Dream to explore: 5-HT2a as adaptive temperature parameter for sophisticated affective inference

Relative to other neuromodulators, serotonin (5-HT) has received far less attention in machine learning and active inference. We will review prior work interpreting 5-HT1a signaling as an uncertainty parameter with opponency to dopamine. We will then discuss how 5-HT2a receptors may promote more exploratory policy selection by enhancing imaginative planning (as sophisticated affective inference). Finally, we will briefly comment on how qualitatively different effects may be observed across low and high levels of 5-HT2a signaling, where the latter may help agents to change self-adversarial policies and break free of maladaptive absorbing states in POMDPs.

The Half-Empty/Full Glass in Mental Health: A Reference-Dependent Computational Model of Evaluation in Psychopathology

Evaluation (the process attributing value to outcomes) underlies “hot” aspects of cognition, such as emotion, affect, and motivation. In several psychopathologies, such as depression and addiction, impairments in evaluation are critical. Contemporary theories highlight the reference-dependent nature of evaluation, whereby outcomes are evaluated relative to their context. Surprisingly, reference-dependent evaluation remains to be explored in the context of psychopathology. We offer a computational theory of how impaired reference-dependent evaluation might underlie mental illness. The theory proposes that evaluation derives from comparing an outcome against a reference point parameter and by weighting any discrepancy by an uncertainty parameter. Maladaptive evaluation is proposed to occur when these parameters do not reflect the true context statistics. Depending on which parameter is altered, different forms of maladaptive evaluation emerge, each associated with specific clinical conditions. This model highlights how the concept of reference-dependent evaluation can elucidate several clinical conditions, including perfectionism, depression, and addiction.

Uncertainty in Product Modelling within the Development Process

This paper gives an overview about how to locate uncertainty in product modelling within the development process. Therefore, the process of product modelling is systematized with the help of characteristics of product models and typical working steps to develop a product model. Based on that, it is possible to distinguish between product modelling uncertainty, mathematic modelling uncertainty, parameter uncertainty, simulation uncertainty and product model uncertainty.

Uncertainty Characterization of Engineering Failure Data

How to quantitatively measure the uncertainty of engineering failure data is an important but still unsolved task in probabilistic risk analysis. This paper aims to fill the gap first by specifying the requirements for a robust uncertainty measure to meet the criteria. Complexity and uncertainty measurements in computational complexity, classical statistical mechanics and information theory are also reviewed for possible inspiration. In this paper, a new groundbreaking parameter, which is related to reliability or survival function, is selected to characterize the uncertainty of engineering failure data with given probabilistic distributions. The uncertainty formulae based on the Shannon entropy and the new uncertainty parameter for various distribution functions are also provided. Finally, several examples are given to demonstrate the applicability of the new uncertainty measure in durability and reliability analyses.

The Optimal Robust Portfolio Model Based on CDaR

This paper proposed a robust portfolio model, using CDaR to measure portfolio risk. Due to uncertainty parameter, we constructed ellipsoidal uncertainty set as the parameters uncertainty set to maximize the portfolio return. We verified the operable of the model with numerical simulation. The result shows that the risk is higher compared to without robust case, which is helpful to cautious investment for investors.