Potential Artifacts of Sequential State Estimation Invariants

In sequential estimation methods often used in general climate or oceanic calculations of the state and of forecasts, observations act mathematically and statistically as forcings as is obvious in the innovation form of the equations. For purposes of calculating changes in important functions of state variables such as total mass and energy, or in volumetric current transports, results are sensitive to mis-representation of a large variety of parameters including initial conditions, prior uncertainty covariances, and systematic and random errors in observations. Errors are both stochastic and systematic, with the latter, as usual, being the most intractable. Here some of the consequences of such errors are first analyzed in the context of a simplified mass-spring oscillator system exhibiting many of the issues of far more complicated realistic problems. The same methods are then applied to a more geophysical barotropic Rossby wave plus western boundary current system. The overall message is that convincing trend and other time-dependent determinations in "reanalyis" like estimates requires a full understanding of both models and observations.

Practical Nonparametric Sampling Strategies for Quantile-Based Ordinal Optimization

Given a finite number of stochastic systems, the goal of our problem is to dynamically allocate a finite sampling budget to maximize the probability of selecting the “best” system. Systems are encoded with the probability distributions that govern sample observations, which are unknown and only assumed to belong to a broad family of distributions that need not admit any parametric representation. The best system is defined as the one with the highest quantile value. The objective of maximizing the probability of selecting this best system is not analytically tractable. In lieu of that, we use the rate function for the probability of error relying on large deviations theory. Our point of departure is an algorithm that naively combines sequential estimation and myopic optimization. This algorithm is shown to be asymptotically optimal; however, it exhibits poor finite-time performance and does not lead itself to implementation in settings with a large number of systems. To address this, we propose practically implementable variants that retain the asymptotic performance of the former while dramatically improving its finite-time performance.

How Do Attitudes and Perceptions Affect Environmental Preferences?

Environmental preferences or willingness to pay (WTP) values tend to be heterogeneous and evolving over time. Attitudes and related theories worked as an alternative observation scope to the more conventional sociodemographic characteristics, explaining preference heterogeneity in environmental economics. Perception as a concept, on the other hand, is too illusive to be exclusively examined so is better treated as an attitude. Although not popular in mainstream environmental economics, the research interest in the attitude–WTP relationship has continued since the late 1990s and has increased and been relatively steady between 2006 and 2020. According to the lessons from the established behavioral models, attitudes are normally categorized as either general or specific. General attitudes are situation-invariant and slow to change, whereas specific attitudes are situational and quick to change. The early pioneering studies of the attitude–WTP relationship used mostly ad hoc measures for environmental attitudes roughly from 1990, followed by the studies of more systematic representation roughly from 2000, and by those of hybrid models roughly from 2010. There were segmentation-based and parameterization-based approaches to incorporating attitudinal characteristics into valuation models. In particular, parameterization has appeared in three generations: indirect inclusion of indicators, sequential estimation using factor analysis, and integrated hybrid models. As future prospects, first, general environmental attitudes might play an important role in the coming decade because of their relative stability (i.e., situation invariant), comparability, and wide influence, determining environmental preferences and behaviors. Second, a potential difference between the segmentation-based and parameterization-based approaches requires further investigation. Third, the role of hybrid models and the payment parameter that is arbitrarily constrained demand more studies for accurate estimation of mean WTP values. The evolving nature of human preferences could be understood only when the observation scope for latent attitudes is enlightened enough to guide studies of environmental economics, to lead environmental policies, and to accomplish sustainable development.

Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results

An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a sequential estimation method, this approach reduces the expense of computation than genetic algorithm and yields results with the same accuracy. Comparing with standard Particle filter, proposed method can adjust control parameters to adapt to a rapidly changing environment. This approach is demonstrated on the shallow water sound propagation data which was collected during the ASIAEX 2001 experiment. The results indicate that the geoacoustic parameters are well estimated and source–receiver distance are also well determined.