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Latent goal models for dynamic strategic interaction.

ABSTRACT: Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience. However, most previous studies have used experimental paradigms in which choices are discrete (and few), play is static, and optimal solutions are known. Yet in natural environments, interactions between agents typically involve continuous action spaces, ongoing dynamics, and no known optimal solution. Here, we seek to bridge this divide by using a "penalty shot" task in which pairs of monkeys competed against each other in a competitive, real-time video game. We modeled monkeys' strategies as driven by stochastically evolving goals, onscreen positions that served as set points for a control model that produced observed joystick movements. We fit this goal-based dynamical system model using approximate Bayesian inference methods, using neural networks to parameterize players' goals as a dynamic mixture of Gaussian components. Our model is conceptually simple, constructed of interpretable components, and capable of generating synthetic data that capture the complexity of real player dynamics. We further characterized players' strategies using the number of change points on each trial. We found that this complexity varied more across sessions than within sessions, and that more complex strategies benefited offensive players but not defensive players. Together, our experimental paradigm and model offer a powerful combination of tools for the study of realistic social dynamics in the laboratory setting.

SUBMITTER: Iqbal SN

PROVIDER: S-EPMC6472832 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Latent goal models for dynamic strategic interaction.

Iqbal Shariq N SN Yin Lun L Drucker Caroline B CB Kuang Qian Q Gariépy Jean-François JF Platt Michael L ML Pearson John M JM

PLoS computational biology 20190311 3

Understanding the principles by which agents interact with both complex environments and each other is a key goal of decision neuroscience. However, most previous studies have used experimental paradigms in which choices are discrete (and few), play is static, and optimal solutions are known. Yet in natural environments, interactions between agents typically involve continuous action spaces, ongoing dynamics, and no known optimal solution. Here, we seek to bridge this divide by using a "penalty ...[more]

PMID: 30856172

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Project description:BackgroundA global plan has been set to end human deaths from dog-mediated rabies by 2030 ("Zero-by-30"), but whether it could be achieved in some countries, such as China, remains unclear. Although elimination strategies through post-exposure prophylaxis (PEP) use, dog vaccination, and patient risk assessments with integrated bite case management (IBCM) were proposed to be cost-effective, evidence is still lacking in China. We aim to evaluate the future burdens of dog-mediated human rabies deaths in the next decade and provide quantitative evidence on the cost-effectiveness of different rabies-control strategies in China.MethodsBased on data from China's national human rabies surveillance system, we used decision-analytic modelling to estimate dog-mediated human rabies death trends in China till 2035. We simulated and compared the expected consequences and costs of different combination strategies of the status quo, improved access to PEP, mass dog vaccination, and use of IBCM.ResultsThe predicted human rabies deaths in 2030 in China will be 308 (95%UI: 214-411) and remain stable in the next decade under the status quo. The strategy of improved PEP access alone could only decrease deaths to 212 (95%UI: 147-284) in 2028, remaining unchanged till 2035. In contrast, scaling up dog vaccination to coverage of 70% could eliminate rabies deaths by 2033 and prevent approximately 3,265 (95%UI: 2,477-3,687) extra deaths compared to the status quo during 2024-2035. Moreover, with the addition of IBCM, the "One Health" approach through mass dog vaccination could avoid unnecessary PEP use and substantially reduce total cost from 12.53 (95%UI: 11.71-13.34) to 8.73 (95%UI: 8.09-9.85) billion US dollars. Even if increasing the total costs of IBCM from 100 thousand to 652.10 million US dollars during 2024-2035, the combined strategy of mass dog vaccination and use of IBCM will still dominate, suggesting the robustness of our results.ConclusionsThe combined strategy of mass dog vaccination and IBCM requires collaboration between health and livestock/veterinary sectors, and it could eliminate Chinese rabies deaths as early as 2033, with more deaths averted and less cost, indicating that adding IBCM could reduce unnecessary use of PEP and make the "One Health" rabies-control strategy most cost-effective.

Project description:BackgroundUnderstanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques.MethodsWe employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (WI, 2016-17, as baseline), Wave II (WII, 2017-18), and Wave III (WIII, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use.OutcomesFour distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at WI, however, S3 became the prominent use pattern (32.5%) by WIII. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., ORS2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., ORS3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction.InterpretationThis is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU.FundingThe Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.

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Latent goal models for dynamic strategic interaction.

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Latent goal models for dynamic strategic interaction.

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