Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task‐relevant features. The authors focused on the recognition heuristic, so the task‐relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty‐three three‐member groups performed an inference task in which they had to infer which of two German companies had (...) the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed. (shrink)

The primary impediment to formulating a general theory for adaptive evolution has been the unknown distribution of fitness effects for new beneficial mutations. By applying extreme value theory, Gillespie circumvented this issue in his mutational landscape model for the adaptation of DNA sequences, and Orr recently extended Gillespie's model, generating testable predictions regarding the course of adaptive evolution. Here we provide the first empirical examination of this model, using a single-stranded DNA bacteriophage related to phiX174, and find that (...) our data are consistent with Orr's predictions, provided that the model is adjusted to incorporate mutation bias. Orr's work suggests that there may be generalities in adaptive molecular evolution that transcend the biological details of a system, but we show that for the model to be useful as a predictive or inferential tool, some adjustments for the biology of the system will be necessary. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received (...) regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

In this article complexity results for adaptive logics using the minimal abnormality strategy are presented. It is proven here that the consequence set of some recursive premise sets is $\Pi _1^1 - complete$ . So, the complexity results in (Horsten and Welch, Synthese 158:41–60,2007) are mistaken for adaptive logics using the minimal abnormality strategy.

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme (...) requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme. (shrink)

The adaptive object model method is an effective way to develop dynamic and configurable adaptive software. It has the characteristics of metamodel, description drive, and runtime reflection. First, the core idea of the adaptive object model is explained; then, the five modes of establishing the metamodel in the adaptive object model architecture, the model engine, and supporting tools are analyzed; and the basketball tracking algorithm of the adaptive object model is discussed. Secondly, a two-dimensional joint (...) information strategy is proposed to improve the tracking effect. When the basketball is in a very complex environment, there will always be some color information in the background that is the same as the target, which affects the effect of basketball tracking. Therefore, this paper proposes a Camshift tracking method based on the significance of histograms, through real time. The basketball movement is compared with the background histogram to continuously adjust the basketball movement tracking model. These two methods can better establish the tracking model of the basketball adaptive object, reduce the interference of background information, and achieve the effect of stable tracking of the target. The simulation experiment results show that the method proposed in this paper can effectively improve the accuracy of the basketball goal model and achieve stable tracking of the goal. (shrink)

This paper proposes an adaptive neural network control approach for a direct-current system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing (...) works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results. (shrink)

The information theoretic principle of rational adaptation predicts that individuals with aphasia adapt to their language impairments by relying more heavily on comparatively unimpaired non-linguistic knowledge to communicate. This prediction was examined by assessing the extent to which adults with chronic aphasia due to left-hemisphere stroke rely more on conceptual rather than lexical information during verb retrieval, as compared to age-matched neurotypical controls. A primed verb naming task examined the degree of facilitation each participant group received from either conceptual event-related (...) or lexical collocate cues, compared to unrelated baseline cues. The results provide evidence that adults with aphasia received amplified facilitation from conceptual cues compared to controls, whereas healthy controls received greater facilitation from lexical cues. This indicates that adaptation to alternative and relatively unimpaired information may facilitate successful word retrieval in aphasia. Implications for models of rational adaptation and clinical neurorehabilitation are discussed. (shrink)

It is known that the text of the Qur'an is artistic prose that has reached an unprecedented level in terms of its unique style, superiority, and robustness. Likewise, it can be said for hadith texts reach the peak of eloquence and beauty. Scholars have paid attention to the Qur'an and Hadith texts for centuries in their scientific studies. There are also poets among those who care. Inspired by both texts, they tried to use their style in their odes and literary (...) products through quotation, reference, compensation, and similar arts. From this point of view, our research aims to examine the phenomenon of religious intertextuality in African Arabic poetry -on the condition that the study is limited to the West African region- and to explain its places. In addition to learning to what extent the African poet was influenced by the style, language structure, and methods of the Qur'an and hadith texts, it also explores the relationship adopted between his literary references. What gives this study important is that it attempts to apply one of the modern critical theories (intertextuality) to Arabic poetry from West Africa, which many researchers do not know about. This research will prove the rooting of the art of Arabic poetry in West Africa through its intertextuality with the ancient Arab heritage, as well as the validity of its use as a scientific material for contemporary theories. During the research, it was determined that various religious intertextuality was used in the poems of African poets. The poets did not adhere to a certain form in their poems, on the contrary, they used different methods at every stage of their poems. This revealed their culture and knowledge of Arabic literature and poets and showed the extent of their devotion to the Qur'an and Sunnah. Likewise, by referring to historical events and religious people with a religious dimension, they revealed creativity in their poems. For this reason, they were able to paint the most beautiful artistic paintings, which impressed both the reader and the listener. (shrink)

One important difference between adaptive and nonadaptive explanations can be found in the evolutionary sequence of structural and functional modifications. Phylogenetic analysis (cladistics) provides a powerful methodology for distinguishing exaptation from adaptation, by indicating whether character traits have predated, accompanied, or followed evolution of particular functions. Such analysis yields falsifiable hypotheses that can help to distinguish causal relationships from mere correlation.

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence models to predict the ground settlement of earth pressure balance shield tunnelling in the Bangkok (...) MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution, particle swarm optimization, genetic algorithm, and ant colony optimizer to tune the adaptive neuro-fuzzy inference system. To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including preprocessed and normalized, preprocessed and nonnormalized, non-preprocessed and normalized, and non-preprocessed and nonnormalized datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value of 7.98 and a correlation coefficient value of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance shield tunnelling. (shrink)

Although there is a large and growing literature on children’s developing concepts of illness transmission, little is known about how children develop contagion knowledge before formal schooling begins, and how these informal learning experiences can impact children’s health behaviors. Here we asked two important questions: First, do children’s informal learning experiences, such as their experiences reading storybooks, regularly contain causal information about illness transmission; and second, what is the impact of this type of experience on children’s developing knowledge and behavior? (...) In Study 1, we examined whether children’s commercial books about illness regularly contain contagion-relevant causal information. In Study 2, we ran a pilot study examining whether providing children with causal information about illness transmission in a storybook can influence their knowledge and subsequent behavior when presented with a contaminated object. The results from Study 1 suggest that very few (15%) children’s books about illness feature biological causal mechanisms for illness transmission. However, results from Studies 2 suggest that storybooks containing contagion-relevant explanations about illness transmission may encourage learning and avoidance of contaminated objects. Altogether, these results provide preliminary data suggesting that future research should focus on engaging children in learning about contagion and encouraging adaptive health behaviors. (shrink)

This article illustrates instrumental variable (IV) estimation by examining an unexpected finding of the research on career adaptability and job satisfaction. Theoretical and empirical arguments suggest that in the general population, people’s abilities to adapt their careers are beneficial to their job satisfaction. However, a recent meta-analysis unexpectedly found no effect when personality traits are controlled for. We argue that a reverse effect of job satisfaction on career adaptability, originating from affective tendencies tied to personality, might explain this null effect. (...) Our argument implies that the estimates obtained with traditional ordinary least squares (OLS) regressions are biased by endogeneity, a correlation between an explanatory variable and the error term in a regression model. When experimental manipulations are impossible, IV estimations, such as two-stage least squares (2SLS) regressions, are one possible solution to the endogeneity problem. Analyzing three waves of data from a sample of 836 adults, the concurrent and time-lagged effect of job satisfaction on career adaptability was revealed to be more consistent than the reverse. Our results provide an explanation, rooted in affective dispositions, as to why recent meta-analytical estimates unexpectedly found that career adaptability does not predict job satisfaction at the interindividual level. We also discuss IV estimation in terms of its limits, weight the interpretation of its estimates against the temporality criterion for causal inference, and consider its possible extension to analyses of change. (shrink)

Adaptive management is commonly identified as a way to address situations where ecological and social uncertainty exists. Two discourses are common: a focus on experimentation, and a focus on collaboration. The roles of experimental and collaborative adaptive management in contemporary practice are reviewed to identify tools for bridging the discourses. Examples include broadening the scope of contributions during the buy-in and goal-setting stages, using conceptual models and decision support tools to include stakeholders in model development, experimentation using indicators (...) of concern to stakeholders, an experimental focus that reflects the level of statistical confidence required by management, and the engagement of stakeholders in data interpretation so that those affected by management outcomes can learn and adapt accordingly. In this context, a framework of questions that managers can use to reflect on both ecological and social uncertainties as they relate to individual management contexts is proposed. (shrink)

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices to read and write shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of (...) enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to safety and security; enhancement as unnecessary, unnatural or aberrant; and fairness, equality, and distributive justice. Most researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening. However, researchers unanimously felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood. (shrink)

Contemporary biological research has suggested that some host–microbiome multispecies systems (referred to as “holobionts”) can in certain circumstances evolve as unique biological individual, thus being a unit of selection in evolution. If this is so, then it is arguably the case that some biological adaptations have evolved at the level of the multispecies system, what we call hologenomic adaptations. However, no research has yet been devoted to investigating their nature, or how these adaptations can be distinguished from adaptations at the (...) species-level (genomic adaptations). In this paper, we cover this gap by investigating the nature of hologenomic adaptations. By drawing on the case of the evolution of sanguivory diet in vampire bats, we argue that a trait constitutes a hologenomic adaptation when its evolution can only be explained if the holobiont is considered the biological individual that manifests this adaptation, while the bacterial taxa that bear the trait are only opportunistic beneficiaries of it. We then use the philosophical notions of emergence and inter-identity to explain the nature of this form of individuality and argue why it is special of holobionts. Overall, our paper illustrates how the use of philosophical concepts can illuminate scientific discussions, in the trend of what has recently been called metaphysics of biology. (shrink)

Dunbar (1993) emphasizes the role of cooperative language use in the evolution of human linguistic capacity and neglects to consider the role that manipulative language use would have played. I argue that as group size and neocortieal size increased during human evolution, the adaptive value of using language to benefit oneself at the expense of others would also have increased. I discuss how selection pressures for manipulative language use would have operated in the contexts of mating, status striving, and (...) social exchange. (shrink)

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed (...) with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)

No other scientific theory has had as tremendous an impact on our understanding of the world as Darwin's theory as outlined in his Origin of Species, yet from the very beginning the theory has been subject to controversy. The Evolution of Darwinism focuses on three issues of debate - the nature of selection, the nature and scope of adaptation, and the question of evolutionary progress. It traces the varying interpretations to which these issues were subjected from the beginning and the (...) fierce contemporary debates that still rage on and explores their implications for the greatest questions of all: Where we come from, who we are and where we might be heading. Written in a clear and non-technical style, this book will be of use as a textbook for students in the philosophy of science who need to become familiar with the background to the debates about evolution. (shrink)

Much has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at (...) times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests. (shrink)