting irritability in early and late adolescence for males. Shared environment was not a primary influence on irritability for males or females. Unique, nonshared environmental factors suggested strong effects early for males followed by an attenuating influence, whereas unique environmental factors were relatively stable for females. CONCLUSIONS: Genetic effects on irritability are developmentally dynamic from middle childhood through young adulthood, with males and females displaying differing patterns. As males age, genetic influences on irritability increase while nonshared environmental influences weaken. Genetic contributions are quite strong in females early in life but decline in importance with age. In girls, nonshared environmental influences are fairly stable throughout development.The National Institutes of Health/National Institute of Mental HealthPublishe

Habitat complexity influences selection of thermal environment in a common coral reef fish

Coral reef species, like most tropical species, are sensitive to increasing environmental temperatures, with many species already living close to their thermal maxima. Ocean warming and the increasing frequency and intensity of marine heatwaves are challenging the persistence of reef-associated species through both direct physiological effects of elevated water temperatures and the degradation and loss of habitat structure following disturbance. Understanding the relative importance of habitat degradation and ocean warming in shaping species distributions is critical in predicting the likely biological effects of global warming. Using an automated shuttle box system, we investigated how habitat complexity influences the selection of thermal environments for a common coral reef damselfish, Chromis atripectoralis. In the absence of any habitat (i.e. control), C. atripectoralis avoided temperatures below 22.9 ± 0.8°C and above 31.9 ± 0.6°C, with a preferred temperature (Tpref) of 28.1 ± 0.9°C. When complex habitat was available, individual C. atripectoralis occupied temperatures down to 4.3°C lower (mean ± SE; threshold: 18.6 ± 0.7°C; Tpref: 18.9 ± 1.0°C) than control fish. Conversely, C. atripectoralis in complex habitats occupied similar upper temperatures as control fish (threshold: 31.7 ± 0.4°C; preference: 28.3 ± 0.7°C). Our results show that the availability of complex habitat can influence the selection of thermal environment by a coral reef fish, but only at temperatures below their thermal preference. The limited scope of C. atripectoralis to occupy warmer environments, even when associated with complex habitat, suggests that habitat restoration efforts in areas that continue to warm may not be effective in retaining populations of C. atripectoralis and similar species. This species may have to move to cooler (e.g. deeper or higher latitude) habitats under predicted future warming. The integration of habitat quality and thermal environment into conservation efforts will be essential to conserve of coral reef fish populations under future ocean warming scenarios

ARB: Advanced Reasoning Benchmark for Large Language Models

Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores.Comment: Submitted to NeurIPS Datasets and Benchmarks Trac

Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages

Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient data for pre-training such models, or are predominantly oral vernaculars without a standardised writing system, precluding fine-tuning. Query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms. Using data from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD can be improved by leveraging representations developed for ASR (wav2vec 2.0: the English monolingual model and XLSR53 multilingual model). Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD. Nevertheless, we find that wav2vec 2.0 representations (either English or XLSR53) offer large improvements (56-86% relative) over state-of-the-art approaches on our endangered language datasets