Rational Inattention during a Randomized Controlled Trial

Route choice, travel time variability, and rational inattention

We present a geometric approach to the finite Rational Inattention (RI) model, recasting it as a convex optimization problem with reduced dimensionality that is well suited to numerical methods. We provide an algorithm that outperforms existing RI computation techniques in terms of both speed and accuracy in both static and dynamic RI problems. We further introduce methods to quantify the impact of numerical inaccuracy on the model's outcomes and to produce robust predictions regarding the most frequently implemented actions.

The paper proposes endogenous information choice as a channel through which uncertainty affects price dynamics. I consider a rational inattention model with volatility uncertainty and endogenous information processing capability. According to the model, firms' learning and optimal attention exhibits inertia and asymmetry in response to volatility changes. Firms choose to process more information when uncertainty rises, especially about aggregate conditions, and their pricing behavior changes accordingly. Using a Markov‐switching factor‐augmented vector autoregression (MS‐FAVAR), the paper also documents a significant positive correlation between volatility and firms' responsiveness to macro‐ and microlevel shocks, consistent with model predictions.

Agricultural Input Decisions in the Presence of Complementarities: A Rational Inattention Model

Decisions take time, and the time taken to reach a decision is likely to be informative about the cost of more precise judgments. We formalize this insight in the context of a dynamic model of optimal evidence accumulation. We provide conditions under which the resulting belief dynamics resemble either diffusion processes or processes with large jumps. We then demonstrate that the state-contingent choice probabilities predicted by our model are identical to those predicted by a static rational inattention model, providing a micro-foundation for such models. In the diffusion case, our model provides a normative foundation for a variant of the drift-diffusion model from mathematical psychology.

Decision Making with Rational Inattention

Dynamic rational expectation models are more difficult to solve than standard dynamic models. These technical difficulties have probably hindered the development and use of such models at the empirical level. The purpose of this paper is to introduce the principal methods of solution to potential builders and users of such models.Key wordsRational ExpectationsDynamic ModelsSimulationExpectations

The main problems faced by a dynamic model within a Kalman filter occur when the system experiences unexpected dynamic conditions, a change in data acquisition rate, or when the dynamics of the system are non-linear. To minimize the errors produced from dynamic modelling in unusual conditions, an extended dynamic model is developed in this paper, and its usefulness demonstrated through comparison of the performance of a Kalman filter's response to simulated data with a standard dynamic model and the extended dynamic model. The results show that, in use, the proposed extended dynamic model is superior to a standard dynamic model, due mainly to its ability to adapt to a wider range of dynamic conditions, which in turn ensures the optimization of the Kalman filter and the consequent generation of reliable positioning results.

In this article, a novel approximate analytical solution is presented for solving the standard viral dynamic model. Basically, the standard model is used to study viral dynamics in patients for a wide range of viruses like HIV, HPV, HBV, and HCV. In this research work, the standard model for hepatitis C virus (HCV) is considered in detail; however, the analysis and results can be applicable for all other viruses. This standard model is used to study viral dynamics in patients treated with direct‐acting antiviral agents (DAAs). Power series solution combined with Laplace–Padé resummation method (PSLP) is used to obtain the approximate analytical solutions for the model. To test the capability as well as the validity of the proposed method, results are compared with available viral load data published in the literature and with published simulation results. Good fits are obtained in the comparison for all cases considered. Given that medical specialists and physicians are more interested in solutions that yield direct and simple predictions, it is expected that the proposed approximate analytical solution would be attractive to them and help them to obtain a straightforward and a proper estimation regarding the viral load due to variations in treatment and/or patient’s parameters.

Dynamic procedures, typically motivated by scale-similarity concepts, have proven highly successful and are widely used in Large Eddy Simulation (LES) for determining model coefficients. However, in hybrid Reynolds-Averaged Navier–Stokes (RANS)/LES approaches, excessively coarse grids can undermine scale-similarity-based dynamic procedures: the turbulence mode implied by the test filter scale may be inconsistent with that of the grid filter. In this study, we derive a consistent dynamic model (CDM) by minimizing dependence of stress on the turbulence-resolution length scale, thereby ensuring consistency between RANS and LES modes. The CDM is capable of recovering the standard dynamic model (SDM) in regions far from the wall, while smoothly transitioning to the RANS mode near the wall. A priori tests in homogeneous isotropic turbulence show that, as filter width increases, the SDM coefficient quickly decays toward zero, whereas the CDM remains consistent with Direct Numerical Simulation (DNS)-calibrated values, demonstrating broader applicability. A posteriori tests in turbulent channel flow confirm that the CDM avoids underprediction of CDES near the wall and accurately distinguishes between RANS and LES regions without additional wall treatments. Consequently, mean and fluctuating quantities predicted on coarse grids agree better with experimental data and scaling laws than those from LES and the SDM. In periodic hill simulations, the CDM also predicts separation and reattachment points closer to high-resolution LES, further validating its ability to capture separated flows on coarse grids.

Structural Macroeconometrics provides a thorough overview and in-depth exploration of methodologies, models, and techniques used to analyze forces shaping national economies. In this thoroughly revised second edition, David DeJong and Chetan Dave emphasize time series econometrics and unite theoretical and empirical research, while taking into account important new advances in the field. The authors detail strategies for solving dynamic structural models and present the full range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The authors look at recent strides that have been made to enhance numerical efficiency, consider the expanded applicability of dynamic factor models, and examine the use of alternative assumptions involving learning and rational inattention on the part of decision makers. The treatment of methodologies for obtaining nonlinear model representations has been expanded, and linear and nonlinear model representations are integrated throughout the text. The book offers a rich array of implementation algorithms, sample empirical applications, and supporting computer code. Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.

Investigation of pitch damping derivatives for the Standard Dynamic Model at high angles of attack using neural network

In this paper we will describe the basic features of the standard dynamic model of monopoly union in a critical perspective; starting by the Kidd-Oswald-Jones-McKenna dynamic models, we will suggest some extensions and developments, aimed to underline the importance of the strategic aspects ignored by the standard literature. By means of the theorems and concepts peculiar to the differential game theory, we will show the different solutions, in terms of wages and employment, due to the explicit analysis of the strategic-dynamic interaction between union and firms, together with the biases due to the use of the static models.

The rational inattention model has recently attracted much attention as a promising candidate to model bounded rationality in the research field of decision-making and game theory. The model presumes that the cost of information processing is proportional to the mutual information obtained from signals. It has been reported that the introduction of this information cost can explain various phenomena observed in the market, and applied in a wide range of fields such as finance, bargaining, auction, and policy analysis. However, the rational inattention model does not have a sufficient cognitive foundation, despite the amount of attention it has received. In this study, we will use both a behavioral and neural approach to investigate whether the amount of mutual information corresponds to cognitive costs.KeywordsRational inattentionMutual informationExperiment

Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm’s profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers’ limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1032 .