A kind of rational preference

In recent years, there has been growing interest in leveraging human preferences for drug discovery to build models that capture chemists’ intuition for de novo molecular design, lead optimization, and prioritization for experimental validation. However, existing models derived from human preferences in chemistry are often black-boxes, lacking interpretability regarding how humans form their preferences. Enhancing transparency in human-in-the-loop learning is crucial to ensure that such approaches in drug discovery are not unduly affected by subjective bias, noise or inconsistency. Moreover, interpretability can promote the development and use of multi-user models in drug design projects, integrating multiple expert perspectives and insights into multi-objective optimization frameworks for de novo molecular design. This also allows for assigning more or less weight to experts based on their knowledge of specific properties. In this paper, we present a methodology for decomposing human preferences based on binary responses (like/dislike) to molecules essentially proposed by generative chemistry models, and inferring interpretable preference models that represent human reasoning. Our approach aims to bridge the gap between human-in-the-loop learning and user model interpretability in drug discovery applications, providing a transparent framework that elucidates how human judgments can shape molecular design outcomes.

The proliferation of novel preference theories in financial economics is hampered by a lack of non-experimental evidence and by the theories’ additional complexity which has not been shown to be critical in applications. In this article I present arguments in support of preferences with rank dependency. Using the Survey of Consumer Finances data, I document two widespread patterns inconsistent with expected utility: (i) many households simultaneously invest in well-deversified funds and in poorly-diversified portfolios of stocks; and (ii) some households with substantial savings do not invest anything in equities. I show that portfolio choice models with rank-dependent preferences, plausibly parameterized and under fully rational assumptions, are quantitatively consistent with the observed diversification. These results call for further efforts to integrate the models of rank-dependent preferences in portfolio theory and asset pricing. The decision theoretic literature has long acknowledged the shortcomings of the expected utility theory of Von Neumann and Morgenstern (1944). Several generalizations of the theory are inspired by the experimental evidence and aimed at improving its descriptive accuracy. However, there are few applications of new models of preferences in finance, and expected utility continues to dominate the field. 1 Two principal hurdles slow the proliferation of novel preferences in financial economics: first, the lack of empirical (non-experimental or ‘‘field’’) data confirming that the behavior motivating non-expected utilities is widespread outside the laboratory settings; second, the additional and often significant complexity of the alternative preferences which has not been shown to be critical in applications where the models based on expected utility fall short of reasonable predictions. This article presents empirical and theoretical This paper was previously circulated under the title ‘‘Household Portfolio Diversification.’’ The paper has benefited from the comments of Shlomo Benartzi, Luca Benzoni, Markus Brunnermeier, John Dickhaut, Maria Perozek, Raj Singh, Hersh Shefrin, and workshop participants at Minnesota Finance bag lunch, 2003 Wharton conference ‘‘Household Financial Decision Making and Portfolio Choice’’ and 2003 summer meetings of Econometric Society. I would like to thank Cam Harvey (the editor) and an anonymous referee for many helpful comments and suggestions. A special thanks to Jack Kareken for comments and editorial help on an earlier draft. I am grateful to Donna Zerwitz for editorial assistance. The views expressed herein are those of the author and not necessarily of the Federal Reserve Bank of Minneapolis or the Federal Reserve System. All errors and omissions are the sole responsibility of the author. Address correspondence to Valery Polkovnichenko, Finance 3-122 CSOM, 319 19 th

Human visual behavior on a product significantly affects their purchasing behavior during online shopping. In this study, two experimental studies were performed to investigate human visual behavior and preference for sneakers using an eye tracking technology. The first study discovered that shoelace and vamp areas of interests (AOIs) attracted more attention than the other AOIs. The second study explored the factors affecting human behavior on sneakers, which employed 30 students from different professional backgrounds (i.e., such as fashion and non-fashion disciplines), and examined 24 sneakers, i.e., combinations of four shoelace styles and six vamp materials. The results showed that both genders irrespective of their professional backgrounds were more concerned about the shoelaces than vamps. The shoelace AOI gained more attention of females than males, while the vamp AOI was more concerned by males than females. The vamp AOI was more concerned by non-professionals than professionals, while the shoelace AOI was paid more attention by professionals than non-professionals. Besides, flat or round shoelaces, canvas, and cow leather or cotton flannel vamp materials were more preferred by the participants than the other types. The findings are of great help for the fashion product designers, the manufacturers, and the sellers to provide the product required by the customers.

Some research about the value of road safety has been widely practiced in many countries. One approach that is commonly used for the determination of the value of the safety of road transport is by asking directly to the public about the preferences or willingness to pay in order to improve the safety aspects. Human preferences in this model are built on the behavioral theory with economics perspective, which explains that human behavior is formed by preference. The basic premise of this theory states that every human being has a preference for a number of goods. The problem is that safety is included in the category of non-market goods that are not traded and often rrespondents refuse to answer questions directly related to willingness to pay. Therefore, in order to obtain objective answers from the respondents, the form of questions for the willingness to pay should be linked with other proxies. The purpose of this study was to calculate the willingness to pay on valuation of road safety for specific communities by designing the willingness to pay questions which is associated with the proxies of vehicle maintenance and health maintenance.

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.

This paper examines how behavioral economics influences public policy implementation through a comprehensive review of open-access academic and public materials. It investigates the effectiveness of policies that consider non-rational factors in human behavior, including cognitive biases, emotions, social norms, and habits. It emphasizes the need for policymakers to incorporate behavioral economics principles through training, expert guidance, pilot programs, impact studies, incentives, specialized units, and continuous evaluation. The paper proposes strategies for understanding behavioral psychology, designing incentive systems, creating environments that facilitate sound decision-making, and promoting awareness about the irrational factors that influence human conduct.

We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.

In this paper we first address a long-standing criticism of human rationality and what that means for the role of government. We review and compare much of the literature on rationality and demonstrate that various authors within various fields often mean very different things by the word «rational.» While we make no claims as to whether or not humans always behave rationally, we point out the flawed logic for what is suggested for the role of government as a way of addressing the human irrationality problem. Building on the Mises-Rothbard-Huerta de Soto tradition, we argue that what is more important than perfect rationality is purposeful action. We explain the dynamic nature of the market in which time plays an important role, and humans act with expectations to accomplish goals, learn from past mistakes, discover new information and modify their plans accordingly. Using Hayek’s approach, we discuss the knowledge problem in which data is dispersed among millions of individuals (unknown in its entirety to any central authority) as well as the problems with applying the scientific method exactly as it is used in the natural sciences to the human behavioral sciences. These problems combined, we argue, make for a much more disastrous system than would be a system in which often-irrational individuals would be free to make mistakes for themselves, discover new information and take actions for their own betterment.
 Key words: Rationality, Government Intervention, Equilibrium, Human Action, Purposeful Behaviour.
 JEL Classification: B53, D80, D84, H10, H30.
 Resumen: En este artículo abordamos en primer lugar una vieja crítica de la racionalidad humana y lo que ello implica para el papel que desempeña el gobierno. Revisamos y comparamos gran parte de la literatura sobre ra-cionalidad, y demostramos que distintos autores dentro de diversos campos atribuyen cosas muy diferentes a la palabra «racional». Si bien no hacemos ningún tipo de reivindicación respecto a si o no los seres humanos se com-portan siempre racionalmente, subrayamos la defectuosa lógica por la que se sugiere el papel del gobierno como una forma de abordar el problema de la irracionalidad humana. Siguiendo la tradición Mises-Rothbard-Huerta de Soto, sostenemos que más importante que la racionalidad perfecta es la acción intencionada. Explicamos la naturaleza dinámica del mercado en la que el tiempo juega un papel importante, y los seres humanos actúan con ex-pectativas para lograr sus metas, aprenden de los errores del pasado, des-cubren nueva información y modifican sus planes en consecuencia. Utilizando el enfoque de Hayek, se discute el problema del conocimiento en el que los datos están dispersos entre millones de individuos (desconocidos en su to-talidad para cualquier autoridad central), así como los problemas con la aplicación del método científico tal y como se utiliza en las ciencias de la naturaleza a las ciencias del comportamiento humano. Estos problemas combinados, sostenemos, generan un sistema mucho más desastroso de lo que sería un sistema en el que los individuos con frecuencia irracionales fuesen libres para cometer errores por sí mismos, descubrir nueva información y tomar acciones para su propia mejora.
 Palabras clave: Racionalidad, Intervención del Gobierno, Equilibrio, Acción Humana, Comportamiento Intencionado.
 Clasificación JEL: B53, D80, D84, H10, H30.

Evidence that human preferences, beliefs, and behavior are influenced by sources that are outside the reach of conscious awareness, control, intention, and self-reflection is incontrovertible. Recent advances in neuroscience have enabled researchers to investigate the neural basis of these implicit attitudes, particularly attitudes involving social groups. From this research, a model with three identified neural components related to the automatic activation and regulation of implicit attitudes is beginning to emerge. The amygdala is implicated in the automatic evaluation of socially relevant stimuli, while the anterior cingulate and dorsolateral prefrontal cortices are involved in the detection and regulation, respectively, of implicit attitudes. Further support for this model comes from the inclusion of these regions in current models concerning the cognitive regulation of emotion and the detection of conflict. The identification of a putative neural substrate for implicit attitudes has had a direct impact on psychological research into their nature and operational characteristics. We discuss how this emerging neural model has influenced current research on implicit attitudes and describe the importance of such models for directing future research.

The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.

Several studies have used Wikipedia (WP) data-set to analyse worldwide human preferences by languages. However, those studies could suffer from bias related to exceptional social circumstances. Any massive event promoting exceptional editions of WP can be defined as a source of bias. In this article, we follow a procedure for detecting outliers. Our study is based on 12 languages and 13 different categories. Our methodology defines a parameter, which is language-dependent instead of being externally fixed. We also study the presence of human cyclic behavior to evaluate apparent outliers. After our analysis, we found that the outliers in our data-set do not significantly affect the analysis of preferences by categories among different WP languages. While investigating the possibility of bias related to exceptional social circumstances is always a safe measure before doing any analysis on Big Data, we found that in the case of the first ten years of the Wikipedia data-set, outliers do not significantly affect using Wikipedia data-set as a digital footprint to analyse worldwide human preferences.

Product design typically involves a mix of custom, semi-custom, and standard components. A mathematical model of a design problem may also mix custom models and cataloged models that represent standard elements or families of elements. Further, design problem models are often decomposed into subsystems and delegated to appropriate experts, each of whom may use their own preferred modeling and analysis tools. A degree of coupling usually exists between these subsystems so they cannot be optimized independently, yet it can be difficult to understand their integrated system performance. Ideally, it would be possible to link this set of heterogeneous and distributed models to represent the complete product in a way that facilitates rapid exploration of design tradeoffs and global optimization.

Traditional models of opinion dynamics provide a simplified approach to understanding human behavior in basic social scenarios. However, when it comes to issues such as polarization and extremism, a more nuanced understanding of human biases and cognitive tendencies are required. This paper proposes an approach to modeling opinion dynamics by integrating mental models and assumptions of individuals agents using Bayesian-inspired methods. By exploring the relationship between human rationality and Bayesian theory, this paper demonstrates the usefulness of these methods in describing how opinions evolve. The analysis here builds upon the basic idea in the Continuous Opinions and Discrete Actions (CODA) model, by applying Bayesian-inspired rules to account for key human behaviors such as confirmation bias, motivated reasoning, and human reluctance to change opinions. Through this, This paper updates rules that are compatible with known human biases. The current work sheds light on the role of human biases in shaping opinion dynamics. I hope that by making the model more realistic this might lead to more accurate predictions of real-world scenarios.

Chapter 10 - Preference functions and risk-adjusted performance measures

FGF21 Regulates Sweet and Alcohol Preference