Time pressure is a central aspect of economic decision making nowadays. It is therefore natural to ask how time pressure affects decisions, and how to detect individual heterogeneity in the ability to successfully cope with time pressure. In the context of risky decisions, we ask whether a person’s performance under time pressure can be predicted by measurable behavior and traits, and whether such measurement itself may be affected by selection issues. We find that the ability to cope with time pressure varies significantly across decision makers, leading to selected subgroups that differ in terms of their observed behaviors and personal traits. Moreover, measures of cognitive ability and intellectual efficiency jointly predict individuals’ decision quality and ability to keep their decision strategy under time pressure.

We study learning and selection and their implications for possible effort escalation in a simple game of dynamic property rights conflict: a multi-stage contest with random resolve. Accounting for the empirically well-documented heterogeneity of behavioral motives of players in such games turns the interaction into a dynamic game of incomplete information. In contrast to the standard benchmark with complete information, the perfect Bayesian equilibrium features social projection and type-dependent escalation of efforts caused by learning. A corresponding experimental setup provides evidence for type heterogeneity, for belief formation and updating, for self-selection and for escalation of efforts in later stages.

We test the Average Credible Deviation Criterion (ACDC), a stability measure and refinement for cheap talk equilibria introduced in De Groot Ruiz et al. (Equilibrium selection in cheap talk games: ACDC rocks when other criteria remain silent, Working paper, University of Amsterdam 2012a). ACDC has been shown to be predictive under general conditions and to organize data well in previous experiments meant to test other concepts. In a new experimental setting, we provide the first systematic test of whether and to which degree credible deviations matter for the stability of cheap talk equilibria. Our principal experimental result is that in a setting where existing concepts are silent, credible deviations matter and matter gradually, as predicted by ACDC.

This paper examines how selection affects trust and altruism in a Trust and Modified Dictator Game. Past Trust and Dictator game experiments not allowing partner selection show substantially more trust and altruism than equilibrium predicts. We predict partner selection will cause sorting in which behavior across partner types without selection will be positively correlated with partner choice. This selection pattern will cause trust and altruism to be higher with selection and the increase will be proportional to a maximum possible gain. We find selection has all these effects. We also find greater gains in the Trust than Modified Dictator game consistent with larger possible gains in the Trust game. The results imply that theories ignoring selection will underestimate trust and altruism in markets with selection.

Corrections of correlations for range restriction (i.e., selection) and unreliability are common in psychometric work. The current rule of thumb for determining the order in which to apply these corrections looks to the nature of the reliability estimate (i.e., restricted or unrestricted). While intuitive, this rule of thumb is untenable when the correction includes the variable upon which selection is made, as is generally the case. Using classical test theory, we show that it is the nature of the range restriction, not the nature of the available reliability coefficient, that determines the sequence for applying corrections for range restriction and unreliability.

The study of prediction bias is important and the last five decades include research studies that examined whether test scores differentially predict academic or employment performance. Previous studies used ordinary least squares (OLS) to assess whether groups differ in intercepts and slopes. This study shows that OLS yields inaccurate inferences for prediction bias hypotheses. This paper builds upon the criterion-predictor factor model by demonstrating the effect of selection, measurement error, and measurement bias on prediction bias studies that use OLS. The range restricted, criterion-predictor factor model is used to compute Type I error and power rates associated with using regression to assess prediction bias hypotheses. In short, OLS is not capable of testing hypotheses about group differences in latent intercepts and slopes. Additionally, a theorem is presented which shows that researchers should not employ hierarchical regression to assess intercept differences with selected samples.

Multivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation.

The validity of a test is often estimated in a nonrandom sample of selected individuals. To accurately estimate the relation between the predictor and the criterion we correct this correlation for range restriction. Unfortunately, this corrected correlation cannot be transformed using Fisher's Z transformation, and asymptotic tests of hypotheses based on small or moderate samples are not accurate. We developed a Fisher r to Z transformation for the corrected correlation for each of two conditions: (a) the criterion data were missing due to selection on the predictor (the missing data were MAR); and (b) the criterion was missing at random, not due to selection (the missing data were MCAR). The two Z transformations were evaluated in a computer simulation. The transformations were accurate, and tests of hypotheses and confidence intervals based on the transformations were superior to those that were not based on the transformations.

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.

Corrections of correlations for range restriction (i.e., selection) and unreliability are common in psychometric work. The current rule of thumb for determining the order in which to apply these corrections looks to the nature of the reliability estimate (i.e., restricted or unrestricted). While intuitive, this rule of thumb is untenable when the correction includes the variable upon which selection is made, as is generally the case. Using classical test theory, we show that it is the nature of the range restriction, not the nature of the available reliability coefficient, that determines the sequence for applying corrections for range restriction and unreliability.

Corrections for restriction in range due to explicit selection assume the linearity of regression and homoscedastic array variances. This paper develops a theoretical framework in which the effects of some common forms of violation of these assumptions on the estimation of the unrestricted correlation can be investigated. Simple expressions are derived for both the restricted and corrected correlations in terms of the target (unrestricted) correlation in these situations.

Viruses present an amazing genetic variability. An ensemble of infecting viruses, also called a viral quasispecies, is a cloud of mutants centered around a specific genotype. The simplest model of evolution, whose equilibrium state is described by the quasispecies equation, is the Moran–Kingman model. For the sharp-peak landscape, we perform several exact computations and derive several exact formulas. We also obtain an exact formula for the quasispecies distribution, involving a series and the mean fitness. A very simple formula for the mean Hamming distance is derived, which is exact and does not require a specific asymptotic expansion (such as sending the length of the macromolecules to $\infty$ or the mutation probability to 0). With the help of these formulas, we present an original proof for the well-known phenomenon of the error threshold. We recover the limiting quasispecies distribution in the long-chain regime. We try also to extend these formulas to a general fitness landscape. We obtain an equation involving the covariance of the fitness and the Hamming class number in the quasispecies distribution. Going beyond the sharp-peak landscape, we consider fitness landscapes having finitely many peaks and a plateau-type landscape. Finally, within this framework, we prove rigorously the possible occurrence of the survival of the flattest, a phenomenon which was previously discovered by Wilke et al. (Nature 412, 2001) and which has been investigated in several works (see e.g. Codoñer et al. (PLOS Pathogens 2, 2006), Franklin et al. (Artificial Life 25, 2019), Sardanyés et al. (J. Theoret. Biol. 250, 2008), and Tejero et al. (BMC Evolutionary Biol. 11, 2011)).

Casuarina equisetifolia L. commonly called whistling pine is an economically and industrially important tree species with global significance. Although species possess versatile importance worldwide, efforts imparted for selection and designing a robust model of selection index are inadequate. The selection process, based on quantitative and qualitative traits, identified 15 superior trees from the eastern coastal plain of Odisha. These superior trees showcased exceptional qualitative and quantitative attributes. Correlation analysis highlighted key similarities among various traits like volume and above ground biomass (AGB), volume and diameter at breast height (DBH), DBH and AGB, DBH and Tree Height (TH), crown length (CL), height, AGB and height. Principal component analysis emphasized substantial contributions of traits like DBH, height, CL, crown width, AGB and volume across different clusters. Furthermore, culmination resulted in a comprehensive selection index, integrating both qualitative and quantitative characters, reaching 52.04, signifying superior performance among specific accessions. The current study provides valuable insights into selection and designing optimal selection index of C. equisetifolia, guiding future decisions concerning optimal wood production and resource management.

Research on visual attention has uncovered significant anomalies, and some traditional methods may have inadvertently probed peripheral vision rather than attention. Vision science needs to rethink visual attention from the ground up. To facilitate this, for a year I banned the word “attention” in my lab. This constraint promoted a more precise discussion of attention-related phenomena, capacity limits, and mechanisms. The insights gained lead me to challenge attributing to “attention” those phenomena that can be better explained by perceptual processes, are predictable by an ideal observer model, or that otherwise may not require an additional mechanism. I enumerate a set of critical phenomena in need of explanation. Finally, I propose a unifying theory in which all perception results from performing a task, and tasks face a limit on complexity.

Existing empirical literature provides converging evidence that selective emigration enhances human capital accumulation in the world's poorest countries. However, the within-country distribution of such brain gain effects has received limited attention. Focusing on Senegal, we provide evidence that the brain gain mechanism primarily benefits the wealthiest regions that are internationally connected and have better access to education. Conversely, human capital responses are negligible in regions lacking international connectivity, and even negative in better connected regions with inadequate educational opportunities. These results extend to internal migration, implying that highly vulnerable populations are trapped in the least developed areas.