It is a popular view thatpractical deliberation excludes foreknowledge of one's choice. Wolfgang Spohn and Isaac Levi have argued that not even a purely probabilistic self-predictionis available to thedeliberator, if one takes subjective probabilities to be conceptually linked to betting rates. It makes no sense to have a betting rate for an option, for one's willingness to bet on the option depends on the net gain from the bet, in combination with the option's antecedent utility, rather than on the (...) offered odds. And even apart from this consideration, assigning probabilities to the options among which one is choosing is futile since such probabilities could be of no possible use in choice. The paper subjects these arguments to critical examination and suggests that, appearances notwithstanding, practical deliberation need not crowd outself-prediction. (shrink)

Ratio Juris, Volume 35, Issue 1, Page 38-54, March 2022.

Approach and avoidance motivation may represent important explanatory constructs in understanding how individuals differ. Such constructs have primarily been assessed in self-reported terms, but there are limitations to self-reports of motivation. Accordingly, the present review concentrates on the potential utility of implicit cognitive-behavioral probes of approach and avoidance motivation in modeling and understanding individual differences. The review summarizes multiple lines of research that have documented the utility of such probes to the personality-processing interface. Although multiple gaps in our (...) knowledge exist, and are acknowledged, the value of such implicit cognitive-behavioral assessments is emphasized both in modeling multiple subcomponents of approach and avoidance motivation and in showing that such tendencies matter in ways that transcend momentary experiences or manipulations. (shrink)

The opportunity cost model offers an ultimate explanation of ego depletion that helps to move the field beyond biologically improbable resource accounts. The model's more proximate explanation, however, falls short of accounting for much data and is based on an outdated view of human rationality. We suggest that our own process model offers a better proximate account of self-control fatigue.

Recent neo-Humean theories of laws of nature have placed substantial emphasis on the characteristic epistemic roles played by laws in scientific practice. In particular, these theories seek to understand laws in terms of their optimal predictive utility to creatures in our epistemic situation. In contrast to other approaches, this view has the distinct advantage that it is able to account for a number of pervasive features possessed by putative actual laws of nature. However, it also faces some unique challenges. (...) First, since the view tries to characterize the laws in terms of their predictive utility, any respects in which putative actual laws are sub-optimally predictively useful are inherently problematic. Such "predictive infelicities" can easily be found among our best physical theories. Second, in tying the laws to our epistemic situation, neo-Humeanism raises the possibility that by changing our epistemic situation, we can change the laws themselves, though it is hard to believe that we have such influence over the laws. This paper aims to address both of these challenges, first by presenting a variety of strategies for explaining away predictive infelicities, and then by developing a version of Lewis's "rigidification" strategy to preclude the possibility of our changing the laws. (shrink)

In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic (...) reasoning based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios. (shrink)

To what extent are people able to make predictions about other people’s preferences and values? We report two experiments that present a novel method assessing some of the basic processes in surrogate decision-making, namely surrogate-utility estimation. In each experiment participants formed dyads who were asked to assign utilities to health related items and commodity items, and to predict their partner’s utility judgments for the same items. In experiment one we showed that older adults in long-term relationships were able (...) to accurately predict their partner’s wishes. In experiment two we showed that younger adults who were relatively unfamiliar with one another were also able to predict other people’s wishes. Crucially we demonstrated that these judgments were accurate even after partialling out each participant’s own preferences indicating that in order to make surrogate utility estimations people engage in perspective-taking rather than simple anchoring and adjustment, suggesting that utility estimation is not the cause of inaccuracy in surrogate decision-making. The data and implications are discussed with respect to theories of surrogate decision-making. (shrink)

It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. (...) The experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. They show that the majority of individuals select strategies that are boundedly optimal. Further, individual differences in what people choose to do are successfully predicted by the analysis. Two issues are discussed: the performance of the minority of participants who did not find boundedly optimal adaptations, and the possibility that individuals anticipate what, with practice, will become a bounded optimal strategy, rather than what is boundedly optimal during training. (shrink)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals (...) or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

This paper presents two cognitive models that simulate the production of referring expressions in the iMAP task—a task-oriented dialog. One general model is based on Dale and Reiter’s (1995)incremental algorithm, and the other is a simple template model that has a higher correlation with the data but is specifically geared toward the properties of the iMAP task. The property of the iMAP task environment that is modeled here is that the color feature is unreliable for identifying referents while other features (...) are reliable. The low computational cost of the incremental algorithm for generating referring expressions makes it an interesting starting point for a cognitive model. However, its explanatory power is limited, because it generates uniquely distinguishing referring expressions and because it considers features for inclusion in the referring expression in a fixed order. The first model extends the original incremental algorithm by an ability to adapt to feedback of whether a referring expression was used successfully, but it seems to overpredict the frequency with which distinguishing expressions are made and underpredict the frequency of overspecified referring expressions. The second model produces features for referring expressions purely based on its current estimate of a feature’s utility. Both models predict the observed human behavior of decreasing use of color terms and increasing use of useful feature terms. (shrink)

In this article we explore an argumentative pattern that provides a normative justification for expected utility functions grounded on empirical evidence, showing how it worked in three different episodes of their development. The argument claims that we should prudentially maximize our expected utility since this is the criterion effectively applied by those who are considered wisest in making risky choices (be it gamblers or businessmen). Yet, to justify the adoption of this rule, it should be proven that this (...) is empirically true: i.e. that a given function allows us to predict the choices of that particular class of agents. We show how expected utility functions were introduced and contested in accordance with this pattern in the 18th century and how it recurred in the 1950s when Allais made his case against the neo-Bernoullians. (shrink)

In this paper, I criticize Ethan Jerzak’s view that ‘want’ has only one sense, the mixed expected utility sense. First, I show that his appeals to ‘really’-locutions fail to explain away the counterintuitive predictions of his view. Second, I present a class of cases, which I call “principled indifference” cases, that pose difficulties for any expected utility lexical entry for ‘want’. I argue that in order to account for these cases, one needs to concede that ‘want’ has a (...) sense, according to which wanting is a matter of subjectively preferring p-alternatives to not-p-alternatives. Finally, I introduce some considerations for and against the view that ‘want’ also has another sense, which is roughly synonymous with ‘need’. (shrink)

Expected-utility theory has been a popular and influential theory in philosophy, law, and the social sciences. While its original developers, von Neumann and Morgenstern, presented it as a purely predictive theory useful to the practitioners of economic science, many subsequent theorists, particularly those outside of economics, have come to endorse EU theory as providing us with a representation of reason. But precisely in what sense does EU theory portray reason? And does it do so successfully? There are two strikingly (...) different answers to these questions in the literature. On the one hand, there is the view of people such as David Gauthier that EU theory is an implementation of the idea that reason's only role is instrumental. On the other hand, there is the view suggested by Leonard Savage that the theory is a “formal” and noninstrumental characterization of our reasoning process. (shrink)

This monographic chapter explains how expected utility (EU) theory arose in von Neumann and Morgenstern, how it was called into question by Allais and others, and how it gave way to non-EU theories, at least among the specialized quarters of decion theory. I organize the narrative around the idea that the successive theoretical moves amounted to resolving Duhem-Quine underdetermination problems, so they can be assessed in terms of the philosophical recommendations made to overcome these problems. I actually follow Duhem's (...) recommendation, which was essentially to rely on the passing of time to make many experiments and arguments available, and evebntually strike a balance between competing theories on the basis of this improved knowledge. Although Duhem's solution seems disappointingly vague, relying as it does on "bon sens" to bring an end to the temporal process, I do not think there is any better one in the philosophical literature, and I apply it here for what it is worth. In this perspective, EU theorists were justified in resisting the first attempts at refuting their theory, including Allais's in the 50s, but they would have lacked "bon sens" in not acknowledging their defeat in the 80s, after the long process of pros and cons had sufficiently matured. This primary Duhemian theme is actually combined with a secondary theme - normativity. I suggest that EU theory was normative at its very beginning and has remained so all along, and I express dissatisfaction with the orthodox view that it could be treated as a straightforward descriptive theory for purposes of prediction and scientific test. This view is usually accompanied with a faulty historical reconstruction, according to which EU theorists initially formulated the VNM axioms descriptively and retreated to a normative construal once they fell threatened by empirical refutation. From my historical study, things did not evolve in this way, and the theory was both proposed and rebutted on the basis of normative arguments already in the 1950s. The ensuing, major problem was to make choice experiments compatible with this inherently normative feature of theory. Compability was obtained in some experiments, but implicitly and somewhat confusingly, for instance by excluding overtly incoherent subjects or by creating strong incentives for the subjects to reflect on the questions and provide answers they would be able to defend. I also claim that Allais had an intuition of how to combine testability and normativity, unlike most later experimenters, and that it would have been more fruitful to work from his intuition than to make choice experiments of the naively empirical style that flourished after him. In sum, it can be said that the underdetermination process accompanying EUT was resolved in a Duhemian way, but this was not without major inefficiencies. To embody explicit rationality considerations into experimental schemes right from the beginning would have limited the scope of empirical research, avoided wasting resources to get only minor findings, and speeded up the Duhemian process of groping towards a choice among competing theories. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple clusters, (...) including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

ABSTRACTInspired by the discovery of positive age trends in emotional well-being across adulthood, lifespan researchers have uncovered fascinating age differences in cognition–emotion interactions in healthy adult samples, for example in emotion processing, memory, reactivity, perception, and regulation. Taking stock of this body of research, I identify four trends and five remaining gaps in our understanding of emotional functioning in adulthood. In particular, I suggest that the field should pay stronger attention to the prediction of real-world behaviour. Using the sample case (...) of work functioning, I outline gaps in current knowledge, including the lack of data on middle-aged adults, the neglect of relevant cognitive-emotional mechanisms, and the unclear role of life experience. Filling these gaps will enable progress in research on emotional aging in and beyond the work setting and enhance its practical utility for individuals, organisations, and society. (shrink)

Academic performance occupies an important role in adolescent development. It reflects adolescents’ cognitive ability and also shapes their academic and career paths. Students who are satisfied with their school performance tend to show higher self-esteem, confidence, and motivation. Previous research has suggested that students’ problem behaviors, such as Internet Addiction, and academic values, including intrinsic and utility values, could predict satisfaction with academic performance. However, the influence of IA and academic values has not been thoroughly explored in Chinese contexts (...) where the pressure for academic success is heavy. This study examined the relationships between IA, academic values, and satisfaction with academic performance using two waves of data collected from secondary school students in four cities in mainland China. The matched sample included a total of 2,648 Grade 7 or 8 students. Participants completed the same questionnaire containing validated measures at both waves with a 1-year interval. In line with the hypotheses, multiple regression analyses showed that Wave 1 IA was a significant negative predictor of Wave 2 intrinsic value, utility value, and satisfaction with academic performance and their changes. Results of mediation analyses revealed that only intrinsic value, but not utility value, positively predicted satisfaction with academic performance. Structural equation modeling analyses also showed similar findings. Two observations are concluded from the present findings: IA impaired students’ intrinsic value, utility value, and perceived satisfaction with academic performance; two aspects of academic values demonstrated different influences on satisfaction with academic performance. These findings provide implications for the promotion of academic satisfaction experienced by students and the prevention of negative effects of IA. (shrink)

Lower subjective socioeconomic status (SSS) and higher personal relative deprivation (PRD) relate to poorer health. Both constructs concern people’s perceived relative social position, but they differ in their emphasis on the reference groups people use to determine their comparative disadvantage (national population vs. similar others) and the importance of resentment that may arise from such adverse comparisons. We investigated the relative utility of SSS and PRD as predictors of self-rated physical and mental health (e.g., self-rated health, stress, health complaints). (...) Across 6 studies, self-rated physical and mental health were on the whole better predicted by measures of PRD than by SSS while controlling for objective socioeconomic status (SES), with SSS rarely contributing unique variance over and above PRD and SES. Studies 4 to 6 discount the possibility that the superiority of PRD over SSS in predicting health is due to psychometric differences (e.g., reliability) or response biases between the measures. (shrink)

Affective forecasting refers to the ability to predict future emotions, a skill that is essential to making decisions on a daily basis. Studies of the concept have determined that individuals are often inaccurate in making such affective forecasts. However, the mechanisms of these errors are not yet clear. In order to better understand why affective forecasting errors occur, this article seeks to trace the theoretical roots of this theory with a focus on its multidisciplinary history. The roots of affective forecasting (...) lie mainly in economics, with early claims positing that utility played a role in decision-making. Furthermore, the philosopher Jeremy Bentham’s descriptions of utilitarianism played a major role in our understanding of whether to define utility as a hedonic quality. The birth of behavioural economics resulted in a paradigm shift, introducing the concept of cognitive biases as influences on the accuracy of predicted utility. Daniel Gilbert and Timothy Wilson, the earliest researchers of affective forecasting errors, have proceeded with the concept of the accuracy of predicted affective utility to conduct experiments that seek to determine why our predictions of future affect are inaccurate and how such errors play a role in our decision-making. (shrink)

We introduce an expected-value theory of linguistic modality that makes reference to expected utility and a likelihood-based confirmation measure for deontics and epistemics, respectively. The account is a probabilistic semantics for deontics and epistemics, yet it proposes that deontics and epistemics share a common core modal semantics, as in traditional possible-worlds analysis of modality. We argue that this account is not only theoretically advantageous, but also has far-reaching empirical consequences. In particular, we predict modal versions of reasoning fallacies from (...) the heuristics and biases literature. Additionally, we derive the modal semantics in an entirely transparent manner, as it is based on the compositional semantics of Korean modal expressions that are morphosyntactically decomposed into a conditional and an evaluative predicate. (shrink)

Predictive risk modelling is a computational method used to generate probabilities correlating events. The output of such systems is typically represented by a statistical score derived from various related and often arbitrary datasets. In many cases, the information generated by such systems is treated as a form of evidence to justify further action. This paper examines the nature of the information generated by such systems and compares it with more orthodox notions of evidence found in epistemology. The paper focuses on (...) a specific example to illustrate the issues: The New Zealand Government has proposed implementing a predictive risk modelling system which purportedly identifies children at risk of a maltreatment event before the age of five. Timothy Williamson’s conception of epistemology places a requirement on knowledge that it be explanatory. Furthermore, Williamson argues that knowledge is equivalent to evidence. This approach is compared to the claim that the output of such computational systems constitutes evidence. While there may be some utility in using predictive risk modelling systems, I argue, since an explanatory account of the output of such algorithms that meets Williamson’s requirements cannot be given, doubt is cast upon the resulting statistical scores as constituting evidence on generally accepted epistemic grounds. The algorithms employed in such systems are geared towards identifying patterns which turn out to be good correlations. However, rather than providing information about specific individuals and their exposure to risk, a more valid explanation of a high probability score is that the particular variables related to incidents of maltreatment are just higher amongst certain subgroups in a population than they are amongst others. The paper concludes that any justification of the information generated by such systems is generalised and pragmatic at best and the application of this information to individual cases raises various ethical issues. (shrink)

Psychiatry has witnessed a new wave of approaches to clinical phenotyping and the study of psychopathology, including the National Institute of Mental Health’s Research Domain Criteria, clinical staging, network approaches, the Hierarchical Taxonomy of Psychopathology, and the general psychopathology factor, as well as a revival of interest in phenomenological psychopathology. The question naturally emerges as to what the relationship between these new approaches is – are they mutually exclusive, competing approaches, or can they be integrated in some way and used (...) to enrich each other? In this opinion piece, we propose a possible integration between clinical staging and phenomenological psychopathology. Domains identified in phenomenological psychopathology, such as selfhood, embodiment, affectivity, etc., can be overlaid on clinical stages in order to enrich and deepen the phenotypes captured in clinical staging (‘high resolution’ clinical phenotypes). This approach may be useful both ideographically and nomothetically, in that it could complement diagnosis, enrich clinical formulation, and inform treatment of individual patients, as well as help guide aetiological, prediction, and treatment research. The overlaying of phenomenological domains on clinical stages may require that these domains are reformulated in dimensional rather than categorial terms. This integrative project requires assessment tools, some of which are already available, that are sensitive and thorough enough to pick up on the range of relevant psychopathology. The proposed approach offers opportunities for mutual enrichment: clinical staging may be enriched by introducing greater depth to phenotypes; phenomenological psychopathology may be enriched by introducing stages of severity and disorder progression to phenomenological analysis. (shrink)

The classical theory of preference among monetary bets represents people as expected utility maximizers with concave utility functions. Critics of this account often rely on assumptions about preferences over wide ranges of total wealth. We derive a prediction of the theory that bears on bets at any fixed level of wealth, and test the prediction behaviorally. Our results are discrepant with the classical account. Competing theories are also examined in light of our data.

The coronavirus disease 2019 pandemic led to various government-imposed limitations on social interaction and strict home confinement. Such involuntary social-distancing policies can exacerbate feelings of loneliness and alter emotional well-being. Dysregulation of the hypothalamic-pituitary-adrenocortical axis is a potential mechanism for loneliness’ deleterious health effects. In this study, we explored whether pre-pandemic diurnal cortisol output, a measure of HPA axis function, may predict the propensity to changes in loneliness during long-term COVID-19 home confinement and if extraversion would moderate this relationship. This (...) association has been explored by analysing the impact of COVID-19 pandemic and strict home confinement on social and emotional loneliness in 45 Spanish young adults. Diurnal cortisol levels were measured from five saliva samples obtained across a day just before the pandemic, and data about participants’ perceived loneliness, empathic state, extraversion, and prospective volunteering were obtained both before and during the confinement. Participants’ social and family loneliness increased during long-term strict home confinement, while prospective volunteering tendencies and extraversion decreased. Importantly, after adjusting for relevant confounders, moderation analyses revealed that in young adults with high pre-pandemic extraversion, a higher AUCG predicted a larger increase in social loneliness during confinement, while in individuals with low extraversion, AUCG was negatively related to change in loneliness. Our findings highlight the utility of pre-pandemic diurnal cortisol output in predicting the social impact of COVID-19 home confinement, presenting this hormone as a potential biomarker for a priori identification of at-risk groups during public health crises. (shrink)

This paper deals with the equilibria of games when the agents have multiple objectives and, therefore, their utilities cannot be represented by a single value, but by a vector containing the various dimensions of the utility. Our approach allows the incorporation of partial information about the preferences of the agents into the model, and permits the identification of the set of equilibria in accordance with this information. We also propose an additional conservative criterion which can be applied in this (...) framework in order to predict the results of interaction. The potential application of the theoretical results is shown with an analysis of a mixed oligopoly in which the agents value additional objectives other than their own benefit. These objectives are related to social welfare and to the profit of the industry. The flexibility of our approach provides a general theoretical framework for the analysis of a wide range of strategic economic models. (shrink)

Online music sharing, deemed illegal for invading intellectual property rights under current laws, has become a crucial issue for the music industry in the modern digital age, but few have investigated the potential costs and utilities for individuals involved in such online misbehavior. This study aimed to fill in this gap to predict consumers' intentions to engage in online music sharing and further consider consumers' online music sharing knowledge as a moderator in the research model. The results of repeated measures (...) analysis of variance of costs and utilities of online music sharing not only give more detailed information to grasp empirical implications but also provide some suggestions to the music industry in Taiwan. (shrink)

In a pair of controversy provoking papers1, Kadane and Larkey argued that the normative or prescriptive understanding of expected utility theory recommended that participants in a game maximize expected utility given their assessments of the probabilities of the moves that other players would make. They observed that no prescription, norm or standard of Bayesian rationality recommends how they should come to make probability judgments about the choices of other players. For any given player, it is an empirical question (...) as to whether other players are Bayes rational, judge him to be Bayes rational, etc., just as it is an empirical question as to what the other players’ goals and beliefs are. Participants in the game should, of course, use all empirical information available to them about how other players have behaved and how they are likely to behave in the given context of choice. Kadane and Larkey pointed out that rational players could participate in a game meeting these requirements without the game’s terminating with a Nash equilibrium or a result satisfying any other particular solution concept. (shrink)

TheConceptual Feature framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm”. Healthy adults selected the lexical item most related to a probe word in a 4-item written word array comprising the target (...) and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe-target similarity at least as well as latent semantic analysis ratings which are based on word co-occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

Brain age prediction is a relatively new tool in neuro-medicine and the neurosciences. In research and clinical practice, it finds multiple use as a marker for biological age, for general health status of the brain and as an indicator for several brain-based disorders. Its utility in all these tasks depends on detecting outliers and thus failing to correctly predict chronological age. The indicative value of brain age prediction is generated by the gap between a brain’s chronological age and the (...) predicted age, the brain age gap (BAG). This article shows how the clinical and research use of brain age prediction tacitly pathologizes the states that it is sensitive to. It will be argued that the tacit character of this transformation conceals the need for its explicit justification. (shrink)

Despite decades of research, we still lack a clear explanation for the emergence and persistence of inequality. Here we propose and evaluate a marginal utility of inequality hypothesis that nominates circumscription and environmental heterogeneity as independent, necessary conditions for the emergence of intragroup material inequality. After coupling the Standard Cross-Cultural Sample with newly generated data from remote sensing, we test predictions derived from this hypothesis using a multivariate generalized additive model that accounts for spatial and historical dependence as well (...) as subsistence mode. Our analyses show that the probability a society will be stratified increases significantly as a function of proxies of environmental heterogeneity and environmental circumscription. This supports the hypothesis that increasing environmental heterogeneity and circumscription drives the emergence and persistence of inequality among documented societies across the globe. We demonstrate how environmental heterogeneity and circumscription produce situations that limit individuals’ options so that some may find it in their best interest to give up some autonomy for material gain, while others may find it in their best interest to give up some material resources for another individual’s time or deference. These results support the marginal utility of inequality framework and enable future explorations of the ecological conditions that facilitate the emergence of intragroup inequality through time and across the globe. (shrink)

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox (...) fail to account for the predictive utility of universal laws like All ravens are black. (shrink)

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high‐dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF framework to abstract words using eyetracking via an adaptation of the classical “visual word paradigm” (VWP). Healthy adults (n = 20) selected the lexical item most related to a probe word in a (...) 4‐item written word array comprising the target and three distractors. The relation between the probe and each of the four words was determined using the semantic distance metrics derived from ACF ratings. Eye movement data indicated that the word that was most semantically related to the probe received more and longer fixations relative to distractors. Importantly, in sets where participants did not provide an overt behavioral response, the fixation rates were nonetheless significantly higher for targets than distractors, closely resembling trials where an expected response was given. Furthermore, ACF ratings which are based on individual words predicted eye fixation metrics of probe‐target similarity at least as well as latent semantic analysis ratings which are based on word co‐occurrence. The results provide further validation of Euclidean distance metrics derived from ACF ratings as a measure of one facet of the semantic relatedness of abstract words and suggest that they represent a reasonable approximation of the organization of abstract conceptual space. The data are also compatible with the broad notion that multiple sources of information (not restricted to sensorimotor and emotion information) shape the organization of abstract concepts. While the adapted “VWP” is potentially a more metacognitive task than the classical visual world paradigm, we argue that it offers potential utility for studying abstract word comprehension. (shrink)

Sub-Saharan emerging countries experience electrical shortages resulting in power rationing, which ends up hampering economic activities. This paper proposes an approach for very short-term blackout forecast in grid-tied PV systems operating in low reliability weak electric grids of emerging countries. A pilot project was implemented in Arusha-Tanzania; it mainly comprised of a PV-inverter and a lead-acid battery bank connected to the local electricity utility company, Tanzania Electric Supply Company Limited. A very short-term power outage prediction model framework based on (...) a hybrid random forest algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. Input data sampled at a 15-minute interval included day of the month, weekday, hour, supply voltage, utility line frequency, and previous days’ blackout profiles. The model was composed of an adaptive similar day module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF-ASD blackout prediction model. Furthermore, the efficacy analysis of the short-term blackout prediction of the formulated RF, ASD, and RF-ASD regression and classification algorithms was compared. Considering the stochastic nature of blackouts, their performance was found to be fair in short-term blackout predictions of the test site’s weak grid using limited input data from the point of coupling of the user. The models developed were only able to predict blackouts if they occurred frequently and contiguously, but they performed poorly if they were sparse or dispersed. (shrink)

The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict (...) the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence. -/- . (shrink)

Extended redundancy analysis is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid the overly optimistic assessment, we introduce a new model evaluation approach for ERA, which utilizes computer-intensive resampling methods to assess how well a model performs on unseen data. Specifically, we suggest several new model (...) evaluation metrics for ERA that compute a model’s performance on out-of-sample data, i.e., data not used for model development. Although considerable work has been done in machine learning and statistics to examine the utility of cross-validation and bootstrap variants for assessing such out-of-sample predictive performance, to date, no research has been carried out in the context of ERA. We use simulated and real data examples to compare the proposed model evaluation approach with the conventional one. Results show the conventional approach always favor more complex ERA models, thereby failing to prevent the problem of overfitting in model selection. Conversely, the proposed approach can select the true ERA model among many mis-specified models. (shrink)

It is a popular view thatpractical deliberation excludes foreknowledge of one's choice. Wolfgang Spohn and Isaac Levi have argued that not even a purely probabilistic self-predictionis available to thedeliberator, if one takes subjective probabilities to be conceptually linked to betting rates. It makes no sense to have a betting rate for an option, for one's willingness to bet on the option depends on the net gain from the bet, in combination with the option's antecedent utility, rather than on the (...) offered odds. And even apart from this consideration, assigning probabilities to the options among which one is choosing is futile since such probabilities could be of no possible use in choice. The paper subjects these arguments to critical examination and suggests that, appearances notwithstanding, practical deliberation need not crowd outself-prediction. (shrink)

The problem of algorithmic fairness is typically framed as the problem of finding a unique formal criterion that guarantees that a given algorithmic decision-making procedure is morally permissible. In this paper, I argue that this is conceptually misguided and that we should replace the problem with two sub-problems. If we examine how most state-of-the-art machine learning systems work, we notice that there are two distinct stages in the decision-making process. First, a prediction of a relevant property is made. Secondly, a (...) decision is taken based (at least partly) on this prediction. These two stages have different aims: the prediction is aimed at accuracy, while the decision is aimed at allocating a given good in a way that maximizes some context-relative utility measure. Correspondingly, two different fairness issues can arise. First, predictions could be biased in discriminatory ways. This means that the predictions contain systematic errors for a specific group of individuals. Secondly, the system’s decisions could result in an allocation of goods that is in tension with the principles of distributive justice. These two fairness issues are distinct problems that require different types of solutions. I here provide a formal framework to address both issues and argue that this way of conceptualizing them resolves some of the paradoxes present in the discussion of algorithmic fairness. (shrink)

This paper aims to establish the connection between the theoretical and practical aims of the Office of the Hydrographer of the British Admiralty and Charles Darwin’s (1809–1882) work on coral reefs from 1835 to 1842. I also emphasize the consistent zoological as well as geological reasoning contained in these texts. The Office’s influences have been previously overlooked, despite the Admiralty’s interest in using coral reefs as natural instruments. I elaborate on this by introducing the work of Alexander Dalrymple (1737–1808), the (...) first hydrographer of the Admiralty and a figure who has flown under the radar of the history of coral reef theories. I show that Dalrymple introduced a unified account of coral reefs in which multiple features of the coral reefs, such as their shape, slope of the sides, ridges, channels, and elevation relative to the water, were all explained by the action of the winds and waves—and proposed that one could use these features to predict seafaring conditions around the islands. Then, I show that Darwin’s “Coral Islands” (1835) and his _Coral Reefs_ monograph (1842) spoke to these hydrographical issues and did so, at times, by way of zoological reasoning. It was, for instance, the coral behavior and the related notion of a zoological or botanical _station_ that ultimately proved the biggest blow to the Admiralty’s aim to use the coral reefs as instruments because it eroded many uniform predictions regarding the past or future of a coral reef. Connecting these themes leads us to a surprising conclusion: that Darwin’s theory of coral reefs, long a model instance of Darwin making uniform predictable inferences, was, in actuality, also his first formal encounter with something at times the entire opposite. (shrink)

Is the societal-level of analysis sufficient today to understand the values of those in the global workforce? Or are individual-level analyses more appropriate for assessing the influence of values on ethical behaviors across country workforces? Using multi-level analyses for a 48-society sample, we test the utility of both the societal-level and individual-level dimensions of collectivism and individualism values for predicting ethical behaviors of business professionals. Our values-based behavioral analysis indicates that values at the individual-level make a more significant contribution (...) to explaining variance in ethical behaviors than do values at the societal-level. Implicitly, our findings question the soundness of using societal-level values measures. Implications for international business research are discussed. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...) the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Our aim in this paper was to establish an empirical evaluation for similarity effects modeled by Rubinstein; Azipurua et al.; Leland; and Sileo. These tests are conducted through a sensitivity analysis of two well-known examples of expected utility (EU) independence violations. We found that subjective similarity reported by respondents was explained very well by objective measures suggested in the similarity literature. The empirical results of this analysis also show that: (1) the likelihood of selection for the riskier choice increases (...) as the pair becomes more similar, (2) these choice patterns are consistent with well-known independence violations of expected utility, and (3) a significant proportion of individuals exhibit intransitive choice patterns predicted under similarity effects, but not allowed under generalized expected utility models for risky choice. (shrink)

Is the societal-level of analysis sufficient today to understand the values of those in the global workforce? Or are individual-level analyses more appropriate for assessing the influence of values on ethical behaviors across country workforces? Using multi-level analyses for a 48-society sample, we test the utility of both the societal-level and individual-level dimensions of collectivism and individualism values for predicting ethical behaviors of business professionals. Our values-based behavioral analysis indicates that values at the individual-level make a more significant contribution (...) to explaining variance in ethical behaviors than do values at the societal-level. Implicitly, our findings question the soundness of using societal-level values measures. Implications for international business research are discussed. (shrink)