I am grateful to Joseph B. Kadane for numerous constructive suggestions offered during discussions of this research. The financial sponsorship of the U.S. Department of Transportation through grant DOT-OS-4006 is also acknowledged. The opinions and conclusions expressed herein are solely those of the author.

Econometric analysis of discrete choice has made considerable use of random utility models to interpret observed choice behavior. Much empirical research concerns choice problems in which persons act with partial knowledge of the utilities of the feasible actions. Economists use random expected utility models to analyze such choice problems. A common practice is to specify fully the expectations that persons hold, in which case choice analysis reduces to inference on preferences alone. However, the expectations assumptions made (...) in empirical research rarely have much foundation. To enable more credible research, this paper considers inference when one specifies a set of expectations that decision makers may plausibly hold. I first pose the idea in abstraction and then specialize to binary response with linear utilities, where the analysis is particularly straightforward. I initially assume that decision makers possess unique subjective probability distributions on the states of nature and make choices that maximize expected utility. I later consider the possibility that persons place only partial probabilistic structure on the states of nature and make undominated choices. (shrink)

This paper discusses random utility representations of the Luce model. Earlier works, such as McFadden, Yellott, and Strauss have discussed random utility representations under the assumption that utilities are additively separable in a deterministic and a random part. Under various conditions, they have established that a separable and independent random utility representation exists if and only if the random terms are type III extreme value distributed. This paper analyzes independent random (...) utility representations without the separability condition and with an infinite universal set of alternatives. Under these assumptions, it turns out that the most general random utility representation of the Luce model is a utility function that is an arbitrary strictly increasing transformation of a separable utility function with extreme value distributed random terms. (shrink)

Though the Random Utility Model (RUM) was conceived entirely in terms of ordinal utility, the apparatus through which it is widely practised exhibits properties of cardinal utility. The adoption of cardinal utility as a working operation of ordinal is perfectly valid, provided interpretations drawn from that operation remain faithful to ordinal utility. The article considers whether the latter requirement holds true for several measurements commonly derived from RUM. In particular it is found that (...) measurements of consumer surplus change may depart from ordinal utility, and exploit the cardinality inherent in the practical apparatus. (shrink)

While the paradigm of utility maximisation has formed the basis of the majority of applications in discrete choice modelling for over 40 years, its core assumptions have been questioned by work in both behavioural economics and mathematical psychology as well as more recently by developments in the RUM-oriented choice modelling community. This paper reviews the basic properties with a view to explaining the historical pre-eminence of utility maximisation and addresses the question of what departures from the paradigm may (...) be necessary or wise in order to accommodate richer behavioural patterns. We find that many, though not all, of the behavioural traits discussed in the literature can be approximated sufficiently closely by a random utility framework, allowing analysts to retain the many advantages that such an approach possesses. (shrink)

This paper studies two models of rational behavior under uncertainty whose predictions are invariant under ordinal transformations of utility. The ‘quantile utility’ model assumes that the agent maximizes some quantile of the distribution of utility. The ‘utility mass’ model assumes maximization of the probability of obtaining an outcome whose utility is higher than some fixed critical value. Both models satisfy weak stochastic dominance. Lexicographic refinements satisfy strong dominance.The study of these utility models (...) suggests a significant generalization of traditional ideas of riskiness and risk preference. We define one action to be riskier than another if the utility distribution of the latter crosses that of the former from below. The single crossing property is equivalent to a ‘minmax spread’ of a random variable. With relative risk defined by the single crossing criterion, the risk preference of a quantile utility maximizer increases with the utility distribution quantile that he maximizes. The risk preference of a utility mass maximizer increases with his critical utility value. (shrink)

Cross-classified random effects models have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs. We also introduce several less well-known applications of CCREMs. The flexibility of CCREMs allows (...) these models to be easily extended to address substantive questions. We use the free R package PLmixed to illustrate the estimation of these models, and show how the general language of the CCREM framework can be translated into specific modeling contexts. (shrink)

Research on reasoning about consequential arguments has been an active but piecemeal enterprise. Previous research considered in depth some subclasses ofconsequential arguments, but further understanding of consequential arguments requires that we address their greater variety, avoiding the risk of over-generalisation from specific examples. Ideally we ought to be able to systematically generate the set of consequential arguments, and then engage in random sampling of stimuli within that set. The current article aims at making steps in that direction, using the (...) theory of utility conditionals as a way to generate a large set of consequential arguments, and offering one study illustrating how the theory can be used for the random sampling of stimuli. Itis expected that further use of this method will bring more diversity to experimental research on consequential arguments, and more robustness to models of argumentation from consequences. (shrink)

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete (...) choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level instantiations for random utility models. (shrink)

Expected utility maximization problem is one of the most useful tools in mathematical finance, decision analysis and economics. Motivated by statistical model selection, via the principle of expected utility maximization, Friedman and Sandow (J Mach Learn Res 4:257–291, 2003a) considered the model performance question from the point of view of an investor who evaluates models based on the performance of the optimal strategies that the models suggest. They interpreted their performance measures in information theoretic terms and (...) provided new generalizations of Shannon entropy and Kullback–Leibler relative entropy and called them U-entropy and U-relative entropy. In this article, a utility-based criterion for independence of two random variables is defined. Then, Markov’s inequality for probabilities is extended from the U-entropy viewpoint. Moreover, a lower bound for the U-relative entropy is obtained. Finally, a link between conditional U-entropy and conditional Renyi entropy is derived. (shrink)

Expected utility maximization problem is one of the most useful tools in mathematical finance, decision analysis and economics. Motivated by statistical model selection, via the principle of expected utility maximization, Friedman and Sandow (J Mach Learn Res 4:257–291, 2003a) considered the model performance question from the point of view of an investor who evaluates models based on the performance of the optimal strategies that the models suggest. They interpreted their performance measures in information theoretic terms and (...) provided new generalizations of Shannon entropy and Kullback–Leibler relative entropy and called them U-entropy and U-relative entropy. In this article, a utility-based criterion for independence of two random variables is defined. Then, Markov’s inequality for probabilities is extended from the U-entropy viewpoint. Moreover, a lower bound for the U-relative entropy is obtained. Finally, a link between conditional U-entropy and conditional Renyi entropy is derived. (shrink)

Empirical research often requires a method how to convert a deterministic economic theory into an econometric model. A popular method is to add a random error term on the utility scale. This method, however, ignores stochastic dominance. A modification of this method is proposed to account for stochastic dominance. The modified model compares favorably to other existing models in terms of goodness of fit to experimental data. The modified model can rationalize the preference reversal phenomenon. (...) An intuitive axiomatic characterization of the modified model is provided. Important microeconomic concept of risk aversion is well defined in the modified model. (shrink)

Supply chain management is critical for addressing uncertainties caused by random demand and production yield. In this study, a random yield supply chain, in which the production process of fresh agricultural products is easily affected by factors, such as weather, soil, or plant diseases and insect pests, was considered. Therefore, the actual output is typically less than the planned output. A model in which the producer faces yield uncertainty was considered, and the influence of the producer’s fairness (...) concerns on the planned output of the contract-farming supply chain was considered. Methods to motivate the producer to increase planned output through reasonable contracts were investigated. The results revealed that the producer’s fairness concern considerably influences the optimal expected profit. In the case of the random output, the planned output and maximum expected profits reduce when the farmer exhibits fairness concerns. As the degree of the producer’s fairness concern increases, the planned output and expected profit gradually decrease The cost-sharing contract can effectively encourage the producer to increase his planned output and expected utility. Finally, the results were verified numerically. (shrink)

Models of choice over menus aim at capturing the effect of some behavioral or non-standard element of decision-making on the behavior of a single decision-maker. These models are usually compared with the standard model of choice over menus, in which the decision-maker chooses a menu whose best item is better than that of all other available ones. However, in many empirical settings such as experimental studies, choice data come from a population of decision-makers with possibly heterogeneous attitudes and tastes. (...) This heterogeneity can make the observed choices over menus stochastic. This fact calls for a stochastic characterization of models of choice over menus to be able to better compare and contrast different models empirically. In this paper, I do this task for the standard model, which would be an extension of the random utility model to the realm of choice over menus. In particular, I provide the necessary and sufficient conditions, i.e., axioms on choice data over menus for it to be consistent with a population of decision-makers each of whom behaves according to the standard model. The axioms that characterize the model are the axiom of revealed stochastic preferences over singletons and three rationality axioms. (shrink)

The possibility to interpret expected and nonexpected utility theories in purely probabilistic terms has been recently investigated. Such interpretation proposes as guideline for the Decision Maker the comparison of random variables through their probability to outperform a stochastic benchmark. We apply this type of analysis to the model of Becker and Sarin, showing that their utility functional may be seen as the probability that an opportune random variable, depending on the one to be evaluated, does (...) not outperform a non-random benchmark. Further, the consequent choice criterion is equivalent to a sort of probability of ruin. Possible interpretations and financial examples are discussed. (shrink)

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production, heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and (...) sustainability in the industry. This paper presents an economic model of a microgrid system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression model was optimized by the gravitational search algorithm. The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic, CCHP system, fuel cell, and energy storage devices are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model. (shrink)

This study explores the plausibility of regret minimization as behavioral paradigm underlying the choice of crash avoidance maneuvers. Alternatively to previous studies that considered utility maximization, this study applies the random regret minimization model while assuming that drivers seek to minimize their anticipated regret from their corrective actions. The model accounts for driver attributes and behavior, critical events that made the crash imminent, vehicle and road characteristics, and environmental conditions. Analyzed data are retrieved from the General (...) Estimates System crash database for the period between 2005 and 2009. The predictive ability of the RRM-based model is slightly superior to its RUM-based counterpart, namely the multinomial logit model model. The marginal effects predicted by the RRM-based model are greater than those predicted by the RUM-based model, suggesting that both models should serve as a basis for evaluating crash scenarios and driver warning systems. (shrink)

This note establishes a new identification result for additive random utility discrete choice models. A decision-maker associates a random utility Uj+mj\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U_{j}+m_{j}$$\end{document} to each alternative in a finite set j∈1,…,J\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j\in \left\{ 1,\ldots,J\right\} $$\end{document}, where U=U1,…,UJ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {U}=\left\{ U_{1},\ldots,U_{J}\right\} $$\end{document} is unobserved by the researcher and random with an unknown joint distribution, (...) while the perturbation m=m1,…,mJ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {m}=\left $$\end{document} is observed. The decision-maker chooses the alternative that yields the maximum random utility, which leads to a choice probability system m→Pr1|m,…,PrJ|m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf { m\rightarrow }\left,\ldots,\Pr \left \right) $$\end{document}. Previous research has shown that the choice probability system is identified from the observation of the relationship m→Pr1|m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mathbf {m}\rightarrow \Pr \left $$\end{document}. We show that the complete choice probability system is identified from observation of a relationship m→∑j=1sPrj|m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {m}\rightarrow \sum _{j=1}^{s}\Pr \left $$\end{document}, for any sshrink)

In the presence of incomplete risk attitudes, choices between noncomparable risky prospects are random. A random choice model advanced by Karni, 2021) includes the hypothesis that choices among noncomparable risky prospects are prompted by signals drawn from personal distributions. This paper introduces a scheme designed to elicit subjects’ assessments of their personal likelihoods of choices among noncomparable risky prospects and describes experiments designed to test the aforementioned hypothesis.

Little is known about health of the growing subpopulation of the working poor in Japan. We aimed to evaluate health status and healthcare utilization in relation to income among Japanese working adults. We conducted a one-month prospective cohort study using a health diary in working adults from a nationally representative random sample in Japan. Based on the government criterion, the working poor group was defined as earning an equivalent annual income of less than 1.48 million Japanese-yen. For health status, (...) we measured symptomatic episodes and health-related quality of life (HRQOL). For healthcare utilization, we measured frequencies of visits to a physician or pharmacy, and use of complementary and alternative medicine (CAM). We constructed multiple linear regression models for these measures adjusted for age, gender, and co-morbidity, using annual equivalent income as a 4-level categorical variable. (shrink)

In both theoretical and applied modeling in behavioral sciences, it is common to choose a mathematical specification of functional form and distribution of unobservables on grounds of analytic convenience without support from explicit theoretical postulates. This article discusses the issue of deriving particular qualitative hypotheses about functional form restrictions in structural models from intuitive theoretical axioms. In particular, we focus on a family of postulates known as dimensional invariance. Subsequently, we discuss how specific qualitative postulates can be reformulated so as (...) to allow for conventional statistical hypothesis testing, and we also derive details of a particular likelihood ratio test procedure. An empirical application of the testing procedure is carried out, based on data from a Stated Preference survey. (shrink)

Experimental data on social preferences present a number of features that need to be incorporated in econometric modelling. We explore a variety of econometric modelling approaches to the analysis of such data. The approaches under consideration are: the Random Utility approach ; the Random Behavioural approach ; and the Random Preference approach. These approaches are applied in various ways to an experiment on fairness conducted by Cappelen et al. :818–827, 2007). Various models that we estimate succeed (...) in capturing the key features of the dataset. Conclusions concerning fairness-related behaviour depend crucially on the choice of econometric model. (shrink)

Chaotic responses to COVID-19, political polarization, and pervasive misinformation raise the question of whether some or many individuals exercise irrational moral judgment. We provide the first mathematically correct test for transitivity of moral preferences. Transitivity is the most prominent rationality criterion of the behavioral, biological, and economic sciences. However, transitivity is conceptually, mathematically, and statistically difficult to evaluate empirically. We tested three parsimonious, order-constrained, probabilistic characterizations: First, the weak utility model treats an individual's choices as noisy reflections of (...) a single, deterministic, underlying transitive preference; second, a variant severely limits the allowable response noise; and third, by the general random utility hypothesis, individuals' choices reveal uncertain, but transitive, moral preferences. Among 28 individuals, everyone's data were consistent with the weak utility model and general random utility model, thus supporting both operationalizations. Tightening the bounds on error rates in noisy responses yielded a poorly performing model, thus rejecting the model according to which choices are highly consistent with a single transitive preference. Bayesian model selection favored probabilistic transitive preferences and hence the equivalent random utility hypothesis. This suggests that there is some order underlying the apparent chaos: Rather than presume widespread disregard for moral principles, policymakers may build on navigating and reconciling extreme heterogeneity compounded with individual uncertainty. (shrink)

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur (...) in Web browsers to internal user goals. Web‐site leaving is modeled as an ongoing assessment by the Web user of the expected benefits of continuing at a Web site as opposed to going elsewhere. These cost–benefit assessments are also based on spreading activation models of information scent. Evaluations include a computational model of Web user behavior called Scent‐Based Navigation and Information Foraging in the ACT Architecture, and the Law of Surfing, which characterizes the empirical distribution of the length of paths of visitors at a Web site. (shrink)

The process of gene expression is affected by many extracellular stimulus signals, and the stochasticity of these signals reshapes gene expression. To adapt the fluctuation of the extracellular environment, genes have many strategies for augmenting their survival probability, frequency modulation, and amplitude modulation. However, it is unclear how genes utilize the stochasticity of signals to regulate gene expression and which strategy will be chosen to maximize cellular function. Here, we analyze a simple mechanistic model to clarify the effect of (...) extracellular random input on gene expression and burst kinetics at different timescales. We can see that in different contexts, extracellular noise has different effects on downstream gene expression, effects which include the following: extracellular noise will make the ON-OFF-state dwell time drift, which will influence the burst frequency and burst size of downstream gene expression under different modulation paradigms; comparing the burst parameter or gene expression products under different modulation paradigms, we can see that the amplitude signal is more sensitive in the case of extracellular noise input, whereas the signal in noiseless conditions is more sensitive when the random input is a fast process, which indicates that the amplitude signal is a superior and common signal in gene expression; and extracellular random input will change the bimodality for gene expression, but its influence is different for gene expression products under different modulation paradigms. These qualitative results reveal that extracellular random input can prompt the gene to achieve its function quickly under different modulation paradigms. (shrink)

Resolutions of choice spaces capture hierarchical processes of choice. Cantone, Giar-lotta and Watson (2021) formalize the concept in the setting of deterministic choice. We extend the notion of a resolution to probabilistic choices. Our main result is about the random utility model, also known as the multiple choice model (MCM, characterized by Falmagne in 1978). It states that any resolution of probabilistic choices satisfying the MCM also satisfies the MCM. We provide three proofs, each offering specific (...) insights. In contrast, Luce model (1959) of probabilistic choice is not preserved by resolutions. A probabilistic choice is hierarchical if it is equal to the outcome of some nontrivial resolution. Our second result is a simpler characterization of such choices, as those admitting a shrinkable subset. Here a shrinkable subset is a collection of elements that are, intuitively, indistinguishable from outside the collection. Our final result establishes that special ‘single-point’ resolutions suffice to generate all resolutions. (shrink)

Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: (...) rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with. (shrink)

The optimal investment problem is studied for a continuous time incomplete market model. It is assumed that the risk-free rate, the appreciation rates, and the volatility of the stocks are all random; they are independent from the driving Brownian motion, and they are currently observable. It is shown that some weakened version of Mutual Fund Theorem holds for this market for general class of utilities. It is shown that the supremum of expected utilities can be achieved on a (...) sequence of strategies with a certain distribution of risky assets that does not depend on risk preferences described by different utilities. (shrink)

This chapter inspects the question of conjecture in economics and probability in relation to the assumption of perfect foresight or more technically apodictic certainty. The main theme for this part is appropriated for the query on the practical applicability of probability theory to the randomness of economic events. In other words, how far is it possible to apply probability theory to economic events which are in nature erratic and uncertain? In this chapter, multidisciplinary approach will be adopted in order to (...) reach practitioners and other scholars in social sciences. A comparative analysis between different branches of social sciences such as history, sociology, etc., will allow us to understand how far the practical applicability is appropriate and plausible. Particularly in economics and generally in other social sciences heavy utilization of intricate econometric models is being increasingly criticized as futile. Manipulation and domination of data over scientific knowledge is another part of the problem. The article titled “Scientism and Study of Society” by Hayek is an inspiration for this purpose. (shrink)

Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable (...) feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data. (shrink)

This article examines mixed ℋ -infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network models with randomly occurring coupling delays and actuator faults. The randomly occurring coupling delays are considered to design the complex dynamical networks in practice. These delays complied with certain Bernoulli distributed white noise sequences. The relevant data including limits of actuator faults, bounds of the nonlinear terms, and external disturbances are available for designing the controller structure. Novel Lyapunov–Krasovskii functional is constructed to verify the (...) stability of the error model and performance level. Jensen’s inequality and a new integral inequality are applied to derive the outcomes. Sufficient conditions for the synchronization error system are given in terms of linear matrix inequalities, which can be analyzed easily by utilizing general numerical programming. Numerical illustrations are given to exhibit the usefulness of the obtained results. (shrink)

Stress and mental ill-health carry considerable costs for both individuals and organizations. Although interventions targeting compassion and self-compassion have been shown to reduce stress and benefit mental health, related research in organizational settings is limited. We investigated the effects of a 6-week psychological intervention utilizing compassion training on stress, mental health, and self-compassion. Forty-nine employees of two organizations were randomly assigned to either the intervention or a physical exercise control condition. Multilevel growth models showed that stress and mental ill-health decreased (...) over 3 months in both groups, while self-compassion only increased in the intervention group. There were no significant effects on life satisfaction in any of the groups. The findings show promising results regarding the ability of compassion training within organizations to decrease stress and mental ill-health and increase self-compassion. (shrink)

Analyzing obesity-related factors and classifying individuals based on their risk status are critical for improving the effectiveness of health policies. This study aimed to predict individuals’ predisposition to obesity using classification algorithms. The dataset, obtained from an open-access data source, contains 22,869 records and 16 attributes, including demographic information, physical activities, and dietary habits. Various preprocessing steps were performed on the data to improve model accuracy and reliability by improving data quality. This preprocessing phase included removing missing data, scaling (...) data, and processing outliers. Principal component analysis and linear discriminant analysis were used to identify the most useful attributes by removing irrelevant attributes from the data. After identifying useful features in the dataset, a hybrid model was developed to classify individuals’ obesity levels by leveraging the strengths of various machine learning (support vector machines, decision trees, gradient boosting, random forest, and logistic regression) and deep learning algorithms (autoencoders, convolutional neural networks, and recurrent neural networks), as well as individual classification algorithms. In order to achieve higher classification performance, a hybrid model was developed. K-Fold cross-validation was performed to evaluate the performance of this model alone. The hybrid model demonstrated the highest classification performance among all individual models, with an accuracy rate of 90%. Considering the global impact of obesity, the findings of this study will make significant contributions to many areas such as early diagnosis and risk assessment, development of health policies, reduction of health expenditures, efficient use of resources, and development of personalized intervention strategies. (shrink)

For decades, discrete choice modelling was practically dominated by only two models: multinomial probit and logit. This paper presents a novel alternative—harmonic choice model. It is qualitatively similar to multinomial probit and logit: if one choice alternative greatly exceeds all (falls below at least one of) other alternatives in terms of utility then it is chosen with probability close to one (zero). Compared to probit and logit, the new model has relatively flat tails and it is steeper (...) in the neighborhood of zero (when all alternatives yield the same utility and the decision maker chooses among them at random). (shrink)

Integrating the categorization-elaboration model (CEM), resource complementarity, and human capital perspectives, we investigate whether the founder CEO’s human capital resources can influence the effect of workforce gender diversity, an untapped strategic resource, on venture firm performance. Our main focus lies on knowledge- and technology-intensive venture firms, and we propose that both workforce gender diversity and the CEO’s human capital resources jointly influence venture firm performance. We identify the specific functional impacts of the founder CEO's human capital, encompassing entrepreneurial experience, (...) human resource (HR) management experience, and same-industry experience. Analyzing 1413 venture firms utilizing multilevel analysis (random coefficient modeling), we find that gender diversity does not significantly affect venture firm performance. Nevertheless, the founder CEO’s entrepreneurial experience exerts a positive impact on the interplay between gender diversity and venture firm performance. These findings underscore the strategic role of the founder CEO’s human capital resources in moderating the effects of gender diversity, making significant contributions to the diversity and strategic human capital resource literature. (shrink)

This poster presents results from applying agent-based modeling to an exploration of risk attitudes and rational decision making in the context of group interaction. We are also interested in the place of agent-based modeling and computational philosophy within the computational humanities. Computational philosophy has not typically been included in Digital Humanities; computational work has been done using philosophy texts as a source for analysis (Kinney 2022; Malaterre et al. 2021; Fletcher et al. 2021; Zahorec et al. 2022), but there are (...) few examples of philosophical arguments being made based on computation (Zahorec et al. 2022). Modeling is largely accepted as a computational humanist method (McCarty 2004; So 2022), but typically models are based on data rather than designed simulations as a method analogous to a thought experiment (Mayo-Wilson / Zollman 2021). Agent-based modeling is not as common in computational humanities, but we are interested in its potential; for instance, see work modeling correspondence networks to better understand lost information and biases in correspondence data sets (Buarque / Vogl 2023), or work simulating past human societies (Romanowska et al. 2021). Our project explores Lara Buchak’s theory of risk-weighted rational decision making by extending it to the context of group populations and interactions. Buchak has proposed risk-weighted expected utility maximization, which incorporates individual risk attitudes into the standard expected utility (EU) calculation based on utility and probability of outcomes (Buchak 2017). This theory provides an explanation for differences of behavior seen in human populations, while still allowing those different choices to be rational. Buchak’s theory has previously only been applied to individual decision making; our work uses agent-based modeling to develop and analyze simulations which incorporate a variety of risk attitudes (risk avoidance and risk seeking) into game theoretic interactions. This project is a collaboration between a philosopher and a research software engineer; we have worked together to develop simulations that implement agents with risk attitudes making risky choices. This presentation focuses on results from an implementation of a hawk/dove game with multiple risk attitudes and risk attitude adjustment. In the hawk/dove game cooperation, or playing dove, is better for the population, but acting aggressively, or playing hawk, is better for an individual if they are in a neighborhood of doves. To set up the game, we place agents on a grid and have them play against each of their neighbors, and accumulate payoffs based on the success of their plays. We define risk attitudes 0 through 9, where that number corresponds to the minimum number of neighbors playing dove for an agent to play hawk. An agent with risk attitude 0 is most risk seeking and always plays hawk; an agent with risk attitude 1 will play hawk if at least one neighbor plays dove; an agent with risk attitude 9 always takes the safe choice and plays dove; an agent with risk attitude 4 or 5 is risk neutral (breaking ties in different ways), which corresponds to expected utility. In this simulation we randomly set agent initial risk attitudes, and then every ten rounds agents compare their payoffs with their neighbors and adopt the most successful risk attitude in their neighborhood. The simulation is coded to stop once risk attitude adjustments stabilize. We define 13 different population states based on primary and second majority of risk averse, risk inclined, or risk moderate, and analysis of over 1 million runs indicate that while it’s more likely to stabilize with the population having become majority risk inclined, that is not the only outcome; we also see cases where the population stabilizes as risk neutral, risk avoidant, and many cases with no clear majority. This diversity of stable populations provides support for the argument that no one risk attitude is uniquely rational, but rather that risk attitudes are conventional. This matches what we observe in the real world: that different populations have different risk attitudes. Our simulations include numerous parameters to vary grid size; adjustment frequency, strategy, and wealth comparison; neighborhood sizes for observing previous choice, playing against, and comparing payoffs for adjusting risk attitudes; and different distributions for initializing risk attitudes. Analysis of parameters and adjusted risk attitudes shows that the strongest correlations are the initial distribution of risk attitudes, which tend to be preserved. * * * This poster was accepted and presented at the DH2024 conference in Washington, D.C. (shrink)

ABSTRACT School psychologists are frequently confronted with ethically challenging situations arising from the need to balance multiple parties’ competing interests and the challenge of serving as both student advocate and school employee. Use of a systematic decision-making model has been recommended as a way of improving the quality of school psychologists’ ethical decisions. In the present study, school psychology practitioners were randomly assigned to one of two conditions: a Critical Evaluative condition, requiring the use of a problem-solving approach to (...) resolve an ethical dilemma, or an Intuitive condition, which entailed generating a solution based solely on intuition and professional experience/judgment. Use of a formal decision-making model did not result in higher caliber ethical resolutions. Participants’ age and number of years in the field were significantly correlated with raters’ judgments that solutions considered the consequences of a chosen course of action. Most participants reported that they did not utilize an ethical decision-making model during the course of their practice, relying instead on consultation with peers/colleagues and their own professional experience. Implications of these findings are discussed in the context of school psychology practice. (shrink)

The importance of ethical consumerism to many companies worldwide has increased dramatically in recent years. Ethical consumerism encompasses the importance of non-traditional and social components of a company's products and business process to strategic success - such as environmental protectionism, child labor practices and so on. The present paper utilizes a random utility theoretic experimental design to provide estimates of the relative value selected consumers place on the social features of products.

Integrating the categorization-elaboration model (CEM), resource complementarity, and human capital perspectives, we investigate whether the founder CEO’s human capital resources can influence the effect of workforce gender diversity, an untapped strategic resource, on venture firm performance. Our main focus lies on knowledge- and technology-intensive venture firms, and we propose that both workforce gender diversity and the CEO’s human capital resources jointly influence venture firm performance. We identify the specific functional impacts of the founder CEO's human capital, encompassing entrepreneurial experience, (...) human resource (HR) management experience, and same-industry experience. Analyzing 1413 venture firms utilizing multilevel analysis (random coefficient modeling), we find that gender diversity does not significantly affect venture firm performance. Nevertheless, the founder CEO’s entrepreneurial experience exerts a positive impact on the interplay between gender diversity and venture firm performance. These findings underscore the strategic role of the founder CEO’s human capital resources in moderating the effects of gender diversity, making significant contributions to the diversity and strategic human capital resource literature. (shrink)

Microfinance is a preferred development tool in many developing countries around the world; however, the industry has been facing many challenges in recent years, including the attainment of financial sustainability. Therefore, this study is aimed at investigating the effect of employee turnover on the financial performance of microfinance institutions (MFIs). The study utilized unbalanced panel data of 1561 unique MFIs from 2010 to 2018. The data were then analyzed by conventional econometric techniques such as the pooled ordinary least squares, (...) class='Hi'>random effects model, fixed effects model, Hausman–Taylor, and the two-step system generalized method of moments. Considering the linear relationship, it was discovered that employee turnover has a negative effect on the financial performance of MFIs. However, this relationship is dependent on the proxies of financial performance, legal status, location, profit orientation of the MFIs (for subsample analysis), and the technique(s)/model(s) employed in the analysis. Furthermore, the study did not find empirical evidence to support the quadratic relationship between employee turnover and the financial performance of MFIs. The outcome of this study advances important policy implications for concerned stakeholders. (shrink)

BackgroundUpper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke.AimSynthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke.MethodsPubMed, SCOPUS, and PEDro databases were systematically searched (...) for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols.ResultsTwenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to non-sEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I255%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I273%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline.ConclusionThis review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions. (shrink)

Sentence boundary detection (SBD) represents an important first step in natural language processing since accurately identifying sentence boundaries significantly impacts downstream applications. Nevertheless, detecting sentence boundaries within legal texts poses a unique and challenging problem due to their distinct structural and linguistic features. Our approach utilizes deep learning models to leverage delimiter and surrounding context information as input, enabling precise detection of sentence boundaries in English legal texts. We evaluate various deep learning models, including domain-specific transformer models like LegalBERT and (...) CaseLawBERT. To assess the efficacy of our deep learning models, we compare them with a state-of-the-art domain-specific statistical conditional random field (CRF) model. After considering model size, F1-score, and inference time, we identify the Convolutional Neural Network Model (CNN) as the top-performing deep learning model. To further enhance performance, we integrate the features of the CNN model into the subsequent CRF model, creating a hybrid architecture that combines the strengths of both models. Our experiments demonstrate that the hybrid model outperforms the baseline model, achieving a 4% improvement in the F1-score. Additional experiments showcase the superiority of the hybrid model over SBD open-source libraries when confronted with an out-of-domain test set. These findings underscore the importance of efficient SBD in legal texts and emphasize the advantages of employing deep learning models and hybrid architectures to achieve optimal performance. (shrink)

This chapter first consolidates a set of important heuristic strategies used for constructing innovative scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in mechanics. Twenty-four strategies are identified, and they are predominantly field-general. Patterns in their use suggest a partially organized hierarchy of interconnected strategies and substrategies, contrary to the view that heuristics are simply tried in random order. Strategies at four different size and time scale levels are (...) described, including larger Modeling Cycle Phases of model generation, evaluation, and modification, each of which can utilize many smaller Tactical Heuristics as substrategies, e.g., analogy, or testing predictions from the model. These in turn can utilize Grounded Imagistic Processes, such as imagistic mental simulation, an important alternative to deduction for evaluating a model by running it. The framework links higher level, serially organized processes with lower level, imagery-based processes. Its intermediate degree of organization is neither anarchistic, nor fully algorithmic. Possible benefits of organization are narrowing the search space involved and balancing sources of model construction and criticism for productive creativity. Unorganized, spontaneous processes are also discussed, along with their possible benefits. (shrink)

The research seeks to ascertain the influence of the Needham and Cosgrove-Osborne models on the comprehension of educational foundations among students at the Faculty of Education. Employing a partially controlled experimental design, the researcher randomly selected a control group from Division C, with Division A representing the first experimental group and Division B the second. The study involved 92 participants, with 30 students in each experimental group and 32 in the control group. To ensure statistical equivalence among the three groups, (...) the researcher utilized one-way analysis of variance (ANOVA) to assess variables such as chronological age (in months), intelligence test results, and the previous year's Grade Point Average (GPA). A test was crafted to evaluate concept acquisition, with its validity and reliability duly established. Upon statistical analysis of the students' responses using ANOVA and the Scheffe test to pinpoint differences in means, the findings revealed significant disparities between the average scores of students in the first and second experimental groups, who studied the subject matter using the Needham, Cosgrove, or Osborne models. The researcher posits that the Needham and Cosgrove-Osborne models should be integrated into teaching practices, given their demonstrated effectiveness as instructional paradigms. (shrink)

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, (...) violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. (...) Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

The aim of this paper is to explore the effectiveness of critical thinking for improving the writing skill of undergraduate Arab students who study English Literature at Saudi universities under lockdown circumstances due to the COVID-19 pandemic. At the same time, it explores the impact of implementing Facebook as an online Constructivist tool to improve this skill. A general overview of the status of English language education in Saudi Arabia is briefly presented to shed light on the ongoing English language (...) challenges in learning writing for undergraduate students in the English language and literature departments, which got more manifested due to the current status of education mode with the emergence of the pandemic. Two-group posttest-only randomized experiment was employed to evaluate the effectiveness of the proposed model, using the infusion and constructivism approaches. A total of 40 students enrolled in a literature course at a private university in Saudi Arabia participated in the experiment. The treatment was conducted through utilizing Facebook. The results demonstrated that students’ improvement in English writing was due to the combination of the infusion of a set of critical skills and the constructivist teaching and learning mode. (shrink)

In a recent paper in this journal, Dagsvik derives the class of independent random utility representations that are “equivalent” to the independence-from-irrelevant-alternatives assumption by Luce. In this short note, we clarify the relations between this paper by Dagsvik, and a paper in Lindberg’s 2012 thesis.